Learning price promotion effects on recurring sell-in purchases from simulated store level sales data

When a product is put on promotion to increase its sales, this causes a decrease in the sales of another product in the same product group. This phenomenon happens usually when the promoted product is a substitute for the other product. In this study, we focus on the wholesaler’s revenue maximization problem over the given planning horizon. For this purpose, we constructed a Bayesian hierarchical model for the order quantities observed in the store level data for substitutable products. Order quantities are assumed to have Poisson distributions whose means depend on season, prices and previously ordered quantities for all products in the same group. The customers are assumed to have different price sensitivities, and consumption rates implicit in their historical order quantities. Using a hybrid of different Markov Chain Monte Carlo methods, we update model parameter posterior distributions and predict each retailer’s order quantities in the future. We verified on simulated sales data that the MCMC methods work.