Perceptual Mapping Techniques
Perceptual mapping techniques are useful when we want to compare consumers’ perceptions of multiple competitors on multiple dimensions.
Two methods of perceptual mapping are: 1. Attribute Rating Method (ratings of methods on prespecified attributes) and 2. Overall Similarity Method (judgments on overall similarity of pairs of brands).
1. Attribute Rating Method
Ex. Dow’s Specialty Chemical Group used the attribute rating method to assess consumers’ perceptions of Dow vs. Competitors on 8 attributes:
1. Meets scheduled delivery dates
2. Practices innovation and development
3. Has fair pricing
4. Has consistent products
5. Provides support in solving processing problems
6. Has custom color capability
7. Provides adequate technical literature
8. Withstands high heat distortion temperatures
There are different sorts of mapping software that can be used to show multiple attributes in a two-dimensional space.
If two vectors point in the same general direction, this shows highly correlated attributes that probably convey one underlying idea (i.e. “relieves dryness” and “leaves skin feeling soft” convey the same idea).
If two vectors create a 90-degree angle, then it shows that one attribute is unrelated to the other (i.e. “relieves dryness” and “available in stores where you shop” attributes might form a 90-degree angle because they are unrelated to one another).
If two vectors point in opposite directions of one another (180-degrees), then it captures the fact that the market perceives one attribute trading off the other (i.e. if one is high, the other is low).
2. Overall Similarity Method
The overall similarity method is useful for analyzing attributes we do not verbalize well (such as tastes, odors or aesthetics). Specifically, for a given number of items, the respondent is required to rank the pairs of items from most similar to least similar (i.e. 1 = most similar pair and 15 = least similar pair).
A statistical method known as Multidimensional Scaling (MDS) can be used to create a map of the pair data. The map is created in such a way that the distance between the items being compared match-up with the rankings from the paired data (i.e. those pairs that are most similar are closest together). The statistical map allows us to eyeball the data and make inferences as to the reasons for certain items being more related than others (i.e. items sharing an educational component might be clustered together).