Customer satisfaction and loyalty analysis with classification algorithms and Structural Equation Modeling


AKTEPE A., ERSÖZ S., TOKLU B.

COMPUTERS & INDUSTRIAL ENGINEERING, vol.86, pp.95-106, 2015 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 86
  • Publication Date: 2015
  • Doi Number: 10.1016/j.cie.2014.09.031
  • Title of Journal : COMPUTERS & INDUSTRIAL ENGINEERING
  • Page Numbers: pp.95-106
  • Keywords: Classification algorithms, Customer satisfaction, Customer loyalty, Structural Equation Modeling, WEKA, LISREL, RELATIONSHIP MANAGEMENT, RETENTION, QUALITY, TRUST

Abstract

Businesses can maintain their effectiveness as long as they have satisfied and loyal customers. Customer relationship management provides significant advantages for companies especially in gaining competitiveness. In order to reach these objectives primarily companies need to identify and analyze their customers. In this respect, effective communication and commitment to customers and changing market conditions is of great importance to increase the level of satisfaction and loyalty. To evaluate this situation, level of customer satisfaction and loyalty should be measured correctly with a comprehensive approach. In this study, customers are investigated in 4 main groups according to their level of satisfaction and loyalty with a criteria and group based analysis with a new method. We use classification algorithms in WEKA programming software and Structural Equation Modeling (SEM) with LISREL tools together to analyze the effect of each satisfaction and loyalty criteria in a satisfaction-loyalty matrix and extend the customer satisfaction and loyalty post-analysis research bridging the gap in this field of research. To convert developed conceptual thought to experimental study, white goods industry is exemplified. 15 criteria are used for evaluation in 4 customer groups and a satisfaction-loyalty survey developed by experts is applied to 200 customers with face-to-face interviews. As a result of the study, a customer and criteria grouping method is created with high performance classification methods and good fit structural models. In addition, results are evaluated for developing a customer strategy improvement tool considering method outcomes. (C) 2014 Elsevier Ltd. All rights reserved.