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The heatmap
Emilie Roze avatar
Written by Emilie Roze
Updated over 9 months ago

Available for: Admins, Results Viewers, Campaign owners

In this article :

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 side by side.

There are 2 types of heatmaps on the platform:

  • the survey-level heatmap - available in each survey report to easily view results,

  • the campaign-wide heatmap - to compare scores from one survey to another.
    💡Depart from the campaign, you'll be able to identify which survey it is through the "Asked on" column.

Adapting 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

All the columns do not fit on your screen?
Scroll the heatmap to the left, use the 'full screen' button, or export your heatmap directly to Excel!

Heatmap colors

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

The colors help to highlight:

  • good scores: in green ;

  • alarming scores: in red ;

  • critical scores: in accentuated red.

→ For more information: What is a good or bad score?

Filtering results

You can compare results based on the demographic and organizational data available to you. Thanks to our Heatmap comparison system, you'll be able to configure your heat map yourself to obtain relevant analyses through an intuitive experience.

Good to know:

  • In the heatmap, you'll find only filters and values with data available in the surveyed population, ensuring relevant results.

  • In the heatmap, you can select just one filter and up to 50 different values for a precise and efficient reading.

  • When you filter via the filter button at the top of your page, the heat map is automatically configured with the column corresponding to your filter.

Your heatmap is built as follows:

  • The first column of scores (External Benchmark) corresponds to the average score of a set of companies. It is up to you to define the comparison criteria: global (all companies combined), sector, or company size.

⚠️ It is made available if there are more than 1000 responses across at least 10 companies for the concerned question.

  • The second column of scores (Benchmark) corresponds to the company's overall results,

  • The third to the scores of the population selected in your filter (your scope if you have Results Viewer accesses),

  • The other columns vary according to the analysis filter selected in the 'Compare by' field.

ℹ️ Depending on your level of rights, you may be able to zoom in on a population for a more detailed analysis. 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 departments.

How to analyze your results?

⇒ Select your analysis criteria

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

Identify the filters that will be most useful for your analysis. For example, you may find it useful to produce heatmaps according to departments, job titles, or even places (countries or cities, etc.).

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

  • Who are the populations for which you can set up an action plan?
    → This question will help you choose your analysis dimensions!

  • Within this demographic distribution, are there groups whose averages stand out as particularly high or low?
    → And hop! You have just started your analysis.

⇒ Spot the weak signals

The grid format allows you to easily identify :

  • teams in overall difficulty (red columns),

  • the main subjects of tension (red lines),

  • and the local issues (isolated red dots).

⚠️ Be careful, however, not to over-interpret the results. Sometimes, other criteria 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 their satisfaction is linked to their location, and not to the department specifically.

If you have any doubts about the interpretation of the results, our team of experts will be happy to help you on this subject. Feel free to contact your Project Manager !

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