On your data

Mapping brand / product positioning

What you put in:

A set of overall brand (or product) evaluations, measured on the same set of people. Specifically, you provide a table with a column for each brand you've asked a group of people to rate, and a row for each person's answers. Usually these brands will all be from the same industry sector. In addition, often a small set of "signpost" variables - key demographic, psychographic, media use, or other behavioral measures - will be included to aid in interpretation. CorrExplore works best with between 10 and 50 variables, and it usually takes a little trial and error to figure out which variables you really want to include.

How it builds the map:

Unlike perceptual mapping techniques, the resulting map is not built from any preconceived ideas about the attributes consumers ought to use to judge brand similarity. Instead, it's simply based on correlations between the brand evaluation variables you provide. Clusters of brands in the map indicate brands that go together not on some fixed attributes assumed to be important for brand evaluation, but in terms of actual brand evaluations themselves. This kind of map provides an overview of real brand preference space. More technical details here.

What it communicates

In general, brands that are near each other are similar. However, it's important to dig deeper and ask why each brand is where it is. Any visual map is necessarily an imperfect representation of the true underlying network of correlations between brands. This is why CorrExplore's interactive and animated features are so important. These features are designed to communicate the correlation network instead of oversimplifying it like cluster analysis, factor analysis, or other mapping techniques that produce still images.

Interact with the map to discover:

How different sets of people see brand space differently (by using segmentation).
How brand space is changing over time or in response to news events, your own marketing efforts, or the efforts of competitors (by using segmentation on a time variable).
Why brands are positioned where they are (by hovering your mouse over them to see their correlations; non-interactive maps can't do this because they'd have to show all correlations, making the display too cluttered).
An overview of relationships that suggests ideas for other multivariate analyses you might not have thought of.

Brand demo data coming soon

Just like the political space demo data built into the free version, we're hoping to soon have a demo data set of brand positioning space.

Visually explore correlation networks