A neural network-based predictive decision model for customer retention in the telecommunication sector

Rahul Thangeda, Niraj Kumar and Ritanjali Majhi

Technological Forecasting and Social Change, 2024, vol. 202, issue C

Abstract: Acquiring a new customer is far more expensive than retaining a customer. Hence, customer retention is a key aspect of business for a firm to maintain and improve on its market share and profit. The paper analyses customer retention strategies by employing an artificial neural network-based decision model to a real-life dataset collected from 311 mobile service users in India. Seven linear and non-linear adaptive models are developed using features related to customer dissatisfaction (DSF), customer disloyalty (DLF) and customer churn (CF). Findings of this study suggest that non-linear models are most efficient in predicting customer churn, and both DSF and DLF variables significantly affect the retention strategy. Three groups of customers are discussed in this study in the order of least likelihood of churning to most likelihood. Finally, a priority matrix based on key performance indicators is proposed to help service providers target potential customers to retain.

Keywords: Customer retention; Churn prediction; Artificial neural network; Telecommunication sector (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:202:y:2024:i:c:s0040162524000465

DOI: 10.1016/j.techfore.2024.123250

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