In this repository we provide the source code and the public available datasets used in the paper "Discovering Temporal Regularities in Retail Customers' Shopping Behavior" in which we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain.
The state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customer's temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc.
To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles.
By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors.
R. Guidotti, L. Gabrielli, A. Monreale, D. Pedreschi, F. Giannotti " Discovering Temporal Regularities in Retail Customers' Shopping Behavior", EPJ Data Science, 2018. [Paper](to appear)