Full Taxonomy

Alliant Audiences

MDM ID:

Segment:

Category:

Sub-Category:

Vertical 1:

Vertical 2:

HH/Ind:

Audience Size (PII):

MDM ID	Segment	Category	Sub-Category	Vertical 1	Vertical 2	HH/Ind	Modeled/Known/Inferred	Audience Size (PII)	Estimated Audience Size (Digital)	Segment Description
161.812	Diesel	Auto	Fuel Type	Auto		Household	Known	1,54	3,23	Households that own a vehicle with a diesel engine, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
161.813	Electric	Auto	Fuel Type	Auto		Household	Known	87,24	183,20	Households that own an electric vehicle, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
161.814	Flex-Fuel	Auto	Fuel Type	Auto		Household	Known	5,10	10,70	Households that own a vehicle with a flex-fuel engine, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
161.821	Hybrid	Auto	Fuel Type	Auto		Household	Known	1,26	2,65	Households that own a hybrid vehicle, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
11.989	Clunker Owner In-Market for New Car	Auto	In-Market	Auto		Household	Modeled	3,68	7,72	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.984	In-Market for Financing	Auto	In-Market	Auto	Financial Services	Household	Modeled	16,94	35,57	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.985	In-Market for Insurance	Auto	In-Market	Auto	Financial Services	Household	Modeled	25,50	53,55	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.991	In-Market for New Economy Car	Auto	In-Market	Auto		Household	Modeled	16,60	34,87	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.990	In-Market for New Green Car	Auto	In-Market	Auto		Household	Modeled	15,94	33,47	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
161.810	In-Market for New Large SUV	Auto	In-Market	Auto		Household	Modeled	9,28	19,48	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously purchased large SUVs.
11.992	In-Market for New Luxury Car	Auto	In-Market	Auto		Household	Modeled	16,98	35,66	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.993	In-Market for New Mini Van	Auto	In-Market	Auto		Household	Modeled	14,07	29,55	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.994	In-Market for New Sedan	Auto	In-Market	Auto		Household	Modeled	13,20	27,72	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
161.811	In-Market for New Small SUV	Auto	In-Market	Auto		Household	Modeled	13,45	28,25	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously purchased small SUVs.
11.995	In-Market for New Sports car	Auto	In-Market	Auto		Household	Modeled	524,24	1,10	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.996	In-Market for New SUV	Auto	In-Market	Auto		Household	Modeled	13,64	28,65	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.997	In-Market for New Truck	Auto	In-Market	Auto		Household	Modeled	17,96	37,71	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.986	In-Market for New Vehicle	Auto	In-Market	Auto		Household	Modeled	19,55	41,06	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.998	In-Market for New Wagon	Auto	In-Market	Auto		Household	Modeled	17,91	37,62	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.987	In-Market for Parts & Service	Auto	In-Market	Auto		Household	Modeled	35,91	75,41	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.988	In-Market for Used Vehicle	Auto	In-Market	Auto		Household	Modeled	79,56	167,08	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
11.999	Multi-Car Owner In-Market for New Car	Auto	In-Market	Auto		Household	Modeled	21,58	45,31	Built from automotive ownership data that is sourced from nationwide dealership and service department records. The source data is used as a study group, that is then joined to an offline cooperative of multichannel transactions (via digital and offline channels from DTC businesses) , demographics and life stage data (recent home move, new parents, retired, etc.) to identify households who have the same indicators of those who have previously made vehicle purchases.
12.163	Acura Switchers	Auto	Loyalty	Auto		Household	Inferred	1,85	3,89	Households that Alliant has identified to be a defector from Acura, meaning they have owned an Acura but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.164	Audi Switchers	Auto	Loyalty	Auto		Household	Inferred	1,07	2,24	Households that Alliant has identified to be a defector from Audi, meaning they have owned a Audi but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.165	Auto Class Switchers	Auto	Loyalty	Auto		Household	Inferred	19,18	40,29	Households that Alliant has identified to be a defector from auto classes, meaning they do not have a repeat history of purchasing a specific vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.166	Auto Make Switchers	Auto	Loyalty	Auto		Household	Inferred	61,69	129,55	Households that Alliant has identified to be a defector from auto makes, meaning they do not have a repeat history of purchasing a specific vehicle make. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.129	Auto Style Loyalists	Auto	Loyalty	Auto		Household	Inferred	29,70	62,37	Households that Alliant has identified to be loyal in their continued purchase of an auto style. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.167	Auto Style Switchers	Auto	Loyalty	Auto		Household	Inferred	17,48	36,72	Households that Alliant has identified to be a defector from auto style, meaning they do not have a repeat history of purchasing that vehicle style. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.130	BMW Loyalists	Auto	Loyalty	Auto		Household	Inferred	309,29	649,50	Households that Alliant has identified to be loyal in their continued purchase of a BMW. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.168	BMW Switchers	Auto	Loyalty	Auto		Household	Inferred	2,32	4,87	Households that Alliant has identified to be a defector from BMW, meaning they have owned a BMW but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.132	Cadillac Loyalists	Auto	Loyalty	Auto		Household	Inferred	220,65	463,37	Households that Alliant has identified to be loyal in their continued purchase of a Cadillac. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.170	Cadillac Switchers	Auto	Loyalty	Auto		Household	Inferred	2,24	4,70	Households that Alliant has identified to be a defector from Cadillac, meaning they have owned a Cadillac but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.133	Chevrolet Loyalists	Auto	Loyalty	Auto		Household	Inferred	4,07	8,55	Households that Alliant has identified to be loyal in their continued purchase of a Chevrolet. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.171	Chevrolet Switchers	Auto	Loyalty	Auto		Household	Inferred	15,10	31,71	Households that Alliant has identified to be a defector from Chevrolet, meaning they have owned a Chevrolet but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.173	Convertible Switchers	Auto	Loyalty	Auto		Household	Inferred	1,54	3,24	Households that Alliant has identified to be a defector from convertibles, meaning they have owned a convertible but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.135	Coupe 2-door Loyalists	Auto	Loyalty	Auto		Household	Inferred	766,49	1,61	Households that Alliant has identified to be loyal in their continued purchase of a coupe 2-door. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.174	Coupe 2-door Switchers	Auto	Loyalty	Auto		Household	Inferred	4,38	9,20	Households that Alliant has identified to be a defector from coupe 2-doors, meaning they have owned a coupe 2-door but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.136	Crossover Loyalists	Auto	Loyalty	Auto		Household	Inferred	993,28	2,09	Households that Alliant has identified to be loyal in their continued purchase of a crossover. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.137	Dodge Loyalists	Auto	Loyalty	Auto		Household	Inferred	1,06	2,23	Households that Alliant has identified to be loyal in their continued purchase of a Dodge. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.176	Dodge Switchers	Auto	Loyalty	Auto		Household	Inferred	8,82	18,52	Households that Alliant has identified to be a defector from Dodge, meaning they have owned a Dodge but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.138	Ford Loyalists	Auto	Loyalty	Auto		Household	Inferred	4,49	9,43	Households that Alliant has identified to be loyal in their continued purchase of a Ford. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.177	Ford Switchers	Auto	Loyalty	Auto		Household	Inferred	16,10	33,80	Households that Alliant has identified to be a defector from Ford, meaning they have owned a Ford but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.139	Full Size Car Loyalists	Auto	Loyalty	Auto		Household	Inferred	2,43	5,11	Households that Alliant has identified to be loyal in their continued purchase of a full size car. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.178	Full Size Car Switchers	Auto	Loyalty	Auto		Household	Inferred	6,99	14,68	Households that Alliant has identified to be a defector from full size Cars, meaning they have owned a full size car but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.140	Full Size SUV Loyalists	Auto	Loyalty	Auto		Household	Inferred	4,61	9,68	Households that Alliant has identified to be loyal in their continued purchase of a full size SUV. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.179	Full Size SUV Switchers	Auto	Loyalty	Auto		Household	Inferred	9,49	19,93	Households that Alliant has identified to be a defector from full size SUV, meaning they have owned a full size SUV but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.141	Full Size Truck Loyalists	Auto	Loyalty	Auto		Household	Inferred	6,67	14,01	Households that Alliant has identified to be loyal in their continued purchase of a full size truck. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.180	Full Size Truck Switchers	Auto	Loyalty	Auto		Household	Inferred	10,25	21,53	Households that Alliant has identified to be a defector from full size truck, meaning they have owned a full size truck but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.181	Full Size Van Switchers	Auto	Loyalty	Auto		Household	Inferred	587,54	1,23	Households that Alliant has identified to be a defector from full size van, meaning they have owned a full size van but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.142	GMC Loyalists	Auto	Loyalty	Auto		Household	Inferred	386,21	811,03	Households that Alliant has identified to be loyal in their continued purchase of a GMC. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.182	GMC Switchers	Auto	Loyalty	Auto		Household	Inferred	4,36	9,15	Households that Alliant has identified to be a defector from GMC, meaning they have owned a GMC but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.143	Hatchback Loyalists	Auto	Loyalty	Auto		Household	Inferred	657,22	1,38	Households that Alliant has identified to be loyal in their continued purchase of a hatchback. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.144	Honda Loyalists	Auto	Loyalty	Auto		Household	Inferred	1,62	3,41	Households that Alliant has identified to be loyal in their continued purchase of a Honda. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.184	Honda Switchers	Auto	Loyalty	Auto		Household	Inferred	9,80	20,59	Households that Alliant has identified to be a defector from Honda, meaning they have owned a Honda but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.145	Hyundai Loyalists	Auto	Loyalty	Auto		Household	Inferred	496,27	1,04	Households that Alliant has identified to be loyal in their continued purchase of a Hyundai. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.185	Hyundai Switchers	Auto	Loyalty	Auto		Household	Inferred	4,53	9,52	Households that Alliant has identified to be a defector from Hyundai, meaning they have owned a Hyundai but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.186	Infiniti Switchers	Auto	Loyalty	Auto		Household	Inferred	1,10	2,32	Households that Alliant has identified to be a defector from Infiniti, meaning they have owned a Infiniti but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.146	Jeep Loyalists	Auto	Loyalty	Auto		Household	Inferred	455,97	957,54	Households that Alliant has identified to be loyal in their continued purchase of a Jeep. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.188	Jeep Switchers	Auto	Loyalty	Auto		Household	Inferred	5,27	11,06	Households that Alliant has identified to be a defector from Jeep, meaning they have owned a Jeep but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.147	Kia Loyalists	Auto	Loyalty	Auto		Household	Inferred	295,04	619,58	Households that Alliant has identified to be loyal in their continued purchase of a Kia. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.189	Kia Switchers	Auto	Loyalty	Auto		Household	Inferred	3,37	7,08	Households that Alliant has identified to be a defector from Kia, meaning they have owned a Kia but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.190	Land Rover Switchers	Auto	Loyalty	Auto		Household	Inferred	327,12	686,94	Households that Alliant has identified to be a defector from Land Rover, meaning they have owned a Land Rover but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.148	Lexus Loyalists	Auto	Loyalty	Auto		Household	Inferred	345,76	726,10	Households that Alliant has identified to be loyal in their continued purchase of a Lexus. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.191	Lexus Switchers	Auto	Loyalty	Auto		Household	Inferred	1,38	2,90	Households that Alliant has identified to be a defector from Lexus, meaning they have owned a Lexus but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.192	Lincoln Switchers	Auto	Loyalty	Auto		Household	Inferred	871,58	1,83	Households that Alliant has identified to be a defector from Lincoln, meaning they have owned a Lincoln but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.193	Luxury Car Switchers	Auto	Loyalty	Auto		Household	Inferred	152,04	319,29	Households that Alliant has identified to be a defector from luxury cars, meaning they have owned a luxury car but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.194	Mazda Switchers	Auto	Loyalty	Auto		Household	Inferred	1,65	3,46	Households that Alliant has identified to be a defector from Mazda, meaning they have owned a Mazda but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.149	Mercedes-Benz Loyalists	Auto	Loyalty	Auto		Household	Inferred	294,34	618,11	Households that Alliant has identified to be loyal in their continued purchase of a Mercedes-Benz. