Machine Learning

Retail Recommender System for Loyalty Personalization

Led the development of a recommender system for loyalty customers using point-of-sale transaction data and promotional calendar data. The system drives more relevant weekly offer personalization and improves conversion rates by matching customers with the most relevant promotional offers based on purchase history, category affinity, and promotional timing.

Domain

Retail

Methods

  • Collaborative Filtering
  • Feature Engineering
  • Recommendation Algorithms
  • A/B Testing

Tools

  • Python
  • PySpark
  • Databricks
  • MLflow

Impact / Outcome

Improved weekly offer personalization and conversion rates for loyalty customers through data-driven recommendation.