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

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

## Insights in the list view

If you are using our best-practice question packs, you will see results for those questions showing the average on the 10-point scale, the change since the last round, as well as a benchmark.

The** score** displayed is always the most recent statistically valid score from your survey for that topic or question. Similarly, the **timeline** **graph** shows 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.

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, including the distribution of the score and any comments linked to the topic or question.

This way you can link to changes of on how employees answered a certain question over time, e.g. to any measures you took to improve this or to any other external events.

## Insights in the Heat Map

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

Below the average score you also see a **standard deviation** and the **NPS** score for the specific question/topic.

## 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.

## Understanding the eNPS

The NPS, or in Leapsome´s case the Employee NPS, is a separate, now 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. NPS cannot easily be converted into other metrics that are more familiar and easier to understand (e.g. %). Therefore, you have to know the basic properties:

- Range: The NPS is between
**-100**(when each individual person is critically adjusted) and**+100**(when each individual person is positively adjusted) - Most companies
**consider a value >0 to be okay,**because this means that there are more promoters than critics. From 50 on, you can already speak of a very good NPS. - For an even more precise interpretation or classification you usually have to
**research the corresponding benchmarks for your industry.**

So if you have an NPS of e.g. 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 among your employees and that you are a very attractive employer.

You can also read more about the theoretical background of the eNPS here.

## Comments

The comment section shows all the anonymous comments made to specific questions. If you want to learn more on what a specific user meant, you can start an anonymous conversation with them. Leapsome´s machine learning based sentiment score automatically identify whether a comment was positive, neutral or negative and thus provide 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.

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