Scalable Representation of Customer Purchase Preferences through Co-Purchase History

Abstract

In the competitive e-commerce landscape, accurately measuring customer preferences and effectively representing customer segments are essential for driving personalized marketing and product offerings. Current data-driven methods often rely on resource-intensive algorithms, and there is a need for a systematic and scalable framework for extracting product sets that represent specific purchasing preferences. This study proposes an unsupervised, efficient framework that leverages purchase history data to derive product sets that best represent known customer segments and product categories. Utilizing an item-based top-N recommendation technique, the proposed method tracks co-purchase histories and generates relevant novel segment variants, capturing hidden purchase preference attributes and delivering a more accurate depiction of customer behavior. Evaluation with real-world customer data from a Korean retail and e-commerce platform network substantiates the practical applicability of the suggested framework in forecasting the probability of purchasing target products, outperforming other prediction techniques. By adopting this scalable and readily implementable approach, businesses can effectively make well-informed decisions regarding product offerings, promotional campaigns, and personalized recommendations, ultimately improving customer engagement and sales.