Retail
E-commerce Personalization Engine
Leading Fashion Retailer
+35%
Conversion Rate
Increase in overall conversion rate
+28%
Average Order Value
Higher basket sizes from recommendations
+45%
User Engagement
More time spent browsing products
$8M
Revenue Impact
Additional annual revenue generated
The Challenge
The client struggled with low conversion rates and generic customer experiences. Their existing recommendation system was rule-based and couldn't adapt to individual user preferences in real-time.
Our Solution
We built a sophisticated personalization engine using collaborative filtering and deep learning models. The system analyzes user behavior, purchase history, and contextual signals to deliver personalized product recommendations, content, and offers in real-time across web and mobile.
Technology Stack
PythonTensorFlowRedisPostgreSQLKubernetesFastAPI
Project Timeline
4 months from concept to production
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