By Andrew Dalglish - 14th November 2016
Which attributes most closely define your brand? Which other brands are challenging you for this space? How differentiated is your brand and where might there be white space?
These are all critical questions for a marketer or indeed anyone managing a brand, but answering them isn’t always straightforward, as the following example illustrates. It’s from a real-life study of how buyers of corporate IT systems perceive different vendor brands.
In this survey we asked buyers whether they associated our client’s brand and thirteen competitors with ten attributes which previous qualitative research had identified as important in this market. For each attribute, their answer could either be that they did associate the brand with it, or they didn’t.
Here’s what we found. Each cell shows the percentage of respondents stating that they associated the brand (row) with the attribute (column):
This illustrates three problems with this kind of data.
First, it’s overwhelmingly complicated and hard to make any sense of (bear with me though as it will soon become much more palatable!).
Second, there’s what’s referred to as a ‘brand dominance’ or ‘halo’ effect at play where high profile, well-liked brands outperform on all attributes. This isn’t necessarily because they ‘own’ these attributes, but simply that their prominence or generally positive sentiment means that they’re felt to perform well across the board. If we heat map our table on the basis of relative association with each attribute it becomes clear that brands N is demonstrating this effect as it scores higher than all other brands on all attributes.
And third, there’s also an ‘attribute dominance’ effect where some attributes are associated with many brands. This is especially prevalent in markets where there is a strong category stereotype, e.g. ‘reliability’ is an attribute associated with many banks. For example, attribute 3 shows this effect.
Correspondence Analysis (also known as Brand Mapping) is a valuable tool to address these issues. It simplifies the data, presents it in an easier to understand visual format and at the same time corrects for the brand and attribute dominance effects.
To achieve this, Correspondence Analysis calculates the percentage of people that should be expected to associate a brand with each attribute. It then flags instances where a brand scores markedly lower or higher than would be expected and in doing so removes the brand dominance effect.
Take brands M and N in our example. On attribute 10, brand N scores higher than brand M (75% versus 51%). However, we’d expect that as brand N regularly sees scores >70% whereas it’s very unusual for brand M to score in the 50’s. As such, Correspondence Analysis would flag that attribute 10 is a key defining feature of brand M, but is probably just noise for brand N.
The same exercise is then performed for attributes to remove the attribute dominance effect. How often would we expect brands to be associated with this attribute, and in reality, does the frequency of association match this expectation?
Having removed the dominance effects, the final step is to express the data in an easier to understand format. To do so, a Brand Map is created which visually represents how different brands are positioned relative to each other and relative to different attributes. There are various ways of visualising a brand map, but the ‘moon plot’ style used in this study is one of the clearest.
This map provides a wealth of information in a relatively straightforward manner.
Attributes are shown outside the circle. The larger the font size, the more powerful this attribute is in distinguishing brands from each other. We can see that attribute 2 is an especially strong differentiator.
Brands are shown within the circle and a brand’s position is important in three respects:
Brand maps like these are invaluable tools for those tasked with managing a brand. Just like a General surveying the battlefield, they’re able to instantly see where their brand is positioned, identify threats to that territory and spot opportunities to ‘flank’ competitors.
Read more about our approach to business-to-business (B2B) branding research.
Andrew has specialised in B2B research for over a decade and co-founded Circle Research in 2006. He is a columnist for B2B Marketing Magazine, a regular contributor to Research Live and frequent speaker at leading events such as the B2B Leaders Forum, Customer Experience Live and the Social Media World Forum. Andrew is a Chartered Member of the MRS, teaches the MRS B2B research course and holds an MA in Psychology from Aberdeen University alongside an MSc in Marketing from Strathclyde University.