Behavioral entropy and profitability in retail

Riccardo Guidotti, Michele Coscia, Dino Pedreschi, Diego Pennacchioli

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution


Human behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer's behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatio-temporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products.

Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467382731
Publication statusPublished - 2 Dec 2015
EventIEEE International Conference on Data Science and Advanced Analytics, DSAA 2015 - Paris, France
Duration: 19 Oct 201521 Oct 2015


ConferenceIEEE International Conference on Data Science and Advanced Analytics, DSAA 2015


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