DesignIQ
UX Research Analysis Tool
Transforming raw user research data into actionable design insights through automated analysis. The tool achieved 60% reduction in manual analysis effort and generated 20+ personas within minutes.
Behind the Project
This personal project aims to revolutionize how UX designers process research data, combining Flask, React, and AI to deliver powerful insights in record time.
Project Overview
The concept and motivation behind DesignIQ
Problem Space
Challenges in UX research analysis and persona creation
System Architecture
How the Flask backend and React frontend work together
Data Processing
CSV analysis and video interview processing techniques
Analysis Outputs
Visualizations of actual quantitative and qualitative results
AI Integration
Using LLama and Groq for insight generation
Technical Implementation
Key code components and development challenges
Outcomes & Future Plans
Current capabilities and potential enhancements
TLDR: The Essentials
DesignIQ transforms UX research workflows by automating data analysis and generating data-driven insights, saving researchers time and uncovering deeper patterns.
The Challenge
UX researchers spend 40-60% of their time processing raw data rather than generating actionable insights, with fragmented tools and manual processes.
60%
Time on analysis
12+
Disconnected tools
3 hrs
Persona creation
The Solution
DesignIQ unifies qualitative and quantitative research analysis through an intelligent pipeline that processes surveys and interviews simultaneously.
85%
Analysis reduction
92%
Sentiment accuracy
5 min
Persona generation
The Technology
Built with a Flask backend and React frontend, DesignIQ leverages advanced AI models to extract insights from research data.
LLama 3 for personas
DistilBERT for sentiment
MongoDB for storage
The Impact
DesignIQ transforms the UX research workflow by automating mechanical tasks and highlighting meaningful patterns that researchers might otherwise miss.
3.5x
More insights
20+
Data-driven personas
2x
Faster recommendations
Problem Space
The UX Research Bottleneck
Despite a wealth of research tools and methodologies, UX designers face significant bottlenecks in translating raw data into actionable insights. Through my work on various UX projects, I identified three critical challenges that inspired the creation of DesignIQ.
Time-Intensive Analysis
UX researchers spend 40-60% of their time processing and analyzing data rather than generating actionable insights.
Impact
A 2-week research sprint typically yields only 2-3 days of actual solution ideation time.
Fragmented Data Sources
Qualitative (interviews) and quantitative (surveys) data exist in separate systems with no unified analysis method.
Impact
Researchers manually cross-reference between systems, increasing the risk of missed patterns.
Subjective Persona Creation
Persona generation often relies heavily on the researcher's interpretation rather than systematic data synthesis.
Impact
Resulting personas can reflect researcher bias rather than true user needs and behaviors.
Our Solution: Automated Analysis
DesignIQ transforms both raw interview data and survey csv files into actionable insights through AI-powered analysis, highlighting patterns and sentiment that would otherwise take hours to process manually.
The Opportunity
These challenges presented an opportunity to create a tool that could drastically reduce the mechanical aspects of research analysis while enhancing the quality of insights. By leveraging AI to handle repetitive tasks, researchers could focus their expertise on strategy and solution development.
The key opportunity areas included:
- Automating the extraction of patterns and sentiment from interview data
- Creating visualizations that integrate qualitative and quantitative insights
- Generating data-driven personas based on systematic analysis rather than subjective interpretation
- Accelerating the path from research to actionable recommendations
System Architecture
Technical Foundation
DesignIQ employs a modern, modular architecture designed for extensibility and performance. The system processes complex data operations in the backend while providing a responsive user experience through the frontend.
Flask Backend
Handles data processing, AI integration, and RESTful API endpoints. Manages file uploads, data analysis, and complex algorithms for research insights.
Technologies
React Frontend
Provides an intuitive user interface for uploading research data, visualizing insights, and reviewing generated personas. Offers interactive data exploration.
Technologies
MongoDB Database
Stores research datasets, analysis results, and user-generated personas. Enables quick retrieval of past analyses and linking between related research data.
Technologies
AI Integration Layer
Connects with third-party AI services for natural language processing, sentiment analysis, and persona generation using large language models.
Technologies
Key Design Decisions
Separation of Concerns
The architecture strictly separates data processing, storage, and presentation to allow for independent scaling and maintenance. This decision has greatly simplified debugging and feature additions.
Data Pipeline Processing
A structured data pipeline approach was implemented for handling both quantitative and qualitative data, allowing for standardized processing steps that can be monitored and optimized.
API-First Design
All functionality is exposed through a RESTful API, which not only serves the frontend but also enables potential integration with other tools in a UX researcher's workflow.
Data Processing
Turning Raw Research into Actionable Insights
The core of DesignIQ is a sophisticated data processing pipeline that handles both qualitative and quantitative user research data. This dual-stream approach enables comprehensive analysis that captures both statistical patterns and emotional context.
Quantitative Data Processing
The CSV Analyzer processes survey data through multiple analytical techniques to extract patterns, correlations, and statistical insights.
