Analyze customer survey feedback with AI, Google Sheets & Slack reports

Here’s a clean, English-only template description you can paste into the n8n “Description” field.

Overview

This workflow analyzes customer survey responses, groups them by sentiment (positive / neutral / negative), generates themes and insights with an AI agent, and delivers a consolidated report to your destinations (Google Sheets, Slack). It runs on a daily schedule and uses batch-based AI analysis for accuracy.

Flow: Schedule → Fetch from Sheets → Group & batch (Code) → AI analysis → Aggregate → Save/Notify (Sheets, Slack)

What You’ll Need

• A survey data source (Google Sheets recommended)
• AI model credentials (e.g., OpenAI or OpenRouter)
• Optional destinations: Google Sheets (summary sheet), Slack channel

Setup

1. Data source (Google Sheets)

   • In Get Survey Responses, replace YOUR_SHEET_ID and YOUR_SHEET_NAME with your sheet details.
   • Ensure the sheet includes columns like: 満足度 (Rating), 自由記述コメント (Comment), 回答日時 (Timestamp).

2. AI model

   • Add credentials to your preferred LLM node (OpenAI/OpenRouter).
   • Keep the prompt’s JSON-only requirement so the structured parser can consume it reliably.

3. Destinations

   • Save to Sheet: set your output documentId / sheetName.
   • Slack: set the target channelId on the Slack node.

How It Works

1. Daily Schedule Trigger — starts the workflow at your chosen time.
2. Get Survey Responses (Sheets) — reads survey data.
3. Group & Prepare Data (Code) — classifies by rating (>=4: positive, =3: neutral, <3: negative) and creates batches (max 50 per batch).
4. Loop Over Batches — feeds each sentiment batch to the AI separately for cleaner signals.
5. Analyze Survey Batch (AI Agent) — returns structured JSON: themes, insights, recommendations.
6. Add Metadata (Code) — attaches original sentiment and item counts to each AI result.
7. Aggregate Results (Code) — merges all batches; outputs Top Themes, Key Insights, Priority Recommendations, and an Executive Summary.
8. Save to Sheet / Slack — appends the summary to a sheet and posts highlights to Slack.

Data Assumptions (Columns)

Your source should include at least:

• 満足度 (Rating) — integer 1–5
• 自由記述コメント (Comment) — string
• 回答日時 (Timestamp) — ISO string or date

Outputs

• Consolidated summary containing:

  • Top themes (with example quotes)
  • Key insights
  • Priority recommendations
  • Executive summary

• Destinations: Google Sheets (one row per run) and Slack (high-level highlights)

Customize

• Adjust sentiment thresholds (e.g., require >=5 for positive) or batch size (default 50) in the Code node.
• Tailor the AI prompt or the output JSON schema to your domain.
• Add more outputs (CSV export, database insert, additional channels) in parallel after the Aggregate step.

Before You Run (Checklist)

• Add credentials for Sheets / AI / Slack in Credentials
• Update documentId, sheetName, and Slack channelId
• Confirm your column names match the Code node references
• Verify schedule time and timezone (e.g., Asia/Tokyo)

Troubleshooting

• Parser errors on AI output: ensure the model response is JSON-only; reduce temperature or simplify schema if needed.
• Only some batches run: check batch size in Loop and ensure each sentiment bucket actually contains responses.
• No output to Sheets/Slack: verify credentials, IDs, and required fields; confirm permissions.

Security & Template Notes

• Do not include credentials in the template file. Users add their own after import.
• Use Sticky Notes to document Overview, Setup, Processing Logic, and Output choices. This template already includes guideline-friendly notes.