Audience commonality and measurement

• US 20080086741A1
• Filed: 04/06/2007
• Published: 04/10/2008
• Est. Priority Date: 10/10/2006
• Status: Abandoned Application

• Current Assignee: Quantcast
• Original Assignee: Quantcast
• Inventors: Feldman, Konrad, Sutter, Paul
• Application Number:
  US11/784,299
• Publication Number:
  US 20080086741A1
• Time in Patent Office: Days
• US Class Current: 725/13
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
• CPC Class Codes: H04N 21/4755 for defining user preferences, e.g. favourite actors or genre retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38

Abstract

Audience commonality metrics for characterizing the relationship between networked media channels based on audience overlap of identified visitor entities and their related media consumption histories. Audience commonality metrics may be scalars or multi-dimensional metrics and may take into account and/or be used in conjunction with data related to on- or off-network media channels, on- or off-network activities, sociographics and/or demographics. The current invention may be used in the design of networked advertising campaigns, identification of new or unusual market segments and/or valuation of media buys. A system according to the current invention comprises access to a configuration, an input for receiving audience commonality data, an audience commonality metrics engine and an output for providing calculated audience commonality metrics. Data related to identified visitor entities may be received, determined and/or inferred from resources such as a cookie, log file, sniffer, firewall, proxy server, client agent, tracking pixel and/or tool

Claims

1. A method of characterizing multiple networked media channels by calculating audience commonality metrics, the method comprising the steps of:

identifying a set of one or more object media channels;

identifying multiple sets of subject media channels wherein each subject media channel set comprises one or more subject media channels; and

,

calculating audience commonality metrics for each set of subject media channels wherein the step of calculating audience commonality metrics for one set of subject media channels comprises the steps of;

identifying visitor entities;

accessing media consumption histories associated with visitor entities; and

,

assessing the degree of audience overlap between the set of object media channels and the subject media channels based at least in part on the identified visitor entities and their related media consumption histories.

2. The method of claim 1 wherein:

audience overlap requires visitor entities common to every media channel within the one set of subject media channels and all object media channels within the set of object media channels.

3. The method of claim 1 wherein:

audience overlap requires visitor entities common to every media channel within the one set of subject media channels and a configurable number of the object media channels within the set of object media channels.

4. The method of claim 1 wherein:

the step of assessing the degree of audience overlap comprises the step of comparing the number of identified visitor entities compared to the expected number of identified visitor entities.

5. The method of claim 1 wherein:

an audience commonality metric comprises a scalar value.

6. The method of claim 1 wherein:

an audience commonality metric comprises a multi-dimensional profile.

7. The method of claim 1 wherein:

an audience commonality metric comprises a category.

8. The method of claim 1 further comprising the step of:

storing at least some audience commonality metrics in a database.

9. The method of claim 8 wherein:

the database is selected from the list of;

a monolithic database, a distributed database, a database of distributed files and cookies.

10. The method of claim 1 wherein:

at least one set of media channels comprises at least one networked advertising destination.

11. The method of claim 1 wherein:

at least one set of media channels comprises a pair of networked advertising destinations.

12. The method of claim 1 wherein:

a media channel is selected from the list of;

a website, a webpage, a video stream and a music stream.

13. The method of claim 1 wherein:

a visitor entity may represent a group of individuals forming a logical agglomerative grouping or a subset thereof.

14. The method of claim 13 wherein:

a logical agglomerative grouping or subset thereof is selected from the list of;

a business, an organization, a department, a family, a social network and a household.

15. The method of claim 1 wherein:

a visitor entity comprises a visitor entity selected from the list of;

a globally unique visitor entity, a locally unique visitor entity or a presumably unique visitor entity.

16. The method of claim 1 further comprising the step of:

identifying media channel market segments by selecting groups of media channels based at least in part on audience commonality metrics.

17. The method of claim 16 wherein:

the step of identifying media channel market segments is based at least in part on additional data selected from the list of;

demographic data, sociographic data, and psychographic data.

18. The method of claim 1 further comprising the step of:

ranking sets of subject media channels with respect to a set of object media channels based at least in part on audience commonality metrics.

