The Challenge
Most e-commerce sites bombard shoppers with irrelevant products. Our conversion rates were flat, and customers were bouncing fast. I remember a shopper saying, 'Why do I keep seeing stuff I'd never buy?' The team wanted to make every visit feel personal, but with thousands of SKUs and millions of sessions, it felt impossible.
Our Approach
We didn't want to push products for the sake of it. We built a recommendation engine that learned from every click, search, and purchase. The tool didn't just look at what was popular—it looked at what mattered to each shopper, in real time. We ran A/B tests, tweaked algorithms, and made sure the system explained its picks. Customers could give feedback, so the tool kept getting smarter. The result? Product suggestions that felt like a personal shopper, not a pushy salesperson.
The Implementation
We started with the homepage and cart, then expanded to email and push notifications. Training was hands-on, with real user journeys and live feedback. Integration with our e-commerce platform was seamless, and the team kept a close eye on opt-outs and satisfaction. As the tool proved itself, we rolled it out sitewide, always listening and improving.
The Results
Conversion rates climbed as shoppers found what they wanted faster, average order values increased, and customer feedback shifted from frustration to delight, with many saying the site 'finally gets me.'
