Market Research Reports

Market Research Reports is a community skill for generating structured market analysis documents, covering market sizing, competitive landscape analysis, trend identification, customer segmentation, and forecast modeling for business intelligence workflows.

What Is This?

Overview

Market Research Reports provides tools for creating comprehensive market analysis documents from structured data inputs. It covers market sizing that estimates total addressable market, serviceable addressable market, and serviceable obtainable market using top-down and bottom-up methodologies, competitive landscape analysis that maps competitors across market positioning dimensions with feature comparison matrices, trend identification that surfaces market movements and emerging opportunities from industry data sources, customer segmentation that groups potential buyers by demographics, behavior patterns, and purchase criteria, and forecast modeling that projects market growth trajectories using historical data and growth rate assumptions. The skill enables analysts to produce structured research reports for strategic decision-making.

Who Should Use This

This skill serves business analysts preparing market intelligence reports, product managers researching new market opportunities, and strategy consultants producing competitive analysis deliverables.

Why Use It?

Problems It Solves

Market research reports assembled manually from disparate data sources take significant time to compile and format. Market size calculations require consistent methodology application that manual approaches may not maintain. Competitive analysis matrices built in spreadsheets lack structured formatting for presentation. Forecast projections require parameterized models that produce different scenarios based on growth assumptions.

Core Highlights

Market sizer calculates TAM, SAM, and SOM with configurable methodologies. Competitor mapper creates positioning matrices with feature comparisons. Trend analyzer identifies market movements from structured data inputs. Forecaster projects growth trajectories across multiple scenarios.

How to Use It?

Basic Usage

class MarketSizer:
  def __init__(
    self,
    total_population:
      int,
    avg_spend: float,
    target_pct:
      float = 0.3,
    capture_pct:
      float = 0.05
  ):
    self.population = (
      total_population)
    self.spend = avg_spend
    self.target = (
      target_pct)
    self.capture = (
      capture_pct)

  def tam(self) -> float:
    return (
      self.population
      * self.spend)

  def sam(self) -> float:
    return (
      self.tam()
      * self.target)

  def som(self) -> float:
    return (
      self.sam()
      * self.capture)

  def report(
    self
  ) -> dict:
    return {
      'TAM': round(
        self.tam(), 2),
      'SAM': round(
        self.sam(), 2),
      'SOM': round(
        self.som(), 2),
      'methodology':
        'top-down'}

Real-World Examples

class CompetitiveMatrix:
  def __init__(
    self,
    features:
      list[str]
  ):
    self.features = (
      features)
    self.competitors = {}

  def add_competitor(
    self,
    name: str,
    scores: dict
  ):
    self.competitors[
      name] = scores

  def ranking(
    self,
    feature: str
  ) -> list[tuple]:
    ranked = [
      (name,
       scores.get(
         feature, 0))
      for name, scores
      in self.competitors
        .items()]
    return sorted(
      ranked,
      key=lambda x:
        x[1],
      reverse=True)

  def summary(
    self
  ) -> dict:
    results = {}
    for name, scores\
        in self.competitors\
          .items():
      avg = sum(
        scores.values()
      ) / max(len(
        scores), 1)
      results[name] = (
        round(avg, 2))
    return dict(
      sorted(
        results.items(),
        key=lambda x:
          x[1],
        reverse=True))

Advanced Tips

Use both top-down and bottom-up market sizing approaches to triangulate estimates and increase confidence in projections. Build competitive matrices with weighted features based on customer priority surveys to reflect actual buying criteria. Create multiple forecast scenarios covering conservative, moderate, and aggressive growth assumptions.

When to Use It?

Use Cases

Calculate TAM, SAM, and SOM for a new product entering an established market category. Build a competitive analysis matrix comparing product features across market players. Generate a market growth forecast with multiple scenario projections for investor presentations.

Related Topics

Market research, competitive analysis, market sizing, business intelligence, customer segmentation, market forecasting, and strategic planning.

Important Notes

Requirements

Market data inputs for sizing calculations and trend analysis. Industry knowledge for meaningful competitive comparisons. Data processing tools for structured report generation.

Usage Recommendations

Do: cite data sources for all market size estimates to establish credibility. Validate competitive analysis with multiple data points rather than single observations. Present forecasts as ranges with stated assumptions rather than single point estimates.

Don't: present market size estimates without documenting the methodology and assumptions used. Build competitive matrices with biased scoring that favors a predetermined conclusion. Extrapolate trends beyond time horizons where the underlying assumptions remain reasonable.

Limitations

Market size estimates are inherently approximate and depend on the quality of input data and assumptions. Competitive analysis reflects a point in time and requires regular updates as the market evolves. Forecast accuracy decreases with longer projection periods and volatile market conditions.