Historically, capturing diagnostic insight in a standard concept or pack test involved exposing respondents to stimuli and then asking them what they liked and disliked about the offering via open-ended questions. While this approach certainly has its merits, it requires respondents to enter intelligible verbatims which then have to be coded to quantify the understanding. Alternatively, verbatim responses are frequently converted into word clouds, which can be somewhat misleading as they are based solely on word count and do not account for context.
In recent years, more technologically advanced and effective tools have emerged to provide more efficient and robust diagnostic insight. Heatmapping is one of those solutions that has increased in popularity because it synthesizes perceptions in an aesthetically pleasing format that is easily understandable to seasoned researchers as well as market research novices.
Originally, heatmaps were a custom offering, meaning that they took several hours (if not multiple days) to process and generate data. Toluna recognized the need for faster diagnostic insight and integrated automated heatmapping capabilities within our research platform. Users of the platform can swiftly and easily upload concept or pack images and then launch a survey to our online panel of 30 million members to quickly collect and distill consumer insight on their offering. Once the survey is live, clients receive immediate access to our online data viewing tool that allows them to view the heatmap data in a few different ways:
- The image is overlaid on a 10×10 grid and the clicks are tallied in each of the blocks, with the frequencies displayed in each block.
- The number of clicks corresponds with a color gradient, so the more clicks an area receives the more red it becomes.
- We can gain consumer insight on specific likes, dislikes, or areas that they find eye-catching or even confusing.
In addition to providing clients with a swift, high-level view of a concept or package, the heatmaps are more visually appealing than word clouds and are more cost-efficient than open-end coding.
To illustrate the effectiveness of heatmaps, we recently ran a parallel study in which half the respondents were exposed to an image and were then asked to type in everything they liked about the image. The other half were exposed to the same image but were asked instead to utilize the heatmapping tool and click on the areas of the image they specifically found most appealing.
Our sample was composed of 478 US consumers. 234 completed the heatmapping exercise and 244 answered the open-ended question. The sample was balanced to nationally representative age and gender quotas.
We created a word cloud from the open-ended responses and compared this to heat map output.
If this is the only tool we had to rely on, it would be difficult to understand what respondents actually like about this image. It seems as though respondents may have liked the child and the message on the chalkboard, but that isn’t rapidly clear or certain.
In comparison, there is no doubt when utilizing the heatmapping approach as to what is driving appeal for the image. A quick look confirms our hypothesis from the word cloud that respondents like the message on the chalkboard. However, the blackboard is covered in writing, and the heatmap reveals that respondents find the “Say No To No” claim most appealing, with attention concentrated on the first “No”. We were not able to derive this level of insightful detail from the word clouds.
Therefore, in addition to being time and cost-efficient, heatmaps also provide a more precise answer about sources of appeal or areas of enhancement for your offerings. Ensure that you are gleaning the maximum level of insight. Modernize and expedite your package and concept testing process with heatmapping from Toluna. Connect with a member of our team today.
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