Next Article in Journal
Measuring Bikeshare Access/Egress Transferring Distance and Catchment Area around Metro Stations from Smartcard Data
Previous Article in Journal
Quantifying Bicycle Network Connectivity in Lisbon Using Open Data
Article Menu

Export Article

Open AccessArticle
Information 2018, 9(11), 288; https://doi.org/10.3390/info9110288

A Hybrid Swarm Intelligent Neural Network Model for Customer Churn Prediction and Identifying the Influencing Factors

King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan
Received: 23 September 2018 / Revised: 3 November 2018 / Accepted: 5 November 2018 / Published: 17 November 2018
(This article belongs to the Section Artificial Intelligence)
Full-Text   |   PDF [894 KB, uploaded 22 November 2018]   |  

Abstract

Customer churn is one of the most challenging problems for telecommunication companies. In fact, this is because customers are considered as the real asset for the companies. Therefore, more companies are increasing their investments in developing practical solutions that aim at predicting customer churn before it happens. Identifying which customer is about to churn will significantly help the companies in providing solutions to keep their customers and optimize their marketing campaigns. In this work, an intelligent hybrid model based on Particle Swarm Optimization and Feedforward neural network is proposed for churn prediction. PSO is used to tune the weights of the input features and optimize the structure of the neural network simultaneously to increase the prediction power. In addition, the proposed model handles the imbalanced class distribution of the data using an advanced oversampling technique. Evaluation results show that the proposed model can significantly improve the coverage rate of churn customers in comparison with other state-of-the-art classifiers. Moreover, the model has high interpretability, where the assigned feature weights can give an indicator about the importance of their corresponding features in the classification process. View Full-Text
Keywords: churn prediction; Particle Swarm Optimization; neural networks; classification; feature weighting churn prediction; Particle Swarm Optimization; neural networks; classification; feature weighting
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Faris, H. A Hybrid Swarm Intelligent Neural Network Model for Customer Churn Prediction and Identifying the Influencing Factors. Information 2018, 9, 288.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top