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2004
ACM

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

13 years 2 months ago
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...
Chad M. Cumby, Andrew E. Fano, Rayid Ghani, Marko
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2004
Where KDD
Authors Chad M. Cumby, Andrew E. Fano, Rayid Ghani, Marko Krema
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