Behavioral entropy and profitability in retail

Abstract

Human behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer's behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatio-temporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products

Discovering temporal regularities in retail customers’ shopping behavior

“…For example, the retail manager could employ the collective footprints by promoting for each customer the shopping in her favorite day and time window by applying a tailored discount. Thus making the regular customers even more regular and also more profitable [3]. Furthermore, the analysis of the regular sub-sequences enables the retail manager to push customers which generally alternate one-shop weeks with no-shopping weeks in performing consecutive one-shop weeks in order to obtain special temporal discounts.…”

“…1 we summarize the definitions and the notations introduced in this section. Since in this work we are not considering the shopping location or the content of the basket [3], we model a shopping session s as a tuple s = customer, timestamp, amount (see Fig. 1-(a)) For each customer, we summarize the temporal information of a set of shopping sessions by introducing the notion of temporal purchasing unit (unit in short) defined as follows: Definition 1 (Temporal purchasing unit) Given a period τ ofd days, a temporal purchasing unit U of a customer c is a matrix U ∈ R t×d , where d is the number of day-intervals in τ with d ≤d, t is the number of time windows considered for each day-interval, and U ij estimates the relevance of the purchases in the i-th time window of the j-th day-interval.…”

“…The temporal component of customer purchases is analyzed in [3][4][5]. In [4] it is examined the role of personal characteristics in time spent shopping.…”

“…The temporal analysis presented is based on conventional and modified self organizing maps. In [3,6] the authors use a sophisticated version of entropy to study the customers' behaviors in retail data both from the basket and the spatio-temporal point of view. In particular, they define a procedure to group similar baskets by exploiting a frequent pattern mining approach and creating in this way classes of probability for certain sets of items frequently purchased together.…”

“…Most of the works in the literature focus on what customers buy [1,2]. Just a few of them have exploited also the temporal dimension as a feature for enriching their models based primarily on the items purchased [3][4][5][6]. However, it has been given very few attention to the temporal dimension of shopping sessions considered on its own in order to extract a customer model which helps in understanding the purchase temporal habits.…”