Abstract
Background:Customer churn is a term that refers to the rate at which customers leavethebusiness.Churncould be due tovariousfactors, including switching to a competitor, cancelling their subscriptionbecause ofpoor 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, companiescananticipate future revenue. Thisarticlewill look at which variables in theNet Promoter Score (NPS)dataset influence customer churn in Malaysia's telecommunications industry. The aim of This study wastoidentify the factorsbehind customer churn andpropose achurn predictionframeworkcurrently lackinginthe telecommunications industry. Methods:This study applied data mining techniques totheNPSdataset from a Malaysian telecommunications company in September 2019 and September 2020, analysing 7776 records with30 fields to determine which variableswere significant for the churn prediction model.We developed a propensity forcustomer churn using the Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbours Classifier, Classification and Regression Trees(CART), Gaussian Naïve Bayes, and Support Vector Machineusing 33variables. Results:Customer churn is elevated for customers with a lowNPS.However, an immediate helpdeskcanact as a neutral partyto ensure that thecustomer needs are met andtodeterminean employee's abilityto obtain customer satisfaction. Conclusions:It can be concludedthat CART has the most accuratechurn prediction(98%).However,theresearchis prohibited from accessing personal customer information under Malaysia's data protection policy.Resultsare expected for other businessesto measurepotential customerchurnusingNPS scores to gather customer feedback.
Highlights
Customer retention and customer satisfaction are essential for a business to succeed.[1]
The results of the mediating effects of a customer's partial defection on the relationship between churn determinants and total defection show that some churn determinants influence customer churn, either directly or indirectly through a customer's status change, or both; a customer's status change explains the relationship between churn determinants and the probability of churn.[6]
According to the results of this study, the Net Promoter Score (NPS) feedback rating appears to be a partial mediator between some churn determinants and customer churn
Summary
Customer retention and customer satisfaction are essential for a business to succeed.[1]. Predicting churning customers early on can be a valuable revenue source.[5] The results of the mediating effects of a customer's partial defection on the relationship between churn determinants and total defection show that some churn determinants influence customer churn, either directly or indirectly through a customer's status change, or both; a customer's status change explains the relationship between churn determinants and the probability of churn.[6] This study hypothesised that changes in Net Promoter Score (NPS) can indicate whether churn determinants directly or indirectly influence churn. 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.
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