Two-dimensional analysis of customer behavior in traditional and electronic banking

Neda Abdolvand · Saeedeh Rajaee Harandi

22 citations0/0 files storedDigital Business

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Customer churn and segmentation Customer Service Quality and Loyalty Banking Sector Performance and Management

Abstract

The explosive growth of information technology has encouraged organizations to use new technologies in all areas. These changes are also visible in the banking industry. In this situation, customers have used both electronic and traditional services, and each has its own features. According to expectancy-value theory, customer value and expectations both affect customer buying behavior and should be considered by banking managers in designing their strategies. Better understanding of these features will help banks to provide customers with the services they need and, as a result, maintain them. Hence, using expectancy-value theory as a framework this study proposed a two-dimensional approach to examine the customers' behavior in both electronic and traditional banking in Iran. To this end, the data of one-year transactions of XYZ bank (one of the largest private banks of Iran) customers, consisting of one million records, was used and customers were clustered using the RFM model - recency, frequency, and monetary. The CRISP-DM methodology was used to extract knowledge from customer data and the K-Mean algorithm was used for clustering. Customer lifetime value was also calculated using Lawshe criteria. According to the results, customers who use both electronic and traditional bank services are more valuable. The two-dimensional approach proposed and provides a two-sided view of the customer, and thereby provides a more comprehensive knowledge and understanding of customer behavior, and contributes to the body of literature in this field. Using the proposed model, different banks can better classify their customers and can provide the right product / service to the right person.

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References

2008Developing a fuzzy TOPSIS approach based on subjective weights and objective weights 1999Customer satisfaction and its measurement in hospitality enterprises 2008Classifying the segmentation of customer value via RFM model and RS theory 2004Integrating AHP and data mining for product recommendation based on customer lifetime value 2014Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation 2022Digital transformation and the emergence of the Fintech sector: Systematic literature review 2019RFM model for customer purchase behavior using K-Means algorithm 2011Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study

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