SYSTEM AND METHODS FOR GENERATING QUANTITATIVE PRICING POWER AND RISK SCORES

• US 20090259522A1
• Filed: 03/23/2009
• Published: 10/15/2009
• Est. Priority Date: 05/02/2006
• Status: Abandoned Application

• Current Assignee: Vendavo, Inc.
• Original Assignee: Vendavo, Inc.
• Inventors: Gray, Allan David Ross, Johnson, Jeffrey D., Callioni, Gianpaolo, Gorlov, Vlad, Rapperport, Jamie, Mehra, Amit, Geraghty, Sean
• Application Number:
  US12/408,862
• Publication Number:
  US 20090259522A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• 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
• CPC Class Codes: Y02P 90/82 Energy audits or management systems therefor

Abstract

The present invention relates to a quantitative pricing power and pricing risk score generator and methods therefore, useful in association with an integrated price management system. Such a system may include a segment generator, a segment pricing power analyzer, and a segment pricing risk analyzer. The segment generator may receive or generate segments associated with a customer. Segment generation uses transaction level data which includes product attribute information, market data, and transaction history data. The segment pricing power analyzer and risk analyzer identify pricing power and risk factors, and assign raw scores to each factor, generates a weight for each raw score for each of the pricing power factors, and generates the quantitative pricing power and risk scores for each segment by computing a weighted average of the factors using the generated weights for each of the raw scores. Weights may be assigned according to default values or may be generated via an optimization.

Claims

1. A method for generating quantitative pricing power and pricing risk scores, useful in association with an integrated price management system, the method comprising:

receiving at least one segment associated with a customer;

identifying at least one pricing power factor;

identifying at least one pricing risk factor;

assigning a raw pricing power score for each of the at least one pricing power factor for each of the at least one segment;

assigning a raw pricing risk score for each of the at least one pricing risk factor for each of the at least one segment;

generating a pricing power weight for each of the raw pricing power score for each of the at least one pricing power factor for each of the at least one segment;

generating a pricing risk weight for each of the raw pricing risk score for each of the at least one pricing risk factor for each of the at least one segment;

generating the quantitative pricing power score for each of the at least one segment by computing a weighted average of the raw pricing power score for each at least one pricing power factor using the generated pricing power weight for each of the raw pricing power score for each of the at least one pricing power factor; and

generating the quantitative pricing risk score for each of the at least one segment by computing a weighted average of the raw pricing risk score for each at least one pricing risk factor using the generated pricing risk weight for each of the raw pricing risk score for each of the at least one pricing risk factor.

2. The method as recited in claim 1, wherein the at least one segment is generated.

3. The method as recited in claim 2, wherein the segment is generated using transaction level data.

4. The method as recited in claim 3, wherein the transaction level data includes product attribute information, market data, and transaction history data.

5. The method as recited in claim 1, wherein the at least one pricing power factor includes at least one of price variance, approval escalations, win ratios, and elasticity.

6. The method as recited in claim 1, wherein the at least one pricing risk factor includes at least one of total sales, sales trends, margin, and percent of total spend.

7. The method as recited in claim 1, wherein the pricing power weight is a default power weight.

8. The method as recited in claim 1, wherein the pricing risk weight is a default risk weight.

9. The method as recited in claim 1, wherein the pricing power weight is determined via optimization.

10. The method as recited in claim 1, wherein the pricing risk weight is determined via optimization.

11. A quantitative pricing power and pricing risk score generator, useful in association with an integrated price management system, the quantitative pricing power and pricing risk score generator comprising:

a segment generator configured to receive at least one segment associated with a customer;

a segment pricing power analyzer configured to identify at least one pricing power factor, assign a raw pricing power score for each of the at least one pricing power factor for each of the at least one segment, generate a pricing power weight for each of the raw pricing power score for each of the at least one pricing power factor for each of the at least one segment, and generate the quantitative pricing power score for each of the at least one segment by computing a weighted average of the raw pricing power score for each at least one pricing power factor using the generated pricing power weight for each of the raw pricing power score for each of the at least one pricing power factor; and

a segment pricing risk analyzer configured to identify at least one pricing risk factor, assign a raw pricing risk score for each of the at least one pricing risk factor for each of the at least one segment, generate a pricing risk weight for each of the raw pricing risk score for each of the at least one pricing risk factor for each of the at least one segment, and generate the quantitative pricing risk score for each of the at least one segment by computing a weighted average of the raw pricing risk score for each at least one pricing risk factor using the generated pricing risk weight for each of the raw pricing risk score for each of the at least one pricing risk factor.

12. The quantitative pricing power and pricing risk score generator of claim 11, wherein the segment generator is further configured to generate the at least one segment.

13. The quantitative pricing power and pricing risk score generator of claim 12, wherein the segment generator generates the segment using transaction level data.

14. The quantitative pricing power and pricing risk score generator of claim 13, wherein the transaction level data includes product attribute information, market data, and transaction history data.

15. The quantitative pricing power and pricing risk score generator of claim 11, wherein the at least one pricing power factor includes at least one of price variance, approval escalations, win ratios, and elasticity.

16. The quantitative pricing power and pricing risk score generator of claim 11, wherein the at least one pricing risk factor includes at least one of total sales, sales trends, margin, and percent of total spend.

17. The quantitative pricing power and pricing risk score generator of claim 11, wherein the segment pricing power analyzer assigns a default power weight to the pricing power weight.

18. The quantitative pricing power and pricing risk score generator of claim 11, wherein the segment pricing risk analyzer assigns a default risk weight to the pricing risk weight.

19. The quantitative pricing power and pricing risk score generator of claim 11, wherein the segment pricing power analyzer generates the pricing power weight via an optimization.

20. The quantitative pricing power and pricing risk score generator of claim 11, wherein the segment pricing risk analyzer generates the pricing risk weight via an optimization.