Demand estimation using auction price analysis

• US 7,076,459 B2
• Filed: 03/21/2001
• Issued: 07/11/2006
• Est. Priority Date: 03/21/2001
• Status: Expired due to Fees

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Beyer, Dirk, Zhang, Alex Xin, Liu, Tongwei, Ward, Julie Ann
• Primary Examiner(s): Bashore, Alain L.
• Application Number:
  US09/813,907
• Publication Number:
  US 20020161622A1
• Time in Patent Office: 1,938 Days
• Field of Search: 705/53, 705/37, 705/35
• US Class Current: 705/37
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• 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 method for generating a demand estimate for a product includes gathering a set of auction data which is relevant to the product, removing from the auction data all but a highest bid from each unique bidder in the auction data, and correcting a bias in the auction data caused by a set of characteristics of an auction from which the auction data is obtained. In one embodiment, the auction data is obtained from an on-line auction which is characterized by bidders not necessarily knowing the start time of the auction.

Claims

1. A computer-based method for generating a demand estimate for a product, comprising:

removing from a set of auction data all but a highest bid from each unique bidder in the auction data;

determining the demand estimate by correcting a bias in the auction data caused by a set of characteristics of an auction from which the auction data is obtained using a computer-based mechanism such that the demand estimate provides an estimate of a likelihood that a consumer will purchase the product.

2. The method of claim 1, further comprising gathering the auction data.

3. The method of claim 2, wherein gathering the auction data includes searching an auction web site for the product.

4. The method of claim 2, wherein gathering the auction data includes obtaining the auction data from an auction web site.

5. The method of claim 1, wherein correcting the bias includes applying a statistical model to the auction data.

6. The method of claim 1, further comprising generating a graph of the demand estimate.

7. The method of claim 1, further comprising generating a table containing the demand estimate.

8. A system for generating a demand estimate for a product, comprising:

a set of auction data including a set of bids for the product;

price analyzer that determines an estimate of a likelihood that a consumer will purchase the product by removing from the auction data all but a highest bid from each unique bidder in the auction data and correcting a bias in the auction data caused by a set of characteristics of an auction from which the auction data is obtained.

9. The system of claim 8, further comprising means for gathering the auction data.

10. The system of claim 9, wherein the means for gathering the auction data includes means for searching an auction web site for the product.

11. The system of claim 9, wherein the means for gathering the auction data includes means for obtaining the auction data from an auction web site.

12. The system of claim 8, wherein the price analyzer corrects the bias by applying a statistical model to the auction data to obtain the demand estimate.

13. The system of claim 8, wherein the price analyzer generates a graph of the demand estimate.

14. The system of claim 8, wherein the price analyzer generates a table containing the demand estimate.

15. A method for generating a demand estimate for a product, comprising:

gathering a set of auction data that pertains to the product from an auction web site using a web client;

removing from the auction data all but a highest bid from each unique bidder in the auction data;

determining an estimate of a likelihood that a consumer will purchase the product by correcting a bias in the auction data caused by a set of characteristics of an auction corresponding to the auction data.

16. The method of claim 15, wherein determining an estimate comprises determining an estimate of a likelihood that a consumer will purchase the product at a set of possible prices for the product.

17. The method of claim 15, wherein gathering a set of auction data includes searching the auction web site for a product that is similar to the product.

18. The method of claim 15, wherein correcting a bias includes applying a statistical model to the auction data.

19. The method of claim 15, further comprising generating a graph of the demand estimate.

20. The method of claim 15, further comprising generating a table containing the demand estimate.