Customer Churn Prediction Based on Big Data and Machine Learning Approaches

Ziyu Zhu ()
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Ziyu Zhu: Michigan State University, Eli Broad College of Business

A chapter in Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023), 2023, pp 4-15 from Springer

Abstract: Abstract Telecom companies are facing fierce competition in the market. For telecom operators, customers are living. Due to the high upfront investment in acquiring new customers, they prefer to retain existing customers rather than acquire new ones. The loss of old customers means that telecom operators are losing their share in the telecom market. To prevent customer churn, business analysts and customer relationship management (CRM) analysts need to understand and analyze the behavioral patterns of existing customer churn data. The study used three models (i.e., LGBM, Logistic Regression, and Random Forest) to construct a valid and accurate churn prediction model for the telecom industry. Furthermore, the empirical evaluation results suggest that Logistic Regression selected by the AUC metric is the most suitable model. These results provide an understanding of customer features and preferences to predict customers that might to be churn and the reasons for churn and to take preventive measures in advance.

Keywords: Customer Churn Prediction Model; Logistic Regression model; Prediction Algorithms; telecom (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-142-5_2

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DOI: 10.2991/978-94-6463-142-5_2

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