Why do we buy certain items when we buy them? A new study published in EPJ Data Science analyzes personal retail data to extract a temporal purchasing profile, which is able to summarize whether and when a customer makes a purchase. Its results show that certain patterns and types of shoppers are detectable, which can be used both by customers to enable personalized services, and by the retail market chain for providing offers and discounts tailored to the individual shoppers personal temporal profile.
The availability of huge amounts of retail data stimulates challenging questions that can be answered by analyzing different aspects of customers’ shopping sessions. Retail data is a complex type of data containing various dimensions: what customers buy, when and where they make the purchases, and which is the relevance of the purchase in terms of money spent or number of items purchased.
The most important dimension for understanding how customers schedule their shopping time is obviously the temporal one. We use this dimension as the main building block to construct individual temporal purchasing profiles, which put in correlation the customers’ temporal habits with other information such as the amount of expenditure and number of purchased items. This knowledge enables novel marketing strategies tailored to the temporal and systematic behavior of each customer, and also new innovative services based on recommendations for shopping time schedule and for increasing customers’ awareness.
Our aim is to understand whether and when a customer typically makes retail purchases. Which of these temporal aspects of the shopping behavior are more systematic? Which are the regular sequences of the temporal patterns?
Retail data is a complex type of data containing various dimensions: what customers buy, when and where they make the purchases, and which is the relevance of the purchase in terms of money spent or number of items purchased.
Individual and collective temporal profiles
A temporal purchasing profile is able to describe the regular and characteristic temporal behaviors of a customer. Each individual has their own regularities and habits outlining their behavior and making them a unique part of the mass.
The analysis of individuals provides the basis for understanding the common regular patterns also at collective level. Thus, we define individual and collective temporal profiles which can be employed for the analysis of the temporal dimension of the customers’ shopping sessions. These models enable a customer segmentation based on temporal components of purchases and permit to perform explorative analyses of individuals under a new point of view.
For each customer we can consequently extract: (i) their temporal purchasing profile as the set of temporal footprints and sequence of footprints summarizing whether and when a customer typically purchases, and (ii) the collective perspective for making comparable the individual and not-comparable profiles, so that the shopping routines shared by different customers can be analyzed.
Daily, one-shop, and occasional shopping behavior
This analytical approach is applied to different case studies based on real data, containing 7 years of retail data for 91k customers, with the goal of discovering customers’ temporal purchasing patterns and grouping the customers’ profiles to identify sets of customers with similar temporal behavior. The proposed framework empowers the discovery of various customers’ segmentation with respect to different temporal aspects.
In particular, the study reveals three main typical collective behaviors characterizing the whole collection of customers on the basis of when they go to the shopping center: daily spending behavior capturing purchases made every day; one-shop spending behavior, characterizing a regularity with a week containing a predominant shopping session; and an occasional spending behavior, describing not habitual shopping sessions related to a very small expenditure amount.
Among one-shop spending behaviors the analysis captures a further classification with respect to the expenditure amount: normal spending behavior less than €50, high spending behavior with a typical expenditure between €50 and €100, and higher spending behavior with an expenditure higher than €100.
By analyzing the number of different purchasing behaviors at individual and collective level we identify two categories of customers that we name regular and changing: a customer represented with a high number of temporal purchasing behavioral patterns is classified as changing, while a customer with a small number of temporal purchasing behaviors is classified as regular. Finally, we found various and diversified regular sequences explaining how the customers typically combine and follow their shopping behavior with respect to the temporal point of view.
Liked the blog? Now read the research:
Discovering temporal regularities in retail customers’ shopping behavior