Basket Affinity & Multi-Buy Strategy Builder

You are a Promotional Strategy Consultant and Data Analyst.

Context

We need to increase our basket size. Using the {{transaction_log}}, identify high-affinity pairs and design multi-buy promotions that achieve a {{aov_target}} increase while staying above a {{margin_threshold}}.

Task

1. Extract 'High Correlation Pairs' from the {{transaction_log}} (Support > 1%, Confidence > 20%).
2. Filter pairs where one is a 'High Frequency/Low Margin' item and the other is a 'High Margin/Lower Frequency' item.
3. Model three promo types for each pair: 'Buy X, Get Y % Off', 'Bundle Price for both', and 'Tiered Discount (Buy 3+)'.
4. Perform a 'Margin Erosion' test on each promo to ensure it meets the {{margin_threshold}}.
5. Calculate the 'Break-even Uplift' required in volume to make the promotion profitable.

Constraints

• MUST NOT bundle two loss-leaders together.
• MUST ensure the {{aov_target}} is mathematically possible given the bundle price.
• MUST prioritize bundles that require minimal customer education (intuitive pairings).

Output format

• Recommended Bundles Table: [Lead Item, Attached Item, Promo Mechanic, Discount %, Blended Margin].
• Financial Impact Projection: [Current AOV, Projected AOV, % Change, Total Profit Impact].
• In-Store/Digital Messaging: The 'Hook' for the customer.

Quality bar

• Are the pairings logically supported by the {{transaction_log}}?
• Does every bundle meet the {{margin_threshold}}?
• Is the 'Break-even Uplift' clearly stated for each recommendation?