A New Model for Best Customer Segment Selection Using Fuzzy TOPSIS Based on Shannon Entropy

In today’s competitive market, for a business firm to win higher profit among its rivals, it is of necessity to evaluate, and rank its potential customer segments to improve its Customer Relationship Management (CRM). This brings the importance of having more efficient decision making methods considering the current fast growing information era. These decisions usually involve several criteria, and it is often necessary to compromise among possibly conflicting factors. In this paper a new extension of fuzzy Techniques for Order Preferences by Similarity to Ideal Solution (TOPSIS) based on Shannon entropy concept for customer segment selection is proposed. Fuzzy set theories are also employed due to the presence of vagueness and imprecision of information. The contribution of this paper is that it provides a framework for MCDM which considers vagueness and ambiguity as well as allowing to set multiple aspiration levels for customer segment selection problems in which ‘‘the more/higher is better’’ (e.g., benefit criteria) or ‘‘the less/lower is better’’ (e.g., cost criteria).At the end, a numerical example of this approach is shown to illustrate its effectiveness.

Keywords:

Fuzzy set theories , CRM , Customer Segment selection , MCDM , TOPSIS , Shannon entropy