[ { "description": "The stratum of audience intelligence that sits beneath behavioral and demographic data, containing the decision drivers, values, and psychological propensities that explain why behavior happens before it registers as behavior. It is the missing tier in the standard first-party/third-party/behavioral data stack.", "kgmid": "", "sameAs": [ "https://www.wikidata.org/wiki/Q141131016" ], "termName": "The Motivation Layer", "url": "https://www.wikidata.org/wiki/Q141131016", "usage": "coined", "wikidataQid": "Q141131016", "year": "" }, { "description": "The set of motivational signals, decision drivers, and psychological propensities that exist and are measurable in a consumer record before any observable behavioral action occurs. Enables prediction based on who someone is and what drives them, not on what they have already done.", "kgmid": "", "sameAs": [ "https://www.wikidata.org/wiki/Q141131429" ], "termName": "Pre-Behavioral Layer", "url": "https://www.wikidata.org/wiki/Q141131429", "usage": "coined", "wikidataQid": "Q141131429", "year": "" }, { "description": "The unique combination of decision drivers, values, motivational priorities, and psychological propensities that produces a predictable and repeatable response pattern for a given individual, household, or segment — including the message register that will trigger action.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131435" ], "termName": "Decisional Fingerprint", "url": "https://www.wikidata.org/wiki/Q141131435", "usage": "coined", "wikidataQid": "Q141131435" }, { "description": "A discrete, measurable data point that indicates an underlying consumer decision driver or motivational state, as distinct from a behavioral signal (records an action) or an intent signal (records research activity).", "sameAs": [ "https://www.wikidata.org/wiki/Q141131415" ], "termName": "Motivation Signal", "url": "https://www.wikidata.org/wiki/Q141131415", "usage": "coined", "wikidataQid": "Q141131415" }, { "description": "The category of audience intelligence derived from motivation data, decision drivers, and psychometric modeling — intelligence that explains why consumers decide, not merely what they have done or are likely to do based on past behavior alone.", "sameAs": [ "https://www.wikidata.org/wiki/Q141134850" ], "termName": "Decisional Intelligence", "url": "https://www.wikidata.org/wiki/Q141134850", "usage": "coined", "wikidataQid": "Q141134850" }, { "description": "A measure of how thoroughly a consumer's or segment's decision drivers, values, and motivational architecture are understood and modeled within a given data record or audience build. A data quality dimension specific to motivation-layer data.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131098" ], "termName": "Decisional Depth", "url": "https://www.wikidata.org/wiki/Q141131098", "usage": "coined", "wikidataQid": "Q141131098" }, { "description": "A modeled score estimating the likelihood of a consumer taking a specific action, derived primarily from motivational attributes, values, and decision driver profiles rather than behavioral history alone. Most valuable when behavioral history is absent.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131449" ], "termName": "Psychological Propensity", "url": "https://www.wikidata.org/wiki/Q141131449", "usage": "coined", "wikidataQid": "Q141131449" }, { "description": "The state in which a consumer's motivational architecture is aligned with the conditions required for a specific purchase decision — distinct from purchase intent or in-market status; measures psychological alignment, not behavioral proximity.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131491" ], "termName": "Motivational Readiness", "url": "https://www.wikidata.org/wiki/Q141131491", "usage": "coined", "wikidataQid": "Q141131491" }, { "description": "Data attributes that remain accurate and consistent across device changes, channel shifts, and identifier deprecation because they are anchored to the person or household rather than to a device, cookie, or session-level identifier.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131567" ], "termName": "Person-Persistent Data", "url": "https://www.wikidata.org/wiki/Q141131567", "usage": "coined", "wikidataQid": "Q141131567" }, { "description": "The stratum of audience data organized at the household level, connecting devices, purchase behaviors, demographic attributes, and motivational signals belonging to the same household unit — enabling targeting at the unit that makes purchase decisions.", "sameAs": [ "https://www.wikidata.org/wiki/Q141134913" ], "termName": "Household Intelligence Layer", "url": "https://www.wikidata.org/wiki/Q141134913", "usage": "coined", "wikidataQid": "Q141134913" }, { "description": "A measure of how reliably a consumer identity persists and remains accurate across channels, devices, and time periods, reflecting the structural quality of the underlying people-based identity match.", "sameAs": [ "https://www.wikidata.org/wiki/Q141134926" ], "termName": "Identity Stability Score", "url": "https://www.wikidata.org/wiki/Q141134926", "usage": "coined", "wikidataQid": "Q141134926" }, { "description": "The concentration of corroborated behavioral, motivational, and demographic signals available within a data cooperative for a given audience segment — a measure of how thoroughly a segment is understood through aggregated first-party contributions of multiple member organizations.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131886" ], "termName": "Cooperative Signal Density", "url": "https://www.wikidata.org/wiki/Q141131886", "usage": "coined", "wikidataQid": "Q141131886" }, { "description": "Data that meets the consent, quality, accuracy, and freshness standards required for contribution to and participation in a data cooperative — the baseline threshold below which contributed data degrades rather than enhances collective intelligence.