Topography is a turnkey solution created to explore a number of entities, such as brands, that can be rated on a list of attributes. This methodology gives you an important understanding of consumers' perceptions of each entity, perceived similarities between them, each entity's most differentiating attributes, and a visual comparison of ratings among the entities in aggregate as well as individually by each attribute.
Star Rating vs. Sliders
You'll need to choose whether you want respondents to rate each brand with stars (the default mode) or sliders. If you choose star rating, you can use 5, 7, or 9 stars for each attribute. Choosing sliders gives you more flexibility — you can choose from the library of pre-written Likert scales, edit the answers, write your own custom answers, and even adjust the scores from 1 to 99 (provided automatically when an answer is chosen). By default, the platform assumes 10 points for one star or the lowest answer on your Likert Scale, going all the way up to 50, 70, or 90 for a top rating.
⚠️ Note: If you decide to edit the Likert scale or adjust the scoring, make sure you know exactly what you're doing, since it may drastically affect your model and data visualization.
Grouped by Entity vs. Grouped by Attribute
The last important decision is how to group your two lists. By default, they'll be grouped by entities (or brands, in our example). That means each brand will be presented as a separate question, with attributes listed as sub-questions below. This grouping may be easier on respondents since it helps them activate memories of all their experiences with a given brand or entity, enabling them to rate each by all attributes you're testing.
If you switch to "group by attribute," each question will ask about one attribute at a time (such as "Food healthiness") and will contain all compared brands on the page. It might introduce a higher cognitive toll on respondents, since they'll have to access more memories across brands to answer each question. Even so, in some cases this might be more valuable since it helps focus attention on comparing all brands across a given attribute.
⚠️ Note: When grouping by attribute, the experiment will take as many questions as there are items in the list you're presenting.
Results
On the Results page, there are several ways to view your results. We use Bayesian Averages (BA) to calculate the following:
- Perceptual Map — treats each attribute as a separate dimension or axis in a multidimensional space. It looks for a balance among all the forces and positions your tested entities in relation to one another within this space.
- Topography view — an interactive 3D model of the perceptual map, where score averages are expressed as height. Great for visual presentations to represent your data.
- Quadrant view — visualizes the aggregate data on a simple graph to compare entities. Customize your plot view by clicking the respective dropdowns to assign an attribute to the x-axis, y-axis, size of dot, and color of dot.
- Excel exports — contain the raw data for Topography results.
Respondent View
See how this question type looks for respondents.