Optimal Pricing at Grocery Stores
Revenue maximization via optimal pricing policies based on purchase history
Traditional revenue optimization relies on generic demand assumptions
This approach generally has practical limitations: either the demand estimation is too broad and not personalized to a particular grocery store, or there is not enough data from a store to estimate demand.
We use Optimal Policy Trees that leverage data from all grocery stores and use intrinsic features such as customer demographics to cluster stores and make optimal pricing recommendations.
Combine the power of complex demand modeling and interpretable policy learning
In the example policy tree shown here, it uses age, income, household size, and homeownership to separate the households into cohorts where different prices lead to optimal revenue. As an example, it identifies that it is optimal to choose the highest price for households with two adults, no children and income above 150k.
Illustrative Optimal Policy Tree prescribing the prices that optimize revenue
Global optimality leads to increased revenue
Why is the Interpretable AI solution unique?
A novel approach to policy learning
Optimal Policy Trees are the first scalable and optimal solution to this class of problem
Flexible demand modeling
This framework allows for complex and accurate modeling of demand while maintaining interpretable policy recommendations