Machine Learning-Powered Churn Analysis: Identifying Key Indicators and Predictive Patterns in Customer Behavior

EasyChair Preprint 14446

Abstract

In today's highly competitive market, customer retention is crucial for business sustainability and growth. This study explores the application of machine learning (ML) techniques in predicting customer churn, with a focus on identifying key behavioral indicators and developing robust predictive models. By analyzing vast datasets that include customer interactions, transaction histories, and demographic information, the study uncovers critical patterns that precede customer attrition. The research employs various ML algorithms, such as decision trees, logistic regression, and neural networks, to model customer behavior and predict churn with high accuracy. Feature importance analysis highlights the most significant predictors, enabling businesses to proactively address potential churn triggers. The findings demonstrate that ML-powered churn analysis not only enhances prediction accuracy but also offers actionable insights into customer behavior, allowing companies to implement targeted retention strategies and improve overall customer satisfaction. This research underscores the potential of machine learning as a powerful tool in understanding and mitigating customer churn, ultimately contributing to long-term business success.

Keyphrases: Machine Learning (ML), Machine Learning-Powered Churn Analysis, Predictive Patterns in Customer Behavior, business sustainability and growth, understanding and mitigating customer churn

@booklet{EasyChair:14446,
  author    = {Adeoye Ibrahim},
  title     = {Machine Learning-Powered Churn Analysis: Identifying Key Indicators and Predictive Patterns in Customer Behavior},
  howpublished = {EasyChair Preprint 14446},
  year      = {EasyChair, 2024}}