Churn Prediction and Retention in Banking, Telecom and IT Sectors Using Machine Learning Techniques

Jain, Himani; Yadav, Garima; Manoov, R.

doi:10.1007/978-981-15-5243-4_12

Cited by 27 publications

(13 citation statements)

References 10 publications

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“…Several types of research have been conducted to identify the most influential features that affect employee churn [13]. Some of the statistical techniques used are not computationally efficient because these techniques are based on model accuracy [14].…”

Section: Data Mining Techniques For Predicting Employee Turnovermentioning

confidence: 99%

Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry

Naz¹,

Siddiqui

Koo

et al. 2022

Applied Sciences

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Employee churn analytics is the process of assessing employee turnover rate and predicting churners in a corporate company. Due to the rapid requirement of experts in the industries, an employee may switch workplaces, and the company then has to look for a substitute with the training to deal with the tasks. This has become a bottleneck and the corporate sector suffers with additional cost overheads to restore the work routine in the organization. In order to solve this issue in a timely manner, we identify several ML techniques that examine an employee’s record and assess factors in generalized ways to assess whether the resource will remain to continue working or may leave the workplace with the passage of time. However, sensor-based information processing is not much explored in the corporate sector. This paper presents an IoT-enabled predictive strategy to evaluate employee churn count and discusses the factors to decrease this issue in the organizations. For this, we use filter-based methods to analyze features and perform classification to identify firm future churners. The performance evaluation shows that the proposed technique efficiently identifies the future churners with 98% accuracy in the IoT-enabled corporate sector organizations.

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Section: Data Mining Techniques For Predicting Employee Turnovermentioning

confidence: 99%

Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry

Naz¹,

Siddiqui

Koo

et al. 2022

Applied Sciences

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“…Authors report that the usage of random forest method exhibits better classification accuracy compared to the others. In [16], authors focus on the churn prediction and retention of IT, banking, and telecom sectors. For this aim, classification performance of logistic regression, random forest, support vector machine, and XGBoost models are compared, comprehensively.…”

Section: Literature Backgroundmentioning

confidence: 99%

The Effectiveness of Homogeneous Classifier Ensembles on Customer Churn Prediction in Banking, Insurance, and Telecommunication Sectors

Kilimci

2022

International Journal of Computational and Experimental Science and Engineering

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The prediction of customer churn is a big challenging problem for companies in different sectors such as banking, telecommunication, and insurance. It is a crucial estimation for many businesses since obtaining new customers frequently costs more than holding present ones. For this reason, analysts and researchers are focus on to investigate reasons behind of customer churn analyzing behaviors of them. In this paper, an ensemble-based framework is proposed to predict the customer churn in various sectors, namely banking, insurance, and telecommunication. To demonstrate the effectiveness of proposed ensemble framework, k-NN, logistic regression, naïve Bayes, support vector machine, decision tree, random forest, multilayer perceptron algorithms are employed. Moreover, the effects of the inclusion of feature extraction process are investigated. Experiment results indicate that that random forest algorithm is capable to predict churn customers with 89.93% of accuracy in banking, 95.90% of accuracy in telecommunication, and 77.53% of accuracy in insurance sectors when feature extraction procedure is carried out.

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“…Several evaluation methods were used to determine the proposed model's performance, including accuracy and recall assessments used in previous studies (Wei and Chiu, 2002;Fawcett, 2006;Huang et al, 2009;Khan et al, 2010;Do et al, 2017;Jain et al, 2021;). Problems regarding customer churn prioritize the effectiveness of predicting its true positive data (Ullah et al, 2019).…”

Section: Model Evaluationmentioning

confidence: 99%

“…Different ways to reduce or manage churn rate have been reported, including investing in retention activities, targeted marketing, campaign management, and customer relationship management (CRM) (Chen and Popovich, 2003). Building and maintaining long-term relationships with customers through CRM can gain both empathy and loyalty (Jain et al, 2021), and loyal customers can become great ambassadors in the market and help attract new business (Amin et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have demonstrated that ensemble learning is best for prediction models, including both classification and regression problems and it has been proven that the approach can increase model performance through generalization and error adjustment (Jain et al, 2021). Stacking techniques are particularly complex and timeconsuming, and so this study employs bagging in the form of random forest (RF) and extreme gradient boosting (XGBoost) trees for their simplicity and robustness in predicting customer churn (Do et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Customer Churn using ensemble learning: Case Study of a Fixed Broadband Company

Dhini¹,

Fauzan²

2021

IJTech

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Technology advancement has developed a shift perception towards better service from internet providers, and the power to move easily to another provider to secure improved quality results in customer churn. Internet service providers must detect the risk of churn at the earliest opportunity if they want to retain their customers. This study aimed to predict churn using recent developments in machine learning approaches, and customer data from one of the biggest fixed broadband companies in Indonesia was selected as a case study. Ensemble learning is the collaboration of meta-algorithms to improve model performance, and two such approaches were performed in this study, namely random forest and extreme gradient boosting (XGBoost). The results show that the ensemble learning models outperform classical technique and XGBoost is the best algorithm for predicting customer churn. Customers are thereby clustered as being at high, medium, or low risk of churn, and the company can specify particular retention strategies towards each customer cluster.

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Churn Prediction and Retention in Banking, Telecom and IT Sectors Using Machine Learning Techniques

Cited by 27 publications

References 10 publications

Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry

Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry

The Effectiveness of Homogeneous Classifier Ensembles on Customer Churn Prediction in Banking, Insurance, and Telecommunication Sectors

Predicting Customer Churn using ensemble learning: Case Study of a Fixed Broadband Company

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