Skill: Evaluate Store Performance
Domain
retail
Description
Evaluates retail store performance using sales metrics, operational efficiency, and customer experience indicators for portfolio optimization.
Tags
retail, store, performance, sales, operations, analytics
Use Cases
- •Store benchmarking
- •Performance ranking
- •Closure decisions
- •Investment prioritization
Proprietary Business Rules
Rule 1: Sales Performance Metrics
Revenue and comp sales analysis.
Rule 2: Operational Efficiency
Labor, shrink, and expense ratios.
Rule 3: Customer Metrics
Traffic, conversion, and satisfaction.
Rule 4: Portfolio Classification
Store tier and action classification.
Input Parameters
- •
store_id(string): Store identifier - •
sales_data(dict): Sales performance - •
operational_data(dict): Operational metrics - •
customer_data(dict): Customer metrics - •
market_data(dict): Trade area demographics - •
benchmark_data(dict): Performance benchmarks
Output
- •
performance_score(float): Overall rating - •
sales_analysis(dict): Sales metrics - •
operational_assessment(dict): Efficiency analysis - •
customer_assessment(dict): Customer metrics - •
classification(string): Store tier - •
recommendations(list): Performance actions
Implementation
The evaluation logic is implemented in store_evaluator.py and references data from store_benchmarks.json.
Usage Example
python
from store_evaluator import evaluate_store
result = evaluate_store(
store_id="STR-001",
sales_data={"revenue": 5000000, "comp_growth": 0.03, "transactions": 150000},
operational_data={"labor_pct": 0.12, "shrink_pct": 0.01, "sqft": 25000},
customer_data={"traffic": 200000, "conversion": 0.75, "nps": 45},
market_data={"population": 100000, "median_income": 65000},
benchmark_data={"avg_sales_sqft": 180, "avg_conversion": 0.70}
)
print(f"Store Performance Score: {result['performance_score']}")
Test Execution
python
from store_evaluator import evaluate_store
result = evaluate_store(
store_id=input_data.get('store_id'),
sales_data=input_data.get('sales_data', {}),
operational_data=input_data.get('operational_data', {}),
customer_data=input_data.get('customer_data', {}),
market_data=input_data.get('market_data', {}),
benchmark_data=input_data.get('benchmark_data', {})
)