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Journals / Academic Platform Journal of Engineering and Smart Systems / 2022 / Cilt: 10 - Sayı: 3

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

Levent ÇALLI
Sena KASIM
Pages
115–123
Publication Date
2022-09-30
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Additionally, it has been observed that the number of products and customer features has a relatively higher weight for the prediction of churner.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Companies must retain their customers and maintain long-term relationships in industries with intense competition.
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Key Findings & Data:While customer churn research is prevalent in B2C (Business to Customer) business models such as the telecoms and retail sectors, customer churn analysis in B2B (business to business) models is a relatively emerging topic.
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Conclusion & Impact:Additionally, it has been observed that the number of products and customer features has a relatively higher weight for the prediction of churner.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Customer Churn · SaaS · Machine Learning

Abstract

Companies must retain their customers and maintain long-term relationships in industries with intense competition. Customer churn analysis is defined in the literature as identifying customers who may leave a company to take appropriate marketing precautions. While customer churn research is prevalent in B2C (Business to Customer) business models such as the telecoms and retail sectors, customer churn analysis in B2B (business to business) models is a relatively emerging topic. In this regard, the study carried out a customer churn analysis by considering an ERP (enterprise resource planning) company with a software as a service (SaaS) business model. Different machine learning algorithms analyzed ten features determined by selection methods and expert opinions. According to the analysis results, the random forest algorithm gave the best result. Additionally, it has been observed that the number of products and customer features has a relatively higher weight for the prediction of churner.

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Cite This Article

ÇALLI, L., KASIM, S. (2022). Using Machine Learning Algorithms to Analyze Customer Churn in the Software as a Service (SaaS) Industry. Academic Platform Journal of Engineering and Smart Systems, Cilt: 10 - Sayı: 3, 115–123. https://ajindex.com/articles/730580

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References (1)

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