System & method for online advertising

• US 20060010029A1
• Filed: 04/28/2005
• Published: 01/12/2006
• Est. Priority Date: 04/29/2004
• Status: Abandoned Application

• Current Assignee: Intellectual Ventures Fund 45 LLC (Intellectual Ventures LLC)
• Original Assignee: Intellectual Ventures Fund 45 LLC (Intellectual Ventures LLC)
• Inventors: Gross, John N.
• Application Number:
  US11/117,928
• Publication Number:
  US 20060010029A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration

Abstract

A system and method for determining and identifying clusters/kernels of linkings in communities of online users. The analysis is based at least in part on an entropy analysis, such as by dividing a population into cyber neighborhoods, which can be geographic based and/or logically related.

Claims

1. A method of analyzing advertising effect across a number of Internet sites comprising:

(a) dividing a set of webpages into a plurality of measurement windows consisting of a plurality of separate web pages;

(b) measuring an adoption value for an item between said plurality of separate webpages for at least one measurement window;

wherein information for said item has been provided to at least some online users in connection with online advertising presented with said set of webpages;

(c) comparing the adoption value in said at least one measurement window for said plurality of separate webpages with a nominal adoption value using a cross-entropy analysis;

(d) determining whether clusters of adoption exist in each said measurement window based at least in part on the results of step (c).

2. The method of claim 1 wherein said measurement windows correspond to web pages from a common geographic region.

3. The method of claim 1 wherein said measurement windows correspond to web pages from a common online website and/or domain.

4. The method of claim 1 wherein said measurement windows correspond to web pages presented to a single logical subgroup of users of an online site.

5. The method of claim 4, wherein said single logical subgroup consists of message postings for a topic on an online message board.

6. The method of claim 4, wherein said single logical subgroup of users of an online site correspond to members of a cyberspace based purchase circle sharing a common geographic characteristic.

7. The method of claim 1 wherein said measurement windows are synthesized at least in part from a plurality of separate web pages from multiple websites and/or domains.

8. The method of claim 1 wherein said measurement windows are determined at least in part by identifying trendsetter web pages and/or trendsetter websites.

9. The method of claim 1 wherein said measurement windows are determined at least in part by evaluating one or more web logs (blogs).

10. The method of claim 1, further including a step:

measuring a change in adoption value in time.

11. The method of claim 1, further including a step:

adjusting online advertising to at least some online users based on said adoption value.

12. The method of claim 1, further including a step:

adjusting online advertising to different measurement windows of web pages based on measuring adoption rates within said respective measurement windows.

13. The method of claim 1, further including a step:

computing an expected economic activity and/or likelihood of commercial success of said item based at least in part on said adoption value.

14. A method of monitoring effects of advertising on Internet web pages including the steps:

(a) providing online advertising for an item in association with presenting one or more web pages at a website to a first set of users;

(b) dividing said first set of users into separate logical groups;

(c) measuring a rate of adoption of said item in said separate logical groups using at least in part an entropy analysis to identify kernels and/or clusters within said first set of users.

15. The method of claim 14 wherein said rate of adoption of said item in said separate logical groups is measured by reference to web pages associated with said first set of users.

16. The method of claim 15, wherein said web pages associated with said first set of users include at least some web logs.

17. The method of claim 15, wherein said web pages associated with said first set of users include at least some message groups.

18. The method of claim 15, wherein said web pages associated with said first set of users include at least some subscriber content queues maintained by users of a content purchase and/or rental website.

19. The method of claim 14, wherein said first set of users include members of a cyberspace purchase circle comprised of individuals sharing at least some common geographic demographic data.

20. The method of claim 14, wherein said first set of users include members of a cyberspace purchase circle comprised of individuals sharing at least one of a common Internet domain characteristic, a common workplace characteristic, a common educational characteristic, and/or a common personal interest characteristic.

21. The method of claim 14, wherein said separate logical groups are classified at least in part by geographic characteristics obtained from explicit user profiles or implicit demographics information derived from user interactions.

22. The method of claim 14, further including a step:

predicting further demand for said item or related items based on step (c).

23. A method of providing advertising to online community population the method comprising the steps of:

(a) dividing the online community population into a plurality of measurement windows, which measurement windows include monitored members of the online community distributed across a plurality of subgroups logically associated with an Internet website;

(b) providing advertising for an item to said online community population including to said plurality of subgroups;

(c) measuring a first acceptance distribution for the item across the monitored members by identifying members within the plurality of measurement windows who have expressed an interest in the item;

(d) comparing the first acceptance distribution with a normal acceptance distribution using a cross-entropy analysis;

(e) determining whether kernels of acceptance for the item have formed in the online community population.

24. The method of claim 23, further including a step:

performing the operation of step (c) before step (b), and repeating steps (d) and (e) to determine an effect of said advertising.

25. The method of claim 24, wherein a measurement is made to determine if said advertising is causing imitation between members of said online community population.

26. The method of claim 23, further including a step:

correlating characteristics of said item to said online community population to determine said measurement windows.

27. The method of claim 23 further including a step:

limiting said plurality of measurement windows to individuals who are determined to be trendsetters.

28. The method of claim 23 further including a step:

measuring an influence of item adoptions made by a first group of persons within a first measurement window on item adoptions made by a second group of persons within a second measurement window.

29. The method of claim 23 further including a step:

limiting said plurality of measurement windows to individuals who are determined to not be trend laggards.

30. A system for providing online Internet related advertising including:

an Internet accessible web server, which web server is configured with one or more software routines adapted to perform the following operations;

(a) measuring a first rate of item adoption within a group of users;

(b) identifying adoption clusters and/or kernels in said group of users least in part based on an evaluation that said first rate of item adoption exceeds a threshold value.