Perceptual Mapping

Perceptual Mapping

Perceptual mapping is also known as multidimensional sealing. It is a graphics technique and is a crucial strategic management tool that attempts to visually display the perceptions about different interrelationships of objects. It offers a unique ability to communicate the complex relationships between variables like marketplace competitors and the criteria used by buyers in making purchase decisions and recommendations, IQ of employees, various traits of people, position of a product, product line, brand, or company relative to their competition. etc.

Perceptual mapping can be used to plot the interrelationships of consumer products, industrial goods, institutions, as well as populations. Virtually any subjects that can be rated on a range of attributes can be mapped to show their relative positions in relation both to other subjects as well as to the evaluative attributes. Perceptual maps may be used for market segmentation, concept development and evaluation, and tracking changes in marketplace perceptions among other uses.

There are two methods of perceptual mapping:

Overall Similarity Method: This is used to identify how similar or how dissimilar the traits of the object are. The attributes are identified and then they are mapped. The advantage of this method is that hidden attributes are exposed. But one must have a thorough knowledge about the objects in question to be able to apply this method.

Attribute Based Method: Here the perception is mapped based on the attributes. A disadvantage of this method is one might miss out on an important attribute or may not specify an important attribute.

Perceptual mapping involves two steps: (1) data collection and (2) data analysis and presentation.

Among the various mathematical and statistical methods used to produce perceptual maps, it has been found—and published research to this effect—that multiple discriminant analysis provides the most reliable methodology. Among the reasons for this are:

1. Discriminant analysis has a close linkage between product points and attribute locations.

2. Discriminant analysis maps do not change if attributes are added that are linear combinations of those already present in the perceptual space.

3. Discriminant analysis is alone in paying attention to “between product”information, after scaling it so that “within product” differences are equal for each dimension and uncorrelated. That means that DA uses a “yardstick” to give every dimension common metric (in terms of equal unexplained variance).

4. Discriminant analysis is the most efficient method in terms of cramming into a space of low dimensionality the most information about how products differ.

5. Unlike mapping based on distances or similarities, DA make use of attribute ratings, which are easy and natural for respondents, and useful for their content even if mapping is not done with them.

Employing this methodology, respondents are never asked about similarities among products or subjects; they are asked to rate products on attributes, and similarities are inferred from differences in respondents’ ratings.

The data required for perceptual mapping thus comes from rating scales where the subjects of the map, from products to populations, are described on the basis of selected attributes. The validity of the map depends on both the overall set ofattributes and the subjects of the study as well as the subset of attributes and subjects evaluated by each respondent.

Multiple discriminant analysis uses the “F ratio” to determine attribute and product or subject location in the perceptual space. The F ratio is a ratio of the variance between ratings of different products/subjects to the variance of ratings within products/subjects. In an attribute study, these variations among ratings are generally of two types:

1. The differences between products/subjects, revealed in the difference between average ratings for different products.

2. The differences within products, revealed in the differences among respondents’ ratings of the same product. An attribute would have a higher F ratio either if its product averages were more different from one another, or if there were more agreement among respondents rating the same product.

Perceptual maps can have any number of dimensions but the most common is two dimensions. A huge change in the objective function value, which shows the error estimate of the map, means the correct picture will be obtained when the map is viewed in 3 dimension.

Name: Dibyangana Saha

Roll Number 12132

Finance Batch of 2009-11,

SIBM Bangalore.