Method and a system for simultaneous pricing and merchandising

• US 8,285,599 B1
• Filed: 03/28/2012
• Issued: 10/09/2012
• Est. Priority Date: 07/18/2011
• Status: Active Grant

• Current Assignee: Nor1, Inc. (Oracle Corporation)
• Original Assignee: Nor1, Inc. (Oracle Corporation)
• Inventors: Hines, Andrew, Norins, Arthur L.
• Primary Examiner(s): FADOK, MARK A
• Application Number:
  US13/432,843
• Time in Patent Office: 195 Days
• Field of Search: 705/26, 705/27
• US Class Current: 705/26.1
• CPC Class Codes: G06Q 30/0212 Chance discounts or incentives
• CPC Class Codes: G06Q 30/0223 based on inventory
• CPC Class Codes: G06Q 30/0252 based on events or environment, e.g. weather or festivals
• CPC Class Codes: G06Q 30/0253 During e-commerce, i.e. online transactions
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0613 Third-party assisted

Abstract

One embodiment of the instant invention is a computer-implemented method for processing transaction-related data that includes at least the following steps of: receiving seller data about a seller; receiving buyer data about a buyer; generating a seller profile; generating a buyer profile; receiving transaction data about a current transaction between the seller and the buyer for an offering; generating a transaction composite for the current transaction; determining a classification rule for each transaction composite class; and classifying the transaction composite into a particular transaction composite class based on comparing the classification rule to: i) the generated seller profile, ii) the generated buyer profile, or iii) the current transaction.

Claims

1. A computer-implemented method, comprising:

determining at least one offer from at least one seller to at least one buyer, wherein the at least one seller and at least one buyer are parties to at least one current transaction and wherein the determining at least one offer comprises;

determining, by a computer system, a demand for at least one offering from the at least one seller, wherein the demand is calculated based, at least in part, on;

1) at least one transaction composite,

2) at least one product preference function, wherein the at least one product preference function is based at least in part on;

i) a plurality of offerings,

ii) a price of each offering from the plurality of offerings,

iii) a transaction composite class of the at least one transaction composite,

wherein the at least one at least one transaction composite comprises;

i) at least one seller profile having at least one seller statistical score, wherein the at least one seller profile is independent from the transaction data for the at least one current transaction,

ii) at least one buyer profile having at least one buyer statistical score, wherein the at least one buyer profile is independent from the transaction data for the at least one current transaction, and

iii) transaction data for the at least one current transaction;

wherein the at least one product preference function determines at least one ranked subset of offerings from the plurality of offerings, wherein the at least one ranked subset of offerings identifies a purchasing probability for each offering from the at least one ranked subset of offerings when each offering is being offered with at least one other offering from the at least one ranked subset of offerings, and wherein the at least one product preference function evaluates the purchasing probability of each offering from the at least one ranked subset of offerings based, at least in part, on at least one relationship between the price of each offering in the at least one ranked subset of offerings and the purchasing probability of each offering from the at least one ranked subset of offerings, and

3) at least one price sensitivity function, wherein the at least one price sensitivity function is based at least in part on;

i) the at least one ranked subset from the plurality of offerings,

ii) the price and the purchasing probability of each offering from at least one ranked subset,

iii) the transaction composite class of the at least one transaction composite,

wherein the at least one price sensitivity function determines the at least one relationship between the price of each offering from the at least one ranked subset of offerings and the purchasing probability of each offering from the at least one ranked subset of offerings;

calculating, by the computer system, a supply prediction for the at least one offering, wherein the supply prediction is determined based at least in part on;

i) historical demand data indicating previous fulfillment of the at least one offering,

ii) current demand data indicating at least one received but unfulfilled order availability of the at least one offering, and

iii) capacity limit for fulfilling orders for the at least one offering;

calculating, by the computer system, a revenue prediction, wherein the revenue prediction determines a maximum expected revenue based at least on the determined demand for the at least one offering and the supply prediction for the at least one offering; and

providing the at least one offer to the at least one buyer based, at least in part, on;

(1) the determined demand for the at least one offering,

(2) the calculated supply prediction, and

(3) the calculated revenue prediction.

