Discovering temporal regularities in retail customers’ shopping behavior

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.

Human migration: the big data perspective

“…Market basket analysis and the study of food consumption have been widely used in the literature for different purposes, such as defining individual indicators of customer predictability [80], studying GDP trends [81], analyzing customers with respect to their temporal purchasing patterns [83], and classifying them as residents or tourists according to their shopping profile [82]. Exploiting retail data to study the migration phenomenon from an individual and collective point of view that is not exposed to social sanctions and with multiple observations in time can bring to the light novel results useful for better understanding the migration phenomenon and also for developing well-being policies.…”

Privacy Risk for Individual Basket Patterns

“…In [10] the authors propose two indexes that consider the level of repetitiveness in both the basket composition and also in the temporal and spatial dimension of shopping purchases, i.e., when and where the customers go to the supermarket. Other forms of customer profiling on market basket data like those described in [11,12] adopt ad vector based modeling.…”

Impact of natural disasters on consumer behavior: Case of the 2017 El Niño phenomenon in Peru

“…The following works [ 16 – 19 ] analyzed customer behavior by studying the sequence of purchases through credit and debit cards. For instance, in [ 16 ], the authors used the Sequitur algorithm [ 20 ] to classify user spending behavior and characterize people’s lifestyles according to their temporal purchase sequences.…”

“…[ 17 , 18 ] used a detection algorithm to characterize people purchase sequences characterized by the merchant category code (MCC) [ 21 ]. Finally, another model based on retail customer data [ 19 ] identified temporal regularities in buying behavior. The authors grouped weekly customer buying patterns using a k-means clustering algorithm to extract groups of behaviors G u per user.…”