Solutions & Tools
Personalized Product Recommendations for E-commerce Retailer
- Objective: To increase customer engagement, boost conversion rates, and enhance revenue by delivering personalized product recommendations to shoppers based on their browsing behavior, purchase history, and preferences.
- Challenges : Low conversion rates due to a one-size-fits-all product recommendation approach. Limited insight into individual customer preferences, leading to missed cross-selling and up-selling opportunities. High bounce rate on product pages, with customers leaving without finding relevant products
Solution Approach
Data Collection & Processing
- Aggregated data from various sources, including user browsing history, purchase history, product ratings, and clicks.
- Used additional data like customer demographics, location, and product interaction to better understand customer preferences.
Machine Learning Model for Recommendations
- Employed collaborative filtering and content-based filtering models to create personalized recommendations based on similar users and similar products.
- Used deep learning algorithms (e.g., neural collaborative filtering) to generate dynamic recommendations in real time
- Developed specific recommendation categories like “Similar Products,” “Frequently Bought Together,” and “Trending Now” to cater to different stages of the customer journey.
Integration & Real-time Deployment
- Integrated the recommendation engine with the e-commerce website and mobile app to provide instant, personalized recommendations
- A/B tested recommendation models to optimize placement and style for highest engagement on product and checkout pages
Performance Monitoring & Continuous Improvement
- Monitored key metrics like click-through rate (CTR), conversion rate, and average order value (AOV) to assess recommendation effectiveness.
- Regularly updated recommendation models with new data to improve relevance and accuracy over time.