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.195	Mercedes-Benz Switchers	Auto	Loyalty	Auto		Household	Inferred	1,18	2,48	Households that Alliant has identified to be a defector from Mercedes-Benz, meaning they have owned a Mercedes-Benz but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.150	Mid Size Car Loyalists	Auto	Loyalty	Auto		Household	Inferred	6,01	12,61	Households that Alliant has identified to be loyal in their continued purchase of a mid-size car. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.196	Mid Size SUV Switchers	Auto	Loyalty	Auto		Household	Inferred	461,54	969,24	Households that Alliant has identified to be a defector from midsize SUVs, meaning they have owned a mid-size SUV but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.197	Mid-Size Car Switchers	Auto	Loyalty	Auto		Household	Inferred	6,68	14,03	Households that Alliant has identified to be a defector from mid-size cars, meaning they have owned a mid-size car but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.198	Mid-Size Truck Switchers	Auto	Loyalty	Auto		Household	Inferred	1,13	2,37	Households that Alliant has identified to be a defector from mid-size trucks, meaning they have owned a mid-sized truck but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.151	Mini Van Loyalists	Auto	Loyalty	Auto		Household	Inferred	1,78	3,75	Households that Alliant has identified to be loyal in their continued purchase of a mini van. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.200	Mini Van Switchers	Auto	Loyalty	Auto		Household	Inferred	3,35	7,04	Households that Alliant has identified to be a defector from mini vans, meaning they have owned a mini van but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.152	Nissan Loyalists	Auto	Loyalty	Auto		Household	Inferred	883,64	1,86	Households that Alliant has identified to be loyal in their continued purchase of a Nissan. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.202	Nissan Switchers	Auto	Loyalty	Auto		Household	Inferred	4,23	8,88	Households that Alliant has identified to be a defector from Nissan, meaning they have owned a Nissan but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.153	Pickup Truck Loyalist	Auto	Loyalty	Auto		Household	Inferred	8,37	17,59	Households that Alliant has identified to be loyal in their continued purchase of a pickup truck. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.203	Pickup Truck Switchers	Auto	Loyalty	Auto		Household	Inferred	7,38	15,50	Households that Alliant has identified to be a defector from pickup trucks, meaning they have owned a pickup truck but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.154	Sedan Loyalists	Auto	Loyalty	Auto		Household	Inferred	14,42	30,28	Households that Alliant has identified to be loyal in their continued purchase of a sedan. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.205	Sedan Switchers	Auto	Loyalty	Auto		Household	Inferred	9,46	19,87	Households that Alliant has identified to be a defector from sedans, meaning they have owned a sedans but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.155	Small Car Loyalists	Auto	Loyalty	Auto		Household	Inferred	10,31	21,64	Households that Alliant has identified to be loyal in their continued purchase of a small car. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.206	Small Car Switchers	Auto	Loyalty	Auto		Household	Inferred	8,07	16,95	Households that Alliant has identified to be a defector from small cars, meaning they have owned a small car but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.156	Small SUV Loyalists	Auto	Loyalty	Auto		Household	Inferred	3,49	7,33	Households that Alliant has identified to be loyal in their continued purchase of a small SUV. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.207	Small SUV Switchers	Auto	Loyalty	Auto		Household	Inferred	5,56	11,68	Households that Alliant has identified to be a defector from small SUV, meaning they have owned a Small SUV but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.157	Small Truck Loyalists	Auto	Loyalty	Auto		Household	Inferred	358,03	751,85	Households that Alliant has identified to be loyal in their continued purchase of a small truck. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.208	Small Truck Switchers	Auto	Loyalty	Auto		Household	Inferred	1,76	3,70	Households that Alliant has identified to be a defector from Small Truck, meaning they have owned a small truck but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.209	Subaru Switchers	Auto	Loyalty	Auto		Household	Inferred	1,44	3,03	Households that Alliant has identified to be a defector from Subaru, meaning they have owned a Subaru but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.158	Toyota Loyalists	Auto	Loyalty	Auto		Household	Inferred	2,51	5,26	Households that Alliant has identified to be loyal in their continued purchase of a Toyota. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.211	Toyota Switchers	Auto	Loyalty	Auto		Household	Inferred	7,04	14,77	Households that Alliant has identified to be a defector from Toyota, meaning they have owned a Toyota but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.212	Utility Van Switchers	Auto	Loyalty	Auto		Household	Inferred	8,86	18,60	Households that Alliant has identified to be a defector from utility vans, meaning they have owned a utility van but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.160	Van Loyalists	Auto	Loyalty	Auto		Household	Inferred	1,96	4,12	Households that Alliant has identified to be loyal in their continued purchase of a van. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.214	Volkswagen Switchers	Auto	Loyalty	Auto		Household	Inferred	1,69	3,56	Households that Alliant has identified to be a defector from Volkswagen, meaning they have owned a Volkswagen but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.215	Volvo Switchers	Auto	Loyalty	Auto		Household	Inferred	656,31	1,38	Households that Alliant has identified to be a defector from Volvo, meaning they have owned a Volvo but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.161	Wagon Loyalists	Auto	Loyalty	Auto		Household	Inferred	966,39	2,03	Households that Alliant has identified to be loyal in their continued purchase of a wagon. Identified by known historic auto ownership data through nationwide dealership and service department records.
12.216	Wagon Switchers	Auto	Loyalty	Auto		Household	Inferred	2,92	6,13	Households that Alliant has identified to be a defector from wagons, meaning they have owned a wagon but do not have a repeat history of purchasing that vehicle type. Identified by known historic auto ownership data through nationwide dealership and service department records.
161.822	0 – 30k Miles	Auto	Mileage Ranges	Auto		Household	Known	11,87	24,93	Households that own a vehicle with mileage ranging between 0-30k, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
161.828	100K+ Miles	Auto	Mileage Ranges	Auto		Household	Known	5,62	11,81	Households that own a vehicle with mileage over 100k, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
161.823	30k – 50k Miles	Auto	Mileage Ranges	Auto		Household	Known	4,56	9,58	Households that own a vehicle with mileage ranging between 30-50k, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
161.825	50k – 75k Miles	Auto	Mileage Ranges	Auto		Household	Known	3,47	7,28	Households that own a vehicle with mileage ranging between 50-75k, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
161.827	75k – 100k Miles	Auto	Mileage Ranges	Auto		Household	Known	4,05	8,51	Households that own a vehicle with mileage ranging between 75-100k, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.109	Auto Lease Propensity	Auto	Ownership	Auto		Household	Modeled	12,93	27,16	This audience consists of households in the top 20% of a model predicting the likelihood that a member leases a car. The model is built using survey data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The combined data identifies households in the cooperative that have shared characteristics.
12.098	Emerging Consumers - Clunker Owners	Auto	Ownership	Auto		Household	Modeled	6,04	12,68	Households that own a clunker car, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the bottom 50% of buying activity in the Alliant cooperative.)
12.099	Emerging Consumers - Economy Car Owners	Auto	Ownership	Auto		Household	Modeled	13,14	27,59	Households that own an economy car, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the bottom 50% of buying activity in the Alliant cooperative.)
12.100	Emerging Consumers - Green Car Owners	Auto	Ownership	Auto		Household	Modeled	3,16	6,63	Households that own a green car, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the bottom 50% of buying activity in the Alliant cooperative.)
12.101	Emerging Consumers - Luxury Car Owners	Auto	Ownership	Auto		Household	Modeled	2,61	5,49	Households that own a luxury car, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the bottom 50% of buying activity in the Alliant cooperative.)
12.102	Emerging Consumers - Mini Van Owners	Auto	Ownership	Auto		Household	Modeled	2,60	5,46	Households that own a mini van, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the bottom 50% of buying activity in the Alliant cooperative.)
12.103	Emerging Consumers - Multi-Car Owners	Auto	Ownership	Auto		Household	Modeled	13,51	28,38	Households that own multiple cars, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the bottom 50% of buying activity in the Alliant cooperative.)
12.104	Emerging Consumers - Sedan Owners	Auto	Ownership	Auto		Household	Modeled	14,55	30,55	Households that own a sedan, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the bottom 50% of buying activity in the Alliant cooperative.)
12.105	Emerging Consumers - Sports Car Owners	Auto	Ownership	Auto		Household	Modeled	330,04	693,08	Households that own a sports car, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the bottom 50% of buying activity in the Alliant cooperative.)
12.106	Emerging Consumers - SUV Owners	Auto	Ownership	Auto		Household	Modeled	9,42	19,79	Households that own a SUV, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the bottom 50% of buying activity in the Alliant cooperative.)
12.107	Emerging Consumers - Truck Owners	Auto	Ownership	Auto		Household	Modeled	6,72	14,11	Households that own a truck, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the bottom 50% of buying activity in the Alliant cooperative.)
12.108	Emerging Consumers - Wagon Owners	Auto	Ownership	Auto		Household	Modeled	2,26	4,74	Households that own a wagon, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the bottom 50% of buying activity in the Alliant cooperative.)
12.113	Empowered Consumers - Clunker Owners	Auto	Ownership	Auto		Household	Modeled	8,17	17,16	Households that own a clunker car, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the top 50% of buying activity in the Alliant cooperative.)
12.114	Empowered Consumers - Economy Car Owners	Auto	Ownership	Auto		Household	Modeled	21,05	44,20	Households that own an economy car, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the top 50% of buying activity in the Alliant cooperative.
12.115	Empowered Consumers - Green Car Owners	Auto	Ownership	Auto		Household	Modeled	5,92	12,43	Households that own a green car, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the top 50% of buying activity in the Alliant cooperative.)
12.116	Empowered Consumers - Luxury Car Owners	Auto	Ownership	Auto		Household	Modeled	4,94	10,38	Households that own luxury cars, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the top 50% of buying activity in the Alliant cooperative.)
12.117	Empowered Consumers - Mini Van Owners	Auto	Ownership	Auto		Household	Modeled	4,49	9,44	Households that own mini vans, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the top 50% of buying activity in the Alliant cooperative.)
12.118	Empowered Consumers - Multi-Car Owners	Auto	Ownership	Auto		Household	Modeled	24,62	51,70	Households that own multiple cars, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the top 50% of buying activity in the Alliant cooperative.)
12.119	Empowered Consumers - Sedan Owners	Auto	Ownership	Auto		Household	Modeled	22,53	47,31	Households that own a sedan, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the top 50% of buying activity in the Alliant cooperative.)
12.120	Empowered Consumers - Sports Car Owners	Auto	Ownership	Auto		Household	Modeled	664,58	1,40	Households that own a sports car, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the top 50% of buying activity in the Alliant cooperative.)
12.121	Empowered Consumers - SUV Owners	Auto	Ownership	Auto		Household	Modeled	17,14	36,00	Households that own a SUV, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the top 50% of buying activity in the Alliant cooperative.)
12.122	Empowered Consumers - Truck Owners	Auto	Ownership	Auto		Household	Modeled	11,54	24,23	Households that own a truck, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the top 50% of buying activity in the Alliant cooperative.)
12.123	Empowered Consumers - Wagon Owners	Auto	Ownership	Auto		Household	Modeled	4,22	8,86	Households that own a wagon, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) who are scored with a payment performance model to identify those who are in the top 50% of buying activity in the Alliant cooperative.)
12.110	Hybrid Cars Propensity	Auto	Ownership	Auto		Household	Modeled	15,05	31,62	This audience consists of households in the top 20% of a model predicting the likelihood that they own or lease at least one hybrid vehicle. The model is built using survey data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The combined data identifies households in the cooperative that have shared characteristics.
12.111	Motorcycle Owner Propensity	Auto	Ownership	Auto		Household	Modeled	15,81	33,20	This audience consists of households in the top 20% of a model predicting the likelihood that they own a new or used motorcycle. The model is built using survey data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The combined data identifies households in the cooperative that have shared characteristics.
