A Survey on Churn Analysis

TL;DR

This survey comprehensively reviews churn prediction techniques across various industries, highlighting differences in definitions and models, and aims to guide researchers in selecting appropriate methods for their specific service contexts.

Contribution

It consolidates diverse churn definitions and models from multiple fields, providing a unified overview to aid in selecting suitable churn analysis approaches.

Findings

Churn definitions vary significantly across industries.

Classification of churn loss, feature engineering, and prediction models.

Guidelines for selecting churn models based on industry context.

Abstract

In this paper, I present churn prediction techniques that have been released so far. Churn prediction is used in the fields of Internet services, games, insurance, and management. However, since it has been used intensively to increase the predictability of various industry/academic fields, there is a big difference in its definition and utilization. In this paper, I collected the definitions of churn used in the fields of business administration, marketing, IT, telecommunications, newspapers, insurance and psychology, and described their differences. Based on this, I classified and explained churn loss, feature engineering, and prediction models. Our study can be used to select the definition of churn and its associated models suitable for the service field that researchers are most interested in by integrating fragmented churn studies in industry/academic fields.