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Survey Results Analyser

Example prompt: "Take the responses from our customer satisfaction survey in Google Sheets, analyse the common themes and sentiment, and email me a summary report."

How to automate survey analysis with GloriaMundo

The Problem

Running a survey is the easy part. Reading through hundreds of free-text responses to find the actual insights is where things stall. Someone has to open the spreadsheet, scan every row, try to spot recurring complaints and praise, and then write up a summary that fairly represents what customers said. For larger surveys, this takes hours and the results are heavily influenced by whoever happened to do the reading — they tend to remember the most extreme responses rather than the most common ones. Quantitative data like satisfaction scores is simpler, but correlating those numbers with the qualitative feedback requires careful cross-referencing that rarely happens.

How GloriaMundo Solves It

We build a workflow that starts by reading all survey responses from your Google Sheet. A code step handles the quantitative side first — calculating averages, distributions, and response counts for any numerical or rating-scale questions. An LLM step then reads through the free-text responses, groups them into recurring themes (e.g. "pricing concerns", "onboarding confusion", "feature requests"), classifies overall sentiment per theme, and pulls representative quotes for each. It combines the quantitative metrics with the thematic analysis into a single summary report. An integration step emails the finished report to you via Gmail, formatted with sections for key metrics, theme breakdowns, and recommended focus areas. Glass Box preview shows you the full analysis before the email is sent, so you can verify the themes make sense and adjust the level of detail.

Example Workflow Steps

  1. Trigger (manual or scheduled): Runs on demand or after a survey closes.
  2. Step 1 (integration): Read all survey responses from the Google Sheet — both rating-scale answers and free-text comments.
  3. Step 2 (code): Calculate quantitative metrics — average satisfaction score, response distribution, completion rate, and any segment breakdowns.
  4. Step 3 (llm): Analyse free-text responses to identify recurring themes, classify sentiment per theme, and select representative quotes. Combine with the quantitative metrics into a structured summary report.
  5. Step 4 (integration): Email the formatted summary report via Gmail with sections for key metrics, theme analysis, and recommended actions.

Integrations Used

  • Google Sheets — source of survey response data, both quantitative scores and free-text feedback
  • Gmail — delivers the finished summary report to the specified recipients

Who This Is For

Product managers, customer success leads, and UX researchers who run regular surveys and need to turn raw responses into actionable insights without spending half a day reading through a spreadsheet. Especially useful for teams running quarterly NPS or CSAT surveys with 50+ responses. Since survey responses may contain personal data, use the Glass Box preview to verify what is being shared in the report before sending.

Time & Cost Saved

Manually analysing 100 survey responses — reading free-text, categorising themes, calculating scores, and writing a summary — takes 3-5 hours. For larger surveys, it can take a full day. This workflow produces a comprehensive analysis in minutes, with consistent theme identification that does not depend on who happens to read the responses. Over a year of quarterly surveys, that is 12-20 hours of analysis work replaced, with more reliable and reproducible results.