12.003	Own a BMW	Auto	Ownership	Auto		Household	Known	3,38	7,09	Households that own a BMW, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.005	Own a Cadillac	Auto	Ownership	Auto		Household	Known	3,32	6,96	Households that own a Cadillac, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.006	Own a Chevrolet	Auto	Ownership	Auto		Household	Known	23,85	50,09	Households that own a Chevrolet, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.007	Own a Chevrolet Blazer	Auto	Ownership	Auto		Household	Known	1,19	2,50	Households that own a Chevrolet Blazer, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.009	Own a Chevrolet Camaro	Auto	Ownership	Auto		Household	Known	933,04	1,96	Households that own a Chevrolet Camaro, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.012	Own a Chevrolet Cruze	Auto	Ownership	Auto		Household	Known	1,03	2,16	Households that own a Chevrolet Cruze, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.013	Own a Chevrolet Equinox	Auto	Ownership	Auto		Household	Known	1,76	3,69	Households that own a Chevrolet Equinox, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.014	Own a Chevrolet Impala	Auto	Ownership	Auto		Household	Known	2,39	5,02	Households that own a Chevrolet Impala, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.016	Own a Chevrolet Malibu	Auto	Ownership	Auto		Household	Known	2,52	5,30	Households that own a Chevrolet Malibu, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.020	Own a Chevrolet Suburban	Auto	Ownership	Auto		Household	Known	1,66	3,49	Households that own a Chevrolet Suburban, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.021	Own a Chevrolet Tahoe	Auto	Ownership	Auto		Household	Known	2,06	4,32	Households that own a Chevrolet Tahoe, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.023	Own a Chrysler	Auto	Ownership	Auto		Household	Known	6,20	13,03	Households that own a Chrysler, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.024	Own a Dodge	Auto	Ownership	Auto		Household	Known	13,84	29,07	Households that own a Dodge, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.025	Own a Dodge Caravan	Auto	Ownership	Auto		Household	Known	2,93	6,15	Households that own a Dodge Caravan, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.030	Own a Dodge RAM	Auto	Ownership	Auto		Household	Known	3,08	6,48	Households that own a Dodge RAM, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.032	Own a Ford	Auto	Ownership	Auto		Household	Known	25,33	53,19	Households that own a Ford, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.034	Own a Ford Escape	Auto	Ownership	Auto		Household	Known	2,59	5,44	Households that own a Ford Escape, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.036	Own a Ford Expedition	Auto	Ownership	Auto		Household	Known	1,73	3,63	Households that own a Ford Expedition, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.037	Own a Ford Explorer	Auto	Ownership	Auto		Household	Known	3,99	8,37	Households that own a Ford Explorer, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.039	Own a Ford Fusion	Auto	Ownership	Auto		Household	Known	1,82	3,81	Households that own a Ford Fusion, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.043	Own a Ford Truck	Auto	Ownership	Auto		Household	Known	7,14	15,00	Households that own a Ford Truck, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.045	Own a GMC	Auto	Ownership	Auto		Household	Known	6,77	14,22	Households that own a GMC, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.046	Own a GMC Envoy	Auto	Ownership	Auto		Household	Known	771,66	1,62	Households that own a GMC Envoy, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.047	Own a GMC Sierra	Auto	Ownership	Auto		Household	Known	2,54	5,33	Households that own a GMC Sierra, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.049	Own a Honda	Auto	Ownership	Auto		Household	Known	14,07	29,55	Households that own a Honda, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.050	Own a Honda Accord	Auto	Ownership	Auto		Household	Known	5,39	11,31	Households that own a Honda Accord, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.051	Own a Honda Civic	Auto	Ownership	Auto		Household	Known	3,87	8,13	Households that own a Honda Civic, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.052	Own a Honda CRV	Auto	Ownership	Auto		Household	Known	2,57	5,39	Households that own a Honda CRV, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.053	Own a Honda Odyssey	Auto	Ownership	Auto		Household	Known	1,94	4,07	Households that own a Honda Odyssey, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.054	Own a Honda Pilot	Auto	Ownership	Auto		Household	Known	1,47	3,08	Households that own a Honda Pilot, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.055	Own a Hyundai	Auto	Ownership	Auto		Household	Known	6,22	13,07	Households that own a Hyundai, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.058	Own a Jaguar	Auto	Ownership	Auto		Household	Known	401,89	843,96	Households that own a Jaguar, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.059	Own a Jeep	Auto	Ownership	Auto		Household	Known	7,68	16,13	Households that own a Jeep, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.060	Own a Jeep Cherokee	Auto	Ownership	Auto		Household	Known	1,21	2,54	Households that own a Jeep Cherokee, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.062	Own a Jeep Wrangler	Auto	Ownership	Auto		Household	Known	2,17	4,55	Households that own a Jeep Wrangler, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.063	Own a Kia	Auto	Ownership	Auto		Household	Known	4,61	9,69	Households that own a Kia, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.064	Own a Land Rover	Auto	Ownership	Auto		Household	Known	463,65	973,66	Households that own a Land Rover, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.065	Own a Lexus	Auto	Ownership	Auto		Household	Known	3,36	7,05	Households that own a Lexus, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.066	Own a Lincoln	Auto	Ownership	Auto		Household	Known	2,13	4,48	Households that own a Lincoln, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.067	Own a Mazda	Auto	Ownership	Auto		Household	Known	3,97	8,35	Households that own a Mazda, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.068	Own a Mercedes-Benz	Auto	Ownership	Auto		Household	Known	3,07	6,44	Households that own a Mercedes-Benz, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.070	Own a Mini	Auto	Ownership	Auto		Household	Known	490,59	1,03	Households that own a Mini, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.071	Own a Mitsubishi	Auto	Ownership	Auto		Household	Known	2,12	4,45	Households that own a Mitsubishi, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.072	Own a Nissan	Auto	Ownership	Auto		Household	Known	10,78	22,64	Households that own a Nissan, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.073	Own a Nissan Altima	Auto	Ownership	Auto		Household	Known	3,01	6,33	Households that own a Nissan Altima, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.074	Own a Nissan Frontier	Auto	Ownership	Auto		Household	Known	815,68	1,71	Households that own a Nissan Frontier, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.075	Own a Nissan Maxima	Auto	Ownership	Auto		Household	Known	1,30	2,74	Households that own a Nissan Maxima, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.076	Own a Nissan Pathfinder	Auto	Ownership	Auto		Household	Known	874,85	1,84	Households that own a Nissan Pathfinder, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.077	Own a Nissan Sentra	Auto	Ownership	Auto		Household	Known	1,57	3,30	Households that own a Nissan Sentra, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.081	Own a Porsche	Auto	Ownership	Auto		Household	Known	382,38	803,00	Households that own a Porsche, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.083	Own a Subaru	Auto	Ownership	Auto		Household	Known	3,04	6,38	Households that own a Subaru, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.084	Own a Toyota	Auto	Ownership	Auto		Household	Known	17,75	37,28	Households that own a Toyota, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.085	Own a Toyota 4Runner	Auto	Ownership	Auto		Household	Known	1,29	2,70	Households that own a Toyota 4Runner, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.086	Own a Toyota Avalon	Auto	Ownership	Auto		Household	Known	878,88	1,85	Households that own a Toyota Avalon, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.087	Own a Toyota Camry	Auto	Ownership	Auto		Household	Known	5,16	10,84	Households that own a Toyota Camry, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.088	Own a Toyota Corolla	Auto	Ownership	Auto		Household	Known	3,28	6,88	Households that own a Toyota Corolla, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.089	Own a Toyota Highlander	Auto	Ownership	Auto		Household	Known	1,46	3,07	Households that own a Toyota Highlander, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.090	Own a Toyota Prius	Auto	Ownership	Auto		Household	Known	1,27	2,67	Households that own a Toyota Prius, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.091	Own a Toyota RAV4	Auto	Ownership	Auto		Household	Known	1,72	3,61	Households that own a Toyota RAV4, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.092	Own a Toyota Sienna	Auto	Ownership	Auto		Household	Known	1,39	2,93	Households that own a Toyota Sienna, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.093	Own a Toyota Tacoma	Auto	Ownership	Auto		Household	Known	1,73	3,62	Households that own a Toyota Tacoma, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.094	Own a Toyota Tundra	Auto	Ownership	Auto		Household	Known	1,31	2,75	Households that own a Toyota Tundra, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.095	Own a Volkswagen	Auto	Ownership	Auto		Household	Known	4,01	8,43	Households that own a Volkswagen, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.096	Own a Volvo	Auto	Ownership	Auto		Household	Known	1,46	3,07	Households that own a Volvo, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.001	Own an Acura	Auto	Ownership	Auto		Household	Known	2,54	5,34	Households that own an Acura, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.002	Own an Audi	Auto	Ownership	Auto		Household	Known	1,47	3,10	Households that own an Audi, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
12.056	Own an Infiniti	Auto	Ownership	Auto		Household	Known	1,54	3,23	Households that own an Infiniti, identified by nationwide dealership and service department records. These households are also known multichannel buyers (via digital and offline channels from DTC businesses) in the Alliant cooperative.
240.368	C-Level Executives Under 50	B2B	Age	Demographics		Household	Known	1,09	2,29	The C-level Executives Under 50 audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.377	Executives In Market for New Vehicle	B2B	Auto	Auto		Household	Modeled	1,60	3,36	The Executives In Market for New Vehicle audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.405	Executives with Apple Buyer Propensity	B2B	Brands	Tech		Household	Modeled	2,89	6,08	The Executives with Apple Buyer Interest audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.407	Executives with Dell Buyer Propensity	B2B	Brands	Tech		Household	Modeled	2,51	5,27	The Executives with Dell Buyer Interest audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.409	Executives with Lyft Buyer Propensity	B2B	Brands	Travel		Household	Modeled	2,13	4,47	The Executives with Lyft Buyer Interest audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.406	Executives with Microsoft Buyer Propensity	B2B	Brands	Tech		Household	Modeled	2,43	5,11	The Executives with Microsoft Buyer Interest audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.408	Executives with Samsung Buyer Propensity	B2B	Brands	Tech		Household	Modeled	2,74	5,76	The Executives with Samsung Buyer Interest audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.412	Executives with AT&T Buyer Propensity	B2B	Brands	Telecom		Household	Modeled	2,90	6,10	The Executives with AT&T Buyer Interest audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.411	Executives with T-Mobile Buyer Propensity	B2B	Brands	Telecom		Household	Modeled	2,43	5,10	The Executives with T-Mobile Buyer Interest audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.410	Executives with Uber Buyer Propensity	B2B	Brands	Travel		Household	Modeled	2,18	4,58	The Executives with Uber Buyer Interest audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.413	Executives with Verizon Buyer Propensity	B2B	Brands	Telecom		Household	Modeled	2,49	5,23	The Executives with Verizon Buyer Interest audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.373	Small Business Owners at Home who use AMEX	B2B	Card Type	Home		Household	Known	199,35	418,63	The Small Business Owners who use AMEX audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.372	Small Business Owners at Home who use Discover Card	B2B	Card Type	Home		Household	Known	141,12	296,35	The Small Business Owners who use Discover Card audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.371	Small Business Owners at Home who use Mastercard	B2B	Card Type	Home		Household	Known	324,47	681,39	The Small Business Owners who use Mastercard audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.374	Small Business Owners at Home who use Visa	B2B	Card Type	Home		Household	Known	466,69	980,05	The Small Business Owners who use Visa audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.369	Executives with Children 0-12	B2B	Family	Demographics		Household	Known	1,64	3,43	The Executives with Children 0-12 audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.370	Executives with Teens	B2B	Family	Demographics		Household	Known	1,07	2,24	The Executives with Teens audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.399	Female C-Suite at Home	B2B	Gender	Home		Household	Known	1,00	2,11	The Female C-Suite audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.397	Female Executives at Home	B2B	Gender	Home		Household	Known	3,83	8,04	The Female Executives audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.400	Male C-Suite at Home	B2B	Gender	Home		Household	Known	917,18	1,93	The Male C-Suite audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.398	Male Executives at Home	B2B	Gender	Home		Household	Known	2,47	5,19	The Male Executives audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.379	Executives Who Have Recently Moved	B2B	New Movers	Demographics		Household	Known	216,21	454,04	The Executives Who Have Recently Moved audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.381	Architecture & Engineering Professionals at Home	B2B	Occupation	Home		Household	Known	118,98	249,86	The Architecture & Engineering Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.382	Arts & Entertainment Professionals at Home	B2B	Occupation	Home		Household	Known	280,25	588,53	The Arts & Entertainment Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.383	Construction & Maintenance Professionals at Home	B2B	Occupation	Home		Household	Known	406,37	853,38	The Construction & Maintenance Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.392	Education Professionals at Home	B2B	Occupation	Home		Household	Modeled	537,34	1,13	The Education Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.380	Finance & Banking Professionals at Home	B2B	Occupation	Home		Household	Known	435,41	914,37	The Finance & Banking Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.384	Food Prep & Hospitality Professionals at Home	B2B	Occupation	Home		Household	Known	132,89	279,07	The Food Prep & Hospitality Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.391	Healthcare Professionals at Home	B2B	Occupation	Home		Household	Known	462,51	971,26	The Healthcare Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.385	IT Professionals at Home	B2B	Occupation	Home		Household	Known	191,17	401,45	The IT Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.386	Legal Professionals at Home	B2B	Occupation	Home		Household	Known	112,80	236,87	The Legal Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.390	Real Estate & Insurance Professionals at Home	B2B	Occupation	Home		Household	Known	697,60	1,46	The Real Estate & Insurance Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.388	Religious Professionals at Home	B2B	Occupation	Home		Household	Known	122,54	257,33	The Religious Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.387	Sales & Marketing Professionals at Home	B2B	Occupation	Home		Household	Known	351,31	737,75	The Sales & Marketing Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.389	Sports & Training Professionals at Home	B2B	Occupation	Home		Household	Known	245,73	516,02	The Sports & Training Professionals audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.376	Democrat Executives	B2B	Party Affiliations	Politics		Household	Known	948,13	1,99	The Democrat Executives audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.375	Republican Executives	B2B	Party Affiliations	Politics		Household	Known	972,84	2,04	The Republican Executives audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.395	Executives Interested in New York Post	B2B	Publications	Media & Entertainment		Household	Modeled	3,12	6,55	The Executives Interested in New York Post audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.394	Executives Interested in New York Times	B2B	Publications	Media & Entertainment		Household	Modeled	2,42	5,09	The Executives Interested in New York Times audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.393	Executives Interested in Wall Street Journal	B2B	Publications	Media & Entertainment		Household	Modeled	1,87	3,93	The Executives Interested in Wall Street Journal audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.396	Executives Interested in Washington Post	B2B	Publications	Media & Entertainment		Household	Modeled	2,86	6,00	The Executives Interested in Washington Post audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.403	C-Level Executives with JetBlue Interest	B2B	Travel	Travel		Household	Modeled	620,84	1,30	The C-Level Executives with JetBlue Interest audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.404	C-Level Executives with United Airlines Interest	B2B	Travel	Travel		Household	Modeled	705,86	1,48	The C-Level Executives with United Airlines Interest audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.401	Domestic Traveling Executives	B2B	Travel	Travel		Household	Known	2,30	4,82	The Domestic Traveling Executives audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
240.402	International Traveling Executives	B2B	Travel	Travel		Household	Known	2,25	4,73	The International Traveling Executives audience enables you to reach this niche segment outside of the office, across devices. They are also known direct-to-consumer buyers within the Alliant DataHub.
