Method and apparatus for analyzing data and advertising optimization

• US 20010020236A1
• Filed: 03/22/2001
• Published: 09/06/2001
• Est. Priority Date: 03/11/1998
• Status: Abandoned Application

• Current Assignee: Mark E. Cannon
• Original Assignee: Mark E. Cannon
• Inventors: Cannon, Mark E.
• Application Number:
  US09/814,622
• Publication Number:
  US 20010020236A1
• Time in Patent Office: Days
• US Class Current: 707/1
• 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. 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.

2. The apparatus of

claim 1

further comprising a graphical user interface with a plurality of icons which provide a plurality of choices for advertising optimization.

3. The system of

claim 1

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.

4. The apparatus of

claim 3

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.

5. The apparatus of

claim 1

further comprising a database mining engine residing in the memory.

6. The apparatus of

claim 5

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.

7. The apparatus of

claim 1

further comprising a data conversion mechanism residing in the memory.

8. The apparatus of

claim 7

wherein the data conversion mechanism comprises a mechanism to convert data from a first data format to a second data format.

9. The apparatus of

claim 8

wherein the first data format is a plurality of television viewing records received from A. C. Nielsen and the second data format is a binary representation of the plurality of television viewing records.

10. The system of

claim 1

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.

11. The apparatus of

claim 3

wherein the plurality of indices comprises at least two of an exposure valuation index, an audience valuation index, an exposure recency index, a response index and a cost index.

12. A computer system for optimizing an advertising schedule, the computer system comprising:

a CPU;

a memory coupled to the CPU;

a database residing in the memory, the database containing a plurality of person-by-person data files, the plurality of person-by-person data;

a database mining engine residing in the memory;

a data conversion mechanism residing in the memory, the data conversion mechanism comprising a mechanism for converting data from a first data format to a second data format; and

a graphical user interface residing in the memory and being executed by the CPU, wherein the graphical user interface provides a plurality of choices for optimizing the advertising schedule according to a plurality of indices.

13. The computer system of

claim 12

wherein the first data format is a plurality of television viewing records received from A. C. Nielsen and the second data format is a binary representation of the plurality of television viewing records.

14. The computer system of

claim 12

wherein the plurality of indices includes an exposure valuation index, an audience valuation index, an exposure recency index, a response index and a cost index.

15. A program product comprising:

an advertising optimization mechanism, the advertising optimization mechanism iteratively modifying a base advertising schedule to achieve an optimal advertising schedule; and

signal bearing media bearing the advertising optimization mechanism.

20. The program product of

claim 15

further comprising a data conversion mechanism, the data conversion mechanism comprising a mechanism for converting data from a first data format to a second data format.

21. The program product of

claim 20

wherein the first data format is a plurality of television viewing records received from A. C. Nielsen and the second data format is a plurality of variable length records which describe changes in media-related access data for a target audience.

22. The program product of

claim 20

wherein the first data format is a plurality of television viewing records received from A. C. Nielsen and the second data format is a binary representation of the plurality of television viewing records.

16. The program product of

claim 16

wherein the signal bearing media comprises transmission media.

17. The program product of

claim 16

wherein the signal bearing media comprises recordable media.

18. The program product of

claim 16

further comprising a plurality of indices which are utilized by the advertising optimization mechanism to iteratively modify the base advertising schedule.

19. The program product of

claim 18

wherein the plurality of indices comprises an exposure valuation index, an audience valuation index, an exposure recency index, a response index and a cost index.

23. A method for advertising optimization, the method comprising the step of iteratively modifying a base advertising schedule according to at least one of a plurality of indices in order to achieve an optimal advertising schedule.

24. The method of

claim 23

wherein the plurality of indices comprises an exposure valuation index, an audience valuation index, an exposure recency index, a response index and a cost index.

25. The method of

claim 23

wherein the step of iteratively modifying a base advertising schedule comprises using a weighted effective frequency method to score and compare a plurality of possible alternative advertising schedules.

26. The method of

claim 25

wherein the step of scoring and comparing a plurality of possible alternative advertising schedules comprises the step of assigning a value to a modified advertising campaign based on previous or anticipated individual or collective advertising exposure.

27. The method of

claim 23

wherein the step of iteratively modifying a base advertising schedule comprises using a time weighted effective frequency method to score and compare a plurality of possible alternative advertising schedules.

28. A computer-implemented method, the method comprising the steps of:

(a) providing an advertising campaign containing a plurality of advertising spots;

(b) identifying one of the plurality of advertising spots as a least valuable advertising spot;

(c) removing the least valuable advertising spot from the advertising campaign;

(d) identifying a plurality of alternative options to add to the advertising campaign;

(e) selecting one of the plurality of alternative options and adding the selected alternative option to the advertising campaign to achieve a modified advertising campaign;

(f) scoring the modified advertising campaign; and

(g) repeating steps b, c, d, e, and f in order to achieve an optimal advertising schedule.