2. The method of claim 1, wherein the at least one offer comprising an offer of at least one of:

merchandise or service.

3. The method of claim 2, wherein the merchandise or the service is subject to uncertain availability.

4. The method of claim 1, wherein the method further comprises initiating at least one electronic request to at least one electronic source to obtain seller data about the at least one seller and generating, by the computer system, from the seller data, at least one seller profile.

5. The method of claim 4, wherein the seller data comprises:

i) at least one seller qualitative characteristic about the at least one seller and at least one seller quantitative characteristic of the at least one seller, or

ii) a plurality of seller qualitative characteristics about the at least one seller, or

iii) a plurality of seller quantitative characteristics of the at least one seller.

6. The method of claim 5, wherein the at least one seller statistical score calculated based at least in part on the at least one seller qualitative characteristic about the at least one seller or the at least one seller quantitative characteristic of the at least one seller, or both.

7. The method of claim 1, wherein the method further comprises initiating at least one electronic request to at least one electronic source to obtain buyer data about the at least one buyer and generating, by the computer system, from the buyer data, at least one buyer profile.

8. The method of claim 7, wherein the buyer data comprises:

i) at least one buyer qualitative characteristic about the at least one buyer and at least one buyer quantitative characteristic of the at least one buyer, or

ii) a plurality of buyer qualitative characteristics about the at least one buyer, or

iii) a plurality of buyer quantitative characteristics of the at least one buyer.

9. The method of claim 8, wherein the at least one buyer statistical score calculated based at least in part on the at least one buyer qualitative characteristic about the at least one buyer or the at least one buyer quantitative characteristic of the at least one buyer, or both.

10. The method of claim 7, wherein the at least one electronic source is a social network source.

11. The method of claim 1, wherein the at least one seller and buyer statistical scores are of type selected from the group of:

sum, average, median, mode and count.

12. The method of claim 7, wherein the buyer data comprises online browsing history or clickstream data.

13. The method of claim 1, wherein the transaction data comprises information about at least one online transaction, at least one offline transaction, or both.

14. The method of claim 5, wherein the at least one seller statistical score utilizes the seller data without associating the seller data with a particular buyer of the at least one seller and without associating the seller data with a particular transaction between the particular buyer and the at least one seller.

15. The method of claim 1, wherein the at least one current transaction is an expression of interest by the at least one buyer to transact with at least one seller for at least one offering.

16. A computer system for processing transaction-related data, comprising:

a) memory having at least one region for storing computer executable program code; and

b) at least one processor for executing the computer executable program code stored in the memory, wherein the computer executable program code comprising;

code to determine at least one offer from at least one seller to at least one buyer, wherein the at least one seller and at least one buyer are parties to at least one current transaction and wherein the code to determine at least one offer comprises;

code to determine a demand for at least one offering from the at least one seller, wherein the demand is calculated based, at least in part, on;

1) at least one transaction composite,

2) at least one product preference function, wherein the at least one product preference function is based at least in part on;

i) a plurality of offerings,

ii) a price of each offering from the plurality of offerings,

iii) a transaction composite class of the at least one transaction composite,

wherein the at least one at least one transaction composite comprises;

i) at least one seller profile having at least one seller statistical score, wherein the at least one seller profile is independent from the transaction data for the at least one current transaction,

ii) at least one buyer profile having at least one buyer statistical score, wherein the at least one buyer profile is independent from the transaction data for the at least one current transaction, and

iii) transaction data for the at least one current transaction;

wherein the at least one product preference function determines at least one ranked subset of offerings from the plurality of offerings, wherein the at least one ranked subset of offerings identifies a purchasing probability for each offering from the at least one ranked subset of offerings when each offering is being offered with at least one other offering from the at least one ranked subset of offerings, and wherein the at least one product preference function evaluates the purchasing probability of each offering from the at least one ranked subset of offerings based, at least in part, on at least one relationship between the price of each offering in the at least one ranked subset of offerings and the purchasing probability of each offering from the at least one ranked subset of offerings, and

3) at least one price sensitivity function, wherein the at least one price sensitivity function is based at least in part on;

i) the at least one ranked subset from the plurality of offerings,

ii) the price and the purchasing probability of each offering from at least one ranked subset,

iii) the transaction composite class of the at least one transaction composite,

wherein the at least one price sensitivity function determines the at least one relationship between the price of each offering from the at least one ranked subset of offerings and the purchasing probability of each offering from the at least one ranked subset of offerings;

code to calculate a supply prediction for the at least one offering, wherein the supply prediction is determined based at least in part on;

i) historical demand data indicating previous fulfillment of the at least one offering,

ii) current demand data indicating at least one received but unfulfilled order availability of the at least one offering, and

iii) capacity limit for fulfilling orders for the at least one offering;

code to calculate a revenue prediction, wherein the revenue prediction determines a maximum expected revenue based at least on the determined demand for the at least one offering and the supply prediction for the at least one offering; and

code to provide the at least one offer to the at least one buyer based, at least in part, on;

(1) the determined demand for the at least one offering,

(2) the calculated supply prediction, and

(3) the calculated revenue prediction.

17. The computer system of claim 16, wherein the method further comprises initiating at least one electronic request to at least one electronic source to obtain seller data about the at least one seller and generating, by the computer system, from the seller data, at least one seller profile.

18. The computer system of claim 17, wherein the seller data comprises:

i) at least one seller qualitative characteristic about the at least one seller and at least one seller quantitative characteristic of the at least one seller, or

ii) a plurality of seller qualitative characteristics about the at least one seller, or

iii) a plurality of seller quantitative characteristics of the at least one seller.

19. The computer system of claim 18, wherein the at least one seller statistical score calculated based at least in part on the at least one seller qualitative characteristic about the at least one seller or the at least one seller quantitative characteristic of the at least one seller, or both.

20. The computer system of claim 16, wherein the method further comprises initiating at least one electronic request to at least one electronic source to obtain buyer data about the at least one buyer and generating, by the computer system, from the buyer data, at least one buyer profile.

21. The computer system of claim 20, wherein the buyer data comprises:

i) at least one buyer qualitative characteristic about the at least one buyer and at least one buyer quantitative characteristic of the at least one buyer, or

ii) a plurality of buyer qualitative characteristics about the at least one buyer, or

iii) a plurality of buyer quantitative characteristics of the at least one buyer.

22. The computer system of claim 21, wherein the at least one buyer statistical score calculated based at least in part on the at least one buyer qualitative characteristic about the at least one buyer or the at least one buyer quantitative characteristic of the at least one buyer, or both.

23. The computer system of claim 20, wherein the at least one electronic source is a social network source.

24. The computer system of claim 16, wherein the at least one seller and buyer statistical scores are of type selected from the group of:

sum, average, median, mode and count.

25. The computer system of claim 20, wherein the buyer data comprises online browsing history or clickstream data.

26. The computer system of claim 16, wherein the transaction data comprises information about at least one online transaction, at least one offline transaction, or both.

27. The computer system of claim 17, wherein the at least one seller statistical score utilizes the seller data without associating the seller data with a particular buyer of the at least one seller and without associating the seller data with a particular transaction between the particular buyer and the at least one seller.

28. The computer system of claim 16, wherein the at least one current transaction is an expression of interest by the at least one buyer to transact with at least one seller for at least one offering.

29. The computer system of claim 16, wherein the at least one offer comprising an offer of at least one of:

merchandise or service.

30. The computer system of claim 29, wherein the merchandise or the service is subject to uncertain availability.