Method and apparatus for analyzing data and advertising optimization

• US 6,286,005 B1
• Filed: 03/11/1998
• Issued: 09/04/2001
• Est. Priority Date: 03/11/1998
• Status: Expired due to Term

• Current Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Original Assignee: Cannon Holdings LLC (Nielsen Holdings plc)
• Inventors: Cannon, Mark E.
• Primary Examiner(s): Alam, Hosain T.
• Assistant Examiner(s): FLEURANTIN, JEAN B
• Application Number:
  US09/038,380
• Time in Patent Office: 1,273 Days
• Field of Search: 707/1, 707/9, 707/10, 707/100-104, 707/7, 705/7-10, 455/2.01, 725/9
• US Class Current: 705/14.66
• 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 30/0269 based on user profile or attribute
• CPC Class Codes: H04H 60/45 for identifying users
• CPC Class Codes: H04H 60/46 for recognising users' preferences
• CPC Class Codes: H04H 60/66 for using the result on distributors' side
• CPC Class Codes: H04N 21/25883 being end-user demographical data, e.g. age, family status or address arrangements for identifying locations of users in broadcast systems H04H60/52
• CPC Class Codes: H04N 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/478 Supplemental services, e.g. displaying phone caller identification, shopping application
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02

Abstract

The most preferred embodiment of the present invention is a computer-based decision support system that includes three main components: a database mining engine (DME); an advertising optimization mechanism; and a customized user interface that provides access to the various features of the invention. The user interface, in conjunction with the DME, provides a unique and innovative way to store, retrieve and manipulate data from existing databases containing media-related audience access data, which describe the access habits and preferences of the media audience. By using a database with a simplified storage and retrieval protocol, the data contained therein can be effectively manipulated in real time. This means that previously complex and lengthy information retrieval and analysis activities can be accomplished in very short periods of time (typically seconds instead of minutes or even hours). Further, by utilizing the advertising optimization mechanism of the present invention, businesses, networks, and advertising agencies can interactively create, score, rank and compare various proposed or actual advertising strategies in a simple and efficient manner. This allows the decision-makers to more effectively tailor their marketing efforts and successfully reach the desired target market while conserving scarce advertising capital. Finally, the user interface for the system provides access to both the DME and the optimization mechanism in a simple and straightforward manner, significantly reducing training time.

Claims

1. A computer system comprising:

a CPU;

a memory coupled to the CPU;

a database residing in the memory, the database comprising a plurality of person-by-person media-related records which describe a series of choices and decisions made by an identified sample audience in relation to a media vehicle;

a database mining engine residing in the memory and being executed by the CPU; and

a scoring mechanism residing in the memory, the scoring mechanism evaluating the series of choices and decisions made by individual persons in the identified sample audience as described in the person-by-person media-related records and generating a score for a proposed advertising schedule based on the person-by-person media-related records in the database, the score of the proposed advertising schedule quantifying the effectiveness of the proposed advertising schedule based upon the series of choices and decisions made bv the individual persons in the identified sample audience.

2. The computer system of claim 1 wherein the scoring mechanism scores the proposed advertising schedule by computing a overall exposure value for each individual member of the sample audience and summing the computed overall exposure values.

3. The computer system of claim 2 wherein the scoring mechanism computes the overall exposure value for each individual member of the sample audience by including a audience valuation value for each individual member based on corresponding demographic characteristics for each individual member.

4. The computer system of claim 2 wherein the scoring mechanism computes the overall exposure value for each individual member of the sample audience by including a exposure valuation value for each individual member based on their individual exposures to the proposed advertising schedule.

5. The computer system of claim 4 wherein the exposure valuation value for each individual member of the sample audience is generated using time weighted effective frequency techniques such that the exposure valuation value for each individual member is determined by the number of exposures of the individual member and the spacing between exposures of the individual member.

6. The computer system of claim 2 wherein data base includes an array of person objects, with each person object corresponding to an individual member of the sample audience, and wherein each of the person objects includes fields representing the age of the corresponding person, and wherein each field representing the age of the corresponding person includes a sequence of bits, with each bit assigned to correspond to one of a plurality of age ranges and wherein the bit corresponding to the age range of the corresponding person is set, such that the data mining engine can quickly retrieve viewing data from persons of one or more selected age ranges by utilizing a boolean operation with a boolean filter having a corresponding sequence of bits with each bit corresponding to a selected age range.

7. An apparatus comprising:

a CPU;

a memory coupled to the CPU;

an advertising optimization mechanism residing in the memory and being executed by the CPU, the advertising optimization mechanism iteratively modifying and scoring a base advertising schedule in order to achieve an optimal advertising schedule.

8. The pparats of claim 7 further comprising a graphical user interface with a plurality of icons which provide a plurality of choices for advertising optimization.

14. The system of claim 7 further comprising at least one index residing in the memory and cooperating with the the advertising optimization mechanism to iteratively modify and score the base advertising schedule.

15. The computer system of claim 14 wherein the selected data fields include person ages, and wherin each of the sequence of bits represents a range of ages.

