Perceptual mapping has been used as a strategic management tool for about thirty
years. It offers a unique ability to communicate the complex relationships between
marketplace competitors and the criteria used by buyers in making purchase decisions
and recommendations. Its powerful graphic simplicity appeals to senior management
and can stimulate discussion and strategic thinking at all levels of all types of
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.
Perceptual mapping involves two steps: (1) data collection and (2) data analysis and
Among the various mathematical and statistical methods used to produce perceptual
maps, POPULUS has found—and published research to this effect—that multiple
discriminant analysis provides the most reliable methodology. Among the reasons for
1. Discriminant analysis has a close linkage between product points and attribute
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.
6. POPULUS research [Fiedler, 1996] has shown that DA was more successful that
Correspondence Analysis at reproducing a known map when the data were
distorted in various ways.
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.POPULUS - 2 - Perceptual Mapping
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
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.
Research by POPULUS has shown that the first alternative provides for a greater
correlation between discrimination and importance.
Data Analysis and Presentation
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
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 discriminant analysis finds the optimal weighted combination of all the
attributes which would produce the highest F ratio of between-product to withinproduct variation. That weighted combination of attributes becomes the first
dimension of the perceptual map
Name : Varun Deshpande
Marketing Group 6