29. The method of

claim 28

wherein the step of scoring the modified advertising campaign comprises the step of using a weighted effective frequency method to score the modified advertising campaign.

30. The method of

claim 28

wherein the step of scoring the modified advertising campaign comprises the step of using a time weighted effective frequency method to score the modified advertising campaign.

31. The method of

claim 28

wherein the step of scoring the modified advertising campaign comprises the step of using at least one index to score the modified advertising campaign.

32. The method of

claim 31

wherein the step of scoring the modified advertising campaign using at least one index to score the modified advertising campaign comprises the step of using a plurality of indices to score the modified advertising campaign.

33. The method of

claim 32

wherein the step of scoring the modified advertising campaign using a plurality of indices comprises the step of using at least two of an exposure valuation index, an audience valuation index, an exposure recency index, a response index and a cost index to score the modified advertising campaign.

34. The method of

claim 33

further comprising the step of using a series of product usage data as an input for the response index.

35. A graphical user interface comprising at least one icon which accesses a plurality of person-by-person records contained in a database via a database mining engine and presents at least one advertising optimization choice to a user of the graphical user interface.

36. The graphical user interface of

claim 35

further comprising a scoring mechanism which provides a score for an advertising campaign based on a plurality of indices.

37. The graphical user interface of

claim 36

wherein the scoring mechanism uses a plurality of indices to score the advertising campaign.

38. The graphical user interface of

claim 37

wherein the plurality of indices comprises at least two of an exposure valuation index, an audience valuation index, an exposure recency index, a response index and a cost index.

39. 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; and

at least one icon which accesses the plurality of person-by-person records contained in the database via the database mining engine and presents at least one advertising optimization choice to a user of the graphical user interface.

40. The computer system of

claim 39

further comprising a scoring mechanism residing in the memory, the scoring mechanism providing a score for an advertising campaign.

41. 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;

a database mining engine residing in the memory, the database mining engine comprising a plurality of Boolean filters used to screen the plurality of person-by-person records contained in the database; and

a graphical user interface residing in the memory and being executed by the CPU, wherein the user interface accesses the person-by-person records in the database via the database mining engine and iteratively optimizes the advertising schedule using a predetermined method.

42. The computer system of

claim 41

wherein the predetermined method is a weighted effective frequency method.

43. The computer system of

claim 41

wherein the predetermined method is a time weighted effective frequency method.

44. The computer system of

claim 41

further comprising a data conversion mechanism, the data conversion mechanism comprising a mechanism for converting data from a first data format to a second data format.

45. The computer system of

claim 44

wherein the first data format is a plurality of television viewing records received from A. C. Nielsen and the second data format is a binary representation of the plurality of television viewing records.

46. A method of calculating a ratio, the method comprising the steps of:

generating a first media-related exposure value;

generating a second media-related exposure value; and

combining the first and second media-related exposure values to create the ratio.

47. The method of

claim 46

wherein the step of combining the first and second media-related exposure values to create the media analysis ratio comprises the step of dividing the first media-related exposure value by the second media-related exposure value.

48. The method of

claim 46

wherein the step of generating the first media-related exposure value comprises the step of selecting a subset of person-by-person media-related access data from a database.

49. The method of

claim 46

wherein the step of generating the second media-related exposure value comprises the step of selecting a subset of person-by-person media-related access data from a database.

50. A method of calculating a media analysis ratio, the method comprising the steps of:

selecting a subset of person-by-person media-related access data from a database thereby generating a first media-related exposure value;

selecting a subset of person-by-person media-related access data from the database thereby generating a second media-related exposure value; and

dividing the first media-related exposure value by the second media-related exposure value to create the media analysis ratio.

51. A method of scoring an advertisement, the method comprising the steps of:

scoring each of a plurality of individual exposures to the advertisement to determine a value for each of the plurality of individual exposures; and

combining the values determined for each of the plurality of individual exposures to achieve an overall score for the advertisement.

52. The method of

claim 51

wherein the step of scoring each of the plurality of individual exposures to an advertisement to determine a value for each of the plurality of individual advertising exposures comprises the step of using a plurality of factors in combination to score each of the plurality of individual exposures to an advertisement.

53. 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; and

a graphical user interface with at least one icon which accesses the plurality of person-by-person records contained in the database via the database mining engine and presents at least one advertising optimization choice to a user of the graphical user interface.

54. The computer system of

claim 53

wherein the graphical user interface further comprises a mechanism for evaluating a plurality of alternative advertising options.

55. The computer system of

claim 54

wherein the mechanism for evaluating a plurality of alternative advertising options comprises a mechanism for distributing advertisements over time and space based on actual or anticipated individual or collective advertising exposure.

