Track: Machine Learning Applications in Marketing
Measuring Customer Similarity and Identifying Cross-Selling Products
Product affinity segmentation discovers the groups of customers with similar purchase preferences for cross-selling opportunities to increase sales and customer loyalty. However, this concept can be challenging to implement efficiently and effectively for actionable strategies, due to the skew and sparsity of the product-level data and the computational complexity of traditional clustering methods. In this work, we propose to partition customers into groups by maximizing their product purchase similarity within the communities in the customer-product bipartite graph. Through a case study using data from a large U.S. retailer, we demonstrate that the proposed method generates interpretable clustering results with distinct product purchase patterns, yields higher response rates, and addresses the computational complexity in the context of big data.