Computer-implemented marketing optimization systems and methods
First Claim
1. A computer-implemented method for determining an offer set, comprising:
- accessing, using one or more data processors, a global set of action items, wherein the global set includes assignable marketing action items, and wherein action items are assignable to a plurality of customers;
generating, using the one or more data processors, initial offer sets for each customer using an objective function and one or more individual constraints, wherein an initial offer set includes one or more action items, and wherein an individual constraint limits assignable action items for a customer;
adjusting, using the one or more data processors, the objective function using one or more aggregate constraints and a Lagrange relaxation technique, wherein an aggregate constraint limits assignable action items for a plurality of customers;
calculating, using the one or more data processors, initial quality values for the initial offer sets using the adjusted objective function and a subgradient algorithm;
generating, using the one or more data processors, revised offer sets for each customer using the adjusted objective function;
calculating, using the one or more data processors, revised quality values for the revised offer sets using the adjusted objective function and the subgradient algorithm; and
determining, using the one or more data processors, an offer set for each customer using the initial quality values and the revised quality values, wherein each offer set is selected from the initial offer sets or from the revised offer sets.
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Abstract
Computer-implemented systems and methods are provided for determining an action item from a global set of action items for a plurality of customers based on an objective function, a plurality of individual constraints, and a plurality of aggregate constraints. A plurality of offer sets is generated for each customer. An approximate highest reduced adjusted objective for each of the offer sets for each customer is calculated, and the customers are bucketed based on the highest adjusted objective value associated with each customer. The buckets are collapsed into a single bucket record containing a plurality of aggregate offer set columns, and an aggregate offer set column is selected from each bucket record for each bucket. Each bucket associated with a selected offer set is disaggregated, and the action item included in the selected offer set is stored in a computer-readable memory.
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Citations
19 Claims
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1. A computer-implemented method for determining an offer set, comprising:
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accessing, using one or more data processors, a global set of action items, wherein the global set includes assignable marketing action items, and wherein action items are assignable to a plurality of customers; generating, using the one or more data processors, initial offer sets for each customer using an objective function and one or more individual constraints, wherein an initial offer set includes one or more action items, and wherein an individual constraint limits assignable action items for a customer; adjusting, using the one or more data processors, the objective function using one or more aggregate constraints and a Lagrange relaxation technique, wherein an aggregate constraint limits assignable action items for a plurality of customers; calculating, using the one or more data processors, initial quality values for the initial offer sets using the adjusted objective function and a subgradient algorithm; generating, using the one or more data processors, revised offer sets for each customer using the adjusted objective function; calculating, using the one or more data processors, revised quality values for the revised offer sets using the adjusted objective function and the subgradient algorithm; and determining, using the one or more data processors, an offer set for each customer using the initial quality values and the revised quality values, wherein each offer set is selected from the initial offer sets or from the revised offer sets. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system for determining an offer set, comprising:
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one or more processors; one or more computer-readable storage mediums containing instructions configured to cause the one or more processors to perform operations including; accessing a global set of action items, wherein the global set includes assignable marketing action items, and wherein action items are assignable to a plurality of customers; generating initial offer sets for each customer using an objective function and one or more individual constraints, wherein an initial offer set includes one or more action items, and wherein an individual constraint limits assignable action items for a customer; adjusting the objective function using one or more aggregate constraints and a Lagrange relaxation technique, wherein an aggregate constraint limits assignable action items for a plurality of customers; calculating initial quality values for the initial offer sets using the adjusted objective function and a subgradient algorithm; generating revised offer sets for each customer using the adjusted objective function; calculating revised quality values for the revised offer sets using the adjusted objective function and the subgradient algorithm; and determining an offer set for each customer using the initial quality values and the revised quality values, wherein each offer set is selected from the initial offer sets or from the revised offer sets. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A computer-program product for determining an offer set, tangibly embodied in a machine-readable storage medium, including instructions configured to cause a data processing apparatus to:
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access a global set of action items, wherein the global set includes assignable marketing action items, and wherein action items are assignable to a plurality of customers; generate initial offer sets for each customer using an objective function and one or more individual constraints, wherein an initial offer set includes one or more action items, and wherein an individual constraint limits assignable action items for a customer; adjust the objective function using one or more aggregate constraints and a Lagrange relaxation technique, wherein an aggregate constraint limits assignable action items for a plurality of customers; calculate initial quality values for the initial offer sets using the adjusted objective function and a subgradient algorithm; generate revised offer sets for each customer using the adjusted objective function; calculate revised quality values for the revised offer sets using the adjusted objective function and the subgradient algorithm; and determine an offer set for each customer using the initial quality values and the revised quality values, wherein each offer set is selected from the initial offer sets or from the revised offer sets.
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Specification