E-commerce Personalization
Boosting sales and engagement with a custom recommendation engine.
The Challenge
A fast-growing online retailer with a large product catalog found that customers were struggling to discover relevant products. Their generic "popular items" lists were ineffective, leading to low conversion rates and cart abandonment. They needed to provide a truly personalized shopping experience for each user.
Our Solution
We designed and deployed a hybrid recommendation engine that combines collaborative filtering (what similar users like) and content-based filtering (product attributes). The system analyzes user browsing history, purchase data, and item metadata in real-time to generate personalized product recommendations on the homepage, product pages, and in email marketing campaigns.
Key Outcomes
- Increased average order value by 12%.
- Boosted overall sales attributed to recommendations by 15%.
- Improved user engagement metrics, including a 25% increase in pages per session.
- Reduced cart abandonment rate by 8%.