Analysis of Customers' Lifetime Value with a Clustering Approach Based on Som Artificial Neural Networks and Markov Chain (Case Study: Pasargad Digital Bank)

Document Type : Article

Authors

1 Faculty of Industrial Management, University of Tehran, Kish International Campus, Iran

2 Department of Management, Faculty of Management and Financial Sciences, Khatam University, Tehran, Iran

3 Department of Technology and Innovation Management, Faculty of Industrial and Technology Management, University of Tehran, Tehran, Iran

4 Department of Industrial Management, Faculty of Management and Social Sciences, Islamic Azad University, North Tehran Branch, Tehran, Iran

Abstract

Clustering of customers and analysis of their lifetime value is one of the basic strategies of policy making in production and marketing. Since the advertising strategies of many businesses are implemented regardless of the financial behavior clusters of customers and the expected income resulting from the dynamic financial behavior of customers, the advertising policy based on the customers’ lifetime value in the dynamic clusters of their financial behavior can save a lot of advertising and marketing costs to manage the costs imposed on businesses. This is despite the fact that the studies conducted in the field of customer lifetime value analysis have not paid attention to advertising strategies based on lifetime value. The existing methods for determining the lifetime value of customers rely on static clustering of customers' status, while customer clustering based on their financial behavior is a dynamic and time-varying phenomenon. Therefore, in this research, the lifetime value analysis of customers and the optimization of advertising strategy in the dynamic clusters of their financial behavior using a self-organizing neural network and dynamic stochastic programming have been discussed. For this purpose, based on the status of each customer belonging to a cluster during consecutive weeks, a Markov chain is formed from the clusters containing customers, and the lifetime value of customers is based on the transition probability matrices of customers' status from one cluster to another, and it is also estimated based on the expected income of customers in each cluster. The results showed that customer clustering with the self-organizing neural network method can explain at least 91% of the changes in the data. Also, the findings showed that the optimal advertising strategy for each cluster of customers with a certain expected income level will lead to different lifetime value for customers, which shows the importance of dynamic clustering of customers and determining the optimal level of advertising cost in each cluster to gain more customer lifetime value.

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References

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Sharif Journal of Industrial Engineering & Management
Volume 41, Issue 1 - Serial Number 1
September 2025
Pages 57-72

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