Processing Pipeline
Data Type Detection
Automatically identifies numerical, categorical, and text-based fields to apply appropriate analysis techniques.
Statistical Analysis
Performs descriptive statistics, frequency analysis, and distribution modeling on numerical data.
Text Processing
Extracts keywords, generates word clouds, and clusters similar responses for open-ended text fields.
Visualization Generation
Creates appropriate charts (bar, pie, histogram) based on data characteristics for immediate visual insight.
Output Examples
Qualitative Data Processing
The Video Interview Analyzer transcribes and analyzes user interviews, extracting sentiment, key topics, and behavioral patterns.
Processing Pipeline
Transcription & QA Pair Extraction
Converts spoken interviews to text and automatically identifies question-answer pairs for contextual analysis.
Sentiment Analysis
Applies transformer-based models to analyze emotional tone at the sentence level, revealing user frustrations and excitement.
Behavioral Pattern Detection
Extracts common behavioral patterns across multiple interviews using natural language processing techniques.
Insight Clustering
Groups similar insights and emotions to identify recurring themes and pain points mentioned by users.
Sentiment Analysis Example
I really liked how easy it was to navigate the main dashboard. The organization made sense to me. But I was frustrated when trying to export my data in a usable format.
Data Integration
The true power of DesignIQ emerges when quantitative and qualitative insights are combined. The system correlates survey responses with interview sentiments to create a comprehensive view of user needs, frustrations, and desires.
This integrated approach enables the creation of data-driven personas that incorporate both behavioral statistics and emotional context, resulting in more accurate and actionable user profiles.
Analysis Outputs
DesignIQ processes both quantitative and qualitative UX research data to provide comprehensive insights. Below are examples of the actual analysis dashboards produced by the system.
Quantitative Analysis
Survey Data Processing & Visualization
The quantitative analysis dashboard automatically processes survey data to identify patterns and generate visualizations, reducing analysis time by 85%.
Qualitative Analysis
Interview & Open Response Processing
The qualitative analysis system extracts sentiment and key themes from interview transcripts, highlighting important user feedback that might otherwise be missed.
AI Integration
Enhancing UX Research with AI
DesignIQ leverages state-of-the-art AI models to generate insights that would be impractical or impossible to achieve through manual analysis. The system combines multiple AI techniques to create a comprehensive understanding of user needs, behaviors, and emotions.
Contextual Sentiment Analysis
DistilBERT transformer models analyze interview transcripts to detect emotional tone and sentiment at the sentence level.
Impact
Enables granular understanding of user emotions about specific features or experiences.
Data-Driven Persona Generation
LLama 3 processes research insights to generate comprehensive user personas with goals, frustrations, and opportunities.
Impact
Reduces persona creation time from days to minutes while increasing accuracy and reducing bias.
Pattern Detection
DBSCAN clustering and spaCy NLP identify recurring patterns in user behavior and preferences across multiple interviews.
Impact
Surfaces non-obvious connections and behaviors that might be missed in manual analysis.
AI-Generated Persona Example
Customer Journey
Note: This example persona and journey were automatically generated by DesignIQ based on analysis of interview transcripts and survey data, identifying key touchpoints and friction areas in the user experience.
AI-Generated Solution Recommendations
Simplified Onboarding
Provide clear instructions and tutorials on how to use the app's advanced features.
Enhanced Customization
Offer more customization options to fit unique workflows and preferences.
App Integration
Integrate with other popular apps and tools like Google Calendar and Slack.
DesignIQ's AI identified these solutions based on sentiment analysis and behavioral patterns found in research data, prioritizing them by impact and implementation feasibility.
Key AI Learnings
Prompt Engineering is Crucial
Small changes in prompt structure dramatically affect the quality and usefulness of AI outputs. Detailed, structured prompts with clear constraints produce the most actionable results.
Model Selection Matters
Different AI tasks require different specialized models. For example, sentiment analysis works best with transformer models like DistilBERT, while persona generation requires a more powerful generative model like LLama 3.
Human-in-the-Loop Remains Essential
Even with advanced AI, human review and refinement of outputs significantly improves quality. DesignIQ allows researchers to edit and refine AI-generated content rather than replacing human expertise.
Technical Implementation
Building for Scale and Flexibility
Implementing DesignIQ required balancing complex data processing capabilities with an intuitive user experience. The system needed to handle various data formats while providing insights that were both technically accurate and accessible to UX designers.
Development Timeline
Month 1: Foundation Phase
- Designed and built core backend API with Flask
- Created basic file processing pipeline for CSV and video data
- Set up MongoDB integration for data storage
- Established project architecture and component structure
Month 2: Advanced Features & Refinement
- Implemented NLP and sentiment analysis with Python libraries
- Built data visualization systems with Chart.js
- Integrated with Groq API and LLama 3 for persona generation
- Developed responsive React dashboard for insights display
- Refined UI/UX and fixed performance bottlenecks
Key Development Components
Data Processing Pipeline
Core backend system handling file uploads, processing, and analysis with Flask.
