Employee survey: Evaluation & Analysis
From raw data to clear organizational insights in 6 steps.
- Step 1: Clean up the results and check the response rate
- Step 2: Review the overall results and individual questions
- Step 3: Segment by teams, locations, and groups
- Step 4: Segment by teams, locations and groups
- Step 5: Presenting and communicating results in a clear and understandable way
- Step 6: From analysis to prioritised actions
How to turn feedback into actionable insights – and automate the analysis process.
Learn step by step how to turn the raw data from your employee survey into reliable insights and uncover organizational signals instead of simply collecting numbers.
Step 1: Clean the results and check the response rate
Before you begin the actual analysis, it is worth taking a closer look at the quality of the data.
Noticeably short completion times compared with the average may indicate questionnaires that were completed inattentively or according to a pattern. Review such cases individually instead of automatically excluding all fast responses.
With easyfeedback’s fake participant detection, you do not have to review every questionnaire individually: suspicious participations are automatically flagged, allowing you to identify potential fake responses at a glance instead of having to infer them manually from completion times.
Next, assess the response rate: A rate between 30% and 50% is already considered representative, while around 70% or more is generally regarded as near-complete coverage.
Also compare the response rate by department or location. Significant differences can already be a signal in themselves — for example, a recent change in leadership, high levels of sick leave, or a large number of open positions in the respective unit.
Step 2: Review the overall results and individual questions
Start by looking at the overall result before turning to individual questions.
For scale-based questions, it is worth looking beyond the average: A score of 3.8 on a five-point scale can result either from mostly neutral responses or from a clearly polarized workforce. Both may produce the same average, but they indicate very different needs for action.
The following metrics are therefore more meaningful:
- Top-2 Box: Share of respondents who agree (the two highest scale values)
- Bottom-2 Box: Share of respondents who disagree (the two lowest scale values)
Only the distribution of these values shows whether there is a genuine need for action or whether the workforce is simply predominantly neutral.
Step 3: Segment by teams, locations, and groups
The most valuable insights usually emerge through segmentation. Compare:
- Vertically: one subgroup (e.g. a department) against the overall result
- Horizontally: two subgroups at the same level against each other (e.g. Sales vs. Purchasing, Location A vs. Location B)
Keep in mind that different group sizes carry different weight: In a department of three people, each individual response has a much greater impact than in a department with 30 employees.
When segmenting the results, also make sure to apply the same minimum group size that you previously agreed internally — for example, with the works council — to ensure anonymity. You can find out more in our article on involving the works council in employee surveys.
Step 4: Qualitatively analyze open-ended responses
Open-ended text fields often provide the most valuable input in an employee survey — but they are also the most time-consuming part of the analysis.
A proven method is to create thematic clusters: Assign comments to topic areas (e.g. leadership, collaboration, workload, professional development) instead of looking at each comment in isolation. A word cloud can also show at a glance which terms are mentioned most frequently.
When analyzing open-ended responses, pay particular attention to identifying information (e.g. names or details that clearly point to a specific person) and remove it before sharing the responses with those responsible for the analysis. Open-text fields are especially sensitive when it comes to anonymity.
When done manually, this is by far the most time-consuming part of the analysis: Every comment has to be read, assigned to a topic, and incorporated into an overall assessment. With the AI-powered text analyses in Result STUDIO, this process is automated. We will show you exactly what that looks like in the next section.
Step 5: Present and communicate the results clearly
Not every stakeholder needs the same level of detail: Executive management and department heads, for example, generally require different levels of analysis.
The following formats have proven effective for presenting results:
- Pie, column, or bar charts for closed-ended questions
- Word clouds for open-ended questions
- Comparisons with previous surveys to highlight trends rather than just snapshots
Communicate the results promptly instead of waiting several months, and avoid unnecessary technical jargon. A statement such as “50% of respondents are above this value and 50% are below it” is easier to understand than an unexplained median value.
Since not every stakeholder needs the same level of detail, easyfeedback’s Result STUDIO allows you to create different reports for different target groups from the same survey — without having to prepare the raw data separately multiple times.
AI-powered analyses in Result STUDIO
The Result STUDIO is easyfeedback’s dashboard tool: You can import results from any survey, compare and contrast them, and create different reports for different target groups.
At the core are the AI-powered analyses: They automatically interpret the results, turning an evaluation that would take several hours manually into insights available within seconds. If you change the data basis in the report using filters or comparisons — for example, by focusing on a single department — the AI analyses automatically run again and provide a new interpretation tailored to that specific data set. Manual analysis simply cannot match this speed.
For text responses:
- Sentiment analysis: Who responded positively, neutrally, or negatively?
- Summary of all text responses: a quick overview without having to read every comment individually
- Topic list: a percentage-based breakdown of the topics that received feedback
For all questions, both qualitative and quantitative:
- Summary: an overview including the overall sentiment
- Top 5 / Low 5: the five greatest strengths and weaknesses
- SWOT analysis
- Recommended actions including the identified signal, a specific measure, and a suggestion for how to measure success
Step 6: Turn the analysis into prioritized actions
The AI analyses already provide you with specific recommended actions, including the underlying signal and a way to measure success. If you have several recommendations at the same time, the next question is prioritization — which action should be implemented first. We explain how to approach this systematically in a separate article: Recommended actions & action planning with the ICE Score.
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