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

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). 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. Destinations

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

How It Works

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