Abstract: Systems and methods for a collaborative offer portal is provided. A proposed offer is received from a manufacturer, including an offer structure and a number of consumers they wish to target. Transaction logs of a retailer are accessed to determine an audience for the offer by calculating a return on investment (ROI) for the customer base using the retailer's records given the offer type. The consumers are then grouped by their ROI distribution, and the ROI for the deal is calculated based upon the offer size in light of this distribution. From the offer ROI a discount percentage to be paid by the retailer versus the merchant can be created. The retailer may then choose to accept the offer for deployment.

Abstract: Systems and methods for a contract-based offer generator is provided. A contract for a promotional offer on a product is received. Data is extracted from the contract. An offer band is accessed, and a plurality of test offers are selected from the offer bank by scoring each offer in the offer bank against the extracted data. The promotional offer and the selected plurality of test offers are deployed in a plurality of retail locations. This is done by maximizing orthogonality between the following variables: store sales, store out of stock rates, number of relevant SKUs carried in each store, temporal effects, discount depth, buy quantity and offer structure.

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 a collaborative offer portal is provided. A proposed offer is received from a manufacturer, including an offer structure and a number of consumers they wish to target. Transaction logs of a retailer are accessed to determine an audience for the offer by calculating a return on investment (ROI) for the customer base using the retailer's records given the offer type. The consumers are then grouped by their ROI distribution, and the ROI for the deal is calculated based upon the offer size in light of this distribution. From the offer ROI a discount percentage to be paid by the retailer versus the merchant can be created. The retailer may then choose to accept the offer for deployment.

Abstract: Systems and methods for optimizing pricing of products within a retailer are provided. Such systems and methods include determining a set of products to be included in an elasticity computation. Next, the number of days to collect transaction logs for a given product is determined, responsive to sales volumes for each given product. These transaction logs are then collected and used to compute the elasticities for these products. Products that were not included for calculation of elasticities have elasticities imputed for them. A set of constraints are received. Optimal prices are then generated based upon the objectives, rules and price elasticities.

Abstract: Methods and apparatus for generating intelligent offers with base prices are provided. In one embodiment, a promotion generator receives a current product base price, and also receives or calculates a remaining promotional program budget, a remaining promotional program duration, and a minimum discounted price for the product using the current product base price and any available previous base price data for the promoted product, creating or updating a predictive model of future product base prices.

Abstract: Systems and methods for a collaborative offer portal is provided. A proposed offer is received from a manufacturer, including an offer structure and a number of consumers they wish to target. Transaction logs of a retailer are accessed to determine an audience for the offer by calculating a return on investment (ROI) for the customer base using the retailer's records given the offer type. The consumers are then grouped by their ROI distribution, and the ROI for the deal is calculated based upon the offer size in light of this distribution. From the offer ROI a discount percentage to be paid by the retailer versus the merchant can be created. The retailer may then choose to accept the offer for deployment.

Abstract: Systems and methods for a contract-based offer generator is provided. A contract for a promotional offer on a product is received. Data is extracted from the contract. An offer band is accessed, and a plurality of test offers are selected from the offer bank by scoring each offer in the offer bank against the extracted data. The promotional offer and the selected plurality of test offers are deployed in a plurality of retail locations. This is done by maximizing orthogonality between the following variables: store sales, store out of stock rates, number of relevant SKUs carried in each store, temporal effects, discount depth, buy quantity and offer structure.

Abstract: Methods and apparatus for generating intelligent offers with base prices are provided. In one embodiment, a promotion generator receives a current product base price, and also receives or calculates a remaining promotional program budget, a remaining promotional program duration, and a minimum discounted price for the product using the current product base price and any available previous base price data for the promoted product, creating or updating a predictive model of future product base prices.

