SYSTEM AND METHOD FOR MANAGING A PLURALITY OF ADVERTISING NETWORKS

• US 20080270164A1
• Filed: 12/20/2007
• Published: 10/30/2008
• Est. Priority Date: 12/21/2006
• Status: Abandoned Application

• Current Assignee: Clickable Inc. (JP Morgan Chase & Co)
• Original Assignee: Clickable Inc. (JP Morgan Chase & Co)
• Inventors: Kunkel, Nelson, Gandhi, Munish, Steward, Vernon, Kidder, David S.
• Application Number:
  US11/960,922
• Publication Number:
  US 20080270164A1
• Time in Patent Office: Days
• US Class Current: 705/1
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0241 Advertisements

Abstract

According to one aspect of the present invention a method and apparatus are described for improving advertising conversions on the Internet. An analysis engine is provided that analyzes raw advertising metrics in order to identify improvements. A treemap based visualization engine allows the user to visualize a tree in two dimensional space. In one embodiment, an action engine includes rapid one-box recommendation that allows the user to take an action to improve an advertising campaign. According to another aspect, a system and method for managing a plurality of advertising networks is also provided. A typical embodiment of the management system integrates the analysis engine, visualization engine, and action engine in order to optimize a user/manager'"'"'s time and effort in organizing, improving, and managing advertising campaigns across a plurality of advertising networks. The presentation and organization (rendered by the visualization engine) of visual displays of advertising information (compiled by analysis engine) effectively reduces the workload in managing the plurality of advertising networks, additionally, recommendations can be based on advertising information (supplied by the action engine). In one example, visual cues in the form of color designations, bring the user'"'"'s attention to advertising nodes on which actions are estimated to have the significant impact. The definition of what a significant impact is may be established by default or in other embodiments can be configurable by each particular user or manager. Once the user'"'"'s attention is brought to a particular advertising node, actions and alerts can be recommended to improve an individual advertising campaign, ad group, keyword, ad copy, and/or ad. In one embodiment, by providing an interface to access to other networks with their own advertising campaigns a plurality of networks can be managed.

Claims

1. A method for managing a plurality of advertising networks, the method comprising acts of:

aggregating advertising metrics related to at least one of the plurality of advertising networks;

analyzing the at least one of the plurality of advertising networks using advertising metrics;

displaying the at least one of the plurality of advertising networks visually;

displaying an indication related to the visual display of the at least one of the plurality of advertising networks that indicates an action exists for the at least one of the plurality of advertising networks; and

indicating, visually, a ranking for a recommendation.

2. The method according to claim 1, further comprising an act of indicating on the visual display of the at least one of the plurality of advertising networks the ranking for the recommendation using a visual cue.

3. The method according to claim 2, wherein the visual cue comprises at least one of color, font, background, texture, size, and shape.

4. The method according to claim 1, wherein aggregating advertising metrics related to the at least one of the plurality of advertising networks further comprises aggregating advertising metrics related to at least one of advertisement driver, advertisement quality, conversion process, cost, and sales.

5. The method according to claim 1, wherein the advertising metrics are associated with an advertising node.

6. The method according to claim 5, further comprising acts of:

visually displaying the advertising node; and

displaying advertising metrics in response to an event.

7. The method according to claim 6, wherein displaying advertising metrics occurs in response to at least one of a browser related event, a temporal event, an update event, and a status event.

8. The method according to claim 1, wherein the act of analyzing at least one of the plurality of advertising networks further comprises an act of weighting advertising metrics.

9. The method according to claim 8, further comprising an act of generating a recommendation value based on the weighted advertising metrics.

10. The method according to claim 1, further comprising an act of generating a recommendation value based on an estimated impact on the at least one of the plurality of advertising networks.

11. The method according to claim 9, further comprising an act of visually indicating at least one recommendation value by graphically rendering an advertising node.

12. The method according to claim 1, wherein the act of analyzing at least one of the plurality of advertising networks further comprises an act of determining a return on investment value.

13. The method according to claim 12, further comprising an act of visually indicating the return on investment value by graphically rendering an advertising node.

14. A system for managing a plurality of advertising networks, the system comprising:

an aggregation engine for aggregating information related to at least one of a plurality of advertising networks;

a visualization engine for rendering information related to a managed advertisement; and

an analysis engine for analyzing advertising metrics, wherein the analysis engine is further adapted to determine recommendations for the at least one of a plurality of advertising networks.

15. The system of claim 14, further comprising a dashboard for visually displaying the at least one of a plurality of advertising networks and information related to the managed advertisement.

