Abstract: Systems and methods for optimizing base pricing of products within a physical retailer are provided. Such systems and methods include first collecting transaction logs for products in a set of physical retail spaces. These logs are validated, adjusted and elasticities between the products are computed. The adjustment may be responsive to the day, by retailer and by a host of external factors (e.g., weather). The adjustment may also include a normalization and filtering out of inaccurate log data. Elasticity is calculated by machine learning models. A set of constraints are then received and used, along with the elasticities to compute the optimal prices for deployment in retailers for further testing.

Abstract: Systems and methods for optimizing base pricing of products within a physical retailer are provided. Such systems and methods include first collecting transaction logs for products in a set of physical retail spaces. These logs are validated, adjusted and elasticities between the products are computed. The adjustment may be responsive to the day, by retailer and by a host of external factors (e.g., weather). The adjustment may also include a normalization and filtering out of inaccurate log data. Elasticity is calculated by machine learning models. A set of constraints are then received and used, along with the elasticities to compute the optimal prices for deployment in retailers for further testing.

Abstract: Systems and methods for optimizing base pricing of products within a physical retailer are provided. Such systems and methods include first collecting transaction logs for products in a set of physical retail spaces. These logs are validated, adjusted and elasticities between the products are computed. The adjustment may be responsive to the day, by retailer and by a host of external factors (e.g., weather). The adjustment may also include a normalization and filtering out of inaccurate log data. Elasticity is calculated by generalized linear models. A set of constraints are then received and used, along with the elasticities.

Abstract: Systems and methods for the generation of a promotional design matrix with recommendation overlay are provided. A product, discount, quantity and offer structure selection are received from a user, or are imputed based upon previous offers. Variants for the discount vale and quantity are generated by incrementing these values. The quantity and its variants are each assigned to a column of a design matrix, while the discount and variants are assigned to the design matrix rows. At each intersection of a row and column of the design matrix a plurality of offer structures based upon the offer structure selection is generated. The system applies recommendation logic to flag offers that are most likely to be successful based upon their performance, feasibility and diversity. This flagging may include placing an icon, such as a star, next to the recommended offers.

Abstract: Systems and methods for optimizing base pricing of products within a physical retailer are provided. Such systems and methods include first collecting transaction logs for products in a set of physical retail spaces. These logs are validated, adjusted and elasticities between the products are computed. The adjustment may be responsive to the day, by retailer and by a host of external factors (e.g., weather). The adjustment may also include a normalization and filtering out of inaccurate log data. Elasticity is calculated by generalized linear models. A set of constraints are then received and used, along with the elasticities.

Abstract: Systems and methods for the generation of a promotional design matrix with recommendation overlay are provided. A product, discount, quantity and offer structure selection are received from a user, or are imputed based upon previous offers. Variants for the discount vale and quantity are generated by incrementing these values. The quantity and its variants are each assigned to a column of a design matrix, while the discount and variants are assigned to the design matrix rows. At each intersection of a row and column of the design matrix a plurality of offer structures based upon the offer structure selection is generated. The system applies recommendation logic to flag offers that are most likely to be successful based upon their performance, feasibility and diversity. This flagging may include placing an icon, such as a star, next to the recommended offers.