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Launched an AI-powered recommendation engine for an online fashion retail platform that personalized product suggestions based on user browsing behavior, purchase history, and clickstream data, improving engagement, conversion, and cross-sell effectiveness.
More case studiesIndustry: Online Fashion Retail
Core business: E-commerce platform selling apparel and accessories to mid-market and enterprise customers
Employees: 120
Geography: US & EU
A fashion e-commerce platform aimed to improve engagement and sales performance but faced limitations in its recommendation capabilities. Despite having access to behavioral and transactional data, the system relied on static logic and lacked real-time adaptability.
Users were not interacting with existing recommendations, leading to missed sales opportunities and lower conversion rates.
The recommendation logic was static and did not account for individual user preferences, browsing behavior, or purchase history.
The platform lacked real-time analytics and adaptive models to dynamically suggest complementary products based on live user activity.
We designed and implemented a scalable AI-powered recommendation engine integrated with the client’s web platform to enable real-time personalization.
Developed using collaborative filtering and content-based models to generate relevant product suggestions for each user.
Monitored clicks, purchases, and browsing history to continuously refine recommendations.
Displayed personalized product suggestions across product pages, homepage, and category pages.
Delivered recommendations through API endpoints to ensure smooth front-end rendering across desktop and mobile.
The AI-powered recommendation engine significantly improved engagement, conversion, and overall revenue performance.
Time on site and interaction with recommended products increased by 22%.
Product page conversions increased by 18%, and average order value grew by 12% due to relevant cross-sell recommendations.
The system served over 30,000 active users daily with real-time updates, maintaining stable performance without degradation.
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