1. TVB Research Conference October 26, 2000

2. THAT PASSIVE METER DOWN THE BLOCK. one

3. BACKGROUND

4. For more than two decades we have been trying to build the passive TV meter.

5. The irony is there are 15,000 passive meters operating in the US right now.

6. They’re called set meters.

7. And though they are eclipsed by peoplemeters...

8. They are and will remain the dominant local metering technique.

9. The problem with set meter is the diary.

10. To solve this, Nielsen is moving choc-a-block to peoplemeters in a Boston test.

11. A clear alternative is to increase the set meter panel and model viewers. Set meters

12. THE SET METER REVISITED

13. A set meter can be thought of as a people- meter that doesn’t measure people.

14. By doing less, it can do more.

15. It does not require the cooperation of all household members.

16. It is totally passive...

17. It has fewer response problems...

18. It costs far less to operate.

19. But set meters do not measure viewing so that information needs to be obtained elsewhere.

20. I believe it is quite possible to model program viewing...

21. ... from set tuning data and independent VPVH estimates...

22. ... with results indistinguishable from peoplemeter viewing data.

23. VIEWER MODELING

24. Modeling viewers is not a new idea.

25. It was first suggested by Ehrenberg and Twyman in 1966.

26. Who argued against repeatedly measuring behavior that shows little variation (i.e. VPS).

27. Modeling was revisited by Kirkham in 1993, related to the successful UK TV Span set meter panel.

28. He reported that 70% of the variation in viewer ratings was explained by household tuning.

29. In the US viewer modeling has been demonstrated by Ephron and Gray.

30. Who recently received ARF funding ($60,000) to perfect the viewer model.

31. It is relatively simple to model viewers because we know a lot about what is going on in set meter homes.

32. We know the demos of everyone in the house- hold, the time of viewing, the set used and the program tuned.

33. If the set in the child’s room is tuned to the Cartoon Channel, the child is likely viewing.

34. If it’s Oprah on the kitchen set, it’s likely woman.

35. If it’s NFL football in the family room, it’s most likely the man.

36. But the key insight is variation in VPVH for a viewer demo will be reflected by...

37. ... variation in the demo composition of the tuned household group.

38. A high Male 18-34 VPVH will be signaled by...

39. ... a high proportion of tuned households with a Male 18-34 in residence.

40. Since the household demo comp will vary by program, by time period, and by station...

41. It ties the model to real differences in VPVH for local programs like News, Syndication and Sports.

42. THE MODEL

43. The following diagram shows the steps in modeling viewing from household tuning data.

44. The demo: Adults 35+ The program: 60 Minutes

45. Estimating Adults 35+ 60 MINUTES 1. Tuned Households (set meter) Yes (Go to 2)

46. Estimating Adults 35+ 60 MINUTES 1. Tuned Households (set meter) Yes (Go to 2) 2. With 35+ adult resident? (set meter) Yes (Go to 3) No (Discard)

47. Estimating Adults 35+ 60 MINUTES 1. Tuned Households (set meter) Yes (Go to 2) 2. With 35+ adult resident? (set meter) Yes (Go to 3) No (Discard) 3. One person household? (set meter) Yes (Add to viewers) No (Go to 4)

48. Estimating Adults 35+ 60 MINUTES 1. Tuned Households (set meter) Yes (Go to 2) 2. With 35+ adult resident? (set meter) Yes (Go to 3) No (Discard) 3. One person household? (set meter) Yes (Add to viewers) No (Go to 4) 4. Estimate probability of 35+ adult viewing in remaining 35+ adult households. (model) (Add to viewers.)

49. Estimating Adults 35+ 60 MINUTES 1. Tuned Households (set meter) Yes (Go to 2) 2. With 35+ adult resident? (set meter) Yes (Go to 3) No (Discard) 3. One person household? (set meter) Yes (Add to viewers) No (Go to 4) 4. Estimate probability of 35+ adult viewing in remaining 35+ adult households. (model) (Add to viewers.) 5. Sum total viewers.

50. That’s the model. Here is a demonstration using live data.

51. Set meter data was taken from a random third A of the NTI peoplemeter panel. A C B

52. Estimating Adults 35+ 60 MINUTES 1. Tuned Households 11,465,000 Set meter

53. Estimating Adults 35+ 60 MINUTES 1. Tuned Households 2. With 35+ adult resident? 10,671,000 11,465,000 Set meter

54. Estimating Adults 35+ 60 MINUTES 1. Tuned Households 2. With 35+ adult resident? 3. One person household? 2,499,000 11,465,000 10,671,000 Set meter

55. Estimating Adults 35+ 60 MINUTES 1. Tuned Households 2. With 35+ adult resident? 3. One person household? 4. Estimate probability of 35+ adult viewing in remaining 35