Market Research Reports Kit

Generate comprehensive market research reports that analyze industries, competitive landscapes, market trends, and growth opportunities. This skill produces structured reports with data-driven insights, competitive analysis matrices, and actionable strategic recommendations.

When to Use This Skill

Choose Market Research Reports Kit when you need to:

• Analyze a market segment's size, growth trajectory, and key drivers
• Create competitive landscape assessments with positioning matrices
• Evaluate market entry opportunities for new products or geographies
• Produce investor-ready market analysis documents with supporting data

Consider alternatives when:

• You need real-time market data feeds (use financial APIs like Bloomberg or Yahoo Finance)
• You need consumer survey analysis (use survey platforms like Qualtrics)
• You need social media sentiment analysis specifically (use social listening tools)

Quick Start

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# Install required packages
pip install requests pandas matplotlib seaborn jinja2 python-dotenv

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import pandas as pd
import matplotlib.pyplot as plt

# Define market research framework
report_structure = {
    "title": "AI-Powered Drug Discovery Market Analysis 2025",
    "sections": [
        "Executive Summary",
        "Market Overview & Sizing",
        "Key Market Drivers",
        "Competitive Landscape",
        "Technology Trends",
        "Regulatory Environment",
        "Market Segmentation",
        "Growth Projections",
        "Strategic Recommendations"
    ]
}

# Market sizing example
market_data = pd.DataFrame({
    "Year": [2022, 2023, 2024, 2025, 2026, 2027],
    "Market Size ($B)": [1.2, 1.8, 2.7, 4.0, 5.8, 8.3],
    "Growth Rate": [None, 0.50, 0.50, 0.48, 0.45, 0.43]
})

plt.figure(figsize=(10, 6))
plt.bar(market_data["Year"], market_data["Market Size ($B)"], color="#4A90D9")
plt.title("AI Drug Discovery Market Size")
plt.ylabel("Market Size (USD Billions)")
plt.xlabel("Year")
plt.savefig("market_size.png", dpi=150, bbox_inches="tight")

Core Concepts

Report Components

Section	Content	Data Sources
Executive Summary	Key findings, market size, top recommendations	Synthesis of all sections
Market Overview	Definition, scope, TAM/SAM/SOM	Industry reports, census data
Competitive Landscape	Player profiles, market share, positioning	Company filings, press releases
Segmentation	By geography, product, end-user, technology	Survey data, transaction data
Trends & Drivers	Technology shifts, regulatory changes, demand	News, patents, policy docs
SWOT Analysis	Strengths, weaknesses, opportunities, threats	Competitive intelligence
Projections	Revenue forecasts, growth scenarios	Historical data, models

Competitive Analysis Matrix

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import pandas as pd
import numpy as np

def competitive_matrix(competitors):
    """Build a competitive analysis matrix."""
    criteria = [
        "Market Share", "Product Breadth", "Technology",
        "Pricing", "Distribution", "Brand Strength"
    ]

    matrix = pd.DataFrame(
        {comp["name"]: [comp["scores"].get(c, 0) for c in criteria]
         for comp in competitors},
        index=criteria
    )

    # Add weighted total
    weights = [0.25, 0.15, 0.20, 0.15, 0.10, 0.15]
    for col in matrix.columns:
        matrix.loc["Weighted Score", col] = np.dot(
            matrix[col].values[:len(weights)], weights
        )

    return matrix.round(2)

competitors = [
    {"name": "Company A", "scores": {
        "Market Share": 8, "Product Breadth": 7,
        "Technology": 9, "Pricing": 6, "Distribution": 8, "Brand Strength": 9
    }},
    {"name": "Company B", "scores": {
        "Market Share": 6, "Product Breadth": 8,
        "Technology": 7, "Pricing": 8, "Distribution": 7, "Brand Strength": 6
    }},
    {"name": "Company C", "scores": {
        "Market Share": 4, "Product Breadth": 5,
        "Technology": 9, "Pricing": 7, "Distribution": 4, "Brand Strength": 5
    }}
]

matrix = competitive_matrix(competitors)
print(matrix)

Configuration

Parameter	Description	Default
report_depth	Analysis depth (basic/standard/comprehensive)	"standard"
time_horizon	Forecast period in years	5
geographic_scope	Markets to cover	"global"
output_format	Report format (PDF, DOCX, HTML)	"pdf"
data_sources	Included data sources	["public"]
chart_style	Visualization theme	"professional"

Best Practices

1. Define scope before researching — Set clear boundaries on market definition, geographic coverage, time period, and segmentation dimensions upfront. A well-scoped report focused on one segment is more useful than a vague report covering everything.

2. Triangulate data from multiple sources — Never rely on a single data source for market sizing. Cross-reference industry reports, company filings, government data, and analyst estimates. Note discrepancies and explain your methodology.

3. Use TAM/SAM/SOM framework for sizing — Present Total Addressable Market (everyone who could buy), Serviceable Addressable Market (your realistic target), and Serviceable Obtainable Market (what you can capture). This grounds projections in reality.

4. Include methodology transparency — Document your data sources, estimation methods, and assumptions. Readers need to evaluate the reliability of your numbers. A market size estimate without methodology is just a guess.

5. Lead with actionable recommendations — End each major section with "so what" implications. Executives reading the report want to know what to do, not just what the data shows. Frame findings as strategic options with trade-offs.

Common Issues

Conflicting market size estimates across sources — Different research firms use different market definitions and methodologies. When estimates diverge by more than 30%, document each source's methodology and present a range rather than a single number. Explain which definition your analysis adopts and why.

Projections that assume straight-line growth — Markets don't grow linearly. Account for S-curve adoption patterns, market maturity effects, and potential disruptions. Build multiple scenarios (base, optimistic, pessimistic) rather than a single projection, and tie growth assumptions to specific drivers.

Competitive data gaps for private companies — Private companies don't publish revenue or market share data. Use proxy indicators: employee count from LinkedIn, web traffic from SimilarWeb, job postings, patent filings, and customer reviews. Combine multiple proxies for a more reliable estimate than any single indicator.