When planning for future promotions, one of the manufacturer’s goals is to maximize the “gain” from competitor products (so that consumers switch from a competing product to thier own) and minimize the “loss” from their own product range. If an increase in promotional sales comes at the cost of another one of the manufacturer’s products, this is called cannibalization, which means that consumers have switched from one of our products they regularly buy, to the one on promotion. Through data analysis, we could create a cannibalization matrix, which is a table that outlines the expected cannibalization effect of certain products when placed on promotion. However, the practical challenges of constructing such a table are significant. Without applying any domain knowledge or business rules, it might be necessary to look up each individual product and calculate its cannibalizing effect on every other product in our range. For most manufacturers, which may sell hundreds or even thousands of products, this would result in a table with tens or even hundreds of thousands of values. While it’s possible to calculate this automatically, there’s no easy way to validate these values without going through them line by line. A better approach is through the application of human knowledge along with various Artificial Intelligence methods; for example, by implementing human rules based on well-founded assumptions, such as cannibalization occurring across individual categories, we could capture 80% of the expected cannibalization effect with just 20% of the modeling effort, and then complement the cannibalization matrix by additional modifications that result from a deeper analysis of the data using AI algorithms. To learn more about promotional planning and pricing, watch this supplementary video to Chapter 3 of The Rise of Artificial Intelligence, a recently published book authored by the Complexica team.