Customer Feedback Analysis with AI, QuickChart & HTML Report Generator

Description

Generative Customer Insights from Feedback Data using AI Agents & Charts

This workflow automates the analysis of customer feedback or social media data from Google Sheets using DeepSeek LLM, transforming raw text into structured semantic insights. The workflow also generates data visualizations and produces a final HTML report, ready for email delivery.

Table of Contents
What This Workflow Does
Pre-conditions and Requirements
Step-by-Step Workflow Explanation
Example Results
Customization Guide

What This Workflow Does

This workflow performs automated semantic analysis on unstructured feedback data (from Google Sheets), using LLM-based agents and a sequence of transformations. It achieves:

Prompt proposal generation**: AI generates generalizable prompts for various analysis dimensions.
Row-level analysis**: Each row of data is evaluated against all prompts.
Output merging and refinement**: Raw analysis outputs are merged, deduplicated, and semantically clustered.
Visualization and report generation**: QuickChart is used to create graphs, and an HTML report is produced.
Email delivery**: The full report is sent automatically via Gmail.

Pre-conditions and Requirements

API Keys**
DeepSeek API Key
Gmail OAuth2 (for sending results)

Google Sheet Access**
A preformatted Google Sheet containing social listening feedback
The sheet must include at least 20 rows for sample prompt generation

n8n Configuration**
Nodes used: Google Sheets, LangChain (LLM/Agent/Parser), Function, Merge, QuickChart (via URL), Gmail
Ensure all credentials are configured properly in n8n’s credential manager

Step-by-Step Workflow Explanation

Google Sheet Import
Retrieves feedback rows from a specific Google Sheet tab
Filters to the first 20 rows for prompt generation

Prompt Proposal Agent
AI generates 3–6 row-level analysis prompts in a structured JSON format
Prompts must be agnostic to product names and column headers

Prompt Injection and Pairing
Each row is paired with all prompts
Combined into a single dataset for row-by-row LLM evaluation

First Iteration of Analysis
An LLM answers all injected prompts row-by-row
Output is parsed and transformed into structured fields

Semantic Merging and Refinement
Merged lists of values from all rows
AI clusters synonyms and regenerates improved prompt definitions

Second Iteration of Analysis
The refined prompts are used to re-analyze each row
A new structured output per row is generated and merged into one object

Summarization and Visualization
AI generates summaries per dimension (e.g., sentiment)
QuickChart visualizations are created and URL-encoded
Cross-dimensional insights and a global narrative are generated

Final Report Generation and Emailing
A final HTML report is generated
Sent to the specified email using Gmail node

Example Results

Customization Guide

1. Modify Data Source
Change the Google Sheet ID or sheet tab
Add filters for specific time periods or product names

2. Adjust Prompt Definitions
Refine the initial prompt agent instruction to tailor the type of analysis

3. Swap LLM Models
Replace DeepSeek with OpenAI or another LLM via LangChain nodes

4. Visual Styling
Customize QuickChart configurations to adjust chart types, colors, legends

5. Report Format
Update the final HTML prompt to reflect brand design or restructure sections

6. Add Report Destinations
Replace Gmail with Google Drive upload, Notion page creation, or Slack post

This end-to-end AI-powered social listening workflow enables scalable, repeatable, and customizable insights generation from unstructured customer feedback.