Customer Churn Prediction Model and Identifying Features to Increase Customer Retention based on User Generated Content

Kassem, Essam Abou el; Hussein, Shereen Ali; Abdel-Rahman, A.; Alsheref, Fahad Kamal

doi:10.14569/ijacsa.2020.0110567

Cited by 13 publications

(8 citation statements)

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“…Yu et al, Zhang et al and Kassem et al stated fee and convenience as characteristics to determine their influence on consumer satisfaction. They discovered that customer retention is affected by customer satisfaction and socio-demographic variables and found that while the industry is in its early stages, the fee is more important [6][7][8].…”

Section: Factors Influence Customer Churnmentioning

confidence: 99%

Customer Churn Prediction in the Telecommunication Industry

2023

AEMPS

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Customer churn is essential for telecom fields because it reduces income when a customer switches from one service provider to another. It is important for managers to help them find the factors influencing customer churn so that they can make decisions and optimize services. In this essay, SPSS will be used to analyze the factors that influence customer churn. We use contrastive analysis to find out the factors of the customer churn and use logistic regression to analyze the degree of influence of different factors. The result indicates phone charge, quality and diversity of services have impacts on customer churn.

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Section: Factors Influence Customer Churnmentioning

confidence: 99%

Customer Churn Prediction in the Telecommunication Industry

2023

AEMPS

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“…Models like Naïve Bayes and Support Vector Machine are also used in large-scale text classification analyses (Chumwatana and Wongkolkitsilp, 2019; Rashid, 2010). Other classification techniques, like hinge loss and logistic loss (Amiri and Dauḿe, 2016), as well as deep learning and logistic regression (El Kassem et al , 2020), have also been used. Lexicon-based classifiers were used by Varsheney and Gupta (2014) and decision trees by Jahromi et al (2014) among other rule-based decision-making techniques (Jahromi et al , 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, customers have different reasons for churning. Big data predictive models may be able to identify customers who are likely to switch, but not all churning customers do so for the same reasons and should not be treated in the same way (El Kassem et al , 2020). There is a need for a model to predict churn customers and provide a strategy of retention depending on their churn factors.…”

Section: Literature Reviewmentioning

confidence: 99%

What social media sentiment tells us about why customers churn

Lappeman

Franco

Warner

et al. 2022

JCM

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Purpose This study aims to investigate the factors that influence South African customers to potentially switch from one bank to another. Instead of using established models and survey techniques, the research measured social media sentiment to measure threats to switch. Design/methodology/approach The research involved a 12-month analysis of social media sentiment, specifically customer threats to switch banks (churn). These threats were then analysed for co-occurring themes to provide data on the reasons customers were making these threats. The study used over 1.7 million social media posts and focused on all five major South African retail banks (essentially the entire sector). Findings This study concluded that seven factors are most significant in understanding the underlying causes of churn. These are turnaround time, accusations of unethical behaviour, billing or payments, telephonic interactions, branches or stores, fraud or scams and unresponsiveness. Originality/value This study is unique in its measurement of unsolicited social media sentiment as opposed to most churn-related research that uses survey- or customer-data-based methods. In addition, this study observed the sentiment of customers from all major retail banks across 12 months. To date, no studies on retail bank churn theory have provided such an extensive perspective. The findings contribute to Susan Keaveney’s churn theory and provide a new measurement of switching threat through social media sentiment analysis.

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“…The customer churn prediction is also useful in various industrial sectors such as banking, insurance, and mobile phone companies. (Kassem et al 2020) These two papers have ample points about the applications of customer churn prediction which provides evidence about the usage of churn prediction using machine learning techniques in the field of telecom industry as well as other fields like banking sector (Lu et al 2014) . Recently, a lot of researchers have done a variety of customer churn prediction in telecommunications using data analytics as it is the part of data science and ML algorithms for customer churn prediction.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Measure of Customer Churn Prediction in Telecom Industry using Adaboost over K Nearest Neighbor Algorithm

2022

pnr

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To enhance and predict the accuracy rate of customer churning in the telecommunication industry using Adaboost algorithm over K Nearest Neighbor algorithm. Materials and methods: Both Adaboost algorithm and K Nearest Neighbor algorithm with a sample size of (N=10) is executed with multiple training along with testing splits for predicting the accuracy of customer churning shows g-power as 75% and threshold value as 0.000 and confidence interval as 95%. The performance of these algorithms are calculated based on the rate of accuracy using customer churn dataset. Results and Discussion: The accuracy of predicting customer churn using Adaboost algorithm(90%) and K Nearest Neighbor algorithm(75%) is obtained. There was an analytical difference between Adaboost and K Nearest Neighbor (p<0.005). Conclusion: Prediction of customer churn using Adaboost algorithm seems to be comparatively better than the K-Nearest-Neighbor algorithm with improved accuracy.

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Customer Churn Prediction Model and Identifying Features to Increase Customer Retention based on User Generated Content

Cited by 13 publications

References 0 publications

Customer Churn Prediction in the Telecommunication Industry

Customer Churn Prediction in the Telecommunication Industry

What social media sentiment tells us about why customers churn

Accuracy Measure of Customer Churn Prediction in Telecom Industry using Adaboost over K Nearest Neighbor Algorithm

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