Customer segmentation and recommender system build for e-commerce company
The Challenge
Our client, a leading e-commerce company1, wanted to deepen its understanding of customer behavior to improve retention and drive more targeted marketing campaigns. The company had a vast amount of transaction data but lacked a systematic approach to segment customers and personalize recommendations. The goal was to leverage this data to create distinct customer segments and recommend relevant products or services.
The Approach
We adopted a two-fold strategy: implementing RFM segmentation and developing an item-based recommender system.
1. RFM Segmentation:
RFM analysis is a proven method for segmenting customers based on their purchasing behavior. We analyzed three key factors:
- Recency: How recently a customer made a transaction.
- Frequency: How often a customer transacts within a given period.
- Monetary: The total value of a customer’s transactions.
By scoring2 customers on these dimensions, we were able to categorize them into distinct segments, such as high-value customers, frequent transactors, or those at risk of churning. This segmentation provided the company with a clearer understanding of where to focus its marketing efforts, ensuring that resources were allocated efficiently.
2. Item-Based Recommender System:
Once the customer segments were established, we turned our attention to building an item-based recommender system. This system analyzed the purchasing patterns within each segment to suggest financial products or services that similar customers had purchased or expressed interest in. By using collaborative filtering techniques, the recommender system could accurately predict which offerings would appeal to specific customer segments.
The Results
The implementation of RFM segmentation and the item-based recommender system can yield significant benefits:
- Improved Targeting: The company could now tailor its marketing campaigns to specific customer segments, resulting in higher engagement rates and better conversion.
- Increased Customer Satisfaction: By recommending relevant products, the company can better affect customer satisfaction, leading to improved retention and loyalty.
- Revenue Growth: The targeted approach also drove revenue opportunities though cross-selling and upselling the right products to the right customers.
Conclusion
By integrating RFM analysis with an item-based recommender system, our consulting team helped the company transition to a more data-driven approach to customer engagement. As online businesses continue to evolve, these strategies will be essential for companies looking to maintain a competitive edge and build lasting customer relationships.