ShopVault increased average order value by 28% and conversion rates by 18% after launching a personalized product recommendation engine powered by real-time user behavior analysis.
Our Role: End-to-end: data pipeline, model development, frontend integration
Increase in average order value
Order Value Increase
Improvement in overall conversion rate
Conversion Lift
Increase in product page click-through rates on recommended items
Click-Through
ShopVault, a rapidly growing online retailer with over 200,000 SKUs, struggled with low product discovery rates. Shoppers were browsing extensively but not finding relevant products, leading to high bounce rates and low average order values. The existing category-based browsing system was static and didn't adapt to individual user preferences, resulting in missed upsell and cross-sell opportunities.
We built a real-time recommendation engine that analyzes user behavior signals — browsing history, cart additions, purchase patterns, and even time spent on product pages — to generate personalized product recommendations. The system uses a hybrid approach combining collaborative filtering, content-based recommendations, and real-time contextual signals to surface the most relevant products at the right moment in the shopper journey.
ShopVault's catalog of 200,000+ products was becoming impossible to navigate. Customers were overwhelmed by choice and unable to find products they wanted.
We designed a multi-signal recommendation system that adapts in real-time to each shopper's behavior and preferences.
The engine integrates seamlessly into ShopVault's storefront, powering "Recommended for You" sections, product page recommendations, and cart-based suggestions.
Within three months, ShopVault saw a 28% increase in average order value and an 18% lift in conversion rates.
The recommendation engine became the highest-converting feature on our site. It pays for itself every single day.
Marcus Rivera
CTO, ShopVault