Challenge
A leading media publisher sought to improve the efficiency and performance of direct mail reengagement campaigns targeting lapsed subscribers. Traditional list targeting approaches often produced inconsistent response rates, particularly among older lapsed audiences who had been inactive for several years.
The publisher needed a more strategic way to identify expired subscribers most likely to respond to an offer and convert to a paid subscription, while improving the overall efficiency of marketing spend.
Solution
Alliant developed a custom net paid optimization model to identify lapsed subscribers with the highest likelihood to respond to direct mail subscription offers. Using data science and predictive analytics, the model evaluated behavioral and engagement signals to score and prioritize individuals most likely to convert into paid subscribers. The approach placed particular emphasis on older lapsed audiences — subscribers inactive for 3–10 years — where traditional targeting methods often struggle to maintain performance. Rather than simply maximizing response volume, the model was designed to improve the quality, efficiency, and profitability of subscriber reactivation efforts.
Results
Alliant’s custom net paid optimization model demonstrated strong aggregate performance across multiple campaigns, achieving a 127 performance index.
