Abstract: A system that predicts promotional cross item (“PCI”) effects for retail items for a store receives historical sales data for the store and stores the historical sales data in a panel data format. The system then aggregates the stored sales data as a first level of aggregation that is aggregated to the store, a product and a time period. The system further aggregates the first level of aggregation aggregated data as a second level of aggregation that is based on a promotional cross effect attribute (“PCEA”) and is aggregated to the store, the time period and a PCEA level. The system derives PCI effect predictor variables from the second level of aggregation and, for each PCEA within a retail item family, forms a regression model. The system then generates estimated model parameters for one or more PCI effects for each PCEA from the regression models.

Abstract: A system that predicts promotional cross item (“PCI”) effects for retail items for a store receives historical sales data for the store and stores the historical sales data in a panel data format. The system then aggregates the stored sales data as a first level of aggregation that is aggregated to the store, a product and a time period. The system further aggregates the first level of aggregation aggregated data as a second level of aggregation that is based on a promotional cross effect attribute (“PCEA”) and is aggregated to the store, the time period and a PCEA level. The system derives PCI effect predictor variables from the second level of aggregation and, for each PCEA within a retail item family, forms a regression model. The system then generates estimated model parameters for one or more PCI effects for each PCEA from the regression models.

Abstract: 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.

Abstract: A system for predicting a lagged promotional effect in response to a promotion of a product in a store receives historical sales data for the product in the store and stores the historical sales data in a panel data format. The stored sales data is aggregated to the store, product and a time period. The system then trains, validates and tests one or more candidate regression models using the historical sales data, and selects one of the one or more candidate regression models based on the validating and testing. The system then scores the selected regression model to determine a sales volume change for the product after the promotion.

Abstract: 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.

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: Based on a metric that represents a value of a game of chance, a payout distribution is optimized with respect to the metric.

Abstract: A set of data is received containing values associated with respective data points, the values associated with each of the data points being characterized by a distribution. The values for each of the data points are expressed in a form that includes information about a distribution of the values for each of the data points. The distribution information is used in clustering the set of data with at least one other set of data containing values associated with data points.

Abstract: Based on a metric that represents a value of a game of chance, a payout distribution is optimized with respect to the metric.

Abstract: Based on a metric that represents a value of a game of chance, a payout distribution is optimized with respect to the metric.

Abstract: A set of data is received containing values associated with respective data points, the values associated with each of the data points being characterized by a distribution. The values for each of the data points are expressed in a form that includes information about a distribution of the values for each of the data points. The distribution information is used in clustering the set of data with at least one other set of data containing values associated with data points.

Abstract: A set of data is received containing values associated with respective data points, the values associated with each of the data points being characterized by a distribution. The values for each of the data points are expressed in a form that includes information about a distribution of the values for each of the data points. The distribution information is used in clustering the set of data with at least one other set of data containing values associated with data points.

Abstract: Based on a metric that represents a value of a game of chance, a payout distribution is optimized with respect to the metric.

Abstract: A set of data is received containing values associated with respective data points, the values associated with each of the data points being characterized by a distribution. The values for each of the data points are expressed in a form that includes information about a distribution of the values for each of the data points. The distribution information is used in clustering the set of data with at least one other set of data containing values associated with data points.