Retail AI: 22% Conversion Lift with Personalized Recommendations
A retail brand was showing the same product suggestions to every visitor. We built a real-time recommendation engine that lifted conversions by 22%.
The Challenge
What was getting in the way
- 01
Every customer saw the same "top sellers" list regardless of browsing history or purchase behavior
- 02
Email open rates and click-throughs were declining because content felt generic
- 03
The existing tech stack had no way to serve personalized content in real time across web and mobile
The Solution
How we solved it
We built a recommendation engine using TensorFlow, deployed on SageMaker with Redis caching for sub-10ms response times. The model updates hourly based on click, cart, and purchase signals. We integrated it into the client's React storefront and their email platform so both channels serve personalized product picks.
Technologies
What We Built
A look inside the project
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Conversion Rate
Avg Order Value
Click-through
Illustration based on actual project deliverable
The Process
Step-by-step delivery
Customer Data
Aggregate click, cart, and purchase signals
Behavior Analysis
Identify patterns and segment customers by intent
AI Model
Train collaborative filtering and ranking models
Real-Time Serving
Deliver recommendations in under 10ms via Redis
Measure & Iterate
A/B test and improve based on conversion data
The Results
The numbers
Higher Conversion Rate
Increase in Customer Engagement
Personalization Across Channels