Taxonomy of Marketing Strategies Using Bank Customers’ Clustering

Abstract

Purpose-The goal of this study is to identify the main clusters of bank customers in order to help commercial banks to better identify their customers and design more efficient marketing strategies.Design/methodology/approach -Data from 250 bank customers were analyzed by using two-step scalable clustering.Findings -Five different clusters of bank customers were identified, namely, favorite customers, creditworthy customers, non-creditworthy customers, passers, and friends. The findings indicated that disparate clusters of bank customers are extremely different based on their loan amount, default risk, account balance, degree of loyalty and profitability for the bank.Practical implications -The differences which were observed between these five clusters of bank customers accentuate the importance of customer clustering and market segmentation in the financial services industry. Customer clustering can help financial institutions to augment their competitiveness by shifting from traditional marketing strategies to target marketing and segmentation-based marketing approaches.Originality/value -The most important contribution of this study is the incorporation of a wide range of factors that can potentially affect customer clustering in the analysis, whereas, the majority of previous studies only focused on a limited number of variables in order to determine the customer clusters. Specifically, the customer clustering in this study was performed by using demographic variables, profitability, loan amount, default risk, account balance, loyalty, account type, account closure history, customer location, and account currency.

Approaches to Clustering in Customer Segmentation

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Cluster-Based Approaches toward Developing a Customer Loyalty Program in a Private Security Company

“…As a result, grouping customers into different segments helped decision-makers identify market segments more clearly and develop more effective marketing and sales strategies for customer retention. In order to identify key customer clusters in the banking sector and create more efficient marketing strategies, Ansari and Riasi [21] employed cluster analyses based on RFM variables. However, Alkharat [9] used financial, behavioral, and geographical variables to create customer segmentation in a telecommunications company.…”

Studying Product Quality by Exploring Credit Card Customers Behaviour via Data Mining Techniques

“…In this research, customers' data were analyzed using two-step and RFM clustering methods. The findings showed the distinction between bank customers' clusters based on the amount of loans, predetermined risk, inventory, loyalty and profitability for the bank (Ansari & Riasi, 2016). Another study was conducted with the aim at creating benefits to the bank such as attracting profitable customers, maximizing investment in sales and delivery channels, creating competitive advantage in the market, and improving their services to feature analysis.…”