Leapsome offers you various views and filter possibilities to make the most of your survey results. You can find out more about **filter options and views ****here****.**

Just a note that Analytics (except for the 'Participation Rate'), are based on the 'snapshot' taken when the response was given. Therefore any changes made in a user's profile settings during the course of the survey round will not presently be indicated in the analytics. Our product team are currently working on a feature to allow for survey data filtering based on the latest data of users. This is currently the case with the 'Participation Rate' which reflects updated user profile settings in real time.

Below we collected some best practices on how to interpret the survey results based on our analytics section:

## Insights in the list view

In our survey module, you will see results showing the average on the 10-point scale and the change since the last round. If you are using our best-practice questions / question categories, you will also see a benchmark. The** score** displayed is always the most recent statistically valid score from your survey for that topic or question.

You can deep-dive into any category or question by clicking on the topic name or the corresponding question. This will provide you with additional insights, with any **comments** linked to the topic or question being visible. The **timeline graph **will also show previous scores if (and only if) they have passed thresholds for statistical significance and anonymity. The grey line within the timeline graph represents the average score over time.

The **bar chart** also provides more granular details displaying the distribution of scores. It uses green for scores 9/10, grey for scores 7/8, and red for 6 and below. This is similarly replicated for questions with a 1-5 scale, where green represents a 4/5 score, grey a 3 score, and red for scores between 1/2.

This way you can link changes in employees answers to certain question over time, e.g. to any measures you took to improve this or to any other external events. If you for example launched a new initiative to allow for more flexible working hours, you can track how this influenced the evolution of the survey results.

## Insights in the Heatmap

Next to the timeline graph, the heatmap gives you great insights into your survey results. The heatmap gives you a helpful **hint on which areas you need to pay the most attention to**. It shows you the segmented results in a visual way that is easy to understand and then make strategic decisions from.

If you for example see that one team is doing really well on a certain topic, it might be worth talking to team members and managers about what it is they actually do and whether this is something that can be scaled to other teams. Below the average score, you also see a **standard deviation** and the **NPS** score for the specific question / topic.

You can find more on the segmentation and filtering options and logic you have here.

## Understanding the Standard Deviation

The standard deviation is a statistical measure of how spread out numbers are. Low standard deviation indicates that the values tend to be close to the mean, i.e. everyone that answered the question is of a similar opinion. If you for example have a standard deviation of 4 and a mean of 6, this means some people may have responded with a 2 and others with a 10, whereas a standard deviation of 1 with a mean of 5 means that all answers were e.g. between 4 and 6.

Therefore finding out which questions had a high standard deviation will show you **which topics polarize** your employees more. If a question has a very high standard deviation, meaning people are not unanimous about it, you could e.g. see whether this question received any written comments that help you understand why opinions diverge.

## Understanding the eNPS

The NPS, or in Leapsome's case the Employee NPS, is an established metric for measuring satisfaction with a product, service, employer, etc. As with any other metric, you need an understanding of the basic characteristics of the metric (e.g. range) and, if necessary, corresponding comparison groups to know what a certain value means. The NPS cannot easily be converted into other metrics that are more familiar and easier to understand (e.g., %). Therefore, it's useful to know the basic properties:

- How it's calculated: an NPS score is calculated by subtracting the percentage of 'detractors' (people giving a rating of 0-6) from 'promoters' (people giving a rating of 9-10). People who give a rating of 7 or 8 are not counted, as they are considered neutral. For example, if 50% of your employees rate a 9 or 10 on a question, 10% rate a 7 or 8, and 40% rate 0-6, you would subtract 40% (the detractors) from 50% (promoters). Meaning your NPS score would be 10.
- Range: The NPS can lie between
**-100**(which would be the case if every individual gave a negative answer) and**+100**(which would be the case if every individual gave a positive answer) - Interpretation: Most companies
**consider a value >0 to be okay**because this means that there are more promoters than critics/detractors. From 50 on, the score can already be considered a very good result. - For an even more precise interpretation, compare the score with
**relevant**benchmarks**for your company size.**

Example: If you achieve an NPS score of 60 for the question "I would recommend [my company] as a great place to work", this means that there are many more promoters than critics/detractors among your employees and that your employees on average believe that your company is a very attractive employer.

You can read more about the theoretical background of the eNPS here. Another resource that can help you to dive deeper into the perks of measuring the eNPS is this Blog.

(Please keep in mind that Leapsome calculates the NPS score for each question or topic automatically but that the score always needs to be interpreted in relation to the content of the questions - not in all cases does the NPS score provide information.)

## Comments

The comment section shows all the (anonymous) comments made to specific questions. If you want to learn more about what a specific user meant, you can start an anonymous conversation with them. Leapsome's machine learning-based sentiment score automatically identifies whether a comment was positive, neutral, or negative and thus provides a bubble chart showing you which topic got the most comments and what their sentiment was.

This way you will easily understand how your employees feel about certain topics. If one topic received a lot of comments, this is a topic that your employees feel strongly about. The graph then shows you whether most comments would be positive, neutral, or negative, so if you had a large bubble tending to the negative side of the spectrum, this would be worth investigating.

You can also filter and segment comments if you want to deep-dive into what an e.g. specific team said.

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