Automated User Research Insight Analysis Workflow
This workflow automates the processing of user research data by importing survey responses from Google Sheets, generating text vectors using OpenAI, and storing them in the Qdrant database. It identifies major groups through the K-means clustering algorithm and utilizes large language models to perform intelligent summarization and sentiment analysis on the group responses. Finally, the insights are automatically exported back to Google Sheets, creating a structured research report. This process enhances analysis efficiency and helps decision-makers quickly gain deep insights.
Tags
Workflow Name
Automated User Research Insight Analysis Workflow
Key Features and Highlights
This workflow automatically imports large volumes of participant responses from Google Sheets survey data, generates text embeddings using OpenAI, and stores them in the Qdrant vector database. It employs a K-means clustering algorithm to identify major respondent groups based on their answers. Leveraging large language models (LLMs), it performs intelligent summarization and sentiment analysis for each cluster. Finally, the detailed insight results are automatically exported back to Google Sheets, producing a structured and comprehensive research insight report.
Core Problems Addressed
Traditional survey data analysis often struggles with large, dispersed response sets, making manual analysis time-consuming and challenging to uncover deep commonalities and sentiment trends. This workflow uses vectorization and clustering techniques to automatically detect patterns and groups within responses. Combined with LLM-generated precise and insightful summaries, it significantly enhances the efficiency and quality of survey data interpretation.
Application Scenarios
- Market research data analysis
- User experience feedback aggregation
- Employee satisfaction survey summarization
- Detailed insights from various questionnaire results
- Any scenario requiring extraction of key insights from large volumes of textual responses
Main Process Steps
- Import Survey Responses — Retrieve participant answers via the Google Sheets node.
- Data Preprocessing — Convert survey responses into “question-answer” pairs to facilitate vectorization.
- Text Embedding Generation and Storage — Generate text embeddings using OpenAI’s text-embedding-3-small model and store them in the Qdrant vector database.
- Create Dedicated Analysis Sheet — Create a new, separate sheet in Google Sheets to store analysis results.
- Extract Survey Question List — Obtain all survey questions for sequential processing.
- Cluster Analysis of Answers — Apply K-means clustering to the answer embeddings for each question, selecting clusters with at least three responses.
- Cluster Answer Summarization and Sentiment Analysis — Use OpenAI’s GPT-4o-mini model to intelligently summarize and assess the sentiment of each cluster’s answers.
- Export Insight Results — Write the generated insight summaries and sentiment scores into the newly created Google Sheets worksheet.
Involved Systems and Services
- Google Sheets: Source of survey data and destination for storing results.
- OpenAI API: For generating text embeddings and performing natural language intelligent summarization.
- Qdrant Vector Store: Efficient storage and retrieval of text embeddings enabling vector-based clustering analysis.
- n8n Workflow Automation Platform: Integrates and orchestrates the above services to automate the entire process.
Target Users and Value
- Market research analysts and data scientists seeking rapid, in-depth survey insights.
- Product managers and UX designers requiring systematic summarization and sentiment evaluation of user feedback.
- Corporate HR and management teams aiming to quickly identify common issues and emotional trends from employee surveys.
- Any teams or individuals handling large volumes of textual questionnaires who want to improve analysis efficiency and insight quality.
By integrating automation and intelligent technologies, this workflow achieves efficient processing and deep insight extraction from research data, greatly reducing manual analysis time and empowering decision-makers to make accurate judgments based on authentic user voices.
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