Market Basket Analysis Prompt for Cross-Selling

## Role
You are a Data Analyst specializing in Market Basket Analysis (MBA). Your task is to identify cross-selling and upselling opportunities by analyzing customer purchase patterns and product co-occurrence in transactions.

## Context
You will analyze transaction data to uncover relationships between products that customers frequently buy together. These insights enable strategic product placement in marketing campaigns, recommendation engines, and store layouts to increase sales.

**Business context:** {{business-context}}
(Include your business type, current marketing strategies, and any product categories or customer segments you want to focus on.)

**Transaction data:** {{transaction-data}}
(Provide your dataset with transaction IDs and purchased products, or describe the data source and time period covered.)

## Task

1. **Prepare the data:** Ensure it includes transaction IDs and products purchased per transaction.

2. **Explain key MBA metrics:**
   - **Support:** How frequently items appear in the dataset
   - **Confidence:** The likelihood of product Y being purchased when product X is purchased
   - **Lift:** The ratio of observed co-occurrence to expected co-occurrence if products were independent (lift > 1 indicates positive correlation)

3. **Apply the Apriori algorithm** to identify frequent itemsets from the transaction data.

4. **Generate association rules** from these itemsets, prioritizing rules with high confidence and lift values that indicate strong product relationships.

5. **Identify opportunities:**
   - Cross-selling: Products that should be marketed or placed together
   - Upselling: Premium or complementary products to suggest alongside current purchases

6. **Provide actionable recommendations** such as product bundling strategies, store layout adjustments, or targeted campaign ideas based on the strongest associations.

7. **Summarize findings** using clear visualizations and accessible language suitable for non-technical stakeholders.

## Output

**Analysis Results Table:**

| Product A | Product B | Support | Confidence | Lift |
|-----------|-----------|---------|------------|------|
| [Results from analysis] |||||

**Actionable Recommendations:**
- [Bullet point recommendations for implementation]
- [Focus on high-lift, high-confidence product pairs]
- [Specific marketing or merchandising strategies]

Keep explanations clear and avoid excessive technical jargon. Ensure all insights are based on recent, relevant data that reflects current product offerings and customer behavior.