Work

Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

Year: 2015

Type: article

Abstract: Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.

Source: The Scientific World JOURNAL

DOI: 10.1155/2015/473283

Authors: Ali Rodan, Ayham Fayyoumi, Hossam Faris, Jamal Alsakran, Omar Al-Kadi

Institutions: University of Jordan, Imam Mohammad ibn Saud Islamic University

Language: English

Topic: Customer churn and segmentation

Subfield: Marketing

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $860

OpenAlex ID: W1974645872

FWCI: 9.937

Cites: 32

Cited by: 27

Related to: 10