RetailGiant: Personalization Engine
We implemented a recommendation engine that improved conversions and revenue via personalized experiences across channels. The system personalizes ranking across web, email, and push, adapting to seasonality and inventory constraints while maintaining diversity.
Challenge
Generic merchandising failed to surface relevant products, causing low engagement and churn. Static rules ignored cold-start users and fast-moving catalogs, leading to stale recommendations.
Background
Catalog velocity and seasonality made manual merchandising unsustainable. New users saw generic lists, and stock or margin constraints weren’t considered in existing logic.
Solution
Hybrid recommendations (content + collaborative) and a real-time user profile store powering on-site and email personalization. We added exploration vs. exploitation controls, business rules for margin and stock, A/B testing pipelines, and feedback loops to improve models continuously.
Implementation
We shipped hybrid recommenders, a real‑time profile store, and guardrails for margin, stock, and diversity. We added explore‑exploit controls, A/B test pipelines, and feedback loops for continuous model improvement. Merchandisers gained tooling to set campaigns and overrides.
Impact
- Higher AOV and repeat purchases
- Relevant merchandising at scale
- Operational tooling for marketers
- Improved cold-start handling
- Better email click-through and conversions
- Fewer dead-end sessions for new users
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