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Customer churn prediction for telecommunication industry: A Malaysian Case Study

Background: Customer churn is a term that refers to the rate at which customers leave the business. Churn could be due to various factors, including switching to a competitor, cancelling their subscription because of poor customer service, or discontinuing all contact with a brand due to insufficien...

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Autores principales: Mustafa, Nurulhuda, Sook Ling, Lew, Abdul Razak, Siti Fatimah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9051585/
https://www.ncbi.nlm.nih.gov/pubmed/35528953
http://dx.doi.org/10.12688/f1000research.73597.1
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author Mustafa, Nurulhuda
Sook Ling, Lew
Abdul Razak, Siti Fatimah
author_facet Mustafa, Nurulhuda
Sook Ling, Lew
Abdul Razak, Siti Fatimah
author_sort Mustafa, Nurulhuda
collection PubMed
description Background: Customer churn is a term that refers to the rate at which customers leave the business. Churn could be due to various factors, including switching to a competitor, cancelling their subscription because of poor customer service, or discontinuing all contact with a brand due to insufficient touchpoints. Long-term relationships with customers are more effective than trying to attract new customers. A rise of 5% in customer satisfaction is followed by a 95% increase in sales. By analysing past behaviour, companies can anticipate future revenue. This article will look at which variables in the Net Promoter Score (NPS) dataset influence customer churn in Malaysia's telecommunications industry.  The aim of This study was to identify the factors behind customer churn and propose a churn prediction framework currently lacking in the telecommunications industry.   Methods: This study applied data mining techniques to the NPS dataset from a Malaysian telecommunications company in September 2019 and September 2020, analysing 7776 records with 30 fields to determine which variables were significant for the churn prediction model. We developed a propensity for customer churn using the Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbours Classifier, Classification and Regression Trees (CART), Gaussian Naïve Bayes, and Support Vector Machine using 33 variables.   Results: Customer churn is elevated for customers with a low NPS. However, an immediate helpdesk can act as a neutral party to ensure that the customer needs are met and to determine an employee's ability to obtain customer satisfaction.   Conclusions: It can be concluded that CART has the most accurate churn prediction (98%). However, the research is prohibited from accessing personal customer information under Malaysia's data protection policy. Results are expected for other businesses to measure potential customer churn using NPS scores to gather customer feedback.
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spelling pubmed-90515852022-05-06 Customer churn prediction for telecommunication industry: A Malaysian Case Study Mustafa, Nurulhuda Sook Ling, Lew Abdul Razak, Siti Fatimah F1000Res Research Article Background: Customer churn is a term that refers to the rate at which customers leave the business. Churn could be due to various factors, including switching to a competitor, cancelling their subscription because of poor customer service, or discontinuing all contact with a brand due to insufficient touchpoints. Long-term relationships with customers are more effective than trying to attract new customers. A rise of 5% in customer satisfaction is followed by a 95% increase in sales. By analysing past behaviour, companies can anticipate future revenue. This article will look at which variables in the Net Promoter Score (NPS) dataset influence customer churn in Malaysia's telecommunications industry.  The aim of This study was to identify the factors behind customer churn and propose a churn prediction framework currently lacking in the telecommunications industry.   Methods: This study applied data mining techniques to the NPS dataset from a Malaysian telecommunications company in September 2019 and September 2020, analysing 7776 records with 30 fields to determine which variables were significant for the churn prediction model. We developed a propensity for customer churn using the Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbours Classifier, Classification and Regression Trees (CART), Gaussian Naïve Bayes, and Support Vector Machine using 33 variables.   Results: Customer churn is elevated for customers with a low NPS. However, an immediate helpdesk can act as a neutral party to ensure that the customer needs are met and to determine an employee's ability to obtain customer satisfaction.   Conclusions: It can be concluded that CART has the most accurate churn prediction (98%). However, the research is prohibited from accessing personal customer information under Malaysia's data protection policy. Results are expected for other businesses to measure potential customer churn using NPS scores to gather customer feedback. F1000 Research Limited 2021-12-13 /pmc/articles/PMC9051585/ /pubmed/35528953 http://dx.doi.org/10.12688/f1000research.73597.1 Text en Copyright: © 2021 Mustafa N et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Mustafa, Nurulhuda
Sook Ling, Lew
Abdul Razak, Siti Fatimah
Customer churn prediction for telecommunication industry: A Malaysian Case Study
title Customer churn prediction for telecommunication industry: A Malaysian Case Study
title_full Customer churn prediction for telecommunication industry: A Malaysian Case Study
title_fullStr Customer churn prediction for telecommunication industry: A Malaysian Case Study
title_full_unstemmed Customer churn prediction for telecommunication industry: A Malaysian Case Study
title_short Customer churn prediction for telecommunication industry: A Malaysian Case Study
title_sort customer churn prediction for telecommunication industry: a malaysian case study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9051585/
https://www.ncbi.nlm.nih.gov/pubmed/35528953
http://dx.doi.org/10.12688/f1000research.73597.1
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