294.350	Top Golf	Brand Propensities	Activities & Interests	Sports		Household	Modeled	13,82	29,03	This audience consists of households in the top 15-20% of a model predicting a purchase from Top Golf. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.357	Alcoholic Beverages	Brand Propensities	Alcoholic Beverages	Food & Beverage		Household	Modeled	14,90	31,29	This audience consists of households in the top 15-20% of a model predicting a purchase from the Alcoholic Beverages category. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.124	Saucey Buyer Propensity	Brand Propensities	Alcoholic Beverages	Food & Beverage		Household	Modeled	14,02	29,44	This audience consists of households in the top 15-20% of a model predicting a purchase from Saucey. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.239	Total wine & more Buyer Propensity	Brand Propensities	Alcoholic Beverages	Food & Beverage		Household	Modeled	15,33	32,20	This audience consists of households in the top 15-20% of a model predicting a purchase from Total wine & more. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.264	WSJwine Buyer Propensity	Brand Propensities	Alcoholic Beverages	Food & Beverage		Household	Modeled	14,76	30,99	This audience consists of households in the top 15-20% of a model predicting a purchase from WSJwine. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.712	Bevmo Buyer Propensity	Brand Propensities	Alcoholic Beverages	Food & Beverage		Household	Modeled	15,60	32,75	This audience consists of households in the top 10-20% of a model predicting a purchase from Bevmo. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.220	Abercrombie & Fitch Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	16,40	34,43	This audience consists of households in the top 15-20% of a model predicting a purchase from Abercrombie & Fitch. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.221	Adidas Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,98	31,45	This audience consists of households in the top 15-20% of a model predicting a purchase from Adidas. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.474	Adore Me Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,37	28,07	This audience consists of households in the top 15-20% of a model predicting a purchase from Adore Me. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.222	ALDO Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,57	32,69	This audience consists of households in the top 15-20% of a model predicting a purchase from ALDO. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.076	Allbirds Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,29	30,02	This audience consists of households in the top 15-20% of a model predicting a purchase from Allbirds. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.223	American Eagle Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,27	27,86	This audience consists of households in the top 15-20% of a model predicting a purchase from American Eagle. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.225	Ann Taylor Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,37	30,18	This audience consists of households in the top 15-20% of a model predicting a purchase from Ann Taylor. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.226	Anthropologie Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,48	32,51	This audience consists of households in the top 15-20% of a model predicting a purchase from Anthropologie. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.261	Ashley Stewart Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,63	26,52	This audience consists of households in the top 15-20% of a model predicting a purchase from Ashley Stewart. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.228	Asics Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,96	29,32	This audience consists of households in the top 15-20% of a model predicting a purchase from Asics. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.229	asos Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	16,65	34,97	This audience consists of households in the top 15-20% of a model predicting a purchase from asos. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.230	Banana Republic Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,41	32,36	This audience consists of households in the top 15-20% of a model predicting a purchase from Banana Republic. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.231	Bare Necessities Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	10,10	21,22	This audience consists of households in the top 15-20% of a model predicting a purchase from Bare Necessities. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.278	BCBG Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	18,40	38,63	This audience consists of households in the top 15-20% of a model predicting a purchase from BCBG. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.232	Belk Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	11,15	23,41	This audience consists of households in the top 15-20% of a model predicting a purchase from Belk. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.235	Bloomingdale's Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,19	31,90	This audience consists of households in the top 15-20% of a model predicting a purchase from Bloomingdale's. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.236	Blue Nile Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,25	29,94	This audience consists of households in the top 15-20% of a model predicting a purchase from Blue Nile. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.285	Bonobos Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,32	32,17	This audience consists of households in the top 15-20% of a model predicting a purchase from Bonobos. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.237	boohoo Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,56	30,58	This audience consists of households in the top 15-20% of a model predicting a purchase from boohoo. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.238	Boot Barn Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,37	25,97	This audience consists of households in the top 15-20% of a model predicting a purchase from Boot Barn. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.286	Brandy Melville Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,31	30,05	This audience consists of households in the top 15-20% of a model predicting a purchase from Brandy Melville. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.239	Brooks Brothers Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,88	29,15	This audience consists of households in the top 15-20% of a model predicting a purchase from Brooks Brothers. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.298	Burlington Coat Factory Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,58	28,53	This audience consists of households in the top 15-20% of a model predicting a purchase from Burlington Coat Factory. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.301	Calvin Klein Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	18,67	39,21	This audience consists of households in the top 15-20% of a model predicting a purchase from Calvin Klein. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.241	Carters Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	11,75	24,68	This audience consists of households in the top 15-20% of a model predicting a purchase from Carters. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.510	Casual Male XL Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,74	30,96	This audience consists of households in the top 15-20% of a model predicting a purchase from Casual Male XL. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.242	Chico's Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	6,64	13,94	This audience consists of households in the top 15-20% of a model predicting a purchase from Chico's. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.316	Chico's FAS Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,20	27,71	This audience consists of households in the top 15-20% of a model predicting a purchase from Chico’s FAS. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.317	Club Monaco Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,06	31,62	This audience consists of households in the top 15-20% of a model predicting a purchase from Club Monaco. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.318	Cole Haan Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	16,02	33,64	This audience consists of households in the top 15-20% of a model predicting a purchase from Cole Haan. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.246	Converse Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,70	26,66	This audience consists of households in the top 15-20% of a model predicting a purchase from Converse. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.300	Cotton On	Brand Propensities	Apparel	Retail		Household	Modeled	14,66	30,78	This audience consists of households in the top 15-20% of a model predicting a purchase from Cotton On. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.247	Crocs Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,60	28,56	This audience consists of households in the top 15-20% of a model predicting a purchase from Crocs. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.250	Dillards Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,53	26,31	This audience consists of households in the top 15-20% of a model predicting a purchase from Dillards. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.137	Dolce Vita Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,14	29,69	This audience consists of households in the top 15-20% of a model predicting a purchase from Dolce Vita. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.251	DSW Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,28	27,89	This audience consists of households in the top 15-20% of a model predicting a purchase from DSW. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.252	Duluth Trading Company Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	11,18	23,49	This audience consists of households in the top 15-20% of a model predicting a purchase from Duluth Trading Company. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.253	Eddie Bauer Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,31	25,85	This audience consists of households in the top 15-20% of a model predicting a purchase from Eddie Bauer. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.254	Express Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	16,44	34,52	This audience consists of households in the top 15-20% of a model predicting a purchase from Express. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.255	Fabletics Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,02	31,54	This audience consists of households in the top 15-20% of a model predicting a purchase from Fabletics. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.108	Famous Footwear Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,39	26,03	This audience consists of households in the top 15-20% of a model predicting a purchase from Famous Footwear. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.256	Foot Locker Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,80	28,98	This audience consists of households in the top 15-20% of a model predicting a purchase from Foot Locker. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.258	Forever 21 Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,46	30,36	This audience consists of households in the top 15-20% of a model predicting a purchase from Forever 21. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.259	Fossil Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,93	29,26	This audience consists of households in the top 15-20% of a model predicting a purchase from Fossil. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.260	Gap Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,47	30,38	This audience consists of households in the top 15-20% of a model predicting a purchase from Gap. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.093	Guess Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,98	29,35	This audience consists of households in the top 15-20% of a model predicting a purchase from Guess. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.264	H&M Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,47	32,49	This audience consists of households in the top 15-20% of a model predicting a purchase from H&M. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.265	Hanes Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,35	25,94	This audience consists of households in the top 15-20% of a model predicting a purchase from Hanes. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.098	Hot Topic Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,06	27,44	This audience consists of households in the top 15-20% of a model predicting a purchase from Hot Topic. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.269	Hush Puppies Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	17,95	37,69	This audience consists of households in the top 15-20% of a model predicting a purchase from Hush Puppies. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.208	Indochino Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,24	32,01	This audience consists of households in the top 15-20% of a model predicting a purchase from Indochino. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.269	J.Crew Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,80	28,98	This audience consists of households in the top 15-20% of a model predicting a purchase from J.Crew. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.270	J.JILL Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,15	27,61	This audience consists of households in the top 15-20% of a model predicting a purchase from J.JILL. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.211	JanSport Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	11,89	24,98	This audience consists of households in the top 15-20% of a model predicting a purchase from JanSport. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.271	JCPenney Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	11,54	24,23	This audience consists of households in the top 15-20% of a model predicting a purchase from JCPenney. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.272	Jordan Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	11,44	24,01	This audience consists of households in the top 15-20% of a model predicting a purchase from Jordan. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.213	JoS. A. Bank Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	16,56	34,77	This audience consists of households in the top 15-20% of a model predicting a purchase from JoS. A. Bank. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.273	Journeys Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,18	25,57	This audience consists of households in the top 15-20% of a model predicting a purchase from Journeys. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.214	JustFab Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,76	26,79	This audience consists of households in the top 15-20% of a model predicting a purchase from JustFab. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.273	Kate Spade Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,07	29,55	This audience consists of households in the top 15-20% of a model predicting a purchase from Kate Spade. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.275	Keds Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,61	26,47	This audience consists of households in the top 15-20% of a model predicting a purchase from Keds. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.177	Kipling Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	11,71	24,60	This audience consists of households in the top 15-20% of a model predicting a purchase from Kipling. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.274	L.L.Bean Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,00	25,20	This audience consists of households in the top 15-20% of a model predicting a purchase from L.L.Bean. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.275	Lady Foot Locker Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	11,24	23,61	This audience consists of households in the top 15-20% of a model predicting a purchase from Lady Foot Locker. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.277	Lane Bryant Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,38	28,10	This audience consists of households in the top 15-20% of a model predicting a purchase from Lane Bryant. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.278	Levi's Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	18,27	38,37	This audience consists of households in the top 15-20% of a model predicting a purchase from Levi's. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.279	Lids Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,59	26,44	This audience consists of households in the top 15-20% of a model predicting a purchase from Lids. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.179	Lilly Pulitzer Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,47	28,29	This audience consists of households in the top 15-20% of a model predicting a purchase from Lilly Pulitzer. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.281	Loft Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,46	28,27	This audience consists of households in the top 15-20% of a model predicting a purchase from LOFT. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.282	Lord & Taylor Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,57	28,49	This audience consists of households in the top 15-20% of a model predicting a purchase from Lord & Taylor. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.283	Lululemon Athletica Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,57	30,60	This audience consists of households in the top 15-20% of a model predicting a purchase from Lululemon Athletica. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.181	Lulus Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,82	31,13	This audience consists of households in the top 15-20% of a model predicting a purchase from Lulus. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.284	Macy's Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,61	28,58	This audience consists of households in the top 15-20% of a model predicting a purchase from Macy's. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.327	Madewell	Brand Propensities	Apparel	Retail		Household	Modeled	15,53	32,62	This audience consists of households in the top 15-20% of a model predicting a purchase from Madewell. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.484	Marshalls Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,83	31,14	This audience consists of households in the top 15-20% of a model predicting a purchase from Marshalls. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.319	Men's Wearhouse Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,43	28,21	This audience consists of households in the top 15-20% of a model predicting a purchase from Men's Wearhouse. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.312	Merrell Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,54	26,34	This audience consists of households in the top 15-20% of a model predicting a purchase from Merrell. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.486	MeUndies Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,46	32,46	This audience consists of households in the top 15-20% of a model predicting a purchase from MeUndies. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.286	Michael Kors Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,14	27,59	This audience consists of households in the top 15-20% of a model predicting a purchase from Michael Kors. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.149	Nautica Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	11,59	24,33	This audience consists of households in the top 15-20% of a model predicting a purchase from Nautica. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.288	Neiman Marcus Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	16,37	34,37	This audience consists of households in the top 15-20% of a model predicting a purchase from Neiman Marcus. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.290	New Balance Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,58	32,72	This audience consists of households in the top 15-20% of a model predicting a purchase from New Balance. