Clustering Retail Products Based on Customer Behaviour

TL;DR

This paper introduces a data-driven clustering method for retail products based on customer behavior, utilizing market basket data and genetic algorithms, validated on real and simulated datasets.

Contribution

It presents a novel clustering approach that relies solely on customer behavior data and formulates it as an optimization problem solved by genetic algorithms.

Findings

The method produces results comparable to expert classifications.

Allowing more clusters reveals additional product structure.

Demonstrated effectiveness on real Czech drugstore data.

Abstract

The categorization of retail products is essential for the business decision-making process. It is a common practice to classify products based on their quantitative and qualitative characteristics. In this paper we use a purely data-driven approach. Our clustering of products is based exclusively on the customer behaviour. We propose a method for clustering retail products using market basket data. Our model is formulated as an optimization problem which is solved by a genetic algorithm. It is demonstrated on simulated data how our method behaves in different settings. The application using real data from a Czech drugstore company shows that our method leads to similar results in comparison with the classification by experts. The number of clusters is a parameter of our algorithm. We demonstrate that if more clusters are allowed than the original number of categories is, the method yields additional information about the structure of the product categorization.