AgentSkillsCN

Evaluate Brand Equity

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SKILL.md

Skill: Evaluate Brand Equity

Domain

consumer_products

Description

Evaluates brand equity through awareness metrics, perception analysis, and financial valuation for brand management decisions.

Tags

brand, marketing, valuation, consumer, equity, research

Use Cases

  • •Brand valuation
  • •Brand health tracking
  • •Competitive positioning
  • •M&A brand assessment

Proprietary Business Rules

Rule 1: Brand Awareness Measurement

Aided and unaided awareness scoring.

Rule 2: Brand Perception Analysis

Attribute and sentiment evaluation.

Rule 3: Financial Valuation

Brand contribution to enterprise value.

Rule 4: Competitive Benchmarking

Relative brand strength assessment.

Input Parameters

  • •brand_id (string): Brand identifier
  • •awareness_data (dict): Awareness metrics
  • •perception_data (dict): Brand perception surveys
  • •financial_data (dict): Revenue and margin data
  • •competitor_data (list): Competitive brand metrics
  • •market_data (dict): Market context

Output

  • •brand_equity_score (float): Overall brand equity
  • •awareness_metrics (dict): Awareness analysis
  • •perception_analysis (dict): Brand perception
  • •financial_value (float): Estimated brand value
  • •competitive_position (dict): Market positioning

Implementation

The evaluation logic is implemented in brand_evaluator.py and references data from CSV files:

  • •equity_dimensions.csv - Reference data
  • •valuation_methods.csv - Reference data
  • •performance_ratings.csv - Reference data
  • •industry_benchmarks.csv - Reference data
  • •nps_interpretation.csv - Reference data
  • •nps_benchmarks.csv - Reference data
  • •price_premium_factors.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from brand_evaluator import evaluate_brand

result = evaluate_brand(
    brand_id="BRAND-001",
    awareness_data={"aided": 0.85, "unaided": 0.45, "top_of_mind": 0.20},
    perception_data={"quality": 4.2, "value": 3.8, "trust": 4.0},
    financial_data={"revenue": 500000000, "brand_premium": 0.15},
    competitor_data=[{"brand": "Competitor A", "awareness": 0.75}],
    market_data={"category_size": 5000000000, "growth_rate": 0.05}
)

print(f"Brand Equity Score: {result['brand_equity_score']}")

Test Execution

python
from brand_evaluator import evaluate_brand

result = evaluate_brand(
    brand_id=input_data.get('brand_id'),
    awareness_data=input_data.get('awareness_data', {}),
    perception_data=input_data.get('perception_data', {}),
    financial_data=input_data.get('financial_data', {}),
    competitor_data=input_data.get('competitor_data', []),
    market_data=input_data.get('market_data', {})
)