Abstract
In this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, 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. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity.
Highlights
The availability of huge amount of retail data stimulates challenging questions that can be answered only by a deep and accurate analysis of different aspects related to customers’ shopping sessions
The main contributions of this work are the following: (i) we define the temporal purchasing profile as the set of temporal footprints and sequence of footprints summarizing whether and when a customer typically purchases and we provide the method for extracting these profiles; (ii) we define the collective perspective for making comparable the individual and not-comparable profiles, so that the shopping routines shared by different customers can be analyzed; (iii) we show the application of the whole analytic framework on a set of case studies a real datasets, one of them containing 7 years of retail data for 91k customers; (iv) we observe how the individual profiles and the collective perspective allow to separate the customers into well defined groups
It is conceptually different from the previous datasets because a transaction does not model a purchase but the fact that a set of items have been observed in the same day. It contains 4298 transactions belonging to 884 users and 9531 brands considered as items. Even though these datasets refer to a time period remarkably shorter than the period observed in UniCoop dataset, in order to show how our methodological framework can be instantiated in other case studies, we report in Sect. 5.7 some crucial analytical results on Ta-Feng and T-Mall datasets
Summary
The availability of huge amount of retail data stimulates challenging questions that can be answered only by a deep and accurate analysis of different aspects related to customers’ shopping sessions. By using methods proposed in the literature it is not possible to capture the temporal purchasing patterns of each customers, which put in correlation their temporal habits with other information such as the amount of expenditure and number of purchased items. This knowledge about the customers is important because enables different marketing strategies tailored to the temporal and systematic behavior of each customer, and new innovative services for the customer based on recommendations for shopping time schedule and for increasing her awareness. We do not claim that ignoring the items purchased and/or the shopping location may lead to an advantage, but we show that observing only the temporal dimension is crucial to completely understand the different times and expenses adopted by the customers when they go to shopping
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