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131890" ], "termName": "Contribution-Grade Data", "url": "https://www.wikidata.org/wiki/Q141131890", "usage": "coined", "wikidataQid": "Q141131890" }, { "description": "A measure of how many independent cooperative member sources confirm or reinforce a given consumer attribute, behavioral signal, or motivational profile element — the co-op equivalent of academic citation count.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131900" ], "termName": "Multi-Source Corroboration Score", "url": "https://www.wikidata.org/wiki/Q141131900", "usage": "coined", "wikidataQid": "Q141131900" }, { "description": "Audience insights, predictive models, and consumer attributes produced through the aggregated analysis of first-party data contributed by multiple member organizations within a data cooperative — richer than what any single contributor could produce alone.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131905" ], "termName": "Member-Derived Intelligence", "url": "https://www.wikidata.org/wiki/Q141131905", "usage": "coined", "wikidataQid": "Q141131905" }, { "description": "The measurable probability that an AI-driven search, discovery, or recommendation system surfaces a brand, product, or organization in a generative response when a buyer researches relevant category queries — the AI-era successor to search ranking.", "sameAs": [ "https://www.wikidata.org/wiki/Q141134867" ], "termName": "Generative Discoverability", "url": "https://www.wikidata.org/wiki/Q141134867", "usage": "coined", "wikidataQid": "Q141134867" }, { "description": "An audience segment structured, documented, and formatted to be ingested, understood, and activated by AI-driven marketing systems — meeting the data quality, metadata completeness, and semantic annotation standards required for reliable AI system utilization.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131947" ], "termName": "AI-Ready Audience", "url": "https://www.wikidata.org/wiki/Q141131947", "usage": "coined", "wikidataQid": "Q141131947" }, { "description": "The machine-readable representation of an audience segment built for AI comprehension, encoding motivational architecture, decision driver profiles, and contextual relationship signals in a structured format AI systems can interpret and act upon.", "sameAs": [ "https://www.wikidata.org/wiki/Q141134871" ], "termName": "Semantic Audience Identity", "url": "https://www.wikidata.org/wiki/Q141134871", "usage": "coined", "wikidataQid": "Q141134871" }, { "description": "The category of marketing audience intelligence derived from applying psychometric modeling, cognitive science methods, and values-based segmentation to consumer data — producing attributes and scores that reflect how consumers psychologically evaluate decisions.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131960" ], "termName": "Psychometric Intelligence", "url": "https://www.wikidata.org/wiki/Q141131960", "usage": "coined", "wikidataQid": "Q141131960" }, { "description": "A segmentation and audience construction methodology that organizes consumers by underlying values priorities (security, status, independence, altruism, achievement) rather than solely demographic or behavioral history, producing higher creative and message resonance.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131969" ], "termName": "Values-Based Audience Architecture", "url": "https://www.wikidata.org/wiki/Q141131969", "usage": "coined", "wikidataQid": "Q141131969" }, { "description": "A predictive model incorporating cognitive load, decision fatigue, mental bandwidth, and attentional dynamics — predicting not just whether a consumer is likely to act, but whether they are in the psychological state to evaluate and respond to a specific message or offer.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131979" ], "termName": "Cognitive Demand Model", "url": "https://www.wikidata.org/wiki/Q141131979", "usage": "coined", "wikidataQid": "Q141131979" }, { "description": "The stratum of audience intelligence that applies behavioral economics principles — loss aversion, default effects, social proof, present bias — to predict how consumer segments will respond to specific offer framings, pricing structures, and message architectures.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131983" ], "termName": "Behavioral Economics Layer", "url": "https://www.wikidata.org/wiki/Q141131983", "usage": "coined", "wikidataQid": "Q141131983" }, { "description": "The accuracy and consistency with which a dataset's motivation-layer attributes (decision drivers, values priorities, psychometric scores, behavioral economics signals) reflect the true underlying psychological characteristics of the consumers they represent.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131994" ], "termName": "Motivation Fidelity", "url": "https://www.wikidata.org/wiki/Q141131994", "usage": "coined", "wikidataQid": "Q141131994" }, { "description": "A data quality dimension measuring how recently the decision drivers, motivational attributes, and psychological propensities within a consumer record have been updated and validated — reflecting how quickly motivation data ages relative to demographic data.