AI-Driven Survey Insight Generation for Large Datasets
Automatically generates deep, AI-powered insights from thousands of survey responses, reducing analysis time by over 90% and uncovering hidden trends.
Extracting actionable insights from large survey datasets is often manual, time-intensive, and prone to oversight. This workflow automates the discovery of common themes by clustering similar responses, summarizing them with AI, and exporting detailed insights directly into Google Sheets.
Documentation
AI-Powered Survey Insights Generator
This powerful workflow automates the challenging task of extracting meaningful insights from large-scale survey responses. By leveraging AI for data clustering and summarization, it transforms raw participant feedback into actionable intelligence, saving significant time and effort in qualitative analysis.
Key Features
- Effortless Google Sheets Integration: Seamlessly import all survey responses from your existing Google Sheets.
- Intelligent AI-Powered Clustering: Automatically group similar participant responses for each question using advanced K-means clustering, identifying common themes within large datasets.
- Comprehensive AI Summarization & Sentiment Analysis: Leverage powerful LLMs (like GPT-4o-mini) to summarize clustered responses, extract key insights, and determine overall sentiment (positive, neutral, negative).
- Structured Export to Google Sheets: Automatically generate and append detailed insights, including response counts, participant IDs, and raw feedback, to a new, organized sheet for easy review and action.
How It Works
The workflow operates in two main phases. Initially, it imports all survey responses from your designated Google Sheet, converting them into question-answer pairs. Each answer is then vectorized using OpenAI embeddings and stored in a Qdrant vector database, enriched with metadata like participant ID and survey name. This prepares your data for advanced AI analysis. Following the data ingestion, a sub-workflow is triggered to generate insights. For each question found in your survey, it queries Qdrant to retrieve all related vectorized answers. These vectors are then processed using a K-means clustering algorithm to identify groups of similar responses. Only clusters containing three or more similar responses are considered for further analysis, filtering out highly unique or irrelevant feedback. For each valid cluster, the workflow fetches the original participant responses and sends them to an OpenAI Large Language Model (LLM). The LLM then summarizes the collective feedback, extracts key insights, and assigns an overall sentiment. Finally, these comprehensive, AI-generated insights—including the number of responses, participant IDs, and raw text—are meticulously formatted and appended to a newly created 'Insights' sheet within your original Google Spreadsheet.