Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines

• Type: Article in International Scientific Journal
• Date: 2013

Authors

Vera L. Miguéis (Author) FEUP; Ana Maria Cunha Ribeiro dos Santos Ponces Camanho (Author) FEUP; João Falcão e Cunha (Author) FEUP

Journal

Title: Expert Systems with Applications

Vol. 40 No. 16

Pages: 6225-6232

ISSN: 0957-4174

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 32 Citations

ISI Web of Science

Scopus - 41 Citations

Scientific classification

FOS: Engineering and technology

Other information

Authenticus ID: P-006-68T

DOI: 10.1016/j.eswa.2013.05.069

Abstract (EN): The profit resulting from customer relationship is essential to ensure companies viability, so an improvement in customer retention is crucial for competitiveness. As such, companies have recognized the importance of customer centered strategies and consequently customer relationship management (CRM) is often at the core of their strategic plans. In this context, a priori knowledge about the risk of a given customer to mitigate or even end the relationship with the provider is valuable information that allows companies to take preventive measures to avoid defection. This paper proposes a model to predict partial defection, using two classification techniques: Logistic regression and Multivariate Adaptive Regression Splines (MARS). The main objective is to compare the performance of MARS with Logistic regression in modeling customer attrition. This paper considers the general form of Logistic regression and Logistic regression combined with a wrapper feature selection approach, such as stepwise approach. The empirical results showed that MARS performs better than Logistic regression when variable selection procedures are not used. However, MARS loses its superiority when Logistic regression is conducted with stepwise feature selection.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8