Surveys > Results > Heatmap

Available for: Admins ✅ Managers ✅

What is the heatmap? 

The heatmap is a color-coded chart that instantly draws your attention to the highest and lowest scores. 

To show you where to focus your efforts, the heatmap allows you to compare the results of different teams (e.g. values of the same attribute) side by side at the end of your surveys. 

ℹAn attribute is a characteristic of your employees, which allows you to group them together to analyze your results from different angles. To learn more about the use of attributes, see our article Setting up Attributes.

Heatmap visualization

Adapt the heatmap to your needs! 

Select data

In order to target your analysis, you have the possibility to filter the questions and scores you want to see appear or even sort the columns by score. 

Adjust the display

You can pin columns, change their width or hide some columns. 

Not all columns fit on your screen?

Scroll the heatmap to the left, use the 'full screen' button or export your heatmap directly to Excel!

Protecting respondents confidentiality

In order to protect the confidentiality of respondents, some averages will not be displayed. They can be identified by the protection icon 🔒.

We hide the averages of the groups for which there are not enough respondents as well as those that are too similar to the parent group (within a few individuals, the same people are surveyed).

For example, within the technical team, 10 coworkers responded to the survey, including the answers of 9 developers and one coworker within the product team. You will have access to the technical team average, but the averages for the developer and product teams will be hidden. This is to avoid compromising the confidentiality of the coworker in the product team by doing a simple calculation.

Heatmap colors

The heat map is designed to highlight which populations are doing best and which need the most attention for a given issue. 

Averages above 4 are considered good scores and are highlighted in green.

Averages below 3.5 are considered poor scores and are highlighted in red. The red background is even more accentuated below 2.5 in order to reveal the subjects and populations that constitute alerts.

For more information, see our article What is a good score or a bad score? 

Filtering results

The view displayed by default corresponds to the entire scope to which you have access; that is, the entire organization for an administrator and the corresponding team for results viewers.

From this view, you can compare the results according to the available demographic and organizational data by selecting the attribute you are interested in in the field 'compare by'.

Population filter for administrators

With an administrator profile, you also have the possibility to zoom on any population in the drop-down list

For example, you may want to understand how newcomers feel in the different departments of your organization. To do so, after selecting in the population field "0-1 seniority years", compare by department. 

You will then be able to quickly view the results of newcomers across the entire company, compared to the company's average score for newcomers.

How to analyze your results?

We recommend that you identify a maximum of three analysis dimensions before producing your heatmap. 

Determine which dimensions will make the most sense for your analysis. For example, you may find it useful to produce heatmaps according to departments, functions, or even places (countries or cities). 

In order to facilitate your reflection, you can start by asking yourself these questions: 

• What are the populations for which you will be able to set up an action plan?

→ This question will help you choose your dimensions of analysis! 

• Within this demographic distribution, are there groups whose averages stand out as particularly high or low?

→ And hop!  You have just started your analysis. 

Be careful, however, not to over-interpret the results. Sometimes, other criteria (external to your Supermood attributes) come into play in the analysis and help explain a score (country culture, different work codes, etc.). 

For example, let’s assume the HR department is happier than the other departments. If all the coworkers from the HR department are in France, and the other departments are spread over several countries including France, maybe the happiness is linked to the country, and not to the department. 

If you have the slightest doubt about the interpretation of the results, our team of experts will be happy to help you on this subject. 

Do not hesitate to contact your account manager!