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Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data

Mansilla, Roberto; Smith, Gavin; Smith, Andrew; Goulding, James

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Authors

Roberto Mansilla

GAVIN SMITH GAVIN.SMITH@NOTTINGHAM.AC.UK
Associate Professor

ANDREW SMITH Andrew.p.Smith@nottingham.ac.uk
Professor of Consumer Behaviour & Analytics



Abstract

Understanding and measuring the predictability of consumer purchasing (basket) behaviour is of significant value. While predictability measures such as entropy have been well studied and leveraged in other sectors, their development and application to very large multi-dimensional data sets present in the retailing sector are less common. While a small number of methods exist, we demonstrate they fail to accord with intuition, leading to the potential for misunderstandings between those who conduct the analysis and those who act on the insights. We delineate the requirements for such a measure in this domain to demonstrate these issues in context. A novel measure is then developed based on entropy to directly measure the predictability of basket composition. The measure is designated as bundle entropy (zero denotes a bundle's total predictability, one the total unpredictability). We empirically compare the proposed bundle entropy against existing measures using two large-scale real-world transactional data sets, each including more than 2,000 households (frequent shoppers) over two years. First, we demonstrate how the proposed measure is the only measure that behaves according to the desired properties. Second, we show empirically that bundle entropy differs noticeably from the other measures. Finally, we consider some use case analyses and discuss the utility of the proposed measure in practice.

Citation

Mansilla, R., Smith, G., Smith, A., & Goulding, J. (2022). Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data. In Proceedings 2022 IEEE International Conference on Big Data (1044-1053). https://doi.org/10.1109/BigData55660.2022.10021062

Conference Name 2022 IEEE International Conference on Big Data (Big Data)
Conference Location Osaka, Japan
Start Date Dec 17, 2022
End Date Dec 20, 2022
Acceptance Date Nov 15, 2022
Online Publication Date Jan 26, 2023
Publication Date Dec 17, 2022
Deposit Date Mar 8, 2023
Publicly Available Date Mar 8, 2023
Publisher IEEE
Pages 1044-1053
Series Title IEEE International Conference on Big Data
Book Title Proceedings 2022 IEEE International Conference on Big Data
ISBN 9781665480468
DOI https://doi.org/10.1109/BigData55660.2022.10021062
Public URL https://nottingham-repository.worktribe.com/output/18230088
Publisher URL https://ieeexplore.ieee.org/abstract/document/10021062

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