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131999" ], "termName": "Decisional Signal Freshness", "url": "https://www.wikidata.org/wiki/Q141131999", "usage": "coined", "wikidataQid": "Q141131999" }, { "description": "The proportion of a target population for which psychometric attributes — motivational profiles, values scores, decision driver classifications, cognitive style indicators — are available and validated within a given dataset.", "sameAs": [ "https://www.wikidata.org/wiki/Q141132004" ], "termName": "Psychometric Coverage", "url": "https://www.wikidata.org/wiki/Q141132004", "usage": "coined", "wikidataQid": "Q141132004" }, { "description": "The measurable gap between a consumer's declared or modeled motivational profile — decision drivers, values, psychological propensities — and their actual observed decision behavior; isolates individuals/segments for whom belief-action alignment cannot be assumed.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131532" ], "termName": "Motivational Dissonance", "url": "https://www.wikidata.org/wiki/Q141131532", "usage": "coined", "wikidataQid": "Q141131532" }, { "description": "Modeled motivational signal data generated to extend coverage into segments where directly observed decision-driver data is sparse, used to fill gaps in psychometric modeling while explicitly labeled as modeled — distinct from directly measured Motivation Signal.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131538" ], "termName": "Synthetic Motivation Signal", "url": "https://www.wikidata.org/wiki/Q141131538", "usage": "coined", "wikidataQid": "Q141131538" }, { "description": "An audience segment constructed from Motivation Layer signals — decision drivers, values, and psychological propensities — rather than from declared demographic filters or platform-side behavioral matching; built from why people are likely to act.", "termName": "Motivation-Defined Audience", "usage": "coined" }, { "description": "The principle and measurable value by which a co-op member's data contribution earns that member a proportional return of collective intelligence — connecting Cooperative Signal Density and Contribution-Grade Data back to individual member value.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131915" ], "termName": "Membership Signal Equity", "url": "https://www.wikidata.org/wiki/Q141131915", "usage": "coined", "wikidataQid": "Q141131915" }, { "description": "A data relationship structured around mutual, ongoing signal contribution and governed exchange, as distinct from a one-directional data license that transfers a fixed dataset for a fee with no ongoing contribution loop.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131932" ], "termName": "Contribution Partnership", "url": "https://www.wikidata.org/wiki/Q141131932", "usage": "coined", "wikidataQid": "Q141131932" }, { "description": "The methodology connecting cognitive-science principles — decision heuristics, cognitive load, information-processing style — to the Motivation Layer's decision-driver signals, modeling not just what motivates a person but how their cognitive processing shapes which messages they can act on.", "sameAs": [ "https://www.wikidata.org/wiki/Q141131988" ], "termName": "Cognitive-Motivational Mapping", "url": "https://www.wikidata.org/wiki/Q141131988", "usage": "coined", "wikidataQid": "Q141131988" }, { "description": "The application layer where Motivation Layer intelligence is translated into designed brand, product, or service experiences — not only ad targeting; the bridge between knowing why a person is likely to act and shaping the experience that meets them at that motivational state.", "sameAs": [ "https://www.wikidata.org/wiki/Q141132007" ], "termName": "Experience Activation Layer", "url": "https://www.wikidata.org/wiki/Q141132007", "usage": "coined", "wikidataQid": "Q141132007" }, { "description": "Attributes that describe who a person is such as their age, gender, ethnicity, occupation, marital status, education, household composition, and more.", "termName": "Demographic Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "popularized" }, { "description": "Signals based on actions someone has taken (e.g., browsing patterns, content consumption, engagement, lifestyle, interests, etc.).", "termName": "Behavioral Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "popularized" }, { "description": "Data describing what people have bought, when, and sometimes how often.", "termName": "Purchase Data (aka Transaction Data)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Data describing attitudes, motivations, preferences, and decision drivers.", "termName": "Psychographic Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Data that describes the underlying why behind consumer decisions including the attitudes, values, needs, and decision drivers that influence choices.", "termName": "Motivation Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "popularized" }, { "description": "Data organized around people/households using stable identity, designed for consistent understanding across channels.", "termName": "People-based Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "popularized" }, { "description": "Model outputs (often scores) that estimate likelihood of a behavior or attribute.", "termName": "Predictive Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Data packaged into segments (audiences) that can be activated in marketing platforms and across channels.", "termName": "Audience Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "popularized" }, { "description": "Data you collect directly from your customers and owned touchpoints (site/app/CRM).", "termName": "First-Party Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Another company's first-party data shared through a direct partnership.", "termName": "Second-Party Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Data obtained from an external provider, not collected directly by you.", "termName": "Third-Party Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Data a customer intentionally and proactively shares (preferences, needs, intentions).", "termName": "Zero-Party Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Marketing data contributed by multiple participating organizations into a shared database (a 'co-op'), where the combined information is used to create insights, attributes, and audiences that individual members can use typically under defined governance and usage rules.", "termName": "Co-op Data (aka Cooperative Data)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "popularized" }, { "description": "Signals indicating research or interest in a topic/category, often from content consumption or searches.", "termName": "Intent Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "popularized" }, { "description": "Signals based on the environment (content/page/program) rather than a person's identity.", "termName": "Contextual Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "popularized" }, { "description": "Data capturing opinions, preferences, beliefs, and stated intent (what people say they value or plan to do).", "termName": "Attitudinal Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "popularized" }, { "description": "Data indicating where someone is or has been (often modeled or aggregated).", "termName": "Location Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "popularized" }, { "description": "A record of actions taken (e.g., 'added to cart,' 'watched video,' 'opened email').", "termName": "Event Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The internal needs or goals that push someone toward a decision (e.g., security, convenience).", "termName": "Consumer Motivations", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The specific psychological factors that most strongly influence an individual's choices, tradeoffs, and timing (what they prioritize when deciding).", "termName": "Decision Drivers", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The study of how people choose, buy, and form preferences—shaped by emotion, identity, social influence, and context.", "termName": "Consumer Psychology", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The process of connecting identifiers (email, device IDs, etc.) to represent a person/household consistently.", "termName": "Identity Resolution", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Identity matching based on direct, high-confidence identifiers (e.g., the same hashed email).", "termName": "Deterministic Matching", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Identity matching based on patterns and likelihood (e.g., signals suggesting two devices belong to the same person).", "termName": "Probabilistic Matching", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A database that links identifiers (emails, devices, households) to represent relationships across channels.", "termName": "Identity Graph", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A data framework that connects devices and identifiers belonging to the same household.", "termName": "Household Graph", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A type of identity graph focused on connecting devices to individuals or households.", "termName": "Device Graph", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The process of converting offline/CRM identifiers into platform-usable audiences.", "termName": "Onboarding", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Converting identifiers (like email) into a fixed string so they can be matched without exposing the raw value.", "termName": "Hashing", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "An email address that's been converted into a fixed, non-readable string using a one-way hashing method, so it can be used as a privacy-protective identifier for matching records across systems without sharing the raw email.", "termName": "Hashed Email (HEM)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A device-level identifier on mobile (e.g., IDFA/GAID) used for advertising and measurement (where available).", "termName": "Mobile Ad ID (MAID)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A browser-based identifier used to recognize users on a site or across sites (depending on type/permissions).", "termName": "Cookie", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Permission given by a user for data collection and/or use under applicable policies/laws.", "termName": "Consent", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A controlled environment where parties can analyze or match data with privacy protections and restrictions.", "termName": "Clean Room", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Using data and statistical methods to forecast likely future outcomes (e.g., propensity to purchase).", "termName": "Predictive Analytics", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Building models that estimate outcomes using patterns in historical data.", "termName": "Predictive Modeling", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The interdisciplinary study of how people think, learn, perceive, and make decisions (drawing from psychology, neuroscience, behavioral economics, and more).", "termName": "Cognitive Science", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A predictable shortcut in thinking that influences decisions (often unconsciously), such as favoring familiar brands or over-weighting recent experiences.", "termName": "Cognitive Bias", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A score estimating the likelihood that someone will take a specific action (e.g., purchase, subscribe).", "termName": "Propensity Score", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The improvement in performance attributable to a tactic or audience relative to a baseline.", "termName": "Lift", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A