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vtmade edited this page Jun 7, 2025 · 1 revision

MicroCrowd Wiki

Welcome to the MicroCrowd documentation hub! This experimental framework transforms consumer data into realistic personas for simulated focus groups and market research insights.

🎯 Project Overview

MicroCrowd bridges the gap between static consumer data and dynamic human insights by creating interactive personas for experimental research. This tool enables researchers to conduct focus group simulations with authentic personality-driven responses, making advanced research methodologies accessible across diverse scenarios.

Current Capabilities

  • CSV-to-Persona Transformation: Convert datasets into rich, multi-dimensional personas
  • Authentic Personalities: Each persona exhibits unique traits and response patterns
  • Natural Conversations: Realistic focus group dynamics with varied participation
  • Professional Moderation: AI moderator follows structured discussion guides
  • Real-time Control: Pause, resume, or end sessions dynamically
  • Export Functionality: Download complete conversation transcripts

🗺️ Development Roadmap

Phase 1: Python Research Backend (In Progress)

MicroCrowd-Researcher: A Python-based backend designed specifically for researchers who need deeper control and customization.

Features in Development:

  • Advanced Persona Development: More sophisticated psychological profiling algorithms
  • Enhanced Natural Language Processing: Improved conversation flow and authenticity
  • Research-Grade Analytics: Built-in statistical analysis and pattern recognition
  • Flexible Data Integration: Support for multiple data formats and sources

Phase 2: Enhanced Analysis & Export (Q3 2025)

  • Comprehensive Data Export: Multiple format options (JSON, CSV, PDF, Excel)
  • Advanced Analytics Dashboard: Real-time sentiment analysis and theme extraction
  • Statistical Reporting: Automated research summaries and insights generation
  • Discussion Pattern Analysis: Identification of conversation trends and dynamics

Phase 3: Research Integration (Q4 2025)

  • Academic Research Tools: Integration with statistical software (R, SPSS, Python)
  • Methodology Templates: Pre-built research frameworks for common studies
  • Collaboration Features: Multi-researcher project management
  • Version Control: Track changes in persona development and study iterations

Phase 4: Advanced AI Features (2026)

  • Multi-language Support: Conduct research in various languages
  • Cultural Context Modeling: Personas reflect cultural and regional differences
  • Longitudinal Studies: Track persona evolution over time
  • Integration APIs: Connect with survey platforms and CRM systems

🔬 Research Applications

Current Use Cases

  • Product Testing: Evaluate features and pricing across demographics
  • Academic Studies: Examine group dynamics and decision-making patterns
  • UX Research: Generate authentic user feedback from diverse segments
  • Rapid Ideation: Brainstorm concepts with varied perspectives
  • Ethics Discussions: Explore different viewpoints before real-world studies

Experimental Nature

This framework is fundamentally experimental, designed for research exploration as AI capabilities advance. It's NOT intended to replace actual focus groups but rather to enable new research possibilities and preparation phases for traditional studies.

🛠️ Technical Stack

Current Implementation

  • Frontend: React + Vite
  • AI Integration: OpenAI GPT models
  • Data Processing: JavaScript-based CSV parsing
  • Export: Basic transcript download

Planned Python Backend

  • Framework: FastAPI for high-performance research APIs
  • AI/ML: Advanced NLP libraries (spaCy, NLTK, transformers)
  • Data Science: pandas, numpy, scikit-learn for analysis
  • Visualization: matplotlib, seaborn for research outputs
  • Database: PostgreSQL for persona and session storage

🤝 Community & Contributions

We welcome contributions from researchers, developers, and data scientists! Whether you're:

  • Fixing bugs or adding features
  • Improving documentation
  • Testing with different datasets
  • Suggesting research methodologies
  • Contributing to the Python backend development

See our Contributing Guidelines for detailed information.

📞 Support & Contact

📄 License

This project is licensed under the MIT License, making it freely available for research, educational, and commercial use.

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