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DERIVING CONSENSUS RANKINGS VIA MULTICRITERIA DECISION MAKING METHODOLOGY

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DERIVING CONSENSUS RANKINGS VIA MULTICRITERIA DECISION MAKING METHODOLOGY
INTRODUCTION
With increasing globalization, local retailers find themselves having to compete with large foreign players by targeting niche markets. To excel and flaunt as a market leader in an ultramodern era and a globalize world, the organizations must strive to harvest from its marketing strategies, benchmarking and company quality policy.
Ranking and selecting projects is a relatively common, yet often difficult task. It is complicated because there is usually more than one dimension for measuring the impact of each criteria and more than one decision maker. This paper considers a real application of project selection for the marketing mix element, using an approach called ELECTRE.
The ELECTRE method has several unique features not found in other solution methods; these are the concepts of outranking and indifference and preference thresholds. The ELECTRE method applied to the project selection problem using SPSS (Statistical Package for the Social Sciences) application.
Our contribution is to show the potential of Marketing mix model in deriving a consensus ranking for benchmarking. According to the feedback from the respondents, we dynamically rank out the best element to be benchmark.
The decision problem faced by management has been translated into our market research problem in the form of questions that define the information that is required to make the decision and how this information obtained. The corresponding research problem is to assess whether the market would accept the consensus rankings derive from benchmarking result from the impact of marketing mix on customer satisfaction using a multi-criteria decision making outranking methodology.

LITERATURE REVIEW
The project ranking problem is, like many decision problems, challenging for at least two reasons. First, there is no single criterion in marketing mix model which adequately captures the effect or impact of each element; in other words, it is a multiple criteria problem. Second,

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