16. The system of claim 15, further comprising an action engine for providing context to the determined recommendations.

17. The system of claim 14, wherein the recommendations comprise at least one of an action and an alert related to the at least one of a plurality of advertising networks.

18. The system of claim 14, wherein the analysis engine is further adapted to estimate an impact on at least one of the plurality of advertising networks based at least in part on the recommendation.

19. The system of claim 18, wherein the visualization engine renders the estimated impact on the advertising network.

20. The system of claim 19, wherein the visualization engine renders the estimated impact as part of the dashboard.

21. The system of claim 14, wherein the visualization engine renders information associated with the managed advertisement as visual aggregates of information.

22. The system of claim 21, wherein the visual aggregates of information comprise a hierarchical organization.

23. The system of claim 21, wherein the visual aggregates of information comprise advertising nodes.

24. The system of claim 14, wherein the visualization engine emphasizes information related to the managed ad using visual cues.

25. The system of claim 23, wherein the visual cues comprise at least one of color, background, texture, size, shape, and font.

26. A system for improving online advertising conversions, said system comprising:

an analysis engine that analyzes the raw advertising metrics to identify one or more improvements;

a visualization engine that allows the user to visualize a tree in two-dimensional space; and

an action engine that allows a user to take an action to improve its advertising campaign.

27. The system according to claim 26, wherein the analysis engine is further adapted to organize advertising elements into a hierarchical arrangement.

28. The system according to claim 27, wherein the analysis engine is further adapted to associate the raw advertising metrics with the organized advertising elements.

29. The system of claim 26, wherein the visualization engine is further adapted to display visual information aggregates.

30. The system of claim 29, wherein the visual information aggregates comprise hierarchical advertising elements.

31. The system of claim 26, wherein the analysis engine is further adapted to provide a recommendation.

32. The system of claim 31, wherein the recommendation comprises, at least in part, one of an action and an alert.

33. The system of claim 31, wherein the action engine is further adapted to generate context for the recommendation.

34. The system of claim 33, wherein the context for the recommendation comprises an estimated impact associated with the recommendation.

35. The system of claim 33, wherein the context for the recommendation comprises analysis performed on the raw advertising metrics associated with the recommendation.

36. The system of claim 33, wherein the action engine is further adapted to highlight significant portions of the context.

37. The system of claim 31, wherein the visualization engine is further adapted to display visual cues related to the recommendation.

38. The system of claim 37, wherein the visual cues comprise at least one of color, font, background, texture, size, and shape.

39. A computer implemented method for improving online advertising conversions, said method comprising:

analyzing the raw advertising metrics to identify improvements to conversion in online advertising;

visualizing a tree in two-dimensional space in a treemap based visualization; and

providing a rapid one-box recommendation that allows a user to take an action to improve its advertising campaign.

40. The method of claim 39, further comprising an act of providing context associated with the action to improve the advertising campaign.

41. The method of claim 39, wherein analyzing the raw advertising metrics further comprises determining if the raw advertising metrics meet a predefined threshold.

42. The method of claim 39, further comprising an act of estimating an impact on the advertising campaign, based on the recommendation.

43. The method of claim 42, wherein the estimated impact is based at least in part on, at least one of, a return on investment, click thru rate, conversions, conversion rate, impressions, unique visits, quality score of a landing page, a value of goods, visits to a desired product page, and average advertising position.

44. A computer implemented advertising system for managing a plurality of advertising networks, the system comprising:

a presentation engine for rendering a visual interface for a user to access functions associated with at least one of the plurality of advertising networks;

an execution engine for providing and executing functions associated with the at least one of the plurality of advertising networks; and

a data engine for analyzing metrics associated with the at least one of the plurality of advertising networks.

45. The system according to claim 44, wherein the data engine is further adapted to receive data from a plurality of advertising networks.

46. The system according to claim 44, wherein the data engine is further adapted to determine whether the analyzed metrics meet a predetermined threshold.

47. The system according to claim 44, wherein the predetermined threshold is associated with a recorded change over time in the analyzed metrics.

48. The system according to claim 47, wherein the analyzed metrics comprise at least one of a return on investment, click thru rate, conversions, conversion rate, impressions, unique visits, quality score of a landing page, a value of goods, visits to a desired product page, and average advertising position.

49. The system according to claim 47, wherein the execution engine is further adapted to provide a recommendation.

50. The system according to claim 49, wherein the presentation engine is further adapted to display the recommendation, associated context, and an option for accepting the recommendation.

51. The system according to claim 50, wherein the associated context comprises the analyzed metrics associated with the at least one of the plurality of advertising networks.

52. The system according to claim 50, wherein the data engine is further adapted to generate an estimated impact on the at least one of the plurality of advertising networks for the recommendation.