Market Research Automation#

Mine user pain points from social media and analyze competitors. Applicable for market validation before product launch, user needs analysis, and competitor feature comparison.

Trigger Conditions#

• Market research
• Competitor analysis
• market research
• competitor analysis
• User research
• survey generation
• TAM SAM SOM
• Market size estimation

Core Capabilities#

Capability 1: Market Sizing — TAM/SAM/SOM Three-Layer Model#

Estimate the Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) for a target market.

Capability 2: In-Depth Competitor Analysis — Feature/Pricing/User Review Comparison Matrix#

Compare multiple competitors across dimensions such as features, pricing, target users, strengths, and weaknesses.

Capability 3: Automatic Generation of User Interview Frameworks and Survey Questionnaires#

Automatically generate structured user survey questionnaires based on the research topic.

Usage Workflow#

Scenario 1: Market Sizing Research#

bash

python3 scripts/market_researcher_tool.py research --market 'AI Writing Tools'

Scenario 2: Competitor Analysis#

bash

python3 scripts/market_researcher_tool.py compete --products 'Jasper,Copy.ai,Notion AI'

Scenario 3: Generate Survey Questionnaire#

bash

python3 scripts/market_researcher_tool.py survey --topic 'AI Writing Tools'

Command Details#

research - Market Research#

Purpose: Estimate market size and generate a TAM/SAM/SOM analysis report.

Parameters:

• --market: Market name (required)
• --output, -o: Output file path (optional, defaults to console output)

Example:

bash

python3 scripts/market_researcher_tool.py research --market 'AI Writing Tools' -o report.md

compete - Competitor Analysis#

Purpose: Compare features, pricing, and user reviews of multiple competitors.

Parameters:

• --products: List of competitors, comma-separated (required)
• --output, -o: Output file path (optional)

Example:

bash

python3 scripts/market_researcher_tool.py compete --products 'Jasper,Copy.ai,Notion AI,ChatGPT' -o compete.md

survey - Generate Survey Questionnaire#

Purpose: Automatically generate a structured user survey questionnaire.

Parameters:

• --topic: Research topic (required)
• --output, -o: Output file path (optional)

Example:

bash

python3 scripts/market_researcher_tool.py survey --topic 'AI Writing Tools' -o survey.md

Output Format#

Market Research Report#

markdown

# 📊 Market Research Automation Report

**Generated on**: YYYY-MM-DD HH:MM

## Key Findings
1. [Key Finding 1]
2. [Key Finding 2]
3. [Key Finding 3]

## Market Size Analysis (TAM/SAM/SOM)
| Metric | Value | Description |
|------|------|------|
| TAM | $XXX Billion | Total Addressable Market |
| SAM | $YYY Billion | Serviceable Available Market |
| SOM | $ZZZ Billion | Serviceable Obtainable Market |

## Actionable Recommendations
| Priority | Recommendation | Expected Outcome |
|--------|------|----------|
| 🔴 High | [Specific recommendation] | [Quantified expectation] |

Competitor Analysis Report#

markdown

# 🔍 In-Depth Competitor Analysis Report

## Competitor Comparison Matrix
| Product | Pricing | User Rating | Target User | Key Strengths | Main Weaknesses |

## Competitive Strategy Recommendations
| Priority | Recommendation | Expected Outcome |

User Survey Questionnaire#

markdown

# 📋 User Survey Questionnaire

## Basic Information
**Q1. What is your current job role?**
○ Product Manager ○ Marketing ○ Content Creation ...

## Current Usage
**Q2. How often do you use AI writing tools?**
○ Multiple times daily ○ Once daily ...

## Pain Points and Needs
**Q3. What feature would you most like to see improved in AI writing tools?**
________________________________________

Prerequisites#

Install Python dependencies before first use:

bash

pip install requests beautifulsoup4 pandas

References#

• X/Twitter API - User discussion data
• Google Trends - Search trend analysis
• Full Market Research Agent Use Case

Notes#

• All analysis is based on data obtained by the script; data is not fabricated.
• Missing data fields are marked "Data Unavailable" rather than guessed.
• It is recommended to combine with human judgment; AI analysis is for reference only.
• The current version uses mock data and can be extended to real API calls.