How This Workflow Works
This workflow processes transaction and product data to discover frequent item combinations bought by customers using an association rule mining algorithm. It lets the user set the minimum frequency for patterns, and enriches the mined rules with product names and revenue estimates. The workflow then applies these rules to a customer’s current shopping basket to generate tailored recommendations and pricing insights.
Key Features:
- Discover frequent item combinations and product affinities
- Enrich rules with product names, prices, and revenue estimates
- Generate tailored product recommendations for the current basket using support, confidence, or revenue rule
- Present recommendations and pricing in a user-friendly view
Step-by-step:
1. Mine Frequent Item Combinations:
The workflow applies an association rule mining algorithm to transaction data to identify products that are often bought together. Adjustable support thresholds control how common a combination must be to appear in the results.
2. Enrich Rules with Product and Revenue Details:
Generated rules are enhanced with product names, prices, and estimated revenue, making them easier to interpret and prioritize from a business perspective.
3. Apply Rules to Current Basket:
The workflow applies the mined association rules to a customer’s current shopping basket to identify the most relevant purchase combinations.
4. Select Top Recommendations:
Association rules are ranked based on configurable criteria such as support, confidence, or potential revenue to surface the most impactful product recommendations.
5. Calculate Basket Price and Present Results:
The total price of the current basket is calculated and presented alongside product recommendations in a clear, user-friendly view that can be exported or shared for further analysis.