Skip to Main content Skip to Navigation
Conference papers

Using Machine Learning Methods to Predict Subscriber Churn of a Web-Based Drug Information Platform

Abstract : Nowadays, businesses are highly competitive as most markets are extremely saturated. As a result, customer management is of critical importance to avoid dissatisfaction that leads to customer loss. Thus, predicting customer loss is crucial to efficiently target potential churners and attempt to retain them. By classifying customers as churners and non-churners, customer loss is equated to a binary classification problem. In this paper, a new real-world dataset is used, originating from a popular web-based drug information platform, in order to predict subscriber churn. A number of methods that belong to different machine learning categories (linear, nonlinear, ensemble, neural networks) are constructed, optimized and trained on the subscription data and the results are presented and compared. This study provides a guide for solving churn prediction problems as well as a comparison of various models within the churn prediction context. The findings co-align with the notion that ensemble methods are, in principle, superior whilst every model maintains satisfying results.
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/hal-03287713
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Thursday, July 15, 2021 - 6:12:36 PM
Last modification on : Friday, August 13, 2021 - 4:29:53 PM
Long-term archiving on: : Saturday, October 16, 2021 - 7:11:31 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2024-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Georgios Theodoridis, Athanasios Tsadiras. Using Machine Learning Methods to Predict Subscriber Churn of a Web-Based Drug Information Platform. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.581-593, ⟨10.1007/978-3-030-79150-6_46⟩. ⟨hal-03287713⟩

Share

Metrics

Record views

65