Imagine you run an online advertising campaign with loyalty offers to keep customers from leaving. This framework is like a smart calculator that tells you exactly which customers should get which incentive (discount, bonus, free trial) and when—so you keep the most valuable users for the least cost.
Marketing and advertising teams overspend on generic retention campaigns and churn-prevention offers because they don’t know which customers to target or how big an incentive is really needed. This framework jointly optimizes who to target and what incentive to offer, to maximize retention impact per dollar spent.
If implemented in a live environment, the moat would come from proprietary historical user behavior data and offer-response data, plus the optimization logic embedded into existing adtech/CRM workflows (making switching costly).
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Solving large-scale optimization problems (e.g., millions of users × multiple incentive levels) in near-real time; also data quality and feature engineering for robust uplift/response prediction.
Early Adopters
Unlike generic churn models that only predict who might leave, this framework is explicitly prescriptive: it decides both the optimal retention incentive and the optimal target segment jointly, likely under budget or ROI constraints. That joint optimization of incentive size and targeting is more advanced than standard segmentation plus rule-based offers.