Market Basket Analysis
This workflow demonstrates how the data for the Market Basket Analysis was generated. This supermarket offers 48 Products across 10 Categories. Ten-Thousand Shopping Baskets were generated containing between 2 and 15 Products each.
Sales data was generated by first estimating the affinity between Categories and Products. For example, a strong affinity was assumed to exist between the “Meat & Fish” Category and the “Vegetables” Category as Customers shopping for one typically purchases the other. But affinities were also assumed to exist between Products spanning different Categories. For example, the workflow assumes there is a relationship between “Cornflakes” (in the “Cereal” Category) and “Full Cream Milk” (in the “Dairy” Category).
The workflow generates the part-worth Willingness To Pay (WTP) Customers have for each of the Categories and each of the Products.
The supermarket sells 48 Products across 10 Categories. Yogurt falls within the Dairy Category and is priced at $5.99.
There is a part-worth WTP for each Category. Customers place an average value on the Dairy Category of $12.
The Category Correlations and Mean WTP values are combined to generate part-worth WTP Category Distributions.
Product Correlation Matrix
Product Correlations are also transformed into a Correlation Matrix just as the Category Correlations were.
The Product Correlations and Mean WTP values are again combined to generate part-worth WTP Product Distributions.
Customers only buy their top choices so it is necessary to shred their demand for less desirable Products. There is a 90% chance Customers will not buy the next Nth Product.