A Survey on Statistical Models for Estimating Churn Rate

A Survey on Statistical Models for Estimating Churn Rate

Kapil1 Prof. Pankaj Raghuwanshi2

ABSTRACT: Customer churn, the phenomenon of customers discontinuing their relationship with a business, poses a significant threat to the sustainability and profitability of companies across industries. As markets become increasingly competitive, retaining existing customers has proven to be more cost-effective than acquiring new ones. In this context, machine learning (ML) has emerged as a powerful tool for analyzing customer behavior and predicting churn with high accuracy. By leveraging vast datasets and sophisticated algorithms, businesses can proactively identify at-risk customers and take targeted actions to retain them. The success of churn prediction largely depends on the quality and relevance of input features. Important features include customer demographics, transaction frequency, service usage patterns, complaint records, and engagement metrics. Feature engineering, which involves creating new features or transforming existing ones, is a critical step in improving model performance This paper presents a comprehensive survey of statistical models for forecasting churn rates along with associated challenges that the sector faces.

Keywords: Churn Rate, Statistical Modelling, Machine Learning, Deep Learning, Regression Analysis.