Let's imagine we have weekly sales data of a given set of products, and we're interested in forcasting the next week. Each product belongs to a product cluster (or "family"). I've tried both forecasting each product individually and forecasting an aggregate value for each family and then distributing this total using the typical "weights" of each product in the respective family, which work rather well depending on the family.
But what if there is another attribute that can be used to cluster past orders, such as the client? Is there a way this information can be introduced in the models?
Note: the same product can be sold to different clients.