AI Forecasting & Personalization

Business Problem

Manual forecasting and non-personalized campaigns created planning inefficiencies and weaker customer targeting.

Architecture

• Feature pipelines for reusable forecasting inputs
• Ensemble forecasting with XGBoost, Prophet, Random Forest, and regression models
• Collaborative and similarity-based recommendation components for personalization
• Automated retraining and monitoring for drift-prone signals

Technical Decisions

• Used a mix of forecasting models rather than a single approach to fit different planning horizons and feature availability.
• Added popularity-based fallbacks to reduce cold-start degradation in recommendation workflows.
• Invested early in reusable feature pipelines to reduce repeated manual engineering effort.

Production Challenges

• Operationalized retraining to respond to concept drift.
• Tracked RMSE, MAE, MAPE, CTR, and recommendation acceptance to connect model quality to business use.
• Built fallback behavior for sparse or cold-start recommendation segments.

Outcomes