Investigating Predictive Models for Data Analytics to Understand Customer Churn and Contributing Factors

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SIVABHARATHI G, Dr. K. CHITRA

Abstract

 


Customer churn is a pressing concern for businesses across industries. Losing customers can lead ­­to lower profits, reduced revenues, and potential loss of business, which can be detrimental to a company's success. As a result, it is crucial for companies to identify the reasons behind customer ­­churn and develop effective strategies to retain their customers. One promising approach to tackling this problem is through the analysis of historical data.


                By analyzing customer behavior and transactional data, companies can develop a predictive model for customer churn and identify the factors that contribute to it. In addition, this approach can also help companies identify the most profitable service types and estimate the amount of revenue loss due to customer churn. In this research study, we aim to achieve these objectives through quantitative research methods, which involve analyzing numerical data and applying statistical techniques to compare the accuracy of various prediction models. By gaining a better understanding of customer churn and its impact on business, companies can develop effective strategies to retain their customers and improve overall business profitability.


 

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SIVABHARATHI G, Dr. K. CHITRA