System and methods for business to business price modeling using price change optimization

• US 7,680,686 B2
• Filed: 08/29/2006
• Issued: 03/16/2010
• Est. Priority Date: 08/29/2006
• Status: Active Grant

• Current Assignee: Vendavo, Inc.
• Original Assignee: Vendavo, Inc.
• Inventors: Tellefsen, Jens E., Kachani, Soulaymane
• Primary Examiner(s): Sterrett; Jonathan G.
• Assistant Examiner(s): CHOI, PETER H
• Application Number:
  US11/468,013
• Publication Number:
  US 20080059280A1
• Time in Patent Office: 1,295 Days
• Field of Search: 705/10, 705/400
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0283 Price estimation or determination

Abstract

The present invention relates to business to business market price control and management systems. More particularly, the present invention relates to systems and methods for generating price modeling and optimization modules in a business to business market setting wherein price changes are optimized to achieve desired business results.

Claims

1. A method for determining an optimal set of price changes in an integrated price management system, said method comprising:

setting an optimization goal, said goal maximizing profit, sales or volume;

defining pricing goals and constraints based on said optimization goal, wherein the constraints may be selected from the group of;

a maximum price increase, maximum price decrease for a business segment, or intersection of business segments;

defining a selected business segment, wherein the selected business segment may be at least one of static and dynamic, and wherein the defining the selected business segment includes accessing a pre-defined segmentation library, selecting one or more appropriate pre-defined business segments from the pre-defined segmentation library, and combining the selected one or more appropriate pre-defined business segments to generate the selected business segment;

defining a selected product combination;

receiving, from a database, sales history data and win/loss classification data for the selected product combination;

generating, by a computer, a price elasticity demand model and a win probability model for the selected product combination using the sales history data and win/loss classification data, wherein the price elasticity demand model and the win probability model are generated using a set of multivariate, parametric, non-separable algorithms;

wherein the price elasticity demand model is generated by;

defining an aggregate elasticity range for said selected business segment;

defining a self elasticity for selected products in said selected product combination;

determining a set of current prices for said product combination; and

defining a single elasticity for each product in said selected product combination, wherein said single elasticity is defined at said current price;

generating, by a computer, optimized price changes for said selected product combination and business segment using the price elasticity demand model and the win probability model, wherein the optimized price changes meet the defined pricing goals and constraints; and

outputting the optimized price changes to a deal manager for deal negotiations.

2. The method of claim 1 further comprising:

selecting an existing customer;

generating said optimized price changes for said selected product combination for said existing customer.

3. The method of claim 2 further wherein said elasticity range is an estimate.

4. The method of claim 3 further comprising:

using said estimated elasticity to optimize price changes;

analyzing the impact of the optimized price changes;

adjusting optimized prices consistent with business goals; and

setting adjusted prices as optimized prices.

5. A method for determining an optimal set of price changes in an integrated price management system, said method comprising:

setting an optimization goal, said goal maximizing profit, sales or volume;

defining pricing goals and constraints based on said optimization goal, wherein the constraints may be selected from the group of;

a maximum price increase, maximum price decrease for a business segment, or intersection of business segments;

defining a plurality of selected business segments, wherein the selected business segments may be at least one of static and dynamic, and wherein the defining the selected business segment includes accessing a pre-defined segmentation library, selecting one or more appropriate pre-defined business segments from the pre-defined segmentation library, and combining the selected one or more appropriate pre-defined business segments to generate the selected business segments;

defining a plurality of selected product combinations;

generating multiple simultaneous segmentation from said selected business segments;

receiving, from a database, sales history data and win/loss classification data for the selected product combination;

generating, by a computer, a price elasticity demand model and a win probability model for the selected product combination using the sales history data and win/loss classification data, wherein the price elasticity demand model and the win probability model are generated using a set of multivariate, parametric, non-separable algorithms;

wherein the price elasticity demand model is generated by;

defining an aggregate elasticity range for said selected business segment;

defining a self elasticity for selected products in said selected product combination;

determining a set of current prices for said product combination; and

defining a single elasticity for each product in said selected product combination, wherein said single elasticity is defined at said current price;

generating, by a computer, optimized price changes for said selected product combination and business segment using the price elasticity demand model and the win probability model, wherein the optimized price changes meet the defined pricing goals and constraints; and

outputting the optimized price changes to a deal manager for deal negotiations.

6. A computer readable medium having executable instructions which when executing cause the operation on a computer the steps of:

defining pricing goals and constraints;

defining a selected business segment, wherein the selected business segment may be at least one of static and dynamic, and wherein the demand model module accesses a pre-defined segmentation library, selecting one or more appropriate pre-defined business segments from the pre-defined segmentation library, and combining the selected one or more appropriate pre-defined business segments to generate the selected business segment;

defining a selected product combination;

generating optimized price changes for said selected product combination and business segment; and

providing the optimized price changes to a user for deal negotiations.

7. The computer readable medium having executable instructions as recited in claim 6 further comprising the steps of:

selecting an existing customer;

generating said optimized price changes for said selected product combination for said existing customer.

8. The computer readable medium having executable instructions as recited in claim 7 further wherein said elasticity range is an estimate.

9. The computer readable medium having executable instructions as recited in claim 8 further comprising the steps of:

using said estimated elasticity to optimize price changes;

analyzing the impact of the optimized price changes;

adjusting optimized prices to be consistent with business goals; and

setting adjusted prices as optimized prices.