Challenge
Generic product recommendations (bestsellers, related products) did not drive conversions. Merchants needed personalized recommendations based on browsing behavior, purchase history, and similar customer patterns.
Solution
Built AI-powered recommendation engine analyzing customer behavior data. Machine learning model predicts relevant products based on collaborative filtering and content similarity. Dynamic widget displays recommendations on product pages, cart, homepage. A/B testing for algorithm optimization.
Impact
Automated personalization at scale, driving cross-sell and discovery beyond manual merchandising.
Tech Stack
Project Overview
We built an AI-powered recommendation engine for a Shopify store managing thousands of SKUs. Manual merchandising at that scale is a losing game - someone has to decide which products appear in the “you might also like” section, and those decisions are almost always based on gut feel or what’s already popular. This project replaced that guesswork with a system that learns from actual customer behavior.
The Problem with Generic Recommendations
The store’s existing setup showed the same bestsellers to every visitor. A first-time buyer browsing kitchen gear saw the same recommendations as a returning customer who had already bought three items in that category. The system had no memory, no context, and no way to distinguish between them.
The real cost wasn’t just low click-through rates. It was the cross-sell opportunities being left on the table at every cart interaction, and the long-tail SKUs that never surfaced because the algorithm always pushed the same top-10 products. As the catalog grew, the problem compounded.
Technical Approach
Behavioral Data Collection
We started by building a solid tracking layer. Three types of signals feed the model: page views (which products were browsed and for how long), add-to-cart events (a stronger purchase-intent signal than views), and completed purchases. These are collected via the Shopify Storefront API and stored for model training.
Hybrid Recommendation Model
The engine combines two complementary algorithms. Collaborative filtering works on the principle that customers with similar browsing patterns tend to buy similar things - “people who viewed this also bought…” at the pattern level, not just the product level. Content-based similarity measures proximity between products based on attributes like category, price range, and tags.
Neither approach works perfectly alone. Collaborative filtering struggles with new products that lack interaction data. Content-based similarity can be too narrow. The hybrid model uses both, weighting them dynamically based on available data.
Widget Placement and Processing Architecture
Recommendations appear in three locations: the product page (for customers still deciding), the cart page (for cross-sell at the moment of highest purchase intent), and the homepage (for returning visitors, personalized based on their history).
We chose a split processing architecture. The base model runs on a daily batch job - computationally inexpensive and covers the majority of recommendation accuracy. Within-session signals (what the customer clicked in the last 10 minutes) are processed in real-time to adjust recommendations without waiting for the next batch cycle. This keeps response times fast while still capturing fresh intent signals.
A/B Testing
Algorithm updates go through a structured A/B test before full rollout. New model variants are served to 20% of traffic, and we compare CTR, conversion rate, and average order value against the control. Changes only ship when the improvement is statistically significant.
Results
- Recommendation CTR improved 3.2x over the previous static setup
- Cross-sell revenue increased 28%
- Average order value up 15%
The key takeaway from this project: personalization at scale is less about having a fancy model and more about having clean behavioral data and a disciplined testing process. The model itself is only as good as the signals it learns from.