Customer Churn Prediction using Machine Learning Approach: A Comprehensive Study

Abstract

Churn is a term that combines” Change” and” Turn.” The ability to predict customer churn is a signnificant concern for service providers. In today’s market, customers are increasingly discerning and seek to access the best services available in their daily lives. This pursuit of superior services often leads to churn or attrition for organizations. Consequently, forecasting churn has emerged as one of the most formidable challenges faced by service providers. The complexity of churn prediction is heightened by the vast amount of customer data, its sparsity, and the imbalanced nature of this data. This paper highlights the research conducted by various scholars on Customer Churn Prediction (CCP) methodologies within the telecommunications sector.

Introduction of Churn, It’s Types and Customer Relationship Manager (CRM) :

In the quest of finding better services, customers switch from one company to another, this movement is well-known as “customer churn”. The fundamental reason behind customer churn is disappointment with the services, astonishing charges, unfriendly offers, and ferocious consumer provision [1]. The rate of churn is also known as the attrition rate. Identifying churners is a classification problem and whether a customer is availing services or not is fundamental. The world is going through technological advancements every day, and service providers are trying at their level best to provide the best services to their customers by integrating new technical features. Conversely, clients are looking for the greatest deals and high-caliber services at reasonable costs. Sebastiaan Hoppner asserts that maintaining current consumers is less expensive than acquiring new ones [2]. In general, there are two types of churners: those that are voluntary and those that are involuntary or passive [3]. A consumer is considered to have voluntarily switched from one service provider to another when they stop using their services from the former supplier. On the other hand, involuntary churn occurs when a service provider terminates a customer’s account due to non-payment or other reasons [4]. Two subcategories of voluntary churn are incidental or cyclical churn and deliberate or active churn [5]. Incidental churn is the result of customers’ changing locations or lifestyles. Reasons for purposeful churn include convenience, cost of service, technological improvements, social, psychological, and quality aspects [6]. The basic classification of churners is visualized in Figure-1 [7].

Objectives: The objective of this work is to consolidate existing research into a single document, thereby assisting researchers and scholars in their analysis and future investigations in this field. Additionally, this study also proposes a general model for predicting customer churn in the telecommunications industry, which will serve as a valuable resource for emerging researchers in this area.

Methods: The practice and advancement of computer systems that can operate independently of human intervention and learn from their experiences is known as machine learning, which is one of the most prominent features of the computer industry. To optimize efficacy, various statistical models and algorithms are implemented. Supervised machine learning, a subtype of machine learning and artificial intelligence, is characterized by its reliance on labelled data. The labelled dataset is utilized in supervised machine learning to train the algorithm for accurate data classification and outcome prediction. The second type of machine learning is unsupervised learning approaches. In this machine learning category, the user is not obligated to oversee or train the model. This machine learning enables the model to autonomously uncover previously unnoticed patterns and information. Unsupervised machine learning mostly addresses unlabeled data. The third category of machine learning is reinforcement learning. This category allows the agent to learn from its experiences by utilizing input from previous acts. Regarding the issue of anticipating churn, supervised machine learning offers optimal support to researchers. Supervised machine learning entails categorizing a tagged input dataset into distinct classes. In churn prediction, the dataset is categorized into two classifications: whether the client has departed from the organization or not. It is a binary classification problem.

Results: The findings demonstrate that machine learning approaches, particularly those utilizing advanced algorithms such as ensemble methods and deep learning, offer significant advantages over traditional statistical techniques. These models are more accurate and predictive, enabling businesses to better identify clients who are at risk and carry out targeted retention campaigns. The study underscores the necessity of selecting appropriate features and continuously improving models to adapt to evolving consumer behavior and market conditions.

Conclusions: This comprehensive study has examined the application of machine learning techniques to customer churn prediction, highlighting its potential to transform how businesses perceive and handle client retention. Numerous ML models have been thoroughly explored in this study, providing insightful information about the effectiveness, drawbacks, and strengths of each model in predicting customer attrition.