How Many Customers Does a Retail Store Have?

TL;DR

This paper presents a maximum likelihood approach to estimate the total number of retail customers, including unmonitored ones, by analyzing shopping patterns and validating the method with simulations and real data.

Contribution

It introduces a novel maximum likelihood method for estimating unmonitored customers in retail, accounting for different customer segments and behaviors.

Findings

The method is accurate even with small sample sizes.

It outperforms naive estimates assuming constant customer distribution.

It helps determine loyalty program penetration in customer segments.

Abstract

The knowledge of the number of customers is the pillar of retail business analytics. In our setting, we assume that a portion of customers is monitored and easily counted due to the loyalty program while the rest is not monitored. The behavior of customers in both groups may significantly differ making the estimation of the number of unmonitored customers a non-trivial task. We identify shopping patterns of several customer segments which allows us to estimate the distribution of customers without the loyalty card using the maximum likelihood method. In a simulation study, we find that the proposed approach is quite precise even when the data sample is very small and its assumptions are violated to a certain degree. In an empirical study of a drugstore chain, we validate and illustrate the proposed approach in practice. The actual number of customers estimated by the proposed method is much higher than the number suggested by the naive estimate assuming the constant customer distribution. The proposed method can also be utilized to determine penetration of the loyalty program in the individual customer segments.