Customer churn data analysis using data mining

Abstract

Churn is the phenomenon of a customer terminating their relationship or subscription with a company or service provider[1]. It represents the rate at which customers stop using a company's product or service during a specific period of time. Attrition rate is an important metric for businesses as it directly impacts revenue, growth and customer retention. In the context of the churn data set, the churn label indicates whether a customer has been churn. Lost customers are those who decide to stop buying the company's products. On the other hand, non-churn customers are those who continue to buy the company's products. Understanding customer churn is critical for businesses to identify the patterns, factors, and metrics that lead to customer churn. By analyzing churn behavior and its associated characteristics, companies can develop strategies to retain existing customers, improve customer satisfaction, and reduce customer churn. Predictive modeling techniques can also be applied to anticipate and proactively address potential customer churn, enabling companies to take proactive steps to retain at-risk customers[2].