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The goal of Segmentation is to obtain groups of Greek customers of a specific product category or service, that react similarly to various product or service categories. 

The goal of segmentation is not an easy thing and most methods fall short of that.

It is a long time, since MARKETECK has abandoned the type of segmentation, which involves grouping customers on the basis of demographic variables, because those variables are generally not closely related to the buying behavior. Such variables account only for 10% or less of the variance observed in the buying process for products or services.

We use the so called approach of BENEFIT SEGMENTATION, which incorporates the critical features, which drive consumers' buying behavior. Segmenting customers on the basis of their reactions to these critical features brings MARKETECK's analysis closer to the above goal of segmentation.

Our analysis by the use of appropriate software follows three stages:

The first stage calculates distances. Two types of calculations are performed: First, ratings' data is standardized to remove bias involving respondent's use of the rating scale. Secondly, it calculates how similar each respondent's utilities are to every other respondent's
In the second stage, a hierarchical cluster analysis on the respondent similarity measures obtained in the preceding stage, using the Ward's method of clustering, is performed.
In the third stage, we have results for solution. The distance reflects the amount of variability within each segment and there might point out the elbow of the curve, where distances start to level out. The elbow indicates the appropriate number of segments to select the problem. On the other hand, the knowledge of the product or service is also needed to make the appropriate determination regarding segments. 

For each segment, it shows the number of respondents, its average utility function and the relative importance of each feature, then by using the relative statistic, it makes easier to identify the type of the important features. For quantitative features like price, we also show the quantitative model, the vector and ideal-point coefficient.

The refinement procedure assumes that each segment has its own multivariance normal distribution, including its own covariance structure.

Respondents are assigned to the group to which they are most similar. Initially, the mean and covariance matrix are derived from the cluster solution. Subsequently, they are recalculated based on the new group formed by reassignment of observations. At each stage of the iterative refinement procedure, the likelihood that each respondent belongs to each group is calculated. This probability is modified to reflect the size of each group. The procedure performs a quadratic discriminant analysis at each stage and then uses the discriminant function and Bayes rule to assign observations to each group.

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