16. The system of claim 7 further comprising a plurality of indices residing in the memory and cooperating with the the advertising optimization mechanism to iteratively modify and score the base advertising schedule.

17. The apparatus of claim 14 wherein the at least one index comprises at least one of an exposure valuation index, an audience valuation index, an exposure recency index, a response index and a cost index.

18. The apparatus of claim 7 further comprising a database mining engine residing in the memory.

19. The apparatus of claim 18 wherein the database mining engine further comprises a plurality of Boolean filters used to screen the plurality of person-by-person records contained in the database.

20. The apparatus of claim 7 further comprising a data conversion mechanism residing in the memory.

21. The apparatus of claim 20 wherein the data conversion mechanism comprises a mechanism to convert data from a first data format to a second data format.

9. A computer system for analyzing data and optimizing an advertising schedule, the system comprising:

a CPU;

a memory coupled to the CPU;

a database residing in the memory, the database comprising a plurality of person-by-person records which describe a series of television choices and decisions made by an identified sample audience in relation to a media vehicle, and wherein the database comprises a plurality of portions, with each of the plurality of portions including person-by-person records corresponding to a different time period and wherein the each of the plurality of portions includes a viewing index that indexes the person-by-person records for the corresponding time period;

a database mining engine residing in the memory, the database mining engine comprising utilizing the plurality of viewing indices to selectively access data from the database; and

a scoring mechanism residing in the memory and being executed by the CPU, wherein the scoring mechanism accesses the person-by-person records in the database via the database mining engine and provides a score for a proposed incremental change in an advertising schedule based on the person-by-person media-related records in the database by computing a overall exposure value for each individual member of the sample audience due to the proposed incremental change and summing the computed overall exposure values, the score thus quantifying the effectiveness proposed incremental change based upon the series of choices and decisions made by each individual member of the sample audience.

10. The computer system of claim 9 wherein the scoring mechanism determines the overall value for each individual member of the sample audience by utilizing a weighted effective frequency method to determine an exposure valuation value for each individual member, and wherein the weighted effective frequency method is determined by the number of exposures of each individual member.

11. The computer system of claim 9 wherein the scoring mechanism determines the overall value for each individual member of the sample audience by utilizing a time weighted effective frequency method to determine an exposure valuation value for each individual member, and wherein the weighted effective frequency method is determined by the number of exposures of each individual member and the time spacing between exposures of the individual member.

12. The computer system of claim 9 wherein the scoring mechanism determines the overall value for each individual member of the sample audience by including a audience valuation for each individual based on corresponding demographic characteristics for each individual member.

13. The computer system of claim 9 wherein the database comprises data adapted from a plurality of television viewing records received from A. C. Nielsen.

22. A computer system with a graphical user interface comprising:

a CPU;

a memory coupled to the CPU;

a database residing in the memory, the database comprising a plurality of person-by-person media-related records which describe a series of choices and decisions made by an identified sample audience in relation to a media vehicle;

a database mining engine residing in the memory and being executed by the CPU;

a scoring mechanism for evaluating a plurality of alternative advertising options based on the plurality of person-by-person media-related records.

23. The computer system of claim 22 wherein the scoring mechanism for evaluating a plurality of alternative advertising options comprises a mechanism for evaluating advertisement value over time and space based on actual or anticipated individual or collective advertising exposure.

24. The computer system of claim 22 wherein the scoring mechanism for evaluating a plurality of alternative advertising options comprises a mechanism for assigning advertising response values to a plurality of media alternatives.

25. The computer system of claim 22 wherein the scoring mechanism for evaluating a plurality of alternative advertising options comprises a mechanism for assigning costs to the plurality of alternative advertising options based on time or space boundaries for the purpose of scoring the plurality of alternative advertising options.

26. The computer system of claim 22 wherein the scoring mechanism for evaluating a plurality of alternative advertising options comprises a mechanism for assigning individual exposure values to the plurality of alternative advertising options according to the value of at least one of a plurality of individual demographic measurements.

27. The computer system of claim 26 wherein the scoring mechanism for assigning individual exposure values comprises a mechanism for displaying the individual exposure values of the at least one of a plurality of individual demographic measurements.

28. The computer system of claim 22 wherein the scoring mechanism for evaluating a plurality of alternative advertising options comprises a mechanism for displaying the estimated influence of advertising messages based on the declining influence of advertising over time.

29. The computer system of claim 22 wherein the scoring mechanism for evaluating a plurality of alternative advertising options comprises a mechanism for displaying the estimated influence of advertising messages based accumulated advertising messages over time.

30. The computer system of claim 22 wherein the scoring mechanism for evaluating a plurality of alternative advertising options comprises a mechanism for assigning advertising value to multiple levels of advertising exposure based on frequency of exposure.

31. The computer system of claim 30 wherein the scoring mechanism for assigning advertising value to multiple levels of advertising exposure based on frequency of exposure further comprises a mechanism for displaying the assigned advertising values.

32. The computer system of claim 30 wherein the scoring mechanism for assigning advertising value to multiple levels of advertising exposure based on frequency of exposure comprises a mechanism for assigning advertising value to multiple levels of advertising exposure based on actual or anticipated exposure to an advertisement.