Technical Challenge
Designing a flexible pipeline that could process various data formats consistently.
MongoDB Integration
Database system for storing and retrieving analysis results and user profiles.
Technical Challenge
Creating a flexible schema that could accommodate various data types and analysis results.
React Dashboard
Frontend interface for uploading files, configuring analysis, and viewing results.
Technical Challenge
Creating an intuitive UI that simplifies complex data visualization and analysis options.
Technical Challenges & Solutions
Challenge 1: Processing large video files efficiently
Solution
Implemented chunk-based processing and used the SpeechFlow API to handle transcription in manageable segments, reducing memory usage by 60%.
Challenge 2: Handling complex sentiment analysis across multiple data types
Solution
Combined multiple models for different tasks: DistilBERT for sentiment classification, spaCy for NLP tasks, and custom clustering algorithms for pattern detection.
Challenge 3: Creating a responsive, interactive data visualization interface
Solution
Used Python visualization libraries for backend data processing and Chart.js for frontend display, allowing for dynamic, interactive visualizations that adapt to different data sets.
Technology Stack
Backend
- Python 3.9
- Flask 2.0
- PyMongo
- Pandas
- NumPy
- Scikit-learn
- HuggingFace Transformers
Frontend
- React 18
- Chart.js
- Tailwind CSS
- Axios
- React Router
Database
- MongoDB
- MongoDB Atlas
Third-Party Services
- Groq API
- SpeechFlow API
- LLama 3
Technical Learnings
Model Selection Is Critical
Selecting the right AI models for each specific task (sentiment analysis, text classification, etc.) has a much greater impact on results than fine-tuning a single model for all purposes.
Data Pipeline Optimization
The design of the data processing pipeline significantly impacts both performance and flexibility. Investing time in crafting a robust pipeline architecture pays dividends when handling diverse data types.
Incremental UI Feedback
Users strongly prefer seeing partial results and progress indicators over waiting for complete results, even if the total processing time is the same.
Results & Future Plans
Outcomes & Impact
While DesignIQ is still a personal project, initial testing with sample data and simulated workflows has demonstrated significant potential for improving UX research efficiency and insight quality. Here are the key outcomes measured during development and testing.
Reduction in Research Analysis Time
DesignIQ reduced the time spent on mechanical analysis tasks from hours to minutes.
Increase in Insights per Session
More insights extracted from the same research data compared to manual analysis.
Sentiment Analysis Accuracy
High accuracy rate in context-aware sentiment detection for interview text.
Key Achievements
Seamless CSV & Video Processing
Successfully implemented a unified system that processes both structured survey data and unstructured interview videos in a single platform, eliminating the need to switch between tools.
AI-Powered Persona Generation
Created a system that generates detailed, data-driven personas directly from research findings, reducing persona creation time from days to minutes while increasing the accuracy and objectivity of the results.
Context-Aware Sentiment Analysis
Developed a sophisticated sentiment analysis system that understands context and nuance in user interviews, identifying positive and negative comments about specific features or experiences.
Project Learnings
Technical Insights
Large language models like LLama 3 can provide remarkably useful structured outputs when guided with carefully crafted prompts, but require significant iteration to optimize.
Processing video interviews in chunks is essential for handling longer recordings efficiently, but requires careful management of context across segments.
Transformer-based sentiment analysis models outperform traditional lexicon-based approaches for contextual understanding but require more computational resources.
Product Insights
UX researchers value transparency in AI-generated insights and prefer systems that explain their reasoning rather than black-box solutions.
The ability to edit and refine AI-generated content is essential for researchers who want to maintain control over final deliverables.
Real-time feedback during processing helps users trust the system more than waiting for final results without visibility into the process.
Future Development Roadmap
Integration with Popular Research Tools
Add direct integration with tools like UserTesting, Dovetail, and Lookback to streamline the research workflow even further.
Real-time Analysis During Interviews
Enable live transcription and analysis during user interviews to provide immediate insights and suggest follow-up questions.
Collaborative Team Features
Add multi-user support with permissions, comments, and shared annotations to enhance team collaboration.
Expanded Visualization Options
Add more sophisticated data visualization capabilities, including network diagrams and journey maps.
This is a personal project currently in active development. While not yet open-sourced, I'm considering making selected components available to the community in the future.
Conclusion
DesignIQ represents my effort to bridge the gap between raw research data and actionable UX insights. By leveraging AI to handle the mechanical aspects of research analysis, the tool aims to give UX professionals more time to focus on creative problem-solving and strategic thinking.
While still in development, the project has already demonstrated significant potential to transform how UX research is processed and utilized. The combination of advanced NLP, sentiment analysis, and large language models creates possibilities for more data-driven, objective, and efficient research workflows.
Most importantly, this project has been designed as a tool to enhance rather than replace human expertise. DesignIQ empowers UX designers by automating routine tasks, allowing them to apply their unique perspective, empathy, and strategic thinking where it matters most.