Market Segment Evaluation and Selection Based on Application of Fuzzy AHP and COPRAS-G Methods

Article excerpt

1. Introduction

Market segmentation becomes an essential element of marketing in industrialized countries and in living of any business (Wedel, Kamakura 2000). Market segmentation is defined as the partitioning of a market into distinct subsets of customers and any subset could be possibly selected as a target market to be reached with a distinct marketing mix (Kotler 1999). In other words, market segmentation makes it possible to find homogeneous smaller markets by this means, helping marketers to recognize marketing opportunities and to develop products and services in a more tailor-made manner (Jang et al. 2002).

Although market segmentation was introduced into the academic marketing literature by Smith (1956), market segmentation continues to be an important focal point of ongoing research and marketing practices (Chaturvedi et al. 1997; Hanafizadeh, Mirzazadeh 2011). Maybe mass marketing will no longer exist in the coming century or it will become vanished (Kuo et al. 2002).There are a lot of advantages of market segmentation over mass marketing. Firstly, it repeatedly helps every company to find a good chance to expand its own market by better satisfying the wants of customers. Secondly, it increases the profitability or effectiveness of the organization to the extent that the economic benefits provided for consumers exceed the costs of the segmentation process (Chiu et al. 2009). Thirdly, the importance of doing marketing segmentation analysis includes better perception of the market to truly position of a product in the marketplace, choosing the appropriate segments for target marketing, discovering opportunities in existing markets, and gaining competitive advantage through product differentiation (Kotler 1980).

There are many market segmentation bases in the literature that were used to divide a market into segments such as geographic, demographic, life style and product benefits (Kazemzadeh et al. 2009). Besides, there are numerous market segmentation methods such as factor analysis, clustering, conjoint, regression, and discriminate analysis. Also recently, using or integrating other fields including data mining, multivariate statistical analysis, fuzzy logic, artificial neural networks, and genetic algorithm becomes a common tool for market segmentation.

After market segmentation, every company needs to evaluate and select target market or markets, and then Market segmentation evaluation is a critical management decision because all other components of a marketing strategy follow it (Wind, Thomas 1994). Also, Market segment evaluation can help in targeting markets, thus it is very important for improving the probability of success in competitive market.

Although much of the marketing literature has proposed various market segmentation techniques, but a review of academic research reveals that existing studies have relatively neglected segment evaluation and selection (Sarabia 1996; Ou et al. 2009). Also most existing studies suggest some general criteria for evaluation of attractiveness of a segment and merely present a model or method for evaluation.

Selecting an appropriate market segment based on evaluation of segments is one of the most complicated and time consuming problems for many companies, due to many feasible alternatives, conflicting objectives and variety of factors (Aghdaie et al. 2011). Market segment evaluation and selection decisions are sophisticated by the fact that the decision-making process must consider various criteria. Therefore market segment evaluation and selection can be viewed as a multiple criteria decision- making (MCDM) problem. Hence, this study has the main objective of proposing a mechanism for market segment evaluation and selection.

The MCDM methods deal with the process of making decisions for finding the optimum alternative in the presence of multiple, usually conflicting, decision criteria.

In this research a hybrid MCDM model encompassing fuzzy analytic hierarchy process (FAHP) and the complex proportional assessment of alternatives with grey relations (COPRAS-G method) are used for market segment evaluation and selection. …

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