Patent application number | Description | Published |
20120284084 | SCALABLE REGRESSION FOR RETAIL PANEL DATA - 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. | 11-08-2012 |
20130185116 | AUTOMATIC DEMAND PARAMETER ESCALATION - A system provides automatic escalation of demand parameters to determine a reliable demand parameter for a level within a sales hierarchy. The system measures difference in demand parameters between a level of interest within the sales hierarchy and a plurality of other levels within the sales hierarchy. The system also compares the differences in demand parameters of the other levels. The system further determines an escalation path for a demand parameter based on the comparison. | 07-18-2013 |
20130185117 | AUTOMATIC DEMAND PARAMETER ESTIMATION - One embodiment is directed generally to a computer system, and in particular to a system for providing automatic estimating of demand parameters. According to certain embodiments, a computer readable medium has instructions stored thereon that, when executed by a processor, cause the processor to determine a reliable demand parameter for a level within a sales hierarchy. The instructions include estimating a demand parameter for a first pool. The estimating is based on blending and comparing with respect to an enlarged pool comprising the first pool as a subset of the enlarged pool to obtain an estimated demand parameter. | 07-18-2013 |
20130211877 | RETAIL PRODUCT PRICING MARKDOWN SYSTEM - A system that determines a pricing markdown schedule for a retail item at a store receives demand parameters of the retail item at the store and one or more constraints, and expresses a price curve and inventory curve as linear combinations of price and inventory coefficients for orthogonal polynomials. The system determines revenue in terms of values of the price and inventory coefficients, determines an initial guess of the price and inventory coefficients, and determines a gradient of the revenue. The system then maximizes the revenue based on the revenue, the initial guesses, the gradient, and the constraints, where the constraints are in terms of the price and inventory coefficients. Based on the maximized revenue, the system then generates the price markdown schedule. | 08-15-2013 |
20130211878 | ESTIMATING ELASTICITY AND INVENTORY EFFECT FOR RETAIL PRICING AND FORECASTING - A system that estimates elasticity and inventory effect for a product pricing or forecasting system receives a sales condition relationship for an item at a store, the relationship comprising an elasticity parameter, an inventory effect parameter and a sales constant. The system receives a demand model for sales of the item in terms of the elasticity parameter and the inventory effect parameter and a base demand for the item selling at the store. The system estimates the sales constant, the estimating comprising generating a theta parameter by taking logarithms of the sales condition relationship. The system uses linear regression to estimate a logarithm of the sales constant and a value of the theta parameter. The system determines a relationship between the elasticity parameter and the inventory effect parameter based on the value of the theta parameter. | 08-15-2013 |
20130346352 | CONSUMER DECISION TREE GENERATION SYSTEM - A system generates a consumer decision tree (“CDT”). The system receives customer purchasing data that includes transactions of a plurality of products each having at least one product attribute. For a product category, the system identifies a plurality of similar products from the purchasing data and one or more attributes corresponding to each similar product. The system assigns the product category as a current level of the CDT, and determines a most significant attribute of the plurality of attributes for the current level. The system forms a next level of the CDT by dividing the most significant attribute into a plurality of sub-sections, where each sub-section corresponds to an attribute value of the most significant attribute. The system then forms a next level of the CDT for each sub-section until a terminal node is identified. | 12-26-2013 |
20140358633 | DEMAND TRANSFERENCE FORECASTING SYSTEM - A demand transference forecast system receives for a category of merchandise de-promoted sales data for each of a plurality of stock keeping units (“SKUs”), similarities between each pair of SKUs in the category, and SKU-store ranging information. The system determines a sales indices of all SKUs in the category across the de-promoted sales data for the category. The system determines Total Assortment Effect (“TAE”) variable quantities for the SKUs across share intervals in the de-promoted sales data based on the sales indices and the similarities. The system then generates a single parameter based demand transference model based on the similarities, the sales indices, and ratios of the share intervals. | 12-04-2014 |
20150100554 | ATTRIBUTE REDUNDANCY REMOVAL - Systems, methods, and other embodiments associated with attribute redundancy removal are described. In one embodiment, a method includes identifying redundant attribute values in a group of attributes that describe two items. The example method also includes generating a pruned group of attributes having the redundant attribute values removed. The similarity of the two items is calculated based, at least in part, on the pruned group of attribute values. | 04-09-2015 |