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πŸ“Š Understanding the AI Dashboard: Structure, Features & Use Cases

The AI Dashboard provides in-depth insights into survey data through modern AI analysis. This article explains the structure, key features, and how they support your decision-making processes.


πŸ” Structure & filter options

The dashboard is divided into three main pages, each of which can be filtered by organisational unit β€” e.g. by team or department.
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πŸ“ For anonymous surveys, teams are only displayed when at least 6 responses are available.

If additional profile attributes are available (e.g. age, gender, tenure), you can also use these for filtering β€” however only for quantitative data.
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πŸ“ˆ Overview & AI analysis

Here you will find a summary of the survey results with in-depth AI analysis:

  • Basic information β€” Survey title and response rate.
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  • Sentiment analysis 🧠 β€” Uses AI to detect the emotional tone of text responses.
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  • Most mentioned topics β€” Groups text responses into topic clusters and shows the most common themes along with sentiment.
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  • Answers β€” Lists all questions with responses, summaries, and number of submissions.
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  • Correlation analysis β€” Shows the three strongest correlations between scale questions, including an interpretation of statistical relevance.
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🚦 Pain points & measures

From insight to action:

  • Pain points ⚠️ β€” Problem areas sorted by importance (high, medium, low).
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  • Recommended measures β€” Concrete suggestions for resolution, divided into short-, medium-, and long-term actions.
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  • Suggested follow-up questions β€” New questions for upcoming surveys to explore identified topics in more depth.
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🧾 Management summary

A compact overview for leaders covering key topics, sentiment, and recommendations for action for quick orientation.

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