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A customer lifetime value model for the banking industry: a guide to marketing actions

A customer lifetime value model for the banking industry: a guide to marketing actions Purpose – The aim of this study is to develop an applicable and detailed model for customer lifetime value (CLV) and to highlight the most important indicators relevant for a specific industry – namely the banking sector. Design/methodology/approach – This study compares the results of the least square estimation (LSE) and artificial neural network (ANN) in order to select the best performing forecasting tool to predict the potential CLV. The performances of the models are compared by the hit ratio, which is calculated by grouping the customers as “top 20 per cent” and “bottom 80 per cent” profitable. Findings – Due to its higher performance; LSE based linear regression model is selected. The results are found to be highly competitive compared with the previous studies. This study shows that, beside the indicators mostly used in the literature in measuring CLV, two additional groups, namely monetary value and risk of certain bank services, as well as product/service ownership‐related indicators, are also significant factors. Practical implications – Organisations in the banking sector have to persuade their customers to use certain routine risk‐bearing transaction‐based services. In addition, the product development strategy has a crucial role to increase the CLV of customers because some of the product‐related variables directly increase the value of customers. Originality/value – The proposed model predicts potential value of current customers rather than measuring current value considered in the majority of previous studies. It eliminates the limitations and drawbacks of the majority of models in the literature through simple and industry‐specific method which is based on easily measurable and objective indicators. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Journal of Marketing Emerald Publishing

A customer lifetime value model for the banking industry: a guide to marketing actions

Yeliz Ekinci; Nimet Uray; Füsun Ülengin
European Journal of Marketing , Volume 48 (3/4): 24 – Apr 8, 2014
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References (57)

Publisher
Emerald Publishing
Copyright
Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.
ISSN
0309-0566
DOI
10.1108/EJM-12-2011-0714
Publisher site
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Abstract

Purpose – The aim of this study is to develop an applicable and detailed model for customer lifetime value (CLV) and to highlight the most important indicators relevant for a specific industry – namely the banking sector. Design/methodology/approach – This study compares the results of the least square estimation (LSE) and artificial neural network (ANN) in order to select the best performing forecasting tool to predict the potential CLV. The performances of the models are compared by the hit ratio, which is calculated by grouping the customers as “top 20 per cent” and “bottom 80 per cent” profitable. Findings – Due to its higher performance; LSE based linear regression model is selected. The results are found to be highly competitive compared with the previous studies. This study shows that, beside the indicators mostly used in the literature in measuring CLV, two additional groups, namely monetary value and risk of certain bank services, as well as product/service ownership‐related indicators, are also significant factors. Practical implications – Organisations in the banking sector have to persuade their customers to use certain routine risk‐bearing transaction‐based services. In addition, the product development strategy has a crucial role to increase the CLV of customers because some of the product‐related variables directly increase the value of customers. Originality/value – The proposed model predicts potential value of current customers rather than measuring current value considered in the majority of previous studies. It eliminates the limitations and drawbacks of the majority of models in the literature through simple and industry‐specific method which is based on easily measurable and objective indicators.

Journal

European Journal of Marketing – Emerald Publishing

Published: Apr 8, 2014

Keywords: Artificial neural network; Least squares estimation; Customer lifetime value; Linear regression; Marketing decision

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