Target pricing system

• US 6,963,854 B1
• Filed: 03/03/2000
• Issued: 11/08/2005
• Est. Priority Date: 03/05/1999
• Status: Expired due to Fees

• Current Assignee: JDA Software Group Incorporated (RedPrairie Holding Incorporated)
• Original Assignee: Manugistics Group Incorporated (RedPrairie Holding Incorporated)
• Inventors: Kolamala, Anupama, Cook, Greg, Guardino, Thomas, Purang, Mudita, Krishnamurthy, Prabhakar, Boyd, Dean, Cooke, Mark, Gordon, Mike, Nandiwada, Ravi, Andersson, Jorgen, Monteiro, Brian, Tai, Chia-Hung Charles, Haas, Steve, Yang, Feng
• Primary Examiner(s): Bashore, Alain L.
• Assistant Examiner(s): CHARLES, DEBRA F
• Application Number:
  US09/517,983
• Time in Patent Office: 2,076 Days
• Field of Search: 705/37, 705/80, 705/36, 705/412, 705/35, 705/5, 705/26, 705/38, 705/1, 705/10, 705/39, 705/23, 705/9, 705/400, 340/825.27, 340/825.26, 370/313
• US Class Current: 705/37
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 30/08 Auctions
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

A business process and computer system known as the “Target Pricing System” (TPS) that generates an optimum bid or value for a competitively bid good or service. The system is resident on one or more host processors in connection with one or more data stores, and includes a product model that defines list values for the bid using stored price data and costs the values using stored cost data, a competitor net price model that calculates an equivalent competitor net price for the value, and

• • a market response model that calculates the probability of winning with the value as a function of price. The system further preferably includes
  • an optimization model that computes the target price of an optimal value that maximizes expected contribution for the bid or value. The system alternately further includes a benefits model for calculating the benefits of using target pricing over a pre-existing approach, and strategic objects which each affect the target price.

Claims

1. A target pricing system used in a computer for obtaining an optimum value, the target pricing system resident on one or more host processors in connection with one or more data stores, the target pricing system comprising:

a product model that prices using a computer a plurality of values using stored price data and estimates costs for each of the values using stored cost data;

a competitor net price model that calculates using a computer an equivalent competitor net price; and

a market response model that calculates using a computer the probability of winning with each of the values as a function of price,

wherein the market response model uses an equation;

$\mathrm{probability}<br><br><br><br>\mathrm{of}<br><br><br><br>\mathrm{winning}=\frac{1}{1+\underset{j\in <br><br>J}{\sum <br><br>}<br><br>{\mathrm{e<br><br>}}^{k_j+m_j}}$

wherein, for J competitors, k_j is a sum of price-independent terms for competitor j and m_j is a sum of price-dependent terms for the competitor j.

2. The system of claim 1, further including

an optimization model that computes the target price of the optimal value that maximizes expected contribution.

3. The system of claim 1, further including a benefits model that calculates one or more benefits of target pricing in comparison to a pre-existing pricing approach.

4. The system of claim 2, wherein the product model, competitor price model, market response model, optimization model, and benefits model are objects implemented in software on the one or more processors of the target pricing system.

5. A target pricing system used in a computer for obtaining an optimum value, the target pricing system resident on one or more host processors in connection with one or more data stores, the target pricing system comprising:

a product model that produces, using a computer, a plurality of bids using stored price data and estimates costs associated with each of the bids using stored cost data;

a competitor net price model that calculates using a computer an equivalent competitor net price; and

a market response model that calculates using a computer the probability of winning with each of the values as a function of price;

wherein the market response model calculates the probability of winning as;

$\mathrm{probability}<br><br><br><br>\mathrm{of}<br><br><br><br>\mathrm{winning}=\frac{1}{1+\underset{j\in <br><br>J}{\sum <br><br>}<br><br>{\mathrm{e<br><br>}}^{k_j+m_j}}$

wherein, for J competitors, k_j is a sum of price-independent terms for competitor j and m_j is a sum of price-dependent terms for the competitor j, and

an optimization model that determines the competitive response to each of the bids and computes a target price that maximizes expected contribution.

