This is like giving your ad platform a crystal ball that predicts which people are most likely to tap on or engage with a mobile ad, so you show fewer ads to people who won’t care and more to those who probably will.
Mobile ad campaigns waste spend by showing ads to users unlikely to respond. This work builds models that predict user responses (e.g., clicks, installs, conversions) so ad networks and advertisers can target more effectively and bid more intelligently.
Quality and volume of historical user–ad interaction data, plus continuous retraining pipelines that adapt to changing user behavior and inventory.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Real-time scoring latency and feature freshness at large scale (high QPS ad auctions) plus concept drift from rapidly changing user behavior.
Early Majority
Focus on fine-grained user response prediction in mobile contexts (on-device and in-app signals, session-based behavior, device characteristics) rather than generic web click prediction, enabling tighter optimization for mobile ad networks and in-app programmatic ads.