Predicting customer shopping lists from point-of-sale purchase data

This paper describes a prototype that predicts the shopping lists for customers in a retail store. The shopping list prediction is one aspect of a larger system we have developed for retailers to provide individual and personalized interactions with customers as they navigate through the retail store. Instead of using traditional personalization approaches, such as clustering or segmentation, we learn separate classifiers for each customer from historical transactional data. This allows us to make very fine-grained and accurate predictions about what items a particular individual customer will buy on a given shopping trip. We formally frame the shopping list prediction as a classification problem, describe the algorithms and methodology behind our system, its impact on the business case in which we frame it, and explore some of the properties of the data source that make it an interesting testbed for KDD algorithms. Our results show that we can predict a shopper's shopping list w...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Chad M. Cumby, Andrew E. Fano, Rayid Ghani, Marko Krema
• Keywords: Data Mining, KDD 2004, Retail Shopping List, Shopping List Prediction, Shopping Trip