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.291	New York & Company Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,94	27,17	This audience consists of households in the top 15-20% of a model predicting a purchase from New York & Company. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.292	Nike Buyer Propensity	Brand Propensities	Apparel	Retail	Sports	Household	Modeled	14,69	30,86	This audience consists of households in the top 15-20% of a model predicting a purchase from Nike. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.331	Nobull	Brand Propensities	Apparel	Retail		Household	Modeled	13,40	28,13	This audience consists of households in the top 15-20% of a model predicting a purchase from Nobull. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.293	Nordstrom Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,58	30,62	This audience consists of households in the top 15-20% of a model predicting a purchase from Nordstrom. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.294	Nordstrom Rack Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,33	30,10	This audience consists of households in the top 15-20% of a model predicting a purchase from Nordstrom Rack. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.080	Oakley Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,22	25,65	This audience consists of households in the top 15-20% of a model predicting a purchase from Oakley. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.295	Old Navy Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,56	28,48	This audience consists of households in the top 15-20% of a model predicting a purchase from Old Navy. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.332	Pacific Sunwear	Brand Propensities	Apparel	Retail		Household	Modeled	14,09	29,60	This audience consists of households in the top 15-20% of a model predicting a purchase from Pacific Sunwear. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.297	Pandora Jewelry Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,48	32,50	This audience consists of households in the top 15-20% of a model predicting a purchase from Pandora Jewelry. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.298	Patagonia Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,05	29,51	This audience consists of households in the top 15-20% of a model predicting a purchase from Patagonia. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.301	Puma Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,23	29,89	This audience consists of households in the top 15-20% of a model predicting a purchase from Puma. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.302	Ralph Lauren Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	16,03	33,66	This audience consists of households in the top 15-20% of a model predicting a purchase from Ralph Lauren. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.303	Ray-Ban Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,34	32,21	This audience consists of households in the top 15-20% of a model predicting a purchase from Ray Ban. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.119	Reebok Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	16,63	34,93	This audience consists of households in the top 15-20% of a model predicting a purchase from Reebok. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.121	Rent the Runway Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,80	33,19	This audience consists of households in the top 15-20% of a model predicting a purchase from Rent the Runway. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.168	Ross Dress For Less Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,84	31,16	This audience consists of households in the top 15-20% of a model predicting a purchase from Ross Dress For Less. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.304	Rue La La Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,89	31,27	This audience consists of households in the top 15-20% of a model predicting a purchase from Rue La La. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.307	Saks Fifth Avenue OFF 5TH Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,90	33,40	This audience consists of households in the top 15-20% of a model predicting a purchase from Saks Fifth Avenue OFF 5TH. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.123	Samsonite Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,75	30,97	This audience consists of households in the top 15-20% of a model predicting a purchase from Samsonite. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.172	Saucony Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,25	27,83	This audience consists of households in the top 15-20% of a model predicting a purchase from Saucony. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.138	Shoe Carnival Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	10,77	22,63	This audience consists of households in the top 15-20% of a model predicting a purchase from Shoe Carnival. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.310	Shopbop Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	22,02	46,25	This audience consists of households in the top 15-20% of a model predicting a purchase from Shopbop. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.311	Sierra Trading Post Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,53	26,31	This audience consists of households in the top 15-20% of a model predicting a purchase from Sierra Trading Post. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.312	Skechers Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,30	25,82	This audience consists of households in the top 15-20% of a model predicting a purchase from Skechers. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.313	Soma.com Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,41	28,16	This audience consists of households in the top 15-20% of a model predicting a purchase from Soma.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.127	Spanx Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,63	28,62	This audience consists of households in the top 15-20% of a model predicting a purchase from Spanx. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.314	Steven Madden Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	19,64	41,24	This audience consists of households in the top 15-20% of a model predicting a purchase from Steven Madden. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.128	Stitch fix Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,17	29,77	This audience consists of households in the top 15-20% of a model predicting a purchase from Stitch fix. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.146	Stride Rite Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,00	25,20	This audience consists of households in the top 15-20% of a model predicting a purchase from Stride Rite. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.147	Sunglass Hut Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,45	28,24	This audience consists of households in the top 15-20% of a model predicting a purchase from Sunglass Hut. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.315	T.J. Maxx Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,87	31,23	This audience consists of households in the top 15-20% of a model predicting a purchase from T.J. Maxx. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.316	Talbots Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,07	27,45	This audience consists of households in the top 15-20% of a model predicting a purchase from Talbots. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.320	The North Face Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,20	27,72	This audience consists of households in the top 15-20% of a model predicting a purchase from The North face. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.539	ThirdLove Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,16	31,83	This audience consists of households in the top 15-20% of a model predicting a purchase from ThirdLove. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.115	Thursday Boot Co. Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	19,20	40,32	This audience consists of households in the top 15-20% of a model predicting a purchase from Thursday Boot Co. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.323	Timberland Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,20	29,81	This audience consists of households in the top 15-20% of a model predicting a purchase from Timberland. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.237	Tommy Bahama Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,13	27,58	This audience consists of households in the top 15-20% of a model predicting a purchase from Tommy Bahama. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.238	Tommy Hilfiger Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,20	29,81	This audience consists of households in the top 15-20% of a model predicting a purchase from Tommy Hilfiger. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.541	Tommy John Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,87	31,23	This audience consists of households in the top 15-20% of a model predicting a purchase from Tommy John. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.324	TOMS Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,08	27,47	This audience consists of households in the top 15-20% of a model predicting a purchase from TOMS. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.325	Tory Burch Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,55	30,56	This audience consists of households in the top 15-20% of a model predicting a purchase from Tory Burch. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.326	UGG Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,76	33,09	This audience consists of households in the top 15-20% of a model predicting a purchase from UGG. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.327	Under Armour Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	11,74	24,65	This audience consists of households in the top 15-20% of a model predicting a purchase from Under Armour. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.226	Uniqlo Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,20	31,93	This audience consists of households in the top 15-20% of a model predicting a purchase from Uniqlo. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.328	Urban Outfitters Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	16,35	34,33	This audience consists of households in the top 15-20% of a model predicting a purchase from Urban Outfitters. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.229	Vans Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,54	30,54	This audience consists of households in the top 15-20% of a model predicting a purchase from Vans. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.330	Vera Bradley Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,96	27,22	This audience consists of households in the top 15-20% of a model predicting a purchase from Vera Bradley. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.331	Victoria's Secret Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,91	29,22	This audience consists of households in the top 15-20% of a model predicting a purchase from Victoria's Secret. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.332	Warby Parker Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,16	31,84	This audience consists of households in the top 15-20% of a model predicting a purchase from Warby Parker. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.333	White House Black Market Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	14,61	30,67	This audience consists of households in the top 15-20% of a model predicting a purchase from White House Black Market. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.263	Wrangler Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	11,65	24,47	This audience consists of households in the top 15-20% of a model predicting a purchase from Wrangler. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.336	Zappos Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,69	28,75	This audience consists of households in the top 15-20% of a model predicting a purchase from Zappos. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.337	ZARA Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	15,55	32,66	This audience consists of households in the top 15-20% of a model predicting a purchase from Zara. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.266	Zumiez Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,58	28,52	This audience consists of households in the top 15-20% of a model predicting a purchase from Zumiez. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.586	Country Outfitter Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,73	26,74	This audience consists of households in the top 10-20% of a model predicting a purchase from Country Outfitter. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.588	Dailylook Buyer Propensity	Brand Propensities	Apparel	Professional Services		Household	Modeled	14,93	31,35	This audience consists of households in the top 10-20% of a model predicting a purchase from Dailylook. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.598	Eyemart Express Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,44	26,12	This audience consists of households in the top 10-20% of a model predicting a purchase from Eyemart Express. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.604	Fit2Run Buyer Propensity	Brand Propensities	Apparel	Sports		Household	Modeled	13,36	28,06	This audience consists of households in the top 10-20% of a model predicting a purchase from Fit2Run. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.610	Generation Tux Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	13,26	27,85	This audience consists of households in the top 10-20% of a model predicting a purchase from Generation Tux. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.633	La Police Gear Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,16	25,53	This audience consists of households in the top 10-20% of a model predicting a purchase from La Police Gear. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.657	Patriot Outfitters Buyer Propensity	Brand Propensities	Apparel	Retail		Household	Modeled	12,65	26,55	This audience consists of households in the top 10-20% of a model predicting a purchase from Patriot Outfitters. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.664	Potpourri Buyer Propensity	Brand Propensities	Apparel	Home		Household	Modeled	13,53	28,40	This audience consists of households in the top 10-20% of a model predicting a purchase from Potpourri. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.339	Advance Auto Parts Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	12,42	26,09	This audience consists of households in the top 15-20% of a model predicting a purchase from Advance Auto Parts. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.340	AutoNation Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	15,41	32,35	This audience consists of households in the top 15-20% of a model predicting a purchase from AutoNation. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.341	AutoZone Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	13,72	28,81	This audience consists of households in the top 15-20% of a model predicting a purchase from AutoZone. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.280	Big O tires Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	13,17	27,67	This audience consists of households in the top 15-20% of a model predicting a purchase from Big O tires. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.296	Budget Truck	Brand Propensities	Auto	Auto		Household	Modeled	14,42	30,29	This audience consists of households in the top 15-20% of a model predicting a purchase from Budget Truck. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.302	Carmax Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	13,30	27,93	This audience consists of households in the top 15-20% of a model predicting a purchase from Carmax. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.303	Carquest Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	11,89	24,97	This audience consists of households in the top 15-20% of a model predicting a purchase from Carquest. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.322	Discount Tire Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	13,00	27,29	This audience consists of households in the top 15-20% of a model predicting a purchase from Discount Tire. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.310	FasTrak	Brand Propensities	Auto	Auto		Household	Modeled	15,85	33,29	This audience consists of households in the top 15-20% of a model predicting a purchase from FasTrak. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.342	Ford Credit Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	10,41	21,87	This audience consists of households in the top 15-20% of a model predicting a purchase from Ford Credit. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.343	Harley-Davidson Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	15,72	33,02	This audience consists of households in the top 15-20% of a model predicting a purchase from Harley-Davidson. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.212	Jiffy Lube Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	13,12	27,55	This audience consists of households in the top 15-20% of a model predicting a purchase from Jiffy Lube. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.182	Meineke Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	12,86	27,01	This audience consists of households in the top 15-20% of a model predicting a purchase from Meineke. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.185	Midas Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	13,66	28,69	This audience consists of households in the top 15-20% of a model predicting a purchase from Midas. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.148	Napa Auto Parts Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	12,18	25,58	This audience consists of households in the top 15-20% of a model predicting a purchase from Napa Auto Parts. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.561	O'Reilly Auto Parts Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	12,41	26,06	This audience consists of households in the top 15-20% of a model predicting a purchase from O'Rielly Auto Parts. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.333	ParkMobile	Brand Propensities	Auto	Auto		Household	Modeled	14,85	31,18	This audience consists of households in the top 15-20% of a model predicting a purchase from ParkMobile. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.154	Pep Boys Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	14,70	30,88	This audience consists of households in the top 15-20% of a model predicting a purchase from Pep Boys. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.344	RockAuto Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	11,59	24,35	This audience consists of households in the top 15-20% of a model predicting a purchase from RockAuto. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.348	SunPass	Brand Propensities	Auto	Auto		Household	Modeled	13,19	27,69	This audience consists of households in the top 15-20% of a model predicting a purchase from SunPass. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.345	Tirerack.com Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	15,87	33,32	This audience consists of households in the top 15-20% of a model predicting a purchase from Tirerack.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.575	Caliber Collision Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	13,58	28,52	This audience consists of households in the top 10-20% of a model predicting a purchase from Caliber Collision. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.693	Tires Plus Buyer Propensity	Brand Propensities	Auto	Auto		Household	Modeled	13,80	28,98	This audience consists of households in the top 10-20% of a model predicting a purchase from Tires Plus. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.347	Big Lots Stores Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	13,61	28,57	This audience consists of households in the top 15-20% of a model predicting a purchase from Big Lots Stores. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.348	BJ's Wholesale Club Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	12,78	26,84	This audience consists of households in the top 15-20% of a model predicting a purchase from BJ's Wholesale Club. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.349	Costco Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	14,24	29,91	This audience consists of households in the top 15-20% of a model predicting a purchase from Costco. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.350	Dollar General Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	10,55	22,16	This audience consists of households in the top 15-20% of a model predicting a purchase from Dollar General. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.351	Dollar Tree Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	12,81	26,90	This audience consists of households in the top 15-20% of a model predicting a purchase from Dollar Tree. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.314	Giant Food	Brand Propensities	Big Box Retail	Retail		Household	Modeled	12,29	25,81	This audience consists of households in the top 15-20% of a model predicting a purchase from Giant Food. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.322	Hannaford	Brand Propensities	Big Box Retail	Food & Beverage		Household	Modeled	11,61	24,38	This audience consists of households in the top 15-20% of a model predicting a purchase from Hannaford. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.352	Kmart Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	13,35	28,03	This audience consists of households in the top 15-20% of a model predicting a purchase from Kmart. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.353	Kohl's Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	11,37	23,87	This audience consists of households in the top 15-20% of a model predicting a purchase from Kohl's. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.519	Office Max Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	13,57	28,49	This audience consists of households in the top 15-20% of a model predicting a purchase from Office Max. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.354	Sam's Club Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	12,00	25,21	This audience consists of households in the top 15-20% of a model predicting a purchase from Sam's Club. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.355	Sears Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	11,72	24,61	This audience consists of households in the top 15-20% of a model predicting a purchase from Sears. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.343	ShopRite	Brand Propensities	Big Box Retail	Retail		Household	Modeled	14,30	30,04	This audience consists of households in the top 15-20% of a model predicting a purchase from ShopRite. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.345	Sprouts Farmers Market	Brand Propensities	Big Box Retail	Food & Beverage		Household	Modeled	16,21	34,03	This audience consists of households in the top 15-20% of a model predicting a purchase from Sprouts Farmers Market. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.356	Staples Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	13,30	27,93	This audience consists of households in the top 15-20% of a model predicting a purchase from Staples. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.357	Target Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	14,38	30,19	This audience consists of households in the top 15-20% of a model predicting a purchase from Target. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.358	Walmart Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	11,81	24,80	This audience consists of households in the top 15-20% of a model predicting a purchase from Walmart. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.563	ALDI Buyer Propensity	Brand Propensities	Big Box Retail	Food & Beverage		Household	Modeled	12,19	25,59	This audience consists of households in the top 10-20% of a model predicting a purchase from Aldi. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.565	At Home Buyer Propensity	Brand Propensities	Big Box Retail	Food & Beverage		Household	Modeled	11,56	24,27	This audience consists of households in the top 10-20% of a model predicting a purchase from At Home. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.599	Family Dollar Stores Buyer Propensity	Brand Propensities	Big Box Retail	Retail		Household	Modeled	13,00	27,30	This audience consists of households in the top 10-20% of a model predicting a purchase from Family Dollar Stores. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.123	Amazon Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	12,17	25,56	This audience consists of households in the top 15-20% of a model predicting big spenders from Amazon. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.124	American Eagle Outfitters Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	14,11	29,63	This audience consists of households in the top 15-20% of a model predicting big spenders from American Eagle Outfitters. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.125	Blue Apron Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Food & Beverage		Household	Modeled	14,47	30,39	This audience consists of households in the top 15-20% of a model predicting big spenders from Blue Apron. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.126	Chewy.com Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Pet	Retail	Household	Modeled	14,46	30,36	This audience consists of households in the top 15-20% of a model predicting big spenders from Chewy.com. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.127	Clinique Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Health & Beauty	Retail	Household	Modeled	13,09	27,49	This audience consists of households in the top 15-20% of a model predicting big spenders from Clinique. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.128	Coach Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	13,77	28,91	This audience consists of households in the top 15-20% of a model predicting big spenders from Coach. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.129	Costco Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail	Food & Beverage	Household	Modeled	12,57	26,39	This audience consists of households in the top 15-20% of a model predicting big spenders from Costco. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.130	Disney Resorts Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Travel		Household	Modeled	12,50	26,25	This audience consists of households in the top 15-20% of a model predicting big spenders from Disney Resorts. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.131	DoorDash Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Food & Beverage		Household	Modeled	14,25	29,93	This audience consists of households in the top 15-20% of a model predicting big spenders from DoorDash. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.132	DraftKings Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Media & Entertainment		Household	Modeled	12,52	26,29	This audience consists of households in the top 15-20% of a model predicting big spenders from DraftKings. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.133	Eddie Bauer Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	13,42	28,19	This audience consists of households in the top 15-20% of a model predicting big spenders from Eddie Bauer. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.134	Gap Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	14,25	29,93	This audience consists of households in the top 15-20% of a model predicting big spenders from GAP. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
228.130	Grubhub Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Food & Beverage		Household	Modeled	14,92	31,32	This audience consists of households in the top 15-20% of a model predicting big spenders from GrubHub. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.136	Lyft Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Travel		Household	Modeled	15,32	32,17	This audience consists of households in the top 15-20% of a model predicting big spenders from Lyft. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.137	Macy's Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	12,10	25,41	This audience consists of households in the top 15-20% of a model predicting big spenders from Macy's. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.138	MGM Grand Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Media & Entertainment	Travel	Household	Modeled	13,93	29,26	This audience consists of households in the top 15-20% of a model predicting big spenders from MGM Grand. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.139	Michael Kors Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	14,44	30,32	This audience consists of households in the top 15-20% of a model predicting big spenders from Michael Kors. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.140	Old Navy Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	14,53	30,52	This audience consists of households in the top 15-20% of a model predicting big spenders from Old Navy. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.141	Saks Fifth Avenue Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	15,37	32,27	This audience consists of households in the top 15-20% of a model predicting big spenders from Saks Fifth Avenue. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.142	Sam's Club Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Food & Beverage		Household	Modeled	12,00	25,19	This audience consists of households in the top 15-20% of a model predicting big spenders from Sam's Club. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.143	Sephora Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Health & Beauty	Retail	Household	Modeled	16,27	34,16	This audience consists of households in the top 15-20% of a model predicting big spenders from Sephora. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.144	Stamps.com Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Professional Services		Household	Modeled	17,79	37,37	This audience consists of households in the top 15-20% of a model predicting big spenders from Stamps.com. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.145	Tiffany Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	14,58	30,62	This audience consists of households in the top 15-20% of a model predicting big spenders from Tiffany. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.146	Ulta Beauty Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Health & Beauty	Retail	Household	Modeled	13,92	29,23	This audience consists of households in the top 15-20% of a model predicting big spenders from Ulta Beauty. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.147	USPS Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Professional Services		Household	Modeled	20,31	42,65	This audience consists of households in the top 15-20% of a model predicting big spenders from USPS. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.148	Walmart Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	12,33	25,90	This audience consists of households in the top 15-20% of a model predicting big spenders from Walmart. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.149	Warby Parker Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	15,20	31,93	This audience consists of households in the top 15-20% of a model predicting big spenders from Warby Parker. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.150	Wayfair Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	13,48	28,31	This audience consists of households in the top 15-20% of a model predicting big spenders from Wayfair. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.151	Wingstop Big Spender Propensity	Brand Propensities	Big Spenders by Brand	QSR	Food & Beverage	Household	Modeled	13,12	27,56	This audience consists of households in the top 15-20% of a model predicting big spenders from Wingstop. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.152	Zara Big Spender Propensity	Brand Propensities	Big Spenders by Brand	Retail		Household	Modeled	15,87	33,32	This audience consists of households in the top 15-20% of a model predicting big spenders from Zara. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.167	Active Wear Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Retail		Household	Modeled	14,22	29,87	This audience consists of households in the top 15-20% of a model predicting big spenders within the Active Wear category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.096	Airlines Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Travel		Household	Modeled	13,48	28,30	This audience consists of households in the top 15-20% of a model predicting big spenders within the Airlines category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.097	Apparel Accessories Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Retail		Household	Modeled	14,30	30,02	This audience consists of households in the top 15-20% of a model predicting big spenders within the Apparel Accessories category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.122	Auto Insurance Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Auto		Household	Modeled	10,57	22,20	This audience consists of households in the top 15-20% of a model predicting big spenders within the Auto Insurance category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.098	Cruise Lines Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Travel		Household	Modeled	14,36	30,15	This audience consists of households in the top 15-20% of a model predicting big spenders within the Cruise Lines category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.099	Delivery Aggregators Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Home		Household	Modeled	14,52	30,49	This audience consists of households in the top 15-20% of a model predicting big spenders within the Delivery Aggregators category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.100	Drug Store Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Retail	Food & Beverage	Household	Modeled	12,29	25,81	This audience consists of households in the top 15-20% of a model predicting big spenders within the Drug/Pharma category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.101	Electronics Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Retail	Tech	Household	Modeled	11,60	24,37	This audience consists of households in the top 15-20% of a model predicting big spenders within the Electronics category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.102	Fitness Center Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Health & Beauty		Household	Modeled	12,68	26,64	This audience consists of households in the top 15-20% of a model predicting big spenders within the Fitness Centers category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.103	Hardware Stores Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Retail	Home	Household	Modeled	13,98	29,36	This audience consists of households in the top 15-20% of a model predicting big spenders within the Hardware Stores category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.104	Health Care Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Health & Beauty		Household	Modeled	14,25	29,93	This audience consists of households in the top 15-20% of a model predicting big spenders within the Healthcare category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.105	Home Furnishings Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Home	Retail	Household	Modeled	13,33	28,00	This audience consists of households in the top 15-20% of a model predicting big spenders within the Home Furnishing category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.106	Home Improvement Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Home	Retail	Household	Modeled	11,38	23,90	This audience consists of households in the top 15-20% of a model predicting big spenders within the Home Improvements category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.107	Home Security Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Home	Tech	Household	Modeled	13,95	29,29	This audience consists of households in the top 15-20% of a model predicting big spenders within the Home Security category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.108	Intimate Apparel Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Retail		Household	Modeled	14,35	30,14	This audience consists of households in the top 15-20% of a model predicting big spenders within the Intimate Apparel category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.109	Jewelry Watches Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Retail		Household	Modeled	14,82	31,13	This audience consists of households in the top 15-20% of a model predicting big spenders within the Jewelry/Watches category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.110	Lodging Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Travel		Household	Modeled	12,39	26,02	This audience consists of households in the top 15-20% of a model predicting big spenders within the Lodging category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.111	Meal Kits Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Food & Beverage		Household	Modeled	18,19	38,20	This audience consists of households in the top 15-20% of a model predicting big spenders within the Meal Kits category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.112	Mobile Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Telecom		Household	Modeled	11,44	24,03	This audience consists of households in the top 15-20% of a model predicting big spenders within the Mobile category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.113	Occasion Gifts Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Retail		Household	Modeled	10,50	22,06	This audience consists of households in the top 15-20% of a model predicting big spenders within the Occasion Gifts category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.114	Online Grocers Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Food & Beverage	Retail	Household	Modeled	15,50	32,54	This audience consists of households in the top 15-20% of a model predicting big spenders within the Online Grocers category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.115	QSR Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Food & Beverage		Household	Modeled	11,90	24,99	This audience consists of households in the top 15-20% of a model predicting big spenders within the QSR category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.116	Rental Cars Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Travel		Household	Modeled	12,98	27,25	This audience consists of households in the top 15-20% of a model predicting big spenders within the Rental Cars category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.117	Sporting Goods Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Retail	Sports	Household	Modeled	11,23	23,58	This audience consists of households in the top 15-20% of a model predicting big spenders within the Sporting Goods category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.118	Supermarkets Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Food & Beverage		Household	Modeled	12,21	25,64	This audience consists of households in the top 15-20% of a model predicting big spenders within the Super Markets category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.119	Ticket Agencies Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Media & Entertainment		Household	Modeled	12,39	26,02	This audience consists of households in the top 15-20% of a model predicting big spenders within the Ticket Agencies category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.120	Travel Agencies Big Spender Propensity	Brand Propensities	Big Spenders by Brand Category	Travel		Household	Modeled	12,98	27,25	This audience consists of households in the top 15-20% of a model predicting big spenders within the Travel Agencies category. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.153	AT&T Mobile Switcher Propensity	Brand Propensities	Brand/Category Switchers	Telecom		Household	Modeled	10,86	22,81	This audience consists of households in the top 15-20% of a model predicting mobile plan switchers from AT&T. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.154	Boost Mobile Switcher Propensity	Brand Propensities	Brand/Category Switchers	Telecom		Household	Modeled	9,23	19,38	This audience consists of households in the top 15-20% of a model predicting mobile plan switchers from Boost Mobile. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.155	LG Mobile Switcher Propensity	Brand Propensities	Brand/Category Switchers	Telecom		Household	Modeled	9,85	20,68	This audience consists of households in the top 15-20% of a model predicting mobile plan switchers from LG. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.156	MetroPCS Mobile Switcher Propensity	Brand Propensities	Brand/Category Switchers	Telecom		Household	Modeled	12,81	26,89	This audience consists of households in the top 15-20% of a model predicting mobile plan switchers from Metro PCS. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.157	Sprint Mobile Switcher Propensity	Brand Propensities	Brand/Category Switchers	Telecom		Household	Modeled	10,73	22,53	This audience consists of households in the top 15-20% of a model predicting mobile plan switchers from Sprint. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.158	T-Mobile Switcher Propensity	Brand Propensities	Brand/Category Switchers	Telecom		Household	Modeled	14,64	30,75	This audience consists of households in the top 15-20% of a model predicting mobile plan switchers from T-Mobile. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.159	Verizon Mobile Switcher Propensity	Brand Propensities	Brand/Category Switchers	Telecom		Household	Modeled	15,09	31,69	This audience consists of households in the top 15-20% of a model predicting mobile plan switchers from Verizon. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.166	Propensity to Switch from Allstate Insurance	Brand Propensities	Brand/Category Switchers	Financial Services		Household	Modeled	13,57	28,49	This audience consists of households in the top 15-20% of a model predicting auto insurance switchers from Allstate Insurance. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.165	Propensity to Switch from Farmers Insurance	Brand Propensities	Brand/Category Switchers	Financial Services		Household	Modeled	11,59	24,35	This audience consists of households in the top 15-20% of a model predicting auto insurance switchers from Farmers Insurance. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.164	Propensity to Switch from Geico Insurance	Brand Propensities	Brand/Category Switchers	Financial Services		Household	Modeled	13,05	27,41	This audience consists of households in the top 15-20% of a model predicting auto insurance switchers from Geico Insurance. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.163	Propensity to Switch from Progressive Insurance	Brand Propensities	Brand/Category Switchers	Financial Services		Household	Modeled	11,12	23,35	This audience consists of households in the top 15-20% of a model predicting auto insurance switchers from Progressive Insurance. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.162	Propensity to Switch from Safeco Insurance	Brand Propensities	Brand/Category Switchers	Financial Services		Household	Modeled	12,42	26,07	This audience consists of households in the top 15-20% of a model predicting auto insurance switchers from Safeco Insurance. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.161	Propensity to Switch from State Farm Insurance	Brand Propensities	Brand/Category Switchers	Financial Services		Household	Modeled	11,05	23,21	This audience consists of households in the top 15-20% of a model predicting auto insurance switchers from State Farm Insurance. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
229.160	Propensity to Switch from Travelers Insurance	Brand Propensities	Brand/Category Switchers	Financial Services		Household	Modeled	12,74	26,76	This audience consists of households in the top 15-20% of a model predicting auto insurance switchers from Travelers Insurance. The model is built using brand specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.589	Dashlane Buyer Propensity	Brand Propensities	Computer & Electronics	Tech		Household	Modeled	15,00	31,49	This audience consists of households in the top 10-20% of a model predicting a purchase from Dashlane. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.590	Datacamp Buyer Propensity	Brand Propensities	Computer & Electronics	Tech		Household	Modeled	15,00	31,51	This audience consists of households in the top 10-20% of a model predicting a purchase from Datacamp. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.596	Envato Buyer Propensity	Brand Propensities	Computer & Electronics	Tech		Household	Modeled	16,48	34,60	This audience consists of households in the top 10-20% of a model predicting a purchase from Envato. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.619	Hellotech Buyer Propensity	Brand Propensities	Computer & Electronics	Tech		Household	Modeled	15,28	32,09	This audience consists of households in the top 10-20% of a model predicting a purchase from Hellotech. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.638	Malwarebytes Buyer Propensity	Brand Propensities	Computer & Electronics	Tech		Household	Modeled	13,57	28,49	This audience consists of households in the top 10-20% of a model predicting a purchase from Malwarebytes. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.192	BP Buyer Propensity	Brand Propensities	Convenience & Gas	Oil & Gas		Household	Modeled	13,72	28,82	This audience consists of households in the top 15-20% of a model predicting a purchase from BP. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.163	Circle K Buyer Propensity	Brand Propensities	Convenience & Gas	Oil & Gas		Household	Modeled	12,89	27,07	This audience consists of households in the top 15-20% of a model predicting a purchase from Circle K. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.164	Citgo Buyer Propensity	Brand Propensities	Convenience & Gas	Oil & Gas		Household	Modeled	13,43	28,20	This audience consists of households in the top 15-20% of a model predicting a purchase from Citgo. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.358	Convenience & Gas	Brand Propensities	Convenience & Gas	Oil & Gas		Household	Modeled	14,81	31,11	This audience consists of households in the top 15-20% of a model predicting a purchase from the Convenience & Gas category. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.132	Cumberland Farms Buyer Propensity	Brand Propensities	Convenience & Gas	Oil & Gas		Household	Modeled	12,16	25,54	This audience consists of households in the top 15-20% of a model predicting a purchase from Cumberland Farms. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.306	Marathon Petroleum Buyer Propensity	Brand Propensities	Convenience & Gas	Oil & Gas		Household	Modeled	13,24	27,81	This audience consists of households in the top 15-20% of a model predicting a purchase from Marathon Petroleum. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.232	Wawa Buyer Propensity	Brand Propensities	Convenience & Gas	Food & Beverage	Oil & Gas	Household	Modeled	13,95	29,30	This audience consists of households in the top 15-20% of a model predicting a purchase from Wawa. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.667	Printify Buyer Propensity	Brand Propensities	Convenience & Gas	Professional Services		Household	Modeled	15,18	31,87	This audience consists of households in the top 10-20% of a model predicting a purchase from Printify. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.360	Crest Buyer Propensity	Brand Propensities	CPG	CPG		Household	Modeled	13,77	28,92	This audience consists of households in the top 15-20% of a model predicting a purchase from Crest. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.362	Kraft Buyer Propensity	Brand Propensities	CPG	CPG		Household	Modeled	13,09	27,50	This audience consists of households in the top 15-20% of a model predicting a purchase from Kraft. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.363	L'oreal Buyer Propensity	Brand Propensities	CPG	CPG		Household	Modeled	18,35	38,53	This audience consists of households in the top 15-20% of a model predicting a purchase from L'oreal. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.194	Bumble Buyer Propensity	Brand Propensities	Dating App	Media & Entertainment		Household	Modeled	14,43	30,30	This audience consists of households in the top 15-20% of a model predicting a purchase from Bumble. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.650	Match.com Buyer Propensity	Brand Propensities	Dating App	Media & Entertainment		Household	Modeled	15,67	32,92	This audience consists of households in the top 15-20% of a model predicting a purchase from Match.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.081	OkCupid Buyer Propensity	Brand Propensities	Dating App	Media & Entertainment		Household	Modeled	20,50	43,06	This audience consists of households in the top 15-20% of a model predicting a purchase from OkCupid. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.692	Tinder Buyer Propensity	Brand Propensities	Dating App	Media & Entertainment		Household	Modeled	14,05	29,50	This audience consists of households in the top 10-20% of a model predicting a purchase from Tinder. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.365	1-800-Flowers Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	14,54	30,54	This audience consists of households in the top 15-20% of a model predicting a purchase from 1-800-Flowers. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.290	AliExpress	Brand Propensities	Ecommerce	Retail		Household	Modeled	14,78	31,04	This audience consists of households in the top 15-20% of a model predicting a purchase from AliExpress. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.367	Amazon Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	14,20	29,81	This audience consists of households in the top 15-20% of a model predicting a purchase from Amazon. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.130	Craigslist Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	12,38	25,99	This audience consists of households in the top 15-20% of a model predicting a purchase from Craigslist. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.376	delivery.com Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	14,87	31,23	This audience consists of households in the top 15-20% of a model predicting a purchase from delivery.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.379	Ebay Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	12,48	26,22	This audience consists of households in the top 15-20% of a model predicting a purchase from Ebay. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.381	Etsy.com Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	14,29	30,01	This audience consists of households in the top 15-20% of a model predicting a purchase from Etsy.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.385	Go Daddy Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	15,20	31,93	This audience consists of households in the top 15-20% of a model predicting a purchase from Go Daddy. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.386	Google Wallet Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	20,95	44,00	This audience consists of households in the top 15-20% of a model predicting a purchase from Google Wallet. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.388	Groupon Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	15,44	32,43	This audience consists of households in the top 15-20% of a model predicting a purchase from Groupon. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.390	HSN Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	12,63	26,53	This audience consists of households in the top 15-20% of a model predicting a purchase from HSN. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.391	Jet.com Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	15,21	31,94	This audience consists of households in the top 15-20% of a model predicting a purchase from Jet.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.392	Jo-Ann Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	12,90	27,09	This audience consists of households in the top 15-20% of a model predicting a purchase from Jo-Ann. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.393	LinkedIn Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	15,41	32,37	This audience consists of households in the top 15-20% of a model predicting a purchase from LinkedIn. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.394	Meijer Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	11,33	23,80	This audience consists of households in the top 15-20% of a model predicting a purchase from Meijer. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.311	Mercari Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	14,28	29,99	This audience consists of households in the top 15-20% of a model predicting a purchase from Mercari. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.396	Office Depot Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	14,76	31,01	This audience consists of households in the top 15-20% of a model predicting a purchase from Office Depot. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.397	Overstock.com Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	13,91	29,20	This audience consists of households in the top 15-20% of a model predicting a purchase from Overstock.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.399	ProFlowers Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	13,47	28,30	This audience consists of households in the top 15-20% of a model predicting a purchase from ProFlowers. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.402	QVC Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	12,75	26,78	This audience consists of households in the top 15-20% of a model predicting a purchase from QVC. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.407	Shopify Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	14,15	29,72	This audience consists of households in the top 15-20% of a model predicting a purchase from Shopify. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.410	Snapfish Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	13,10	27,50	This audience consists of households in the top 15-20% of a model predicting a purchase from Snapfish. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.414	Tiny Prints Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	11,88	24,95	This audience consists of households in the top 15-20% of a model predicting a purchase from Tiny Prints. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.415	USPS Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	17,33	36,39	This audience consists of households in the top 15-20% of a model predicting a purchase from USPS. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.418	Zulily Buyer Propensity	Brand Propensities	Ecommerce	Retail		Household	Modeled	12,67	26,61	This audience consists of households in the top 15-20% of a model predicting a purchase from Zulily. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.346	StockX	Brand Propensities	Ecommerce	Retail		Household	Modeled	14,39	30,23	This audience consists of households in the top 15-20% of a model predicting a purchase from StockX. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.293	Babbel.com	Brand Propensities	Education			Household	Modeled	15,29	32,11	This audience consists of households in the top 15-20% of a model predicting a purchase from Babbel.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.568	Bartleby Buyer Propensity	Brand Propensities	Education	Education		Household	Modeled	12,89	27,07	This audience consists of households in the top 10-20% of a model predicting a purchase from Bartleby. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.644	Mathway Buyer Propensity	Brand Propensities	Education	Tech		Household	Modeled	13,57	28,49	This audience consists of households in the top 10-20% of a model predicting a purchase from Mathway. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.663	Pluralsight Buyer Propensity	Brand Propensities	Education	Tech		Household	Modeled	14,50	30,45	This audience consists of households in the top 10-20% of a model predicting a purchase from Pluralsight. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.697	Udemy Buyer Propensity	Brand Propensities	Education	Education		Household	Modeled	15,06	31,62	This audience consists of households in the top 10-20% of a model predicting a purchase from Udemy. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.705	Wyzant Buyer Propensity	Brand Propensities	Education	Education		Household	Modeled	14,80	31,08	This audience consists of households in the top 10-20% of a model predicting a purchase from Wyzant. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.288	ABT Electronics	Brand Propensities	Electronics	Tech		Household	Modeled	13,43	28,20	This audience consists of households in the top 15-20% of a model predicting a purchase from ABT Electronics. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.422	Adobe Buyer Propensity	Brand Propensities	Electronics	Tech	Media & Entertainment	Household	Modeled	15,42	32,37	This audience consists of households in the top 15-20% of a model predicting a purchase from Adobe. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.424	Apple Buyer Propensity	Brand Propensities	Electronics	Tech	Retail	Household	Modeled	15,33	32,18	This audience consists of households in the top 15-20% of a model predicting a purchase from Apple. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.428	B&H Photo Video Buyer Propensity	Brand Propensities	Electronics	Tech	Retail	Household	Modeled	13,70	28,77	This audience consists of households in the top 15-20% of a model predicting a purchase from B&H Photo Video. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.427	Best Buy Buyer Propensity	Brand Propensities	Electronics	Retail	Tech	Household	Modeled	13,76	28,90	This audience consists of households in the top 15-20% of a model predicting a purchase from Best Buy. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.429	Bose Buyer Propensity	Brand Propensities	Electronics	Tech	Retail	Household	Modeled	13,37	28,07	This audience consists of households in the top 15-20% of a model predicting a purchase from Bose. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.432	Crutchfield Buyer Propensity	Brand Propensities	Electronics	Retail	Tech	Household	Modeled	8,76	18,40	This audience consists of households in the top 15-20% of a model predicting a purchase from Crutchfield. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.434	Dell Buyer Propensity	Brand Propensities	Electronics	Tech	Retail	Household	Modeled	13,00	27,30	This audience consists of households in the top 15-20% of a model predicting a purchase from Dell. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.437	Garmin Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	12,81	26,90	This audience consists of households in the top 15-20% of a model predicting a purchase from Garmin. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.438	Geek Squad Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	11,57	24,30	This audience consists of households in the top 15-20% of a model predicting a purchase from Geek Squad. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.439	GoPro Buyer Propensity	Brand Propensities	Electronics	Tech	Media & Entertainment	Household	Modeled	13,83	29,04	This audience consists of households in the top 15-20% of a model predicting a purchase from GoPro. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.246	GreatCall Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	7,79	16,36	This audience consists of households in the top 15-20% of a model predicting a purchase from GreatCall. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.440	HP Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	12,15	25,52	This audience consists of households in the top 15-20% of a model predicting a purchase from HP. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.271	iRobot Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	13,49	28,34	This audience consists of households in the top 15-20% of a model predicting a purchase from iRobot. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.445	Lenovo Buyer Propensity	Brand Propensities	Electronics	Tech	Media & Entertainment	Household	Modeled	13,67	28,71	This audience consists of households in the top 15-20% of a model predicting a purchase from Lenovo. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.444	LG Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	14,26	29,94	This audience consists of households in the top 15-20% of a model predicting a purchase from LG. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.446	Microsoft Buyer Propensity	Brand Propensities	Electronics	Tech	Media & Entertainment	Household	Modeled	13,20	27,72	This audience consists of households in the top 15-20% of a model predicting a purchase from Microsoft. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.447	Microsoft Store Buyer Propensity	Brand Propensities	Electronics	Retail	Tech	Household	Modeled	17,53	36,82	This audience consists of households in the top 15-20% of a model predicting a purchase from Microsoft Store. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.450	Motorola Buyer Propensity	Brand Propensities	Electronics	Tech	Telecom	Household	Modeled	19,23	40,39	This audience consists of households in the top 15-20% of a model predicting a purchase from Motorola. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.452	Norton Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	13,64	28,64	This audience consists of households in the top 15-20% of a model predicting a purchase from Norton. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.454	Otterbox Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	19,43	40,79	This audience consists of households in the top 15-20% of a model predicting a purchase from Otterbox. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.456	P.C. Richard & Son Buyer Propensity	Brand Propensities	Electronics	Retail	Home	Household	Modeled	15,70	32,97	This audience consists of households in the top 15-20% of a model predicting a purchase from P.C. Richard and Son. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.457	RadioShack Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	18,30	38,44	This audience consists of households in the top 15-20% of a model predicting a purchase from RadioShack. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.460	Skype Buyer Propensity	Brand Propensities	Electronics	Tech	Telecom	Household	Modeled	14,23	29,88	This audience consists of households in the top 15-20% of a model predicting a purchase from Skype. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.143	Sonos Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	14,82	31,13	This audience consists of households in the top 15-20% of a model predicting a purchase from Sonos. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.461	Sony Buyer Propensity	Brand Propensities	Electronics	Tech	Media & Entertainment	Household	Modeled	15,06	31,63	This audience consists of households in the top 15-20% of a model predicting a purchase from Sony. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.465	Toshiba Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	11,52	24,19	This audience consists of households in the top 15-20% of a model predicting a purchase from Toshiba. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.594	Eero Buyer Propensity	Brand Propensities	Electronics	Tech		Household	Modeled	13,83	29,03	This audience consists of households in the top 10-20% of a model predicting a purchase from Eero. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.650	Nanit Buyer Propensity	Brand Propensities	Electronics	Retail		Household	Modeled	13,63	28,62	This audience consists of households in the top 10-20% of a model predicting a purchase from Nanit. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.468	American Express Buyer Propensity	Brand Propensities	Financial	Financial Services		Household	Modeled	13,82	29,02	This audience consists of households in the top 15-20% of a model predicting a purchase from American Express. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.166	Coinbase Buyer Propensity	Brand Propensities	Financial	Financial Services		Household	Modeled	14,91	31,31	This audience consists of households in the top 15-20% of a model predicting a purchase from Coinbase. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.307	Experian	Brand Propensities	Financial	Financial Services		Household	Modeled	14,78	31,04	This audience consists of households in the top 15-20% of a model predicting a purchase from Experian. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.476	H&R Block Buyer Propensity	Brand Propensities	Financial	Financial Services		Household	Modeled	11,94	25,07	This audience consists of households in the top 15-20% of a model predicting a purchase from H&R Block. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.469	Intuit Buyer Propensity	Brand Propensities	Financial	Financial Services		Household	Modeled	12,12	25,45	This audience consists of households in the top 15-20% of a model predicting a purchase from Intuit. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.292	LifeLock Buyer Propensity	Brand Propensities	Financial	Financial Services		Household	Modeled	12,59	26,45	This audience consists of households in the top 15-20% of a model predicting a purchase from LifeLock. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.477	PayPal Buyer Propensity	Brand Propensities	Financial	Financial Services		Household	Modeled	14,94	31,38	This audience consists of households in the top 15-20% of a model predicting a purchase from PayPal. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.201	Raise.com Buyer Propensity	Brand Propensities	Financial	Financial Services		Household	Modeled	13,69	28,74	This audience consists of households in the top 15-20% of a model predicting a purchase from Raise.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.342	Sezzle	Brand Propensities	Financial	Financial Services		Household	Modeled	11,32	23,77	This audience consists of households in the top 15-20% of a model predicting a purchase from Sezzle. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.363	Tax Preparation	Brand Propensities	Financial	Financial Services		Household	Modeled	13,36	28,05	This audience consists of households in the top 15-20% of a model predicting a purchase from the Tax Preparation category. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.473	Western Union Buyer Propensity	Brand Propensities	Financial	Financial Services		Household	Modeled	13,66	28,69	This audience consists of households in the top 15-20% of a model predicting a purchase from Western Union. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.475	Xoom Buyer Propensity	Brand Propensities	Financial	Financial Services		Household	Modeled	14,47	30,39	This audience consists of households in the top 15-20% of a model predicting a purchase from Xoom. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.578	Canopy Buyer Propensity	Brand Propensities	Financial	Tech		Household	Modeled	14,63	30,72	This audience consists of households in the top 10-20% of a model predicting a purchase from Canopy. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.608	Freshbooks Buyer Propensity	Brand Propensities	Financial	Tech		Household	Modeled	13,97	29,33	This audience consists of households in the top 10-20% of a model predicting a purchase from Freshbooks. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.073	7-Eleven Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	15,69	32,96	This audience consists of households in the top 15-20% of a model predicting a purchase from 7-Eleven. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.473	Acme Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	14,68	30,84	This audience consists of households in the top 15-20% of a model predicting a purchase from Acme. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.475	Albertsons Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	15,30	32,13	This audience consists of households in the top 15-20% of a model predicting a purchase from Albertsons. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.482	Blue Apron Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	15,77	33,11	This audience consists of households in the top 15-20% of a model predicting a purchase from Blue Apron. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.484	Caviar Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	21,78	45,74	This audience consists of households in the top 15-20% of a model predicting a purchase from Caviar. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.298	Cold Stone Creamery	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	14,70	30,87	This audience consists of households in the top 15-20% of a model predicting a purchase from Cold Stone Creamery. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.479	CVS Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Health & Beauty	Household	Modeled	12,38	25,99	This audience consists of households in the top 15-20% of a model predicting a purchase from CVS. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.487	DAVIDsTEA Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	15,68	32,93	This audience consists of households in the top 15-20% of a model predicting a purchase from DAVIDsTEA. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.489	DoorDash Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	14,42	30,29	This audience consists of households in the top 15-20% of a model predicting a purchase from DoorDash. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.490	Drugstore.com Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Health & Beauty	Household	Modeled	8,75	18,38	This audience consists of households in the top 15-20% of a model predicting a purchase from Drugstore.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.101	Duane Reade Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	17,47	36,68	This audience consists of households in the top 15-20% of a model predicting a purchase from Duane Reade. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.491	Eat24 Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	17,73	37,24	This audience consists of households in the top 15-20% of a model predicting a purchase from Eat24. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.216	Five Below Buyer Propensity	Brand Propensities	Food & Drugstore	Retail		Household	Modeled	12,29	25,80	This audience consists of households in the top 15-20% of a model predicting a purchase from Five Below. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.506	Food Lion Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	10,92	22,94	This audience consists of households in the top 15-20% of a model predicting a purchase from Food Lion. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.492	Fresh Direct Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage	Health & Beauty	Household	Modeled	15,27	32,08	This audience consists of households in the top 15-20% of a model predicting a purchase from Fresh Direct. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.508	Freshly Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage	Health & Beauty	Household	Modeled	13,71	28,78	This audience consists of households in the top 15-20% of a model predicting a purchase from Freshly. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.493	GNC Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Health & Beauty	Household	Modeled	13,93	29,25	This audience consists of households in the top 15-20% of a model predicting a purchase from GNC. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.494	Godiva Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	14,86	31,20	This audience consists of households in the top 15-20% of a model predicting a purchase from Godiva. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.495	Grubhub Buyer Propensity	Brand Propensities	Food & Drugstore	QSR	Food & Beverage	Household	Modeled	14,39	30,22	This audience consists of households in the top 15-20% of a model predicting a purchase from Grubhub. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.496	Harry & David Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	13,25	27,82	This audience consists of households in the top 15-20% of a model predicting a purchase from Harry & David. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.497	HEB Grocery Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Food & Beverage	Household	Modeled	14,19	29,81	This audience consists of households in the top 15-20% of a model predicting a purchase from HEB Grocery. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.250	Hint Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	14,92	31,33	This audience consists of households in the top 15-20% of a model predicting a purchase from Hint. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.323	Home Chef	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	14,18	29,78	This audience consists of households in the top 15-20% of a model predicting a purchase from Home Chef. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.501	Instacart Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	15,09	31,69	This audience consists of households in the top 15-20% of a model predicting a purchase from Instacart. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.502	King Arthur Flour Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	12,50	26,26	This audience consists of households in the top 15-20% of a model predicting a purchase from King Arthur Flour. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.503	KL Wine Merchants Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	16,03	33,67	This audience consists of households in the top 15-20% of a model predicting a purchase from KL Wine Merchants. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.504	Kroger Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Food & Beverage	Household	Modeled	11,82	24,83	This audience consists of households in the top 15-20% of a model predicting a purchase from Kroger. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.505	Lindt & Sprungli Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	10,34	21,71	This audience consists of households in the top 15-20% of a model predicting a purchase from Lindt & Sprungli. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.330	Marley Spoon	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	14,58	30,61	This audience consists of households in the top 15-20% of a model predicting a purchase from Marley Spoon. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.507	naturebox.com Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	11,64	24,44	This audience consists of households in the top 15-20% of a model predicting a purchase from naturebox.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.508	Nespresso Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	15,24	32,01	This audience consists of households in the top 15-20% of a model predicting a purchase from Nespresso. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.509	Omaha Steaks Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	12,86	27,02	This audience consists of households in the top 15-20% of a model predicting a purchase from Omaha Steaks. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.480	Peapod Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	13,18	27,69	This audience consists of households in the top 15-20% of a model predicting a purchase from Peapod. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.510	Postmates Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	22,53	47,31	This audience consists of households in the top 15-20% of a model predicting a purchase from Postmates. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.524	Publix Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Food & Beverage	Household	Modeled	13,37	28,08	This audience consists of households in the top 15-20% of a model predicting a purchase from Publix. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.526	Ralphs Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Food & Beverage	Household	Modeled	14,92	31,32	This audience consists of households in the top 15-20% of a model predicting a purchase from Ralphs. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.511	Rite Aid Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Health & Beauty	Household	Modeled	14,11	29,63	This audience consists of households in the top 15-20% of a model predicting a purchase from Rite Aid. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.512	Safeway Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Food & Beverage	Household	Modeled	15,10	31,72	This audience consists of households in the top 15-20% of a model predicting a purchase from Safeway. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.515	Shipt Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	13,73	28,83	This audience consists of households in the top 15-20% of a model predicting a purchase from Shipt. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.531	Stop & Shop Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage	Retail	Household	Modeled	12,80	26,89	This audience consists of households in the top 15-20% of a model predicting a purchase from Stop & Shop. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
294.347	Sun Basket	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	16,38	34,40	This audience consists of households in the top 15-20% of a model predicting a purchase from Sun Basket. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.516	Swanson Vitamins Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage	Health & Beauty	Household	Modeled	12,35	25,93	This audience consists of households in the top 15-20% of a model predicting a purchase from Swanson Vitamins. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.517	The Vitamin Shoppe Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Health & Beauty	Household	Modeled	9,75	20,48	This audience consists of households in the top 15-20% of a model predicting a purchase from The Vitamin Shoppe. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.518	Thrive Market Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	6,69	14,06	This audience consists of households in the top 15-20% of a model predicting a purchase from Thrive Market. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.520	Vitamin World Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Health & Beauty	Household	Modeled	11,84	24,86	This audience consists of households in the top 15-20% of a model predicting a purchase from Vitamin World. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.546	Vons Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Food & Beverage	Household	Modeled	15,23	31,98	This audience consists of households in the top 15-20% of a model predicting a purchase from Vons. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.481	Walgreens Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Health & Beauty	Household	Modeled	13,04	27,39	This audience consists of households in the top 15-20% of a model predicting a purchase from Walgreens. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.547	Wegmans Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage	Retail	Household	Modeled	13,69	28,75	This audience consists of households in the top 15-20% of a model predicting a purchase from Wegmans. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.521	Weight Watchers International Buyer Propensity	Brand Propensities	Food & Drugstore	Health & Beauty	Fitness	Household	Modeled	12,97	27,23	This audience consists of households in the top 15-20% of a model predicting a purchase from Weight Watchers International. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.522	Whole Foods Market Buyer Propensity	Brand Propensities	Food & Drugstore	Retail	Health & Beauty	Household	Modeled	15,10	31,72	This audience consists of households in the top 15-20% of a model predicting a purchase from Whole Foods Market. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
12.523	Wine.com Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage	Alcoholic Beverages	Household	Modeled	14,42	30,28	This audience consists of households in the top 15-20% of a model predicting a purchase from Wine.com. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
226.551	Winn-Dixie Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage	Retail	Household	Modeled	12,74	26,75	This audience consists of households in the top 15-20% of a model predicting a purchase from Winn-Dixie. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.573	Bristol Farms Buyer Propensity	Brand Propensities	Food & Drugstore	Food & Beverage		Household	Modeled	15,15	31,82	This audience consists of households in the top 10-20% of a model predicting a purchase from Bristol Farms. The model is built using brand-specific e-commerce data as a study group, that is then joined to an offline cooperative database of direct-to-consumer purchase transactions, demographics and lifestyle information. The analysis of the combined data is used for predictive modeling to identify other households in the cooperative that have shared characteristics.
960.587	Cub Foods Buyer Propensity	Brand Propensities	Food & Drugstore	Retail		Household	Modeled	11,52	24,18

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