56. The computer system of

claim 54

wherein the mechanism for evaluating a plurality of alternative advertising options comprises a mechanism for assigning advertising response values to a plurality of media alternatives.

57. The computer system of

claim 54

wherein the 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.

58. The computer system of

claim 54

wherein the 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.

59. The computer system of

claim 58

wherein the 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.

60. The computer system of

claim 54

wherein the 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.

61. The computer system of

claim 54

wherein the 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.

62. The computer system of

claim 54

wherein the 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.

63. The computer system of

claim 62

wherein the 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.

64. The computer system of

claim 62

wherein the 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.

65. A method for comparatively scoring a plurality of advertising options comprising the step of using a graphical user interface to evaluate a plurality of alternative advertising options.

66. The method of

claim 65

wherein the step of using a graphical user interface to evaluate a plurality of alternative advertising options comprises the step of distributing advertisements over time and space based on actual or anticipated individual or collective advertising exposure.

67. The method of

claim 65

wherein the step of using a graphical user interface to evaluate a plurality of alternative advertising options comprises the step of assigning advertising response values to a plurality of media alternatives.

68. The method of

claim 65

wherein the step of using a graphical user interface to evaluate a plurality of alternative advertising options comprises the step of assigning costs to the plurality of alternative advertising options based on time or space boundaries to score each of the plurality of alternative advertising options.

69. The method of

claim 65

wherein the step of using a graphical user interface to evaluate a plurality of alternative advertising options comprises the step of assigning individual exposure values to each of the plurality of alternative advertising options according to the value of at least one of a plurality of individual demographic measurements.

70. The computer system of

claim 69

wherein the step of assigning individual exposure values to each of the plurality of alternative advertising options according to the value of at least one of a plurality of individual demographic measurements comprises the step of displaying the individual exposure values of the at least one of a plurality of individual demographic measurements.

71. The method of

claim 65

wherein the step of using a graphical user interface to evaluate a plurality of alternative advertising options comprises the step of displaying the estimated influence of advertising messages based on the declining influence of advertising over time.

72. The method of

claim 65

wherein the step of using a graphical user interface to evaluate a plurality of alternative advertising options comprises the step of displaying the estimated influence of advertising messages based accumulated advertising messages over time.

73. The method of

claim 65

wherein the step of using a graphical user interface to evaluate a plurality of alternative advertising options comprises the step of assigning advertising value to multiple levels of advertising exposure based on frequency of exposure.

74. The method of

claim 73

wherein the step of assigning advertising value to multiple levels of advertising exposure based on frequency of exposure comprises the step of displaying the assigned advertising values.

75. The method of

claim 73

wherein the step of assigning advertising value to multiple levels of advertising exposure based on frequency of exposure comprises the step of assigning advertising value to multiple levels of advertising exposure based on actual or anticipated exposure to an advertisement.

76. A method of calculating a score for an advertising spot, the method comprising the steps of:

determining a separate value for each exposure of each of a plurality of audience members to the advertising spot; and

summing the exposure values for each of the plurality of audience members to calculate the score for the advertising spot.

77. The method of

claim 76

wherein the step of determining a value for each exposure of each of a plurality of audience members to the advertising spot comprises the step of a using a weighted effective frequency method to determine a value for exposing each of a plurality of audience members to the advertising spot.

78. The method of

claim 76

wherein the step of determining a value for each exposure of each of a plurality of audience members to the advertising spot comprises the step of a using a time weighted effective frequency method to determine a value for exposing each of a plurality of audience members to the advertising spot.

79. The method of

claim 76

wherein the step of determining a value for each exposure of each of a plurality of audience members to the advertising spot comprises the step of a using predetermined formula to determine a value for each exposure of each of a plurality of audience members to the advertising spot.

80. The method of

claim 79

wherein the step of a using predetermined formula to determine a value for each exposure of each of a plurality of audience members to the advertising spot comprises the step of using the formula $S_b$

(a)=∑

i=1Na

 



[VIn

(i)×

∏

d=1D

 



VAd

(i)]×

VT

(a)×

VR

(a)÷

VC

(a)

to determine a value for each exposure of each of a plurality of audience members to the advertising spot.

81. The method of

claim 76

wherein the step of summing the exposure values for each of the plurality of audience members to calculate the score for the advertising spot comprises the step of using a using predetermined formula to sum the exposure values for each of the plurality of audience members.

82. The method of

claim 81

wherein the step of the step of using a using predetermined formula to sum the exposure values for each of the plurality of audience members comprises the step of using the formula $\sum _{}^{}$

i=1Na

[VIn

(i)×

∏

d=1D

VAd

(i)]

to sum the exposure values for each of the plurality of audience members.