Using Machine Learning Algorithms to Analyze Customer Churn in the Software as a Service (SaaS) Industry

Çallı, Levent; KASIM>, Sena

doi:10.21541/apjess.1139862

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…In the study by another author [2], an analysis was conducted on customer churn within an enterprise resource planning (ERP) company operating with a software-as-a-service (SaaS) business model. This analysis involved the utilization of various machine learning algorithms to assess ten specific features selected through a combination of methods and expert insights.…”

Section: Background and Related Workmentioning

confidence: 99%

Machine Learning Based Customer Churn Prediction Using Improved Feature Selection Techniques

Sharma,

Patel,

Shrivastava

2023

IRJEAS

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In the contemporary, fiercely competitive business environment, the retention of customers holds utmost significance for the continued growth and profitability of companies. Customer churn, which entails the defection of customers to rival services, stands out as a critical and prevalent challenge in a variety of sectors, spanning from telecommunications to e-commerce. This study delves into the realm of customer churn prediction, presenting an innovative approach that leverages machine learning and advanced feature selection techniques. The research utilizes a comprehensive dataset encompassing customer attributes, historical behavior, and churn labels. Employing state-of-the-art machine learning algorithms, including decision trees, random forests, and support vector machines, a predictive model is developed to identify potential churners accurately. The distinctive aspect of this study lies in its feature selection methodology, which integrates recursive feature elimination (RFE) with feature importance ranking. This hybrid approach refines the feature set, eliminating irrelevant or redundant variables, thereby enhancing model efficiency and interpretability. Empirical results showcase the effectiveness of the enhanced feature selection approach in improving predictive accuracy and model performance.

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Section: Background and Related Workmentioning

confidence: 99%

Machine Learning Based Customer Churn Prediction Using Improved Feature Selection Techniques

Sharma,

Patel,

Shrivastava

2023

IRJEAS

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Machine Learning for Imbalanced Data in Telecom Churn Classification

Dewi,

Santiko,

Safa

et al. 2024

2024 International Conference on Information Technology Research and Innovation (ICITRI)

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Customer Churn Prediction

Anand Parulekar,

Shriram Jamsandekar,

Sanjay Parkar

et al. 2024

International Journal of Innovative Science and Research Techno

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In today’s highly competitive business landscape, customer retention revenue preservation, customer experience improvement, and marketing optimisation are critical factors for sustained growth and profitability. Customer churn predictionis discontinuing their services or purchases, which presents asignificant challenge for businesses across various industries. This project focuses on developing a predictive model to expect customer churn in the banking sector using machine learning techniques. The project involves the collection and analysis of historical customer data, confined account activity, transaction history, demographic information, and customer service interactions. By extracting the right features from this data, a machine learning model is trained to forecast which bank customers are at the highest risk of churning. A critical step in this study was the selection of relevant features that influence customerchurn. Feature selection was guided by domain knowledge and feature importance analysis. The different classifiers were used and then trained on the training dataset further ensuringthe model’s optimal performance. The model’s performance is assessed through various evaluation metrics, including accuracy, precision, and recall. Additionally, the project explores a model illustration to uncover the influential factors contributing to customer churn within the banking context. This project’s outcomes can empower banks to take proactive measures in retaining customers, enhancing their overall experience, and thereby preserving revenue streams. By addressing customer churn, banks can foster long-term relationships, reduce customer acquisition costs, and boost their competitiveness in the financial industry. The results of this project are expected to assist businesses in proactively retaining customers by targeting those at the highest risk of churning. Ultimately, reducing customer churn can lead to increased customer satisfaction, revenue, and long-term business sustainability.

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Using Machine Learning Algorithms to Analyze Customer Churn in the Software as a Service (SaaS) Industry

Cited by 3 publications

References 30 publications

Machine Learning Based Customer Churn Prediction Using Improved Feature Selection Techniques

Machine Learning Based Customer Churn Prediction Using Improved Feature Selection Techniques

Machine Learning for Imbalanced Data in Telecom Churn Classification

Customer Churn Prediction

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