OPTIMIZATION OF CONSUMER OFFERINGS USING PREDICTIVE ANALYTICS

• US 20110307327A1
• Filed: 06/14/2010
• Published: 12/15/2011
• Est. Priority Date: 06/14/2010
• Status: Abandoned Application

• Current Assignee: Fair Isaac Corporation
• Original Assignee: Fair Isaac Corporation
• Inventors: Smirnov, Yuri, Bixby, Todd, Tavares, Gabriel J.
• Application Number:
  US12/815,229
• Publication Number:
  US 20110307327A1
• Time in Patent Office: Days
• US Class Current: 705/14.43
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0244 Optimization

Abstract

Data characterizing a population of consumers that is derived from history-based predictive analytics that describe expected outcomes for each of a plurality of offerings is used by a solver to solve an optimization problem. The solver identifies a subset of the population and assigns one or more of the plurality of offerings to be presented to consumers within the subset of the population while taking into account at least one constraint. Actions to be taken with the identified subset of the population in connection with the assigned one or more offering are then assigned so that at least a portion of the assigned actions can be initiated. Related apparatus, systems, techniques and articles are also described.

Claims

1. An article of manufacture comprising computer executable instructions non-transitorily stored on computer readable media, which, when executed by a computer, causes the computer to perform operation comprising:

receiving data characterizing a population of consumers, the data derived from history-based predictive analytics that describe expected outcomes for each of a plurality of offerings;

using a solver to solve an optimization problem using the data, the solver identifying a subset of the population and assigning one or more of the plurality of offerings to be presented to consumers within the subset of the population while taking into account at least one constraint;

assigning actions to be taken with the identified subset of the population in connection with the assigned one or more offering; and

initiating at least a portion of the assigned actions.

2. An article as in claim 1, wherein the offerings relate to retail products or services.

3. An article as in claim 1, wherein the history-based predictive analytics are derived from historical retail purchases from each consumer using a predictive model trained from historical retail transactions from a plurality of consumers.

4. An article as in claim 1, wherein the solver is a mixed integer programming solver.

5. An article as in claim 4, wherein the optimization problem is a pre-defined linear programming problem.

6. An article as in claim 1, wherein the at least one constraint includes a volume constraint that defines maximum and minimum investment levels for the one or more offerings across the subset of the population.

7. An article as in claim 6, wherein the at least one constraint includes an ineligibility constraint that defines which offerings and/or decisions have been determined as ineligible for certain consumers within the subset of the population.

8. An article as in claim 1, wherein the assigned actions are restricted to a decision space.

9. An article as in claim 8, wherein the decision space is a linear decision space and the restriction is as a function of Ω

, wherein;

10. An article as in claim 9, wherein for each consumer cε

C there is a known number of products R_c in the event E_s is to be assigned to the consumers that will receive the at least one offering.

11. An article as in claim 10, wherein a contribution to an objective value of customer cε

C of buying product pε

P is denoted as W_c,p,s.

12. An article as in claim 11, wherein decision assignments are denoted as x_c,p,s for every customer cε

C and product pε

P in the event s.

13. An article as in claim 12, wherein x_c,p,s=1 if customer c gets product p as a recommendation in the event s, and returns x_c,p,s=0 otherwise.

14. An article as in claim 13, wherein for every event s, a set of binary decisions can be represented as a two-dimensional matrix X whose row entries correspond to the consumers and column entries correspond to the offerings.

15. An article as in claim 14, wherein a vector y of 0-1 decision variables about a choice of consumers being assigned the at least one offering in the event s is also considered.

16. An article as in claim 15, wherein products P are organized in hierarchies based on factors that can share or vary a contribution toward the objective value.

17. An article as in claim 16, wherein V_p represents an allowed number of recommendations for product pε

P,

M_p represents minimal commitment for product pε

P, and

constraints

18. An article as in claim 1, wherein the optimization problem is defined by:

19. An article as in claim 18, wherein the objective function can pertain to one or more of propensity W_c,p, or sales W_c,p*Cost_c,p, or profit W_c,p*M arg in_c,p.

20. An article as in claim 18, wherein the objective function comprises a weighted mix of several objection functions.

21. An article as in claim 18, wherein:

Ω

≡

Ψ

, and

22. An article as in claim 1, wherein the problem is used to determine recommended investor commitments over a span of multiple campaigns or timeframes to attain an optimal metric.

23. An article as in claim 1, wherein the metrics includes:

highest revenue, margin, profit overall, profit by product, product by consumer, and consumer satisfaction.

24. An article as in claim 1, wherein the problem is defined by:

25. An article as in claim 1, wherein the problem is defined as:

26. An article as in claim 1, wherein the problem is defined as:

27. An article as in claim 1, wherein the problem is defined as:

28. An article as in claim 1, wherein the problem is defined as: