What Is Perceptual Mapping, Anyways?

By Kelsey Howell

In 1949, E.L Kaufman, M. W. Lord, T.W. Reese, and J. Volkman published a research paper titled “The Discrimination of Visual Number” concluding that the brain cannot quickly read groups larger than four. Every time we see more than four items, we have to start manually counting.

In survey research, we often try to understand the behavior of groups much larger than four. To do this, we use the common language of percentages. The total number of people shown each question are expressed as 100%, and any smaller subgroups are split from there. 450 out of 800 is unintuitive; 56% easily scans as “somewhat more than half.”

Raw percentages, however, fall flat when trying to compare performance across different metrics. What is a better than average score? What is a worse than average score? These concerns are very relevant to marketing intelligence, especially when trying to assess brand performance against competitors or determine areas of strength or weakness.

For these purposes, we use perceptual mapping:

Perceptual mapping is a survey analysis technique that compares performance in two different traits in relative, rather than absolute terms. That is, it compares results relative to other results.

W5 uses statistical indexing to design perceptual maps. The average percentage on a measure, such as brand awareness, is set as the index, or reference point, and all others are measured proportionally against it.

Mathematically, the index is set = 100, with all other values being turned into proportions of 100. An index value of 115 means that a given percentage is 115% of the average. An index value of 85 means that a given percentage is 85% of the average. To understand the mathematical methods used to calculate these values, see the footnote1.

Once calculated, these can be graphed along a number line. Let’s look at a simplified example with only three brands:  

Brand B performs below average in brand awareness, Brand C performs above average, and Brand A performs roughly equivalent to average.  

This is useful information on its own, but it can become even more powerful when paired with a brand image metric:

Positive x/y values represent above-average performance, while negative values represent below-average performance.

From an analysis point of view, we can see that Brand C is perceived as a strong, well-known brand, while Brand B and A struggle with awareness and positive regard relative to their competition.

In this way, perceptual mapping mirrors the comparative choices consumers and businesses make every day. Whether a brand has 88% positive image or 95% positive image is less important than how those traits stack up against the market as a whole. Perceptual mapping shows the heart of the matter.  

It is also versatile in that perceptual maps can also be set up to interrogate an individual brand, looking at how it performs on the traits that matter most to consumers:

Or they can be used to understand positioning rather than performance, delivering insight into messaging approaches and/or segmentation. The following map suggests the classification of different vacation offerings: 

The primary limitation of perceptual mapping is that it obscures the true prevalence of a particular brand, product, or attribute. For example, in the earlier brand awareness map, all brands depicted had awareness and positive brand image above 50%. Thus, a “weak performer” may still be doing well enough to maintain an aware and loyal audience. Coupling perceptual mapping with more traditional percentage data can give context for both relative and absolute performance, creating a complete market understanding. The “weak performer” might be doing okay, but they could also be doing better. Perceptual mapping allows us to see potential for growth and change. In this way, it is invaluable.

For other breakdowns of concepts used in survey research, read “Sample Size and Soup” and “Conjoint Analysis 101.”

1The following formula will produce an index number relative to the base:

(Original Percentage / Percentage Being Used As Index Base) * 100

Example: Brand A awareness is 80%, Brand B awareness is 65%, Brand C awareness is 90%. The average awareness is 78%. To find the index value of each brand relative to the average awareness:

Brand A: (80% / 78%) * 100 = 102

Brand B: (65% / 78%) * 100 = 83

Brand C: (90% / 78%) * 100 = 115