Churn Prediction in Enterprises with High Customer Turnover
Pages 176 - 191
Abstract
Most research about Machine Learning (ML) models for churn prediction has focused on sectors like telecommunications, while this problem can be particularly challenging in industries with High Customer Turnover (HCT) like food delivery, e-commerce, and gaming. This article addresses this gap by investigating the effectiveness of four alternative ML models that have been effective for churn prediction in particular HCT enterprises - Multilayer Perceptron, SVM, Decision Trees, and Random Forests. We trained and evaluated the models on three representative datasets from distinct sectors, aiming to identify the models and data features that provide the best results. We propose and employ a framework to help achieve this goal. It allowed high-performance churn prediction in our experiments, exploiting mainly the purchase data features of the distinct HCT enterprises. The Random Forest model achieved the best accuracy (around 90%) on the three datasets, and the best precision and F1-score on two of them.
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Published In
Dec 2023
562 pages
ISBN:978-3-031-48315-8
DOI:10.1007/978-3-031-48316-5
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 04 December 2023
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