The values of BI-empowered customer service in telecom

Al-Zadjali, Mohamed; Al‐Busaidi, Kamla Ali

doi:10.1504/ijecrm.2018.096241

Cited by 1 publication

(2 citation statements)

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“…Currently, there is a dearth of analysing the service quality feedback that directly impacts customer touchpoints [15]. An integral component of customer experience management is service quality analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Various data mining techniques are used to guess customer churn by identifying the most severe contributing factor. Supervised data mining algorithms, such as decision trees, genetic algorithms, k nearest neighbours, Naïve Bayes, and support vector machines, are comparatively the most extensively explored for churn prediction [15,17]. From an industry application standpoint, the telecommunication industry is by far the most extensively researched domain for churn prediction [18].…”

Section: Introductionmentioning

confidence: 99%

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An Ensemble Model for Predicting Retail Banking Churn in the Youth Segment of Customers

Pramod

Raman

2022

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(1) This study aims to predict the youth customers’ defection in retail banking. The sample comprised 602 young adult bank customers. (2) The study applied Machine learning techniques, including ensembles, to predict the possibility of churn. (3) The absence of mobile banking, zero-interest personal loans, access to ATMs, and customer care and support were critical driving factors to churn. The ExtraTreeClassifier model resulted in an accuracy rate of 92%, and an AUC of 91.88% validated the findings. (4) Customer retention is one of the critical success factors for organizations so as to enhance the business value. It is imperative for banks to predict the drivers of churn among their young adult customers so as to create and deliver proactive enable quality services.

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Section: Introductionmentioning

confidence: 99%