6. The system of claim 5, further including using a benefits model that calculates one or more benefits of target pricing in comparison to a pre-existing pricing approach.

7. The system of claim 6, wherein the product model, competitor price model, market response model, optimization model, and benefits model are objects implemented in software on the one or more processors of the target pricing system.

8. The system of claim 5, wherein the product model and the competitor price model are n-dimensional with stored data reflective of at least price and cost, and wherein the system pricing the bid, costing the bid, and calculating an equivalent competitor net price are performed by iterative linear interpolation of the stored data.

9. The system of claim 5, wherein the market response model includes coefficients for market response predictors based upon historical data, and for a specific bid, evaluates price and price-independent predictors to generate a market response curve from which an estimated probability of winning a bid is calculated.

10. The system of claim 9, wherein the coefficients are dynamically updated over time based on results of past bids.

11. The system of claim 10, wherein the market response predictors are attributes selected from the group comprised of:

customers, orders, and products.

12. The system of claim 11, wherein the customers attributes are static and variable attributes.

13. The system of claim 6, wherein the benefits model obtains the target price for the specific bid, calculates a bid price using a pre-existing pricing approach, and compares the bid from the pre-existing pricing approach to the market response curve to determine the probability of a successful bid with the pre-existing pricing approach.

14. The system of claim 13, wherein the pre-existing pricing approach is selected from the group comprised of discounting a list price in the product model;

adding to the cost in the product model; and

competitive matching of historical data.

15. The system of claim 5, further including strategic objectives, each of which affect the target price of the bid.

16. The system of claim 15, wherein the strategic objectives are selected from the group comprised of:

a pre-specified maximum or minimum margin on the bid; and

obtaining a pre-specified maximum or minimum success rate on the bid.

17. The system of claim 16, wherein the strategic objectives are specified at the product segment level and market segment level.

18. The system of claim 5, wherein the target pricing system further calculates a target range for the target price using the constraints of the strategic objective objects and determines the target price for the bid that is within the target range.

19. The system of claim 18, wherein the target range is calculated based upon a predetermined plus or minus range around the maximum expected contribution.

20. The system of claim 18, wherein the target range is calculated based upon a predetermined plus or minus range greater and lesser than the calculated optimum target price.

21. The system of claim 5, wherein the target pricing system is resident on one or more processors in a local network of a user of the target pricing system.

22. The system of claim 5, wherein the system includes a target pricing data store including at least the price data, cost data, and historical data, and additional business metrics such as margin, volumes, and revenues.

23. The system of claim 5, wherein the one or more processors of the target pricing system are remotely located from the user of the target pricing system and accessible from a remote interface across the Internet.

24. The system of claim 7, wherein the product model, competitor price model, market response model, optimization model, benefits model, and target pricing data store are resident on the one or more processors of the target pricing system located remotely from the user.

25. An automated method of target pricing a value with one or more processors in connection with one or more data stores, comprising the steps of:

pricing the value using stored list prices in a product model;

costing the value using stored costs in the product model;

calculating an equivalent competitor net price for the value using a competitor net price model;

calculating the probability of winning with the value as a function of price using parameters from a market response model; and

calculating a target price for the value that maximizes expected contribution using an optimization model that determines competitive response to any potential value,

wherein the step of calculating the probability of winning by;

$\mathrm{probability}<br><br><br><br>\mathrm{of}<br><br><br><br>\mathrm{winning}=\frac{1}{1+\underset{j\in <br><br>J}{\sum <br><br>}<br><br>{\mathrm{e<br><br>}}^{k_j+m_j}}$

wherein, for J competitors, k_j is a sum of price-independent terms for competitor j and m_j is a sum of price-dependent terms for the competitor j.

26. The method of claim 25, further including the step of calculating one or more benefits of target pricing in comparison to a pre-existing pricing approach.