19. The method of claim 18 wherein:

the step of ranking sets of subject media channels with respect to a set of object media channels based at least in part on additional data selected from the list of;

demographic data, sociographic data, psychographic data and data related to off-network activity.

20. The method of claim 1 wherein:

the step of assigning an audience commonality metric comprises calculating an audience commonality metric with an algorithm.

21. The method of claim 20 wherein the algorithm is configurable.

22. A method for selecting a set of favorable networked advertising destinations in relation to a target audience comprising the steps of:

characterizing a target audience by identifying one or more characteristic media channels;

identifying a set of favorable networked advertising destinations by selecting networked advertising destinations with favorable audience commonality metrics with respect to one or more of the characteristic media channels wherein;

an audience commonality metric characterizes the extent of audience overlap between sets of media channels based on identified visitor entities and their related media consumption histories.

23. The method of claim 22 wherein:

the extent of commonality represents the measured extent of audience overlap.

24. The method of claim 22 wherein:

the extent of commonality represents the estimated extent of audience overlap.

25. The method of claim 22 wherein:

the extent of commonality represents the historical extent of audience overlap over a specified time period.

26. The method of claim 22 further comprising the step of prioritizing a set of favorable networked advertising destinations based on one or more criteria selected from the list of:

audience commonality metric range, audience commonality metric maximum, audience commonality metric minimum, price of a media buy related to a favorable networked advertising destination, availability of a media buy related to a favorable networked advertising destination and demographics related to a favorable networked advertising destination.

27. The method of claim 22 further comprising the step of prioritizing a set of favorable networked advertising destinations based on audience characteristics.

28. A method for identifying media channels of interest based on the performance of a networked advertising campaign operating on multiple networked advertising destinations comprising the steps of:

identifying the top advertising destinations associated with the networked advertising campaign;

identifying favorable media channels comprising media channels with favorable audience commonality metrics with respect to the top networked advertising destinations;

accessing a history of media channels representing exposures to visitors who engaged the networked advertising campaign; and

,

identifying media channels of interest by finding media channels common to both the set of favorable media channels and the history of media channels.

29. The method of claim 28 wherein the top advertising destinations associated with the networked advertising campaign comprise the networked advertising destinations with the highest ratio of favorable outcomes to campaign exposure

30. A method for analyzing a set of advertising opportunities associated with networked advertising destinations comprising the steps of:

characterizing one or more potential advertising opportunity purchasers for a networked advertising campaign by identifying one or more characteristic media channels per potential advertising opportunity purchaser;

accessing audience commonality metrics for one or more characteristic media channels with respect to one or more networked advertising destinations related to the advertising opportunities;

matching potential advertising opportunity purchasers for a networked advertising campaign with networked advertising destinations related to the advertising opportunities based on the audience commonality metrics.

31. The method of claim 30 wherein the owner of the advertising opportunities uses audience commonality metrics associated with one or more characteristic media channels to set the offer price of the advertising opportunities per potential advertising opportunity purchaser.

32. The method of claim 30 wherein the owner of the advertising opportunities uses audience commonality metrics associated with one or more characteristic media channels to identify one or more potential advertising opportunity purchasers.

33. A system for characterizing the relationship between multiple networked media channels by calculating audience commonality metrics, the system comprising:

access to a configuration comprising;

configuration data identifying a set of one or more object media channels; and

,

configuration data identifying multiple sets of subject media channels wherein each set of subject media channels comprises one or more subject media channels;

an input for receiving audience commonality data for correlating identified users with media consumption events related to media channels;

an audience commonality metrics engine for calculating audience commonality metrics per set of subject media channels with respect to the set of object media channels using the audience commonality data and an algorithm; and

,

an output for providing calculated audience commonality metrics.

34. The system of claim 33 wherein the algorithm is configurable.

35. The system of claim 33 further comprising a database for storing at least some calculated audience commonality metrics.

36. The system of claim 33 further comprising a database for storing at least some audience commonality data for correlating identified users with media consumption events related to media channels.

37. The system of claim 33 further comprising a database for storing at least some portion of the configuration.