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Rough Set Approach to Customer Satisfaction Analysis

  • Salvatore Greco
  • Benedetto Matarazzo
  • Roman Słowiński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)

Abstract

Customer satisfaction analysis has become a hot issue in strategic management. The basis of any decision in this field is the analysis of the answers of a sample of customers to a specific questionnaire. Traditionally, using a methodology called conjoint analysis, the data obtained from the questionnaires are used to build a collective utility function representing customer preferences. This utility function permits to measure the satisfaction of the customers and to determine the most critical features relevant for the appreciation of the considered products or services. In this paper, we propose an alternative methodology to analyze the data from the questionnaire. Our approach is based on the rough set methodology and represents the preferences of the customers by means of simple decision rules such as “if feature α is considered good and feature β is considered sufficient, then the overall evaluation of the product is medium”. The interpretation of the decision rules is simpler and more direct than the interpretation of the utility function given by conjoint analysis. Moreover, the capacity of representing customer preferences in terms of easily understandable “if ..., then...” statements expressed in the natural language makes our approach particularly interesting for Kansei Engineering. The proposed methodology gives also some indications relative to strategic interventions aimed at improving the quality of the offered products and services. The expected efficiency of these interventions is measured, which is very useful for the definition of proper customer satisfaction strategies. Our approach supplies an integrated support to customer satisfaction oriented management.

Keywords

Decision Rule Customer Satisfaction Conjoint Analysis Customer Preference Decision Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Salvatore Greco
    • 1
  • Benedetto Matarazzo
    • 1
  • Roman Słowiński
    • 2
    • 3
  1. 1.Faculty of EconomicsUniversity of CataniaCataniaItaly
  2. 2.Institute of Computing SciencePoznań University of TechnologyPoznań
  3. 3.Institute for Systems ResearchPolish Academy of SciencesWarsawPoland

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