A review of churn prediction models using different machine learning and deep learning approaches in cloud environment

S. Arockia Panimalar; A. Krishnakumar

Authors

S. Arockia Panimalar Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, Coimbatore – 642107, India
A. Krishnakumar Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, Coimbatore – 642107, India

Keywords:

CCP-customer churn-prediction, deep-learning, DNN-deep neural-networks, feature classification, feature-selection, LSTM-long short-term memory, machine-learning

Abstract

Customer churn is portrayed as the event that occurs when the customer quits the organization's services or products. The reasons for this dissatisfaction are the higher costs, low-level understanding of service plan, bad support, high subscription rate, and service quality, etc., Companies ought to be capable in the prediction of that customer behavior perfectly to retain on-hand customers and minimize the churn rate of customers in before occurrence. The study elucidates a review analysis of various churn prediction models, striving in different sectors through utilizing different machine-learning approaches, Deep-learning algorithms, metaheuristic optimization techniques, feature extraction-based methods, and hybrid approaches. The paper also surveys commonly utilized machine-learning techniques on a cloud computing platform to determine customer churn patterns. The churn prediction model, with better precision results, facilitates spotting firms which are near to getting churn and directing firms' focus to minimize overall churn percentage, shaping retention policies, and boosting the company's revenue.

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