An Extended Regularized K-Means Clustering Approach for High-Dimensional Customer Segmentation With Correlated Variables

Abstract

The omnichannel business has becomes a hot topic due to the fast development on e-commerce and the customers' acquaintance with multichannel shopping mode. Various business organizations have started to work on omnichannel business issue in order to satisfy the new trend of customer demand and tend to devote their efforts to both online and offline business. Thus, there is no doubt that understanding the shopping behavior for online customers is vital for the omnichannel business. The RFM (recency, frequency, monetary) model and the k-means clustering method are commonly used to extract customers' information and segment customers, respectively. To extend the RFM model, we divide the total frequency and monetary information into weekly level data, and as a consequence, the number of variables corresponding to one customer increases significantly, leading to the problem of high-dimensional analysis. To address this issue, in this paper we extend the regularized k-means clustering method with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm for independent case to the clustering method with elastic net penalty with a focus on correlated variables. Our simulation results show that the proposed method performs better than the standard k-means method by providing lower error rates and can select variables simultaneously under 4 different scenarios. A real example of an online retailer is presented to illustrate the use of the proposed method and highlight its high potential in clustering high-dimensional applications. In particular, the number of variables is reduced from 108 to 98 without any loss on clustering accuracy.