Abstract: A system and method for automated reactive retail pricing through multilevel hierarchical regression is disclosed. A reactive retail pricing server obtains a set of relevant data, creates a plurality of merchandising sets, and transforms the set of relevant data. Coefficients are calculated for a group model, and are then reverse transformed. Recommended pricing is calculated using the group model, where retailer strategies, retailer goals, and price campaign parameters are applied as constraints. The coefficients are automatically calibrated, and the price campaign is adjusted.

Abstract: Product pricing and inventory optimization systems and methods and user interface elements enable users to optimize inventory (e.g., product quantity or volume) and pricing levels in real time for products for a given time period. Pricing and volume information is automatically determined for products in a product category for a specified time frame based on a shopping criterion defining how a merchandising set including that product is shopped by consumers and based on specified price and volume change limits. The shopping criterion may include a hierarchical order of at least two product relationship parameters, the product relationship parameters including brand relation information, size relation information, feature relation information and price relation information. In certain aspects, the pricing and volume information is also determined based on a brand hierarchy identifying an order of a set of product brands for the product category.