document: 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 with high levels of accuracy , precision , and recall . We believe that this work impacts both the data mining and the retail business community . The formulation of shopping list prediction as a machine learning problem results in algorithms that should be useful beyond retail shopping list prediction . For retailers , the result is not only a practical system that increases revenues by up to 11 % , but also enhances customer experience and loyalty by giving them the tools to individually interact with customers and anticipate their needs .
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