AgentSkillsCN

Analyze Market Basket

分析市场篮子

SKILL.md

Skill: Analyze Market Basket

Domain

retail

Description

Analyzes market basket data to identify product associations, cross-sell opportunities, and optimal product placement strategies.

Tags

retail, analytics, basket-analysis, cross-sell, merchandising, recommendations

Use Cases

  • •Product association discovery
  • •Cross-sell recommendation
  • •Store layout optimization
  • •Bundle pricing strategy

Proprietary Business Rules

Rule 1: Association Rule Mining

Identification of frequent itemsets and association rules.

Rule 2: Lift Calculation

Measurement of association strength beyond random chance.

Rule 3: Temporal Patterns

Analysis of time-based purchasing patterns.

Rule 4: Customer Segmentation

Association patterns by customer segment.

Input Parameters

  • •analysis_id (string): Analysis identifier
  • •transaction_data (list): Transaction records
  • •product_catalog (dict): Product information
  • •time_period (dict): Analysis time range
  • •min_support (float): Minimum support threshold
  • •min_confidence (float): Minimum confidence threshold

Output

  • •association_rules (list): Discovered rules
  • •product_pairs (list): Frequently bought together
  • •recommendations (list): Merchandising recommendations
  • •segment_insights (dict): Patterns by segment
  • •summary_stats (dict): Analysis statistics

Implementation

The analysis logic is implemented in basket_analyzer.py and references data from CSV files:

  • •default_parameters.csv - Reference data
  • •analysis_windows.csv - Reference data
  • •recommendation_thresholds.csv - Reference data
  • •time_patterns.csv - Reference data
  • •segment_definitions.csv - Reference data
  • •cross_sell_rules.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from basket_analyzer import analyze_basket

result = analyze_basket(
    analysis_id="MBA-001",
    transaction_data=[{"txn_id": "T001", "items": ["bread", "milk", "eggs"]}],
    product_catalog={"bread": {"category": "bakery"}, "milk": {"category": "dairy"}},
    time_period={"start": "2025-01-01", "end": "2025-12-31"},
    min_support=0.01,
    min_confidence=0.3
)

print(f"Rules Found: {len(result['association_rules'])}")

Test Execution

python
from basket_analyzer import analyze_basket

result = analyze_basket(
    analysis_id=input_data.get('analysis_id'),
    transaction_data=input_data.get('transaction_data', []),
    product_catalog=input_data.get('product_catalog', {}),
    time_period=input_data.get('time_period', {}),
    min_support=input_data.get('min_support', 0.01),
    min_confidence=input_data.get('min_confidence', 0.3)
)