PROJECTS FOR RETAIL CHAINS AND E-COMMERCE
Building a Product Recommendation Model for a Major Food Retailer
Client: a major food retailer with over 900 stores, 13 million active customers and 100 million purchases
Challenge
With its huge customer base, the retailer struggled to:
Project objectives
The goal was to boost key performance metrics:
Solution
We developed a hybrid recommendation model combining neural network embeddings and collaborative filtering, designed to:
With its huge customer base, the retailer struggled to:
- Provide personalized product recommendations
- Measure the impact of personal discounts
- Manage the product matrix though targeted offers to customers interested in specific products
Project objectives
The goal was to boost key performance metrics:
- Increase margin by 1-2 p.p.
- Raise the average number of purchases by 20%–30%
- Expand the active product matrix by 10%
Solution
We developed a hybrid recommendation model combining neural network embeddings and collaborative filtering, designed to:
- Leverage personal purchase histories and customer preferences, even with very large customer bases
- Ensure continuous retraining on various user signals, such as purchases, searches, comparisons, cart activity, etc.
- Predict future purchases based on purchase frequency
Value delivered
- 1.83% increase in net product margin
- 2.5x growth in conversion rate
- 10% increase in activity across product matrix categories
- Enabled management of personalized offers through customized channels, such as email, mobile apps, push notifications, SMS, etc.
- Enabled segmentation management and user activation through offers based on RFM analysis
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