RetailE-Commerce Retail Group10 weeks6 engineers
Case Study
35%Uplift in Recommendation CTR
Real-time AI recommendation engine and demand forecasting system handling 5M+ product catalog at scale.
Results
Before & After
| Metric | Before | After | Improvement |
|---|---|---|---|
| Recommendation CTR | 2.1% | 2.85% | ↑ 35% |
| Overstock Rate | 18% | 14% | ↓ 20% |
| Customer LTV | $340 | $680 | ↑ 100% |
The Challenge
What We Were Solving
A major e-commerce retailer was using basic collaborative filtering that couldn't handle cold-start problems or real-time personalization at scale.
Our Solution
How We Built It
We built a two-tower neural recommendation system using PyTorch and Databricks, with real-time feature serving via Redis and an A/B testing framework for continuous improvement.
Tech Stack
Technologies Used
PythonDatabricksAzure MLFastAPI
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