Designing a Customer Retention Strategy Using a Deep Learning Based Customer Churn Prediction Model

Document Type : Original Research

Authors
Seyed Mohammad Forghani Dehnavi 1
Ali Rajabzadeh Ghatari 2
Adel Azar 2
Ameneh Khadivar 3
1 Ph.D. Student, Industrial Management, Tarbiat Modares University, Tehran, Iran.
2 Professor, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
3 Associate Professor, Department of Management, Alzahra University, Tehran, Iran.
Abstract
Customer Retention and maintaining customer relationships and preventing customers from Churn is one of the most important tasks of organizations in today's highly competitive markets. In this study, the issue of customer churn and customer retention strategies have been investigated. These issues have been studied through systematic literature review and from different angles such as the field of organization, degree of individualization of customer relationship management, customer segmentation, and selection of key customers, employee engagement and evaluation.

In addition, a model based on deep learning networks has been used to predict customer churn. As a result, a conceptual framework and model is created based on the existing literature in this field and then combined with the customer churn prediction model using deep learning networks. The results show that the use of deep learning in predicting customer churn is a very effective and efficient way to solve the problem of customer retention and customer churn. This approach is not only able to accurately predict which of the organization's customers are turning away from the organization and disconnecting from the organization, but also can accurately identify the factors and parameters affecting customer churn and bring very valuable insight for the organization.
Keywords
customer relationship management
Customer churn
deep learning
Customer Retention Strategies

Subjects

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Organizational Resources Management Researches
Volume 12, Issue 3
December 2022
Pages 98-128