portion of an audience intentionally excluded from marketing to create a comparison baseline.", "termName": "Holdout Group", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The results that happened because of marketing—beyond what would have happened anyway.", "termName": "Incrementality", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Testing a model on historical data to evaluate performance.", "termName": "Backtesting", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "When a model performs well on training data but poorly on new data because it learned noise, not signal.", "termName": "Overfitting", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Creating or transforming input variables to improve model performance.", "termName": "Feature Engineering", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A field that combines psychology and economics to explain why real-world decisions often deviate from 'perfectly rational' behavior.", "termName": "Behavioral Economics", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The ability to understand and communicate why a model produced a given score or outcome (at a level appropriate for the user).", "termName": "Model Explainability", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Segmenting audiences by core values and priorities (e.g., status, stability, altruism, independence) rather than only demographics or behaviors.", "termName": "Values-Based Segmentation", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A standardized rating scale (often 1–5 or 1–7) used to quantify attitudes, agreement, likelihood, or intensity of a trait.", "termName": "Likert Scale", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Modeling approaches used to quantify psychological characteristics (attitudes, motivations, traits) from observed data or structured measures.", "termName": "Psychometric Modeling", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The use of machine learning and other AI techniques to improve marketing decisions—such as who to target, what to say, where to spend, and how to measure impact.", "termName": "AI in Marketing", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A type of AI where algorithms learn patterns from data to make predictions or decisions without being explicitly programmed for every rule.", "termName": "Machine Learning (ML)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "AI/ML used to forecast future outcomes like likelihood to convert, churn, respond, or purchase a category.", "termName": "Predictive AI", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "AI that creates new content such as text, images, audio, video, or code based on patterns learned from large datasets.", "termName": "Generative AI (GenAI)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Data that's structured, governed, and documented well enough to be used reliably in AI/ML systems (clear definitions, consistent formats, usable IDs, and known limitations).", "termName": "AI-Ready Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The historical data used to teach a model patterns and relationships.", "termName": "Training Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "An input variable a model uses—such as a demographic attribute, behavior signal, purchase indicator, or motivation measure.", "termName": "Feature (in Machine Learning)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "When a generative AI system produces information that sounds plausible but is incorrect or unsupported.", "termName": "AI Hallucination (GenAI)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Artificially generated data designed to resemble real data patterns, often used to test systems or protect privacy.", "termName": "Synthetic Data", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Using AI systems to automatically adjust decisions (bids, budgets, audience allocation, creative rotation) toward a goal.", "termName": "AI Optimization", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The practice of structuring content so it's more likely to be surfaced or summarized accurately by AI-driven discovery experiences (AI search, assistants, summaries), not just classic SEO rankings. Encompasses GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization).", "termName": "AIEO (AI Engine Optimization)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A GenAI approach that retrieves relevant information from approved sources and uses it to generate a response grounded in that material.", "termName": "RAG (Retrieval-Augmented Generation)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A platform-ready audience built from your identifiers (e.g., CRM list) or defined segment criteria.", "termName": "Custom Audiences", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "An audience of new prospects who resemble a seed audience (e.g., your best customers).", "termName": "Lookalike Audience", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Excluding people from targeting (e.g., recent buyers, current customers, ineligible audiences).", "termName": "Suppression", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "How often someone sees your ad in a given time period.", "termName": "Frequency", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A control that limits how many times the same viewer or household sees an ad.", "termName": "Frequency Capping", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The number of unique people exposed to your campaign.", "termName": "Reach", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Measuring the additional unique audience reached by adding a new channel to a campaign, often measured when platforms like CTV reach viewers not exposed to