Customer churn prediction for telecommunication industry: A Malaysian Case Study [version 1; peer review: 2 approved]
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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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2021-12-01
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| Series: | F1000Research |
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| Online Access: | https://f1000research.com/articles/10-1274/v1 |
| _version_ | 1818232399186624512 |
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| author | Nurulhuda Mustafa Lew Sook Ling Siti Fatimah Abdul Razak |
| author_facet | Nurulhuda Mustafa Lew Sook Ling Siti Fatimah Abdul Razak |
| author_sort | Nurulhuda Mustafa |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-12T11:05:40Z |
| format | Article |
| id | doaj.art-42a604309f6c44d5acc8cf03a3ef5975 |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-12-12T11:05:40Z |
| publishDate | 2021-12-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-42a604309f6c44d5acc8cf03a3ef59752022-12-22T00:26:25ZengF1000 Research LtdF1000Research2046-14022021-12-011077258Customer churn prediction for telecommunication industry: A Malaysian Case Study [version 1; peer review: 2 approved]Nurulhuda Mustafa0Lew Sook Ling1https://orcid.org/0000-0003-4515-1163Siti Fatimah Abdul Razak2https://orcid.org/0000-0002-6108-3183Telekom Malaysia, Faculty of Business, Ayer Keroh, Melaka, 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Ayer Keroh, Melaka, 75450, MalaysiaFaculty of Information Science and Technology, Multimedia University, Ayer Keroh, Melaka, 75450, MalaysiaBackground: 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.https://f1000research.com/articles/10-1274/v1Customer Churn Net Promoter Score (NPS) Data Mining Techniques Classification and Regression Trees (CART)eng |
| spellingShingle | Nurulhuda Mustafa Lew Sook Ling Siti Fatimah Abdul Razak Customer churn prediction for telecommunication industry: A Malaysian Case Study [version 1; peer review: 2 approved] F1000Research Customer Churn Net Promoter Score (NPS) Data Mining Techniques Classification and Regression Trees (CART) eng |
| title | Customer churn prediction for telecommunication industry: A Malaysian Case Study [version 1; peer review: 2 approved] |
| title_full | Customer churn prediction for telecommunication industry: A Malaysian Case Study [version 1; peer review: 2 approved] |
| title_fullStr | Customer churn prediction for telecommunication industry: A Malaysian Case Study [version 1; peer review: 2 approved] |
| title_full_unstemmed | Customer churn prediction for telecommunication industry: A Malaysian Case Study [version 1; peer review: 2 approved] |
| title_short | Customer churn prediction for telecommunication industry: A Malaysian Case Study [version 1; peer review: 2 approved] |
| title_sort | customer churn prediction for telecommunication industry a malaysian case study version 1 peer review 2 approved |
| topic | Customer Churn Net Promoter Score (NPS) Data Mining Techniques Classification and Regression Trees (CART) eng |
| url | https://f1000research.com/articles/10-1274/v1 |
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