Perceptual mapping is a graphics technique used by asset marketers that attempts to visually display the perceptions of customers or potential customers. Typically the position of a product, product, brand, or company is displayed relative to their competition.
Perceptual maps can have any number of dimensions but the most common is two dimensions. Any more is a challenge to draw and confusing to interpret. In case its multi dimensional then its represented in two dimensions with distance between the objects being adjusted. Displaying consumers’ perceptions of related products is only half the story. Many perceptual maps also display consumers’ ideal points. These points reflect ideal combinations of the two dimensions as seen by a consumer.
A company considering introducing a new product will look for areas with a high density of ideal points. They will also look for areas without competitive rivals. This is best done by placing both the ideal points and the competing products on the same map.
Some maps plot ideal vectors instead of ideal points. The map below, displays various aspirin products as seen on the dimensions of effectiveness and gentleness. It also shows two ideal vectors. The slope of the ideal vector indicates the preferred ratio of the two dimensions by those consumers within that segment. This study indicates there is one segment that is more concerned with effectiveness than harshness, and another segment that is more interested in gentleness than strength.
Perceptual Map of Competing Products with Ideal Vectors
Perceptual maps need not come from a detailed study. There are also intuitive maps (also called judgmental maps or consensus maps) that are created by marketers based on their understanding of their industry. Management uses its best judgment. It is questionable how valuable this type of map is. Often they just give the appearance of credibility to management’s preconceptions.
When detailed marketing research studies are done methodological problems can arise, but at least the information is coming directly from the consumer. There is an assortment of statistical procedures that can be used to convert the raw data collected in a survey into a perceptual map. Preference regression will produce ideal vectors. Multi dimensional scaling will produce either ideal points or competitor positions. Factor analysis, discriminant analysis, cluster analysis, and logit analysis can also be used.
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 of attributes and the subjects of the study as well as the subset of attributes and subjects evaluated by each respondent.
Most studies suffer from too many attributes. Manufacturers and service providers see hundreds of ways in which their products and services differ—or might differ—from those of their competitors. Often the research analyst is unable to impose the discipline necessary to develop a reasonably short list of attributes. In most studies, it is usually desirable (or necessary) to select a subset of attributes for respondents to rate. This can be accomplished by using one of two approaches:
1. Select a subset of most important attributes. Each respondent rates all attributes on importance. The questionnaire is programmed to select a subset of the important attributes for rating. This may assure more meaningful questionnaires for respondents.
2. Randomly select a subset of attributes. The questionnaire randomly selects a subset of attributes for each respondent. This has the advantage that there will be roughly equal sample sizes for each of the evaluative criteria. The obvious disadvantage is that the respondent task may be less interesting.
Data Analysis and Presentation
Multiple discriminate 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. Multiple discriminate analysis finds the optimal weighted combination of all the
Attributes which would produce the highest F ratio of between-product to within product variation. That weighted combination of attributes becomes the first
dimension of the perceptual map.
An example of this is given below, employing attributes describing corporate activities,
such as “Manages operations safely,” Prepared for the future,” “Contributes to balance
of trade,” and “Old fashioned” among others.
This first dimension contains the most information possible for any single perceptual map, in terms of accounting for perceived differences among products/subjects, measured in terms of the variation among ratings of the same product.
Reference: http://en.wikipedia.org/wiki/Perceptual_mapping
http://www.populus.com/files/Perceptual%20Mapping_f_1.pdf
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