Customer Churn Prediction in Telecom Sector Using Machine Learning Techniques

Wagh, Sharmila K.; Wagh, Kishor

doi:10.2139/ssrn.4158415

Cited by 2 publications

(3 citation statements)

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“…This makes it possible for businesses to take proactive steps to keep consumers and lessen the effects of churn. Significant telecom churn prediction research by eminent academics in the field is summarised in this section [39], [40], [41], [42], [43], [44], [45], [46], [47].…”

Section: Literature Reviewmentioning

confidence: 99%

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A Novel Approach for Customer Churn Prediction in Telecom using Machine Learning Models

Chaudhary¹,

Rizvi²,

Kumar³

et al. 2023

Preprint

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For organizations across a range of industries, customer churn is a serious problem.Businesses must identify consumers who are in danger of leaving in order totake actions to keep them. By examining previous data and seeing trends indicatingclient behavior, machine learning provides a potent remedy for forecastingcustomer attrition. In this article, the investigation of the way machine learningtechniques can be utilized to forecast consumer attrition in the telecommunicationindustry has been done. The machine learning models decision trees and randomforests have been trained and assessed using IBM’s dataset of customer behaviorand demographics. These models are compared using performance parameterslike recall, accuracy, F1-score, and precision. According to chosen data, the randomforest model exceeds the competition with a remarkable accuracy of 96%.This study demonstrates the potential of machine learning in predicting customerturnover and offers insightful information for creating strategies that effectivelyretain customers.

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Section: Literature Reviewmentioning

confidence: 99%

“…The machine learning models used by Sharmila K. Wagh et al [47] include a random forest method and a decision tree classifier. When matrices were employed to assess the model, decision tree classifier models initially delivered subpar results on an unbalanced dataset that did not take final accuracy into account.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Approach for Customer Churn Prediction in Telecom using Machine Learning Models

Chaudhary¹,

Rizvi²,

Kumar³

et al. 2023

Preprint

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“…The anticipation of client attrition has become crucial for formulating efficient customer retention tactics in response to intensified market rivalry. Customer churn, which refers to the phenomenon of consumers terminating their membership with a telecommunications service provider, has the potential to result in a decrease in both revenue and market share (Wagh et al, 2023). The costs related to gaining new customers in order to compensate for attrition are significantly elevated, underscoring the need of proactive retention initiatives.…”

Section: Introductionmentioning

confidence: 99%

Churn Prediction in Telecommunication Industry: A Comparative Analysis of Boosting Algorithms

Ogbonna,

Aimufua,

Abdullahi

et al. 2024

dujopas

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The issue of customer churn, which is a major problem in the telecommunications industry, poses several challenges, such as financial implications, client attrition, and increased marketing costs. The advancements achieved in the domain of machine learning and artificial intelligence have significantly expanded the possibilities for forecasting customer churn, presenting a promising resolution for effectively handling customer attrition and enhancing customer retention. This study presents a customer churn prediction model that uses machine learning approaches to assist telecom firms in enhancing customer retention and mitigating churn rates. The study employs machine learning techniques, such as Adaboost, Gradient Boosting, and Extreme Gradient Boosting (XGBoost), in order to evaluate extensive datasets and provide predictions on customer churn via a comparative evaluation. The methodology involves extracting data from the Kaggle data pool, doing further data preparation, and identifying relevant features. The Synthetic Minority Oversampling Technique (SMOTE) is used as a strategy to mitigate the challenges posed by imbalanced data. The dataset is partitioned into training and testing sets at a ratio of 75% to 25%. The XGBoost model demonstrated superior accuracy and recall, positioning itself as the top-performing model among the studied models. The attained accuracy rate was 89.51%. The XGBoost method has a recall rate of 92.48%, which is the highest of the three algorithms evaluated. Gradient boosting follows with a recall rate of 87.69%, while Adaboost achieves a recall rate of 85.13%. These findings underscore the potential of machine learning techniques for addressing the challenges posed by customer churn in the telecommunications industry.

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Customer Churn Prediction in Telecom Sector Using Machine Learning Techniques

Cited by 2 publications

References 10 publications

A Novel Approach for Customer Churn Prediction in Telecom using Machine Learning Models

A Novel Approach for Customer Churn Prediction in Telecom using Machine Learning Models

Churn Prediction in Telecommunication Industry: A Comparative Analysis of Boosting Algorithms

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