Discriminate analysis, a multivariate technique used for market segmentation and predicting group membership is often used for this type of problem because of its ability to classify individuals or experimental units into two or more uniquely defined populations. The discriminant score is the basis for predicting to which group (a purchaser of the manufacturer’s brand or a competitive brand) the particular individual belongs. The discriminant weights of each predictive variable (age, sex, income, etc) indicate the relative importance of each variable. For instance, if age has a low discriminant weight then it is less important than the other variables.

With this information, a classification matrix can be developed that indicates the accuracy of our model that will be used to construct our map. For instance, if our discriminant model correctly classified 95-percent of users of our brand, then only 5-percent were incorrectly classified. Conversely, if the model correctly classifies 92-percent of the competitive brand users, then only 8-percent were incorrectly classified. We consider this a strong model because the number of correct classifications is much higher than what might be expected by chance.

Other Applications of Discrimant Analysis

While our example illustrated how discriminant analysis helped classify users and nonusers of salon brand hair care products based on independent variables, other uses of discriminant analysis include the following:

Product research – Distinguish between heavy, medium, and light users of a product in terms of their consumption habits and lifestyles

Perception/Image research – Distinguish between customers who exhibit favorable perceptions of a store or company and those who do not

Advertising research – Identify how market segments differ in media consumption habits

Direct marketing – Identify the characteristics of consumers who will respond to a direct marketing campaign and those who will not

Conclusion

While discriminant analysis is often used in marketing research for marketing segmentation and predicting group membership, there are more powerful and accurate techniques available. We invite you to learn more about our solutions by contacting us today.

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Author of the Article – Ajay Khatwa(13123)