Abstract: Systems and methods for a collaborative offer portal is provided. A proposed offer is received from a manufacturer, including an offer structure and a number of consumers they wish to target. Transaction logs of a retailer are accessed to determine an audience for the offer by calculating a return on investment (ROI) for the customer base using the retailer's records given the offer type. The consumers are then grouped by their ROI distribution, and the ROI for the deal is calculated based upon the offer size in light of this distribution. From the offer ROI a discount percentage to be paid by the retailer versus the merchant can be created. The retailer may then choose to accept the offer for deployment.

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 offer redemption are provided. In some embodiments, payment data for a product is received via a payment gateway, along with an identifier unique to the payer. This payer identifier is correlated to a database associated with household IDs that have been collected from loyalty programs, pharmacy data and payment collected at a retailer. When the identifier has been associated with a household ID the system may identify applicable promotions that are associated with that household. The details of the offer/promotion are sent back to the point of sale where they are applied to the transaction. Once this is confirmed the adjusted payment is processed and the promotion is cleared from the database.

Abstract: Systems and methods for a collaborative offer portal is provided. A proposed offer is received from a manufacturer, including an offer structure and a number of consumers they wish to target. Transaction logs of a retailer are accessed to determine an audience for the offer by calculating a return on investment (ROI) for the customer base using the retailer's records given the offer type. The consumers are then grouped by their ROI distribution, and the ROI for the deal is calculated based upon the offer size in light of this distribution. From the offer ROI a discount percentage to be paid by the retailer versus the merchant can be created. The retailer may then choose to accept the offer for deployment.

Abstract: Systems and methods for offer redemption are provided. In some embodiments, payment data for a product is received via a payment gateway, along with an identifier unique to the payer. This payer identifier is correlated to a database associated with household IDs that have been collected from loyalty programs, pharmacy data and payment collected at a retailer. When the identifier has been associated with a household ID the system may identify applicable promotions that are associated with that household. The details of the offer/promotion are sent back to the point of sale where they are applied to the transaction. Once this is confirmed the adjusted payment is processed and the promotion is cleared from the database.

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 optimizing promotions within a physical retail space are provided. Electronic tags are deployed throughout the retail space. These tags are wirelessly coupled to a server system, allowing for real time and simultaneous updating of pricing and other promotional variables. These tags enable expansive testing of base pricing, promotion optimization, and sell through criteria. Testing may be performed on a wide range of promotional variables to determine what sorts of values for these variables yield the most effective promotions. Price elasticity for individual products can likewise be tracked through price adjustment testing for determining sell through scheduling. Further, by tracking individual consumers through the retail space, personalized promotions can be presented to the individuals.

Abstract: Systems and methods for optimizing promotions within a physical retail space are provided. Electronic tags are deployed throughout the retail space. These tags are wirelessly coupled to a server system, allowing for real time and simultaneous updating of pricing and other promotional variables. These tags enable expansive testing of base pricing, promotion optimization, and sell through criteria. Testing may be performed on a wide range of promotional variables to determine what sorts of values for these variables yield the most effective promotions. Price elasticity for individual products can likewise be tracked through price adjustment testing for determining sell through scheduling. Further, by tracking individual consumers through the retail space, personalized promotions can be presented to the individuals.

Abstract: Methods and apparatus for generating intelligent offers with base prices are provided. In one embodiment, a promotion generator receives a current product base price, and also receives or calculates a remaining promotional program budget, a remaining promotional program duration, and a minimum discounted price for the product using the current product base price and any available previous base price data for the promoted product, creating or updating a predictive model of future product base prices.

Abstract: A power and risk score calibrator is provided, which receives quantitative power and risk scores for each quantitative segment, and receives qualitative power and risk scores for each qualitative segment. The qualitative segment and the qualitative power and risk scores are defined by a user. The system generates consolidated segments. Then consolidated power and risk scores for each of the consolidated segments are generated, respectively. The gaps between the qualitative power and risk scores and the consolidated power and risk scores are reconciled. From these reconciliations, adjustment factors are generated, which are applied by pricing power and risk value calibrators. The system may also perform a drill down to explain the gap between the qualitative scores and the consolidated scores.