A framework for analysis of the effect of time on shopping behavior
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Due to technological developments, data about how many items a customer buys and how long the customer spends in a supermarket are available. A major problem with the data, however, is that there is no framework that considers the heterogeneity hidden in the data. In this article, we propose a framework that considers heterogeneity in the number of items a customer buys. The first step of our framework is based on the Poisson mixture regression model using a stationary time in the department where the items are sold as its independent variable. This model finds latent homogeneous groups of customers and gives the regression models within each group. It simultaneously classifies the customers into the homogeneous groups. In the second step of our framework, a method to investigate whether another factor (variable) influences the classification into homogeneous groups is presented. This proposed framework is applied to real data collected from the customers, and the effectiveness of the framework is shown. The managerial implications are drawn from the result of the analysis.
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- A framework for analysis of the effect of time on shopping behavior
Journal of Intelligent Information Systems
Volume 41, Issue 1 , pp 91-107
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- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Customer heterogeneity
- RFID data
- POS data
- Poisson mixture regression model
- Odds ratio
- Industry Sectors