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Dergiler / 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
Sayfa
115–123
Yayın Tarihi
2022-09-30
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Companies must retain their customers and maintain long-term relationships in industries with intense competition.
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Temel Bulgular & Veri: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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Sonuç & Katkı:Additionally, it has been observed that the number of products and customer features has a relatively higher weight for the prediction of churner.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler: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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Bu Makaleye Atıf Yap

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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