linear TV ads.", "termName": "Incremental Reach", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Methods used to assign credit for conversions to marketing touchpoints.", "termName": "Attribution", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A statistical approach that estimates how different marketing activities contribute to outcomes over time.", "termName": "Marketing Mix Modeling (MMM)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Attribution models that attempt to assign fractional credit across multiple touches leading to conversion.", "termName": "Multi-Touch Attribution (MTA)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The umbrella term for data-enabled TV advertising across streaming, CTV, addressable, and programmatic environments.", "termName": "Advanced TV", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "TV advertising that uses data to deliver an ad to a specific household on a TV screen.", "termName": "Addressable TV", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Television devices connected to the internet that stream video content and allow advertisers to deliver targeted ads within streaming environments (e.g., Smart TV, Roku, Apple TV).", "termName": "Connected TV (CTV)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Audience targeting for connected TV advertising using available identifiers and segment definitions.", "termName": "CTV Targeting", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Traditional television that leverages data for planning of a media buy. More of an offline planning activity that leverages data from varying channels.", "termName": "Data-Driven Linear TV", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Traditional television viewing through broadcast, cable, or satellite TV with a set schedule.", "termName": "Linear TV", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Linear, ad-supported content delivered via streaming (e.g., Pluto TV, Tubi).", "termName": "FAST (Free Ad-Supported TV)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Video content delivered via the internet, bypassing traditional cable/satellite. Distribution model for services like streaming apps and connected TV platforms (e.g., Hulu, Netflix).", "termName": "Over-the-Top-TV (OTT)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The automated buying and selling of TV advertising using software and data signals, allowing media to be transacted and optimized in real time across streaming and digital video environments.", "termName": "Programmatic TV", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A television with built-in internet connectivity and streaming apps that allows viewers to access digital content directly without external devices. No sticks or dongles are required (e.g., LG, Samsung, Vizio).", "termName": "Smart TV", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Traditional cable or satellite providers. Refers to any service provider that delivers video programming services (e.g., Comcast, DirecTV).", "termName": "MVPD (Multichannel Video Programming Distributor)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Internet-based, streaming pay-TV subscription services that offer live, traditional TV channels (e.g., Sling TV, Hulu + Live TV).", "termName": "vMVPD (Virtual Multichannel Video Programming Distributor)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Free content supported by ads. Streaming video content is available for free to viewers in exchange for watching ads (e.g., YouTube, Tubi).", "termName": "AVOD (Ad-Supported Video on Demand)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Ad-free, subscription-based content. Streaming services where viewers pay a recurring fee to access content without traditional ads (e.g., Netflix, Disney+).", "termName": "SVOD (Subscription Video on Demand)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "A streaming model where viewers pay for individual purchases or rentals rather than subscribing to a service (e.g., iTunes).", "termName": "TVOD (Transactional Video on Demand)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Streaming model where viewers pay a premium price to access newly released or early-window content at home, often shortly after or alongside theatrical release.", "termName": "PVOD (Premium Video on Demand)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "Authentication services that allow cable or satellite subscribers to watch network content across digital devices.", "termName": "TVE (TV Everywhere)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "How well a dataset is fit to support a specific marketing use case—based on factors like accuracy, completeness/coverage, freshness, consistency, and clear documentation of how the data was created and can be used.", "termName": "Data Quality", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "How much of your target population can be represented or matched in a dataset.", "termName": "Data Coverage", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "How often an attribute correctly reflects reality.", "termName": "Data Accuracy", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "How recently data was updated and how quickly it reflects real-world changes.", "termName": "Data Freshness", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The delay between a real-world event and when it appears in a dataset or system.", "termName": "Latency", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" }, { "description": "The policies, legal requirements, and internal rules that govern how data can be collected, used, and shared.", "termName": "Compliance (Privacy + Governance)", "url": "https://alliantinsight.com/insights/marketing-data-glossary/", "usage": "used" } ]