ShopMind — AI-Powered E-Commerce Personalization Engine
38% uplift in conversion rate through real-time AI product recommendations.
The Challenge
Generic product recommendations leaving revenue on the table
A mid-sized e-commerce retailer with 800,000+ SKUs was using basic rule-based recommendations ('customers also bought') that had no understanding of individual user intent, session context, or purchase history. Conversion rates were stagnating at 1.8%, and the marketing team had no ability to personalize at scale. They needed a real-time AI recommendation engine that could handle their catalog scale without slowing down page loads.
Our Approach
Real-time collaborative filtering with session-aware contextual ranking
Behavioral Data Pipeline
Built a real-time event streaming pipeline using Apache Kafka to capture and process click, view, cart, and purchase events from 50,000+ daily active users with sub-100ms latency.
Hybrid Recommendation Model
Developed a hybrid ML model combining collaborative filtering, content-based similarity, and session-aware neural networks — trained on 18 months of transaction history across 2.4M orders.
Real-Time Serving Layer
Built a high-performance recommendation serving API in Python/FastAPI with Redis caching, delivering personalized recommendations in under 50ms for any user at any point in the shopping journey.
A/B Testing Framework
Implemented a rigorous A/B testing framework allowing the product team to test recommendation strategies continuously, with statistical significance calculations built in.
The Solution
A real-time personalization engine that learns and improves continuously
ShopMind's recommendation engine now serves hyper-personalized product suggestions across the homepage, product detail pages, cart, and email campaigns. The model updates recommendations in real time as users browse, incorporating session context alongside long-term preferences. Within 90 days of launch, the platform generated an additional $1.2M in revenue from recommendation-driven purchases.
Project Details
Tech Stack
Services Provided
We went from generic 'you might also like' carousels to a recommendation engine that genuinely understands each shopper. The lift in conversion and AOV exceeded our projections by a significant margin. The Qodeon Labs team moved fast and the technical quality was outstanding.
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