ML-Powered Personalization Engine for E-Commerce
Developed real-time personalization engine increasing conversion rates by 28% and average order value by 35% through intelligent product recommendations.
The Challenge
An e-commerce platform with 5M monthly visitors suffered from low conversion rates (1.2%) and struggled to compete with Amazon's personalized experience. Generic product recommendations weren't driving sales, and cart abandonment was at 78%.
Key Pain Points:
- Conversion rate of only 1.2% (industry average 2.5%)
- 78% cart abandonment rate
- One-size-fits-all product recommendations
- No real-time personalization capabilities
- Limited understanding of customer behavior and preferences
- Losing market share to competitors with better personalization
Our Solution
We built a sophisticated ML-powered personalization engine delivering real-time, context-aware product recommendations.
Our Approach
- 1Implemented comprehensive event tracking across web and mobile
- 2Built real-time feature store using Feast
- 3Developed collaborative filtering and deep learning recommendation models
- 4Created A/B testing framework for continuous model improvement
- 5Implemented real-time personalization API with <50ms latency
- 6Built dynamic email personalization for abandoned cart recovery
- 7Created customer segmentation models for targeted campaigns
- 8Developed explainability layer to understand recommendation drivers
Technologies Used
The Results
The personalization engine now powers 80% of product discovery and has dramatically improved business metrics.
"The personalization engine has become our competitive advantage. Customers now see products they actually want, and our revenue per visitor has skyrocketed."
Related Case Studies
Explore more success stories from similar industries and services
Ready for Similar Results?
Let's discuss how we can help you achieve transformational outcomes with your data.