Customer segmentation in e-commerce: a context-aware quality model for comparing clustering algorithms

Abstract

E-commerce platforms are constantly evolving to meet the ever-changing needs and preferences of online shoppers. One of the ways that is gaining popularity and leading to a more personalised and efficient user experience is through the use of clustering techniques. However, the choice between clustering algorithms should be made based on specific business context, project requirements, data characteristics, and computational resources.  The purpose of this paper was to present a quality framework that allows the comparison of different clustering approaches, taking into account the business context of the application of the results obtained. The validation of the proposed approach was carried out by comparing three methods - K-means, K-medians, and BIRCH. One possible application of the generated clusters is a platform to support multiple variants of the e-commerce user interface, which requires the selection of an optimal algorithm based on different quality criteria. The contribution of the paper includes the proposal of a framework that takes into account the business context of e-commerce customer clustering and its practical validation. The results obtained confirmed that the clustering techniques analysed can differ significantly when analysing e-commerce customer behaviour data. The quality framework presented in this paper is a flexible approach that can be developed and adapted to the specifics of different e-commerce systems.