Abstract: The invention provides, in some aspects, digital data processing methods of generating digital content pieces (e.g., email messages or portions thereof) that are customized in accord with individual recipient behaviors. Such methods include the step of generating and digitally transmitting to a digital data devices of a recipient a digital content piece that (i) has a call to action to which the recipient can respond and (ii) that has a plurality of features selected so as to maximize a probability, P(b1, b2, . . . , bM, x1, x2, . . . , xM), that the recipient will respond to that call to action, where that probability is defined by the relation P(b1,b2, . . . , bM,x1,x2, . . . , xM)=exp(?j=1, . . . , Mbjxj)/(1+exp(?j=1, . . . , Mbjxj)) where x1, x2, . . . , xM are values for each of a plurality, M, of features characterizing the digital content piece and/or the recipient, b1, b2, . . . , bM are respective coefficients for each of the values x1, x2, . . . , xM.

Abstract: The invention provides, in some aspects, digital data processing methods of generating digital content pieces (e.g., email messages or portions thereof) that are customized in accord with individual recipient behaviors. Such methods include the step of generating and digitally transmitting to a digital data devices of a recipient a digital content piece that (i) has a call to action to which the recipient can respond and (ii) that has a plurality of features selected so as to maximize a probability, P(b1, b2, . . . , bM, x1, x2, . . . , xM) , that the recipient will respond to that call to action, where that probability is defined by the relation P(b1,b2, . . . , bM, x1, x2, . . . , xM)=exp(?j=1, . . . , Mbjxj)/(1+exp(?j=1, . . . , Mbjxl)) where x1, x2, . . . , xM are values for each of a plurality, M, of features characterizing the digital content piece and/or the recipient, b1, b2, . . . , bM are respective coefficients for each of the values x1, x2, . . .

Abstract: The invention provides, in some aspects, digital data processing methods of generating digital content pieces (e.g., email messages or portions thereof) that are customized in accord with individual recipient behaviors. Such methods include the step of generating and digitally transmitting to a digital data devices of a recipient a digital content piece that (i) has a call to action to which the recipient can respond and (ii) that has a plurality of features selected so as to maximize a probability, P(b1,b2, . . . ,bM,x1,x2, . . . ,xM), that the recipient will respond to that call to action, where that probability is defined by the relation P(b1,b2, . . . ,bM,x1,x2, . . . ,xM)=exp(?j=1, . . . ,Mbjxj)/(1+exp(?j=1, . . . ,Mbjxj)) where x1,x2, . . . ,xM are values for each of a plurality, M, of features characterizing the digital content piece and/or the recipient, b1,b2, . . . ,bM are respective coefficients for each of the values x1,x2, . . . ,xM.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system determines an optimized pre-pack configuration with an optimized pre-pack allocation and an optimized pre-pack design. The system receives demand data and constraints and initializes a current pre-pack allocation and a current pre-pack design. For the current pre-pack allocation, the system determines a new pre-pack design by solving a multi-choice integer knapsack problem, and then determines if the new pre-pack design is different than the current pre-pack design. When the new pre-pack design is different than the current pre-pack design, the system determines a new pre-pack allocation and assigns the new pre-pack allocation as the current pre-pack allocation and the new pre-pack design as the current pre-pack design and repeats the determining the new pre-pack design and the determining if the new pre-pack design is different than the current pre-pack design until the new pre-pack design is the same as the current pre-pack design.