Tài liệu Modelling importance preferences in customer satisfaction surveys

MODELLING IMPORTANCE PREFERENCES IN CUSTOMER SATISFACTION SURVEYS

ABSTRACT Customer satisfaction measurement, through MUSA model, provides the analysts with the highest and lowest performance indicators, pointing out the leverage opportunities and the weaknesses of the company. An extension of the MUSA methodology for modelling customer importance preferences for service characteristics is presented in this paper. Several approaches in the context of multiobjective linear programming are examined, which give the ability to compare derived and modelled weights of the satisfaction dimensions and to introduce the principles of Kano’s model to MUSA methodology. Finally, the results of an application of the MUSA extension to an educational organization are presented in this paper. Key words: Customer satisfaction analysis, MUSA method, Satisfaction importance modelling, Kano’s model. 1. INTRODUCTION To reinforce customer orientation on a day-to-day basis, a growing number of companies choose customer satisfaction as their main performance indicator. However, customer satisfaction must be translated into a number of measurable parameters directly linked to several aspects of a company’s products/services or else it will remain an abstract and intangible notion. Measurement will provide the analysts with the highest and lowest performance indicators, pointing out the leverage opportunities and the weaknesses of the company. It often happens that derived importance by a preference disaggregation model differs from the stated importance. By the term stated importance we refer to the importance that is given to each criterion by the customers. It is not unreasonable to say that customers tend to rate every criterion as important, when asked freely (Naumann and Giel, 1995). The aim of this paper is to present an extension of the MUSA methodology that helps modelling customer importance preferences for service characteristics. This approach gives the ability to compare derived and modelled weights of the satisfaction dimensions and to extrapolate valuable results. The results of an application of the MUSA extension to an educational organization, give an example of the differentiation between derived and stated importance.