Abstract: Systems, methods, and other embodiments associated with scalable regression for retail panel are described. In one embodiment, a method includes performing a regression that estimates elasticity of demand for a retail item, wherein the regression is performed on a transformation of a demand model that does not include variables associated with base demand or seasonality. In a subsequent processing step, the method includes estimating a base demand and seasonality for the retail item based, at least in part, on the estimated elasticity of demand. The method may be performed in a database that stores retail panel data for the retail item and other retail items.

Abstract: A system determines a markdown pricing sequence for a product. The system receives a sequence of future prices as a function of time for the product based at least on business rules. For each price in the sequence, the system determines a reference price for the product, and then determines an increase in revenue using a demand model. The demand model includes a price elasticity variable that uses the reference price instead of a full price. The system then determines if the sequence of future prices is an optimized sequence based at least in part on the determined increase in revenue.

Abstract: A system determines a revised price on a pricing ladder for a product over a pricing markdown period. For a first time interval of the markdown period, the system computes an optimal price for the product based on an inventory level of the product, where the inventory level is based on a ratio of a current on-hand inventory and a maximal on-hand inventory. The system then determines if the optimal price is approximately less than a current price of the product. When the optimal price is approximately less than a current price of the product, the system assigns to the product a lower price on the price ladder as the current price of the product.

Abstract: Systems, methods, and other embodiments associated with scalable regression for retail panel are described. In one embodiment, a method includes performing a regression that estimates elasticity of demand for a retail item, wherein the regression is performed on a transformation of a demand model that does not include variables associated with base demand or seasonality. In a subsequent processing step, the method includes estimating a base demand and seasonality for the retail item based, at least in part, on the estimated elasticity of demand. The method may be performed in a database that stores retail panel data for the retail item and other retail items.