27. The method of claim 26, wherein the product model and the competitor price model are n-dimensional with stored data reflective of at least price and cost, and the steps of pricing the value, rusting the value, and calculating an equivalent competitor net price are located by iterative linear interpolation of the stored data.

28. The method of claim 25, wherein the step of calculating an equivalent competitor net price further includes the steps of:

retrieving a price from the product model for a specific value; and

applying a discounting model to the price to determine a competitor net price for the specific value.

29. The method of claim 28, further including the step of overriding the calculated equivalent competitor net price if the calculated competitor net price falls outside a predetermined range.

30. The method of claim 25, wherein the market response model includes coefficients for market response predictors based upon historical data, and for a specific value, the step of calculating the probability of winning the bid includes the steps of:

evaluating price-independent predictors; and

generating a market response curve from which an estimated probability of winning with the value is calculated.

31. The method of claim 30, wherein the step of evaluating price-independent predictors is evaluating price independent predictors for at least the customer, the order, and the product.

32. The method of claim 31, further including the step of evaluating static and variable price-independent predictors.

33. The method of claim 26, wherein the step of calculating one or more benefits of target pricing includes the steps of:

obtaining the target price for the specific value;

calculating a target price value using a pre-existing pricing approach; and

comparing the value from the pre-existing pricing approach to a market response curve to determine the probability of a successful bid with the pre-existing pricing approach.

34. The method of claim 33, wherein the step of calculating a target price bid using a pre-existing pricing approach is a step selected from the group of:

discounting the list price from the price model;

adding a predetermined amount to the cost for the value; and

matching a historic rate for the specific value.

35. The method of claim 26, further comprising the steps of:

calculating a specific target price for a performance of a contract;

determining the applicability of one or more strategic objectives to the target price;

calculating a target range for the target bid price that is constrained by the one or more strategic objectives; and

obtaining a target price that is within the target range.

36. The method of claim 35, wherein the step of determining the applicability of one or more strategic objectives is a step selected from the group of:

obtaining a pre-determined maximum or minimum margin on the value; and

obtaining a pre-determined maximum or minimum success rate on the value.

37. The method of claim 25, further including the step of calculating a target range for the value.

38. The method of claim 37, wherein the step of calculating a target range is a step selected from the group of:

calculating a target range from the maximum expected contribution; and

calculating a target range based upon the optimum target price.

39. A target pricing system for obtaining an optimum value, the target pricing system resident on one or more host processors in connection with one or more data stores, the target pricing system comprising:

product model means for creating a product model that prices the value using stored price data and costs the value using stored cost data;

competitor net price model means for creating a competitor net price model that calculates an equivalent competitor net price for the value; and

market response model means for creating a market response model that calculates the probability of winning with the value as a function of price, wherein the market response model means calculates the probability of winning using an equation;

$\mathrm{probability}<br><br><br><br>\mathrm{of}<br><br><br><br>\mathrm{winning}=\frac{1}{1+\underset{j\in <br><br>J}{\sum <br><br>}<br><br>{\mathrm{e<br><br>}}^{k_j+m_j}}$

wherein, for J competitors, k_j is a sum of price-independent terms for competitor j and m_j is a sum of price-dependent terms for the competitor j.

40. The system of claim 39, further including

an optimization model means for creating an optimization model that determines the competitive response to any potential value and computes the target price that maximizes expected contribution.

41. The system of claim 39, further including a benefits model means for creating a benefits model that calculates one or more benefits of target pricing in comparison to a pre-existing pricing approach.

42. The system of claim 41, wherein the product model means, competitor net price model means, market response model means, optimization model, and benefits model are objects implemented in software on the one or more processors of the target pricing system.

43. The system of claim 39, wherein the product model and the competitor price model are n-dimensional with stored data reflective of at least price and cost, and wherein the system pricing the value, costing the value, and calculating an equivalent competitor net price are performed by iterative linear interpolation of the stored data.