A Two-Phase Clustering Analysis for B2B Customer Segmentation

In recent years data mining (DM) has been heavily applied in customer relationship management (CRM). The objective of this paper is the categorization of customer records into several groups in the Business-to-Business (B2B) context, using clustering analysis. The obtained categories are then used to make suitable marketing strategy recommendations for each group of customers. This is accomplished by first using the Length, Recency, Frequency, Monetary (LRFM) customer lifetime value model, which scores customers according to four attributes, the relationship length with the company (L), recency of latest transaction (R), purchasing frequency (F), and monetary value of customer (M). Secondly, the paper introduces a proposed enhanced clustering model using the k-means++ algorithm, where customer records are segmented based on their respective LRFM values. Also, the proposed model is integrated with a bootstrapping phase, where the selection of the number of clusters is performed by employing both the Calinski-Harabasz and Rand cluster validity indices. In addition, the firmographics data of each customer are taken into account by analyzing groups based on both the sale sector, and the location of customers, as means of enhancing the clustering analysis. Finally, the clustering results are evaluated and discussed. This study is performed on a dataset obtained from a well-known, multi-national, Fast-Moving Consumer Goods (FMCG) company in Egypt, resulting in useful insights into the nature of their customers.