Iterative process for large scale marketing spend optimization

A computer-readable storage medium storing instructions that, if executed by a computing system having a processor, cause the computing system to perform a method comprising:

• generating a constraint tree comprising a plurality of constraint tree nodes, each constraint tree node corresponding to a constraint having an associated spend category, an associated lower bound, and an associated upper bound;
• for each of a plurality of spend categories associated with a marketing response model,
• determining an elasticity, and
• determining a current allocation;
• determining a first proposed allocation for each of the spend categories based at least in part on the determined elasticities and a Cobb-Douglas approximation to the marketing response model;
• comparing the first plurality of proposed allocations to the current allocations determined for each of the spend categories;
• and
• in response to determining that a difference value based at least in part on the comparing is greater than a predetermined threshold, determining a second plurality of proposed allocations and comparing the second plurality of proposed allocations to the first proposed allocations.

A system, comprising:

• a component configured to receive, for each of a plurality of revenue types,
• a revenue response model, and
• a target;
• a component configured to receive, for each of a plurality of spend categories associated with the revenue response models, a current spend for the spend category;
• a component configured to generate, based at least in part on the received revenue response models, the received targets, the received current spends, and Cobb-Douglas approximations to the revenue response models, for each of the plurality of spend categories, a first proposed spend;
• a component configured to determine a target miss for the generated first proposed spends;
• and
• a component configured to, in response to determining that the target miss is not below a threshold, generate, based at least in part on the received revenue response models, the received targets, the first proposed spends, and Cobb-Douglas approximations to the revenue response models, for each of the plurality of spend categories, a second proposed spend.