PRICE SENSITIVITY SCORES

• US 20110119071A1
• Filed: 11/08/2010
• Published: 05/19/2011
• Est. Priority Date: 11/13/2009
• Status: Abandoned Application

• Current Assignee: Nomis Solutions Incorporated
• Original Assignee: Nomis Solutions Incorporated
• Inventors: Rohde, Frank, Phillips, Robert L., Raffard, Robin L.
• Application Number:
  US12/941,610
• Publication Number:
  US 20110119071A1
• Time in Patent Office: Days
• US Class Current: 705/1.1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors

Abstract

A price sensitivity score is calculated for a prospect by obtaining data associated with a prospect. A scoring function is determined (which is a function of one or more scoreable variables) based at least in part on training data set. A price sensitivity score is calculated by evaluating the scoring function using the data associated with the prospect.

Claims

1. A method for calculating a price sensitivity score for a prospect, comprising:

obtaining data associated with a prospect;

using a processor to determine a scoring function, which is a function of one or more scoreable variables, based at least in part on training data set; and

calculating a price sensitivity score by evaluating the scoring function using the data associated with the prospect.

2. The method of claim 1, wherein using the processor to determine the scoring function includes using said one or more scoreable variables and potentially one or more non-scoreable variables.

3. The method of claim 1, wherein using the processor to determine the scoring function includes constraining the scoring function to only generate price sensitivity scores that fall within a desired range of price sensitivity scores.

4. The method of claim 3, wherein the price sensitivity scores have a linear relationship with respect to each other.

5. The method of claim 3, wherein the price sensitivity scores have a geometric relationship with respect to each other.

6. The method of claim 3, wherein the price sensitivity scores order prospects in terms of price sensitivity.

7. The method of claim 1, wherein using the processor to determine the scoring function includes:

determining a take up probability function, wherein the take up probability function is a function of one or more scoreable variables and potentially one or more non-scoreable variables; and

using the take up probability function, generate a transformed price sensitivity function which depends only upon one or more scoreable variables.

8. The method of claim 7, wherein using the processor to determine the scoring function further includes generating a scoring function which only depends upon one or more scoreable variables using the transformed price sensitivity function.

9. The method of claim 1, wherein using the processor to determine the scoring function includes:

determining a raw price sensitivity function, which is a function of one or more scoreable variables and potentially one or more non-scoreable variables, using a take up probability function which is a function of one or more scoreable variables and potentially one or more non-scoreable variables; and

integrating the raw price sensitivity function over a distribution of non-scoreable variables to obtain a transformed price sensitivity function which is a function only of one or more scoreable variables.

10. The method of claim 1, wherein using the processor to determine the scoring function includes using a constrained set of possible functions, including the following constraints:

(1);

a candidate raw price sensitivity function ψ

(χ

, β

) must be able to decompose into;

ψ

(χ

,β

)=h(ƒ

(χ

),g(β

)); and

/or

(2);

For some non-scoreable variables χ and

for all scoreable variables β

₁ and β

₂ if ψ

(χ

, β

₁)>

ψ

(χ

, β

₂), then ε

(χ

, β

₁)>

ε

(χ

, β

₂), where ε

is a price elasticity function.

11. The method of claim 1, wherein using the processor to determine the scoring function includes:

determining a best take up probability function based only on one or more non-scoreable variables;

using the best take up probability function to generate and store one or more types of residuals;

determining a best incremental take up probability function which is a function of one or more residuals, one or more non-scoreable pricing variables and one or more scoreable variables; and

determining a price sensitivity scoring function which is only a function of the scoreable variables based at least in part on the best incremental take up probability function.

12. The method of claim 11, wherein the best incremental take up probability function includes a constrained function.

13. The method of claim 11, wherein the one or more types of residuals include a take up error associated with a historic transaction, wherein the take up error is calculated using the best take up probability function and a known take up outcome associated with the historic transaction.

14. A system for calculating a price sensitivity score for a prospect, comprising:

a processor; and

a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to;

obtain data associated with a prospect;

determine a scoring function, which is a function of one or more scoreable variables, based at least in part on training data set; and

calculate a price sensitivity score by evaluating the scoring function using the data associated with the prospect.

15. The system of claim 14, wherein determining the scoring function includes using said one or more scoreable variables and potentially one or more non-scoreable variables.

16. The system of claim 14, wherein determining the scoring function includes constraining the scoring function to only generate price sensitivity scores that fall within a desired range of price sensitivity scores.

17. The system of claim 14, wherein determining the scoring function includes:

determining a take up probability function, wherein the take up probability function is a function of one or more scoreable variables and potentially one or more non-scoreable variables; and

using the take up probability function, generate a transformed price sensitivity function which depends only upon one or more scoreable variables.

18. The system of claim 17, wherein determining the scoring function further includes generating a scoring function which only depends upon one or more scoreable variables using the transformed price sensitivity function.

19. The system of claim 14, wherein determining the scoring function includes:

determining a raw price sensitivity function, which is a function of one or more scoreable variables and potentially one or more non-scoreable variables, using a take up probability function which is a function of one or more scoreable variables and potentially one or more non-scoreable variables; and

integrating the raw price sensitivity function over a distribution of non-scoreable variables to obtain a transformed price sensitivity function which is a function only of one or more scoreable variables.

20. The system of claim 14, wherein determining the scoring function includes using a constrained set of possible functions, including the following constraints:

(1);

a candidate raw price sensitivity function ψ

(χ

, β

) must be able to decompose into;

ψ

(χ

,β

)=h(ƒ

(χ

),g(β

)); and

/or

(2);

For some non-scoreable variables χ and

for all scoreable variables β

₁ and β

₂ if ψ

(χ

, β

₁)>

ψ

(χ

, β

₂), then ε

(χ

, β

₁)>

ε

(χ

, β

₂), where ε

is a price elasticity function.

21. The system of claim 14, wherein determining the scoring function includes:

determining a best take up probability function based only on one or more non-scoreable variables;

using the best take up probability function to generate and store one or more types of residuals;

determining a best incremental take up probability function which is a function of one or more residuals, one or more non-scoreable pricing variables and one or more scoreable variables; and

determining a price sensitivity scoring function which is only a function of the scoreable variables based at least in part on the best incremental take up probability function.

22. The system of claim 21, wherein the best incremental take up probability function includes a constrained function.

23. The system of claim 21, wherein the one or more types of residuals include a take up error associated with a historic transaction, wherein the take up error is calculated using the best take up probability function and a known take up outcome associated with the historic transaction.

24. A computer program product for calculating a price sensitivity score for an prospect, the computer program product being embodied in a computer readable storage medium and comprising computer instructions for:

obtaining data associated with a prospect;

determining a scoring function, which is a function of one or more scoreable variables, based at least in part on training data set; and

calculating a price sensitivity score by evaluating the scoring function using the data associated with the prospect.