📦 Market Research Automation — 市场研究自动化

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

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

Scenario 2: Competitor Analysis

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

Scenario 3: Generate Survey Questionnaire

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:

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:

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:

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

Output Format

Market Research Report

# 📊 Market Research Automation Report
Generated on: YYYY-MM-DD HH:MM
Key Findings
[Key Finding 1]
[Key Finding 2]
[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

# 🔍 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

# 📋 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:

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.