DAS predictive modeling and reporting function

• US 8,533,002 B2
• Filed: 01/31/2011
• Issued: 09/10/2013
• Est. Priority Date: 06/18/2002
• Status: Expired due to Fees

• Current Assignee: Valentine Communications LLC
• Original Assignee: Ewinwin Incorporated
• Inventors: Mesaros, Gregory J.
• Primary Examiner(s): OUELLETTE, JONATHAN P
• Application Number:
  US13/017,961
• Publication Number:
  US 20110125592A1
• Time in Patent Office: 953 Days
• Field of Search: None
• US Class Current: 705/1.1
• 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 30/0224 based on user history
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0258 Registration
• CPC Class Codes: G06Q 30/0267 Wireless devices
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0276 Advertisement creation

Abstract

A system and method facilitating the management of current and prospective customers and customer information is provided. The invention includes an aggregation system adapted to provide management and generation of current and prospective customers. The invention further provides management of current and prospective customer information. Additionally, the aggregation system provides for generating advertisements based at least in part upon market information. Finally, the present invention also includes a printstream aggregation method facilitating the printing of print orders.

Claims

1. An electronic business system, comprising:

memory that stores computer readable instructions;

at least one processor coupled to memory, the processor executing the following computer readable instructions stored in memory to perform a method for offering a volume discount to a plurality of buyers over a finite period of time, the method comprising;

maintaining respective customer profiles associated with current and prospective customers;

determining a likelihood of a potential recipient of a volume discount offer offered over the finite period of time to make a purchase associated with the volume discount offer, the determination based at least in part on the respective customer profiles; and

presenting the volume discount offer to the potential recipient upon the estimated likelihood value exceeding a predetermined threshold.

2. The system of claim 1, wherein the computer readable instructions are further executable to implement a Bayesian-based artificial intelligence technique for generating at least a portion of the respective customer profiles.

3. The system of claim 1, wherein the computer readable instructions are further executable to implement a Bayesian-based artificial intelligence technique for generating the likelihood value.

4. The system of claim 1, wherein the computer readable instructions are further executable to implement a follow-up process with the potential recipient following presentation of the volume discount offer, wherein the follow-up process includes determining a strategy for soliciting the potential recipient in connection with the volume discount offer.

5. The system of claim 1, wherein the computer readable instructions are further executable to implement a follow-up process with a provider of a product or service associated with the volume discount offer, wherein the follow-up process includes determining a strategy for soliciting the potential recipient in connection with the volume discount offer.

6. The system of claim 1, wherein the computer readable instructions are further executable to construct a list of current and prospective customers in accordance with at least information provided by a merchant associated with the volume discount offer.

7. The system of claim 1, wherein the computer readable instructions are further executable to update the likelihood parameters in accordance with at least information provided by a merchant associated with the volume discount offer.

8. The system of claim 1, wherein the computer readable instructions are further executable to monitor a behavior of the potential recipient whereby the volume discount offer is revised based at least in part upon the behavior.

9. The system of claim 1, wherein the computer readable instructions are further executable to monitor broad market pricing information whereby the volume discount offer is revised based at least in part upon the broad market pricing information.

10. The system of claim 1, wherein computer readable instructions are further executable to maintain customer feedback associated with the volume discount offer, whereby the volume discount offer is revised based at least in part on the customer feedback.

11. A computer-implemented method for providing a volume discount offer to a plurality of buyers over a predetermined period of time, the method comprising:

executing non-transitory computer readable instruction stored in memory to maintain a list of current and prospective customers;

executing non-transitory computer readable instruction stored in memory to maintain respective customer profiles for one or more of the current and prospective customers;

generating a likelihood value of a potential recipient of the volume discount offer to make a purchase associated with the volume discount offer based at least in part upon the respective customer profiles; and

transmitting the volume discount offer to the potential recipient upon the estimated likelihood value exceeding a predetermined threshold.

12. The computer-implemented method of claim 11, further comprising employing a Bayesian-based artificial intelligence technique for generating at least a portion of the list of current and prospective customers.

13. The computer-implemented method of claim 11, further comprising employing a Bayesian-based artificial intelligence technique for generating the likelihood value.

14. The computer-implemented method of claim 11, further comprising employing a customer follow-up process in connection with the potential recipient following the presenting the volume discount offer, wherein the follow-up process includes determining a strategy for soliciting the potential recipient in connection with the volume discount offer.

15. The computer-implemented method of claim 11, further comprising employing a merchant follow-up process in connection with a provider of a product or service associated with the volume discount offer, wherein the follow-up process includes determining a strategy for soliciting the potential recipient in connection with the volume discount offer.

16. The computer-implemented method of claim 11, further comprising accepting at least one variable parameter in connection with the generating a likelihood value.

17. The computer-implemented method of claim 11, further comprising examining activity of the potential recipient and presenting or updating the volume discount offer based upon the activity.

18. The computer-implemented method of claim 11, further comprising examining extrinsic market pricing information and presenting or updating the volume discount offer based upon the extrinsic market pricing information.

19. The computer-implemented method of claim 11, further comprising maintaining feedback associated with the volume discount offer and presenting or updating the volume discount based offer upon the feedback.

20. A non-transitory computer readable storage medium having embodied thereon instructions executable by a processor to:

construct a volume discount offer that includes a discounted price for a product or service that varies as a function of a potential recipient of the volume discount offer;

maintain a list of current and prospective customers;

maintain respective customer profiles for one or more of the current and prospective customers;

generate a likelihood value of the potential recipient of the volume discount offer to make a purchase associated with the volume discount offer based at least in part upon the respective customer profiles; and

present the volume discount offer to the potential recipient in response to the estimated likelihood value exceeding a predetermined threshold.