Overview of Frequently bought together model (preview)


Some or all of this functionality is available as part of a preview release. The content and the functionality are subject to change.

The Frequently bought together model empowers the store or the merchandising manager to make data-driven decisions on products’ placement and promotions, based on insights on closely related products. The Frequently bought together model identifies in Point-of-Sale (POS) data, the combinations of products that are most often bought together. The model reveals associated products that could be placed closer together so that retailers can effectively promote cross-selling activity and boost joint sales.

Retailers can compare Frequently bought together revenues, which provide a measurement for:

  • Performance of promotion action, seasonality, and new shelf assortment by comparing two different time periods (typically before and after the action was implemented).

  • Customer behavior by comparing different stores of different geography, population, and surface area.

  • Product groups that drive more revenue by comparing between items.

To use the Frequently bought together model , you should have the following prerequisite knowledge:

  • Microsoft Fabric platform

  • Notebook

  • Lakehouse

  • PySpark/Spark

The Fabric notebook gives retailers the flexibility to:

  • Execute the model in their own cadence.

  • Connect with input data anywhere in Fabric.

  • Fine-tune the model parameters for specific customizations.

  • Choose how to visualize the results.