Analyze Customer Survey Feedback
Daily workflow that groups survey responses by sentiment, uses AI to extract themes and recommendations.
customer support teamsops and IT teams
What it does
You get daily summaries of customer feedback grouped by sentiment with AI-generated themes and actionable recommendations. This workflow automatically pulls data from Google Sheets, processes it through OpenRouter Chat Model, and posts the key highlights directly to your Slack channel for immediate review.
Step by step
- Set up a daily cron trigger to initiate the workflow at a consistent time.
- Configure the Google Sheets node to read from the specific survey response range.
- Define the code batch step to chunk responses into fixed-size arrays for API efficiency.
- Map the final aggregated JSON structure to the target columns in the summary sheet.
Where the LLM does the work
- Craft a system prompt that instructs the model to identify nuanced sentiment and extract key themes from unstructured text.
- Generate specific, actionable recommendations for each identified theme based on customer pain points.
- Format the output into a clean JSON structure with distinct fields for summary, themes, and action items.
Watch out for
Large batches of survey data can exceed token limits so split your chunks to ensure complete processing.
Inconsistent formatting in raw survey responses may confuse the parser so add a cleaning step before analysis.
Tools that fit
OpenRouter Chat Model LLM AI analysis of survey sentiment and themes
Code API Batching data and aggregating results
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Analyze Customer Survey Feedback: Daily workflow that groups survey responses by sentiment, uses AI to extract themes and recommendations. ## Specification - What it does: You get daily summaries of customer feedback grouped by sentiment with AI-generated themes and actionable recommendations. This workflow automatically pulls data from Google Sheets, processes it through OpenRouter Chat Model, and posts the key highlights directly to your Slack channel for immediate review. - Trigger: On a schedule (Scheduled · daily) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - Google Sheets: Survey data source and summary storage - Slack: Posting highlights and notifications - OpenRouter Chat Model: AI analysis of survey sentiment and themes - Code: Batching data and aggregating results ## Known pitfalls, handle each one explicitly in your implementation 1. Large batches of survey data can exceed token limits so split your chunks to ensure complete processing. 2. Inconsistent formatting in raw survey responses may confuse the parser so add a cleaning step before analysis. ## Reference implementation https://n8n.io/workflows/9809 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/analyze-customer-survey-feedback-with-ai/ via usecasesforagents.com
Frequently asked questions
Can I use a different Spreadsheet than Google Sheets?
Yes. Google Sheets is only the example spreadsheet in this recipe. The same flow works with Airtable, Baserow and Excel. Swap the spreadsheet connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for support teams drowning in inbound and looking to triage faster, not just harder.
Ops-IT teams triaging their own internal requests get the same pattern, different queue.