Introduction
The Frequently bought together model provides a rich set of information and insights on its user interface (UI). The Product Groupings view provides analytics based on the product's Frequently bought together revenue KPI. The Frequently bought together revenue KPI of a product (called a main product), is the sum of the sales revenue from the main product, which the system adds to the sales revenues of other products that customers bought together with the main product. You can analyze each product to calculate its Frequently bought together revenue, and the chart shows only target products as defined by Product Criterion (typically, 10 top highest revenues).
Contoso Groceries implements the Frequently bought together model, a feature of Retail data solutions in Microsoft Cloud for Retail, to deal with these challenges and achieve its goals. It wants to apply data-driven insights to address the identified challenges and achieve its objectives.
By applying the following three filters, you can define the point-of-sale (POS) data that the AI/ML Frequently bought together model analyzes:
Retailer or store - POS data identifies this filter. Typical options are retail-chain, shop 1, and shop 2.
Period - You can define this filter in the Frequently bought together model notebook. For example, the period can be related to marketing activities such as new shelf assortment. The filters that are configured via the notebook in this exercise case are Before shelf assortment and After shelf assortment.
Product criterion - This filter extracts the most relevant products that are set from the POS. A typical product criterion could be 10 highest Frequently bought together revenues or 10 top selling products Frequently bought together revenues.
Personas and scenarios
In these exercises, you assume the persona of Megan, the data scientist. Your task is to extract insights that are related to the various challenges that Contoso Groceries is currently dealing with, as outlined in the Retail Story.