Today we had a lecture on perceptual mapping which helped us understand how the market researched data can be analyzed to get some logical conclusions. Below is a brief on the above topic.
Introduction
Perceptual mapping is a market research technique which helps us in mapping the perceptions of the objects in a diagrammatic manner which in turn helps us understand the relation between the positioning of researched object in market. It also helps us to take a broad view of the properties of the product compared with its peers. This helps us strategize as to what our next step should be.
The above technique has been in existence for over twenty years now. It is one of the few advanced multivariate techniques whose popularity has not been affected much over these years. This technique uses different algorithms to generate the outputs which are discussed below. Below are some of the algorithms that are generally in use for perceptual mapping
Discriminant analysis
It is one of the most popular algorithms in use today for applied multivariate mapping. The inputs to discriminant analysis consist of individual respondent ratings of products across attributes. The basic assumption here is that the rating scales are continuous and normally distributed. It is much like regression analysis in that it uses a least squares approaches in an attempt to fit linear models to the data. However, the dependent variable is nominal. That is, for mapping purposes, the dependent variable is the product being rated.
Thus, each product rated by each respondent is an input record, so if a respondent rated five products, which generate five input records. Discriminant analysis then calculates the coefficients to a set of standardized linear equations, called discriminant equations, which explain the differences between the product ratings. Or, said a different way, explains the variance between product ratings.
These algorithms provide a variety of useful statistics to the researcher, such as Eigen values to show you the variance explained by each equation, tests of significance for each equation, multivariate F statistics to show the significance of the group differences, and correlations between the discriminant functions and each attribute variable.
R-Type Factor Analysis
It is seldom used as a mapping procedure in today's MR Field. There are a few empirical studies though that shows it is superior to discriminant analysis. Although you have the same problems with what to do about missing data and selecting the relevant set of variables as we have with discriminant analysis, this procedure overcomes two of the problems with discriminant analysis. All variables are shown on the map, and the inclusion or exclusion of products has no effect on the extracted dimensions.
The inputs to factor analysis are very similar to those for discriminant analysis, product ratings across attributes. However, an additional ingredient is required; we must also collect an importance rating from each respondent for each attribute. These importance ratings are the basis for developing the mapping space. The basic assumptions concerning the distribution and continuity of the rating scales should not be relaxed.
Factor analysis is an interdependence procedure, thus the various differences in product ratings is ignored until after the factor equations are derived. Product locations in the derived space are calculated by averaging the first two factor scores of that product's ratings to define the X and Y coordinates. Or alternatively, plugging the average product scores on each attribute into the two factor scores and calculating the X and Y coordinates.
Ex: - We evaluated a mobile handset perception map which consisted of comparing five different models and understand there positioning.
Submitted By
Arun Ramakrishnan Roll No - 12127 SIBM Bangalore
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