Business Impact
- 14% uplift in session conversion.
- 9% increase in average order value.
- Reduced cold start latency from hours to minutes.
Challenge
Rule-based merchandising and nightly batch models lacked context awareness and failed to personalize for sparse data customers.
Solution
- Unified event collection (clickstream, search, transactions) into a streaming feature pipeline.
- Hybrid recommender (sequence modeling + candidate generation + re-rank layer).
- Contextual bandit exploration to balance novelty & exploitation.
- Real‑time feature updates (recent views, session intent signals).
- Experimentation & metrics platform measuring lift with guardrails.
Architecture Highlights
- Feature store with real-time & offline stores.
- Vector embeddings for user & item similarity.
- Stream processing for incremental updates.
- Online A/B experimentation framework.
Outcomes
Engine enabled rapid iteration on ranking strategies and merchandising policies, creating a continuous improvement loop for conversion KPIs.