Prediction of Customer Retention Rate Employing Machine Learning Techniques

Sharma, Achintya; Gupta, Deepak; Nayak, Natasha V.; Singh, Deepti; Verma, Ankit

doi:10.1109/ici53355.2022.9786903

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…This aids telecommunications service providers in choosing the appropriate marketing strategies and resource allocations for each market segment (e.g. RajaGopal Kesiraju & Deeplakshmi, 2021; Sharma et al, 2022; Weiss, 2005).…”

Section: Literature Reviewmentioning

confidence: 99%

A data‐driven assessment of mobile operator service quality in Ghana

Choi,

Brown,

Komey

2023

E J Info Sys Dev Countries

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The rapid proliferation of mobile services has increased the need for data‐driven oversight of service quality, yet deriving insights from regulator‐collected datasets remains challenging. This study demonstrates techniques to tap the rich potential of drive test measurement data for analytical regulatory and policy decision‐making. Focusing on leading operator MTN in Ghana, we analyzed 4 years of drive test data supplied by the telecom regulator for the capital city of Accra. Three key performance indicators were evaluated—coverage, call setup time, and speech quality. We assessed service quality trends through statistical summaries, data visualization, and machine learning modeling and predicted speech quality scores. Our analysis revealed deteriorating performance post‐2019 and found that the light gradient boosting machine algorithm provided the highest accuracy predictions of speech quality. Overall, this work showcases how regulators can capitalize on vast datasets using big data mining techniques to evaluate network conditions over time and geography, enhancing field measurements for oversight. Our approach and techniques provide a template for evidence‐based policy‐making to uphold consumer service quality as mobile networks evolve.

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Section: Literature Reviewmentioning

confidence: 99%

A data‐driven assessment of mobile operator service quality in Ghana

Choi,

Brown,

Komey

2023

E J Info Sys Dev Countries

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“…CatBoost is becoming popular due to its efficiency features such as fast GPU training, ease of use, and working well with categorical variables [25]. This algorithm has been explored in churn prediction in different sectors, and encouraging performances have been obtained [6,12,26,27]. Let data with samples T = X j , y j j=1,...,m , and X j = x 1 j , .…”

Section: Catboostmentioning

confidence: 99%

“…There are a plethora of machine learning classifiers that have been proposed to analyze customer data for predicting customer churn. These include single classifiers, such as support vector machines, naïve Bayes, decision trees, logistic regression, and k-nearest neighbors, and ensemble classifiers, such as AdaBoost, gradient boosting, XGBoost, CatBoost, and random forests [1,[5][6][7][8][9][10][11][12]. It has been asserted that ensemble classifiers perform better than single classifiers [13].…”

Section: Introductionmentioning

confidence: 99%

Predictive Churn Modeling for Sustainable Business in the Telecommunication Industry: Optimized Weighted Ensemble Machine Learning

Khoh,

Pang,

Ooi

et al. 2023

Sustainability

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Customers are prominent resources in every business for its sustainability. Therefore, predicting customer churn is significant for reducing churn, particularly in the high-churn-rate telecommunications business. To identify customers at risk of churning, tactical marketing actions can be strategized to raise the likelihood of the churn-probable customers remaining as customers. This might provide a corporation with significant savings. Hence, in this work, a churn prediction system is developed to assist telecommunication operators in detecting potential churn customers. In the proposed framework, the input data quality is improved through the processes of exploratory data analysis and data preprocessing for identifying data errors and comprehending data patterns. Then, feature engineering and data sampling processes are performed to transform the captured data into an appropriate form for classification and imbalanced data handling. An optimized ensemble learning model is proposed for classification in this framework. Unlike other ensemble models, the proposed classification model is an optimized weighted soft voting ensemble with a sequence of weights applied to weigh the prediction of each base learner with the hypothesis that specific base learners in the ensemble have more skill than others. In this optimization, Powell’s optimization algorithm is applied to optimize the ensemble weights of influence according to the base learners’ importance. The efficiency of the proposed optimally weighted ensemble learning model is evaluated in a real-world database. The empirical results show that the proposed customer churn prediction system achieves a promising performance with an accuracy score of 84% and an F1 score of 83.42%. Existing customer churn prediction systems are studied. We achieved a higher prediction accuracy than the other systems, including machine learning and deep learning models.

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“…Research conducted by Achintya Sharma; Deepak Gupta; Nikhil Nayak; Deepti Singh; Ankita Verma, with the title "Prediction of Customer Retention Rate Employing Machine Learning Techniques," [13]. This study discusses in the telecommunications sector which has experienced a significant increase in the number of subscribers and technology content as well as competition among telecommunications companies.With the ever-increasing rate of customer churn, and it is more expensive to acquire new customers than to retain existing ones,the company makes customer retention one of the company's main focuses.This study is to compare the accuracy of traditional data mining techniques, namely Logistic Regression, Support Vector Machine (SVM), Decision Tree, XGBoost, Random Forest, Light Gradient Boosting, Gradient Descent Boosting and Cat Boost in predicting customer churn.To get an algorithm that can find the main causes of customer churn from one company to another and ways to increase customer retention.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analysis of Customer Retention Using the Random Forest Algorithm

Suhanda

Nurlaela

Kurniati

et al. 2022

TIERS

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Retaining customers is becoming a measurement focus in an industry with increasing competition. The concept of customer retention has become a research study in the sales industry, because it is difficult to retain customers and easily switch to other brands. Customer repurchase decisions in the business world of sales are very competitive. Customer satisfaction is directly proportional to the retention rate, if the customer is not satisfied then the automatic retention rate will be low. If the company is not able to meet customer expectations, it will have a serious impact on the company, namely moving customers to other services. Service factors, price, profit value, satisfaction and trust affect customer retention. One of the factors that influence consumers to become customer retention is service quality. A predictive customer retention plan is needed with data mining using the random forest algorithm. The random forest algorithm is a method that generates a number of trees from sample data, where the creation of one tree during training does not depend on the previous tree, the decision is based on the most voting. The voting results from several decision trees that are formed are the boundaries that are used as class determination in the classification process and the most votes are the winners and determine the classification class. This study aims to determine and analyze customer loyalty, customer trust and customer satisfaction. So that it can make it easier to monitor customers at the company. The results can be seen with the percentage of about 81.12% customer retention and about 18.87% customer churn. The result of feature evaluation shows that customer_activity has the highest influence on customer retention, followed by subtotal and qty.

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Prediction of Customer Retention Rate Employing Machine Learning Techniques

Cited by 4 publications

References 9 publications

A data‐driven assessment of mobile operator service quality in Ghana

A data‐driven assessment of mobile operator service quality in Ghana

Predictive Churn Modeling for Sustainable Business in the Telecommunication Industry: Optimized Weighted Ensemble Machine Learning

Predictive Analysis of Customer Retention Using the Random Forest Algorithm

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