Abstract: 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.

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: 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.

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 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.