Method for optimizing net present value of a cross-selling marketing campaign

• US 20060218033A1
• Filed: 01/30/2006
• Published: 09/28/2006
• Est. Priority Date: 08/06/1999
• Status: Active Grant

• Current Assignee: Experian Information Solutions Incorporated (Experian plc)
• Original Assignee: Experian Information Solutions Incorporated (Experian plc)
• Inventors: Fishman, Vladimir, Gibiansky, Leonid, Galperin, Yuri
• Granted Patent: US 7,499,868 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0247 Calculate past, present or future revenues
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Abstract

The present invention applies a novel iterative algorithm to the problem of multidimensional optimization by supplying a strict, nonlinear mathematical solution to what has traditionally been treated as a linear multidimensional problem. The process consists of randomly selecting a statistically significant sample of a prospect list, calculating the value of the utility function for each pair of an offer and selected prospects, reducing the original linear multidimensional problem to a non-linear problem with a feasible number of dimensions, solving the non-linear problem for the selected sample numerically with the desired tolerance using an iterative algorithm, and using the results to calculate an optimal set of offers in one pass for the full prospect list.

Claims

1-10. -10. (canceled)

11. A computer-implemented method for optimizing a cross-selling marketing campaign, the method comprising:

selecting from a plurality of customers a statistically significant sample of customers;

calculating a plurality of response probabilities that each represent a probability that a specific customer will respond to a specific promotion;

calculating a plurality of profitabilities that each represent a profitability resulting from a specific customer responding to a specific promotion;

using a non-linear optimization problem related to selecting a marketing campaign with desired expected utility, wherein the non-linear optimization problem takes into account at least some of the response probabilities and at least some of the profitabilities; and

selecting a marketing campaign with desired expected utility at least in part by iteratively solving the non-linear optimization problem on the sample of customers within a pre-defined tolerance, wherein the selected marketing campaign defines which customers receive which promotions.

12. The method of claim 11, wherein selecting a marketing campaign with desired expected utility comprises selecting a marketing campaign that is expected to have a higher net present value than another marketing campaign.

13. The method of claim 11, wherein the non-linear optimization problem further takes into account at least one constraint that limits which customers may receive which promotions such that the selected marketing campaign is a campaign with desired expected utility that satisfies the at least one constraint.

14. The method of claim 11, wherein the non-linear optimization problem further takes into account at least one business goal that is to be met by the marketing campaign such that the selected marketing campaign is a campaign with desired expected utility that meets the at least one business goal.

15. The method of claim 13, wherein the non-linear optimization problem further takes into account at least one business goal that is to be met by the marketing campaign such that the selected marketing campaign is a campaign with desired expected utility that, in addition to satisfying the at least one constraint, meets the at least one business goal.

16. The method of claim 11, wherein the non-linear optimization problem is derived at least in part from a mathematically equivalent linear optimization problem.

17. The method of claim 13, wherein the at least one constraint is expressed as at least one inequality.

18. The method of claim 14, wherein the at least one business goal is expressed as at least one inequality.

19. The method of claim 11, wherein selecting a marketing campaign with desired expected utility further comprises generating a plurality of solicitation values that define which customers receive which promotions.

20. A campaign optimization system comprising:

at least one data repository containing at least one collection of data comprising;

customer data related to a statistically significant sample of customers;

response probability data representing a plurality of probabilities that a specific customer will respond to a specific promotion;

profitability data representing, for each of a plurality of customers, profitability resulting from a specific customer responding to a specific promotion;

an optimization component configured to use a non-linear optimization problem related to selecting a marketing campaign with desired expected utility, wherein the non-linear optimization problem takes into account at least some of the response probability data and at least some of the profitability data; and

a selection component configured to select a marketing campaign with desired expected utility at least in part by iteratively solving the non-linear optimization problem on the sample of customers within a pre-defined tolerance, wherein the selected marketing campaign defines which customers receive which promotions.

21. The system of claim 20, wherein the at least one data repository comprises at least one database.

22. The system of claim 20, wherein the at least one data repository is stored in computer-readable memory.

23. The system of claim 20, wherein the selection component is configured to select a marketing campaign with desired expected utility by selecting a marketing campaign that is expected to have the a net present value that is higher than another marketing campaign.

24. The system of claim 20, wherein the non-linear optimization problem used by the optimization component further takes into account at least one constraint that limits which customers may receive which promotions such that the selected marketing campaign is a campaign with desired expected utility that satisfies the at least one constraint.

25. The system of claim 20, wherein the non-linear optimization problem used by the optimization module further takes into account at least one business goal that is to be met by the marketing campaign such that the selected marketing campaign is a campaign with desired expected utility that meets the at least one business goal.

26. The system of claim 24, wherein the non-linear optimization problem used by the optimization module further takes into account at least one business goal that is to be met by the marketing campaign such that the selected marketing campaign is a campaign with desired expected utility that, in addition to satisfying the at least one constraint, meets the at least one business goal.

27. The system of claim 20, wherein the optimization module derives the non-linear optimization problem at least in part from a mathematically equivalent linear optimization problem.

28. The system of claim 24, wherein the at least one constraint is stored in the at least one data repository and is expressed as at least one inequality.

29. The system of claim 25, wherein the at least one business goal is stored in the at least one data repository and is expressed as at least one inequality.

30. The system of claim 20, wherein the selection module is further configured to generate and store in the at least one data repository a plurality of solicitation values that define which customers receive which promotions.