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

Essam Abou el Kassem · Shereen Adel Ali · Alaa Mostafa · Fahad Kamal Alsheref

32 citations0/0 files storedInternational Journal of Advanced Computer Science and Applications

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Customer churn and segmentation Customer Service Quality and Loyalty Sentiment Analysis and Opinion Mining

Abstract

Customer churn is a problem for most companies because it affects the revenues of the company when a customer switch from a service provider company to another in the telecom sector. For solving this problem we put two main approaches: the first one is identifying the main factors that affect customers churn, the second one is detecting the customers that have a high probability to churn through analyzing social media. For the first approach we build a dataset through practical questionnaires and analyzing them by using machine learning algorithms like Deep Learning, Logistic Regression, and Naïve Bayes algorithms. The second approach is customer churn prediction model through analyzing their opinions through their user-generated content (UGC) like comments, posts, messages, and products or services' reviews. For analyzing the UGC we used Sentiment analysis for finding the text polarity (negative/positive). The results show that the used algorithms had the same accuracy but differ in arrangement of attributes according to their weights in the decision.

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References

2001Customer retention, loyalty, and satisfaction in the German mobile cellular telecommunications market 2019Customer churn prediction in telecom using machine learning in big data platform 2015A comparison of machine learning techniques for customer churn prediction 2002Turning telecommunications call details to churn prediction: a data mining approach 2019A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector 2018Customer churn prediction in telecommunication industry using data certainty 2016Customer churn prediction in the telecommunication sector using a rough set approach 2013Telecommunication subscribers' churn prediction model using machine learning

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2022An Ensemble Model for Predicting Retail Banking Churn in the Youth Segment of Customers 2022What social media sentiment tells us about why customers churn 2024Enhancing churn forecasting with sentiment analysis of steam reviews 2022Predicting Customer Churn on OTT Platforms 2021

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