Thesis Abstract

Abstract
Customer churn, or customer attrition, is a critical challenge faced by businesses across various industries. Predicting and managing customer churn is essential for ensuring the long-term success and sustainability of a business. In recent years, the use of machine learning algorithms has gained popularity for predicting customer churn due to their ability to analyze large volumes of data and identify patterns that may indicate potential churn behavior. This thesis focuses on the application of machine learning algorithms to predict customer churn and provides insights into the factors that influence customer retention. The study begins with an introduction to the concept of customer churn and its significance for businesses. The background of the study highlights the increasing competition in the market and the importance of retaining existing customers. The problem statement identifies the challenges faced by businesses in predicting and managing customer churn, while the objective of the study aims to develop a predictive model that can accurately forecast customer churn. The limitations of the study and its scope are also discussed to provide a clear understanding of the research boundaries. The literature review delves into existing research on customer churn prediction, machine learning algorithms, and their applications in various industries. The review examines different approaches to customer churn prediction, including traditional statistical methods and more advanced machine learning techniques. It also explores the factors that influence customer churn, such as customer demographics, purchasing behavior, and customer satisfaction. The research methodology section outlines the data collection process, data preprocessing techniques, and the selection of machine learning algorithms for customer churn prediction. The study utilizes a dataset of customer interactions and transaction history to train and test the predictive model. Feature engineering and model evaluation techniques are employed to enhance the accuracy and reliability of the predictive model. The discussion of findings presents the results of the predictive model and analyzes the factors that contribute to customer churn. The study identifies key predictors of churn, such as customer tenure, frequency of transactions, and customer feedback. The findings highlight the importance of personalized customer engagement strategies and proactive churn prevention measures to reduce customer attrition. In conclusion, this thesis provides valuable insights into the predictive modeling of customer churn using machine learning algorithms. By developing an accurate and reliable churn prediction model, businesses can proactively identify at-risk customers and implement targeted retention strategies to improve customer loyalty and reduce churn rates. The study contributes to the growing body of research on customer churn prediction and offers practical recommendations for businesses looking to enhance their customer retention efforts.

Thesis Overview

The research project titled "Predictive modeling of customer churn using machine learning algorithms" aims to investigate and develop predictive models to forecast customer churn in the context of business operations. Customer churn, also known as customer attrition, refers to the phenomenon where customers discontinue their relationship with a company or stop using its products or services. This research seeks to leverage machine learning algorithms to analyze historical customer data and identify patterns or signals that can help predict and prevent customer churn. The project is motivated by the significant impact that customer churn can have on a business, including loss of revenue, decreased customer loyalty, and reduced market share. By developing accurate predictive models, businesses can proactively identify customers at risk of churning and implement targeted retention strategies to mitigate churn rates. The research will begin with a comprehensive review of existing literature on customer churn prediction, machine learning techniques, and their applications in the business domain. This literature review will provide a solid foundation for understanding the theoretical underpinnings of customer churn prediction and the various machine learning algorithms that can be employed for this purpose. In the subsequent research methodology chapter, the project will outline the data collection process, feature engineering techniques, model selection criteria, and evaluation metrics for assessing the performance of the predictive models. The research will utilize real-world customer data from a business organization to train and validate the machine learning models. The findings chapter will present the results of the predictive modeling experiments, including the performance metrics of the developed models, such as accuracy, precision, recall, and F1 score. The discussion will delve into the insights gained from the analysis of customer churn patterns and the implications for business decision-making. Lastly, the conclusion and summary chapter will synthesize the key findings of the research and provide recommendations for businesses looking to implement predictive modeling for customer churn prevention. The research will highlight the significance of leveraging machine learning algorithms for enhancing customer retention strategies and improving overall business performance. In conclusion, the project "Predictive modeling of customer churn using machine learning algorithms" aims to contribute to the growing body of knowledge on customer churn prediction and provide practical insights for businesses seeking to reduce churn rates and enhance customer satisfaction.