Competitive Topography
Solution
required
Insights
Strategy
Landscape
Many different attributes make up consumers' attitudes towards any given brand, but which ones really set your brand apart from others? Competitive Topography produces two perceptual maps to identify which brands are seen similarly and why.
- Multidimensional Scale view
This perceputal map places entities and attributes in space, where their relative positions to each other indicate similarity and dissimilarity. - aytm's Topography view
By adding the third dimension of scores, you can explore the hills and valleys on which attributes and entities lie. - Quandrant view
Pick two attributes to serve as x- and y- axies to focus in on how each entity scored.
Competitor analysis
Compare your target product or your overall brand with those of your competitors. As implied by the name, this is the most traditional use.
Portfolio analysis
Curious if your luxury items are viewed differently from your premium ones? Test them against each other to see how they are perceived.
Concept testing
Pit different concept ideas against each other to get a sense for if any really stand apart—and if they happen to stand apart for the right reasons.
It is fairly simple to ask respondents how well an attribute describes a given brand. Looking at the direct results of that question set—such as the Top 2 Box % for each attribute and each brand—can leave you feeling a little lost. Which attribute should you be focusing on? What if they're all important in their own way? How do you combine these results into a meaningful story?
Competitive Topography applies advanced analyses to this data, creating perceptual map visualizations that help you understand the relative nature among the attributes, the brands, and each other. From this perspective, you can easily see which attributes may define your brand from others and which set of brands are seen as relatively similar.
- Precede the Competitive Topography question with a pseudo-screener.
Only have respondents rate items they're familiar with so as not to muddle the data. - Only include attributes which are relevant to all items.
If an attribute may be “not applicable” to some items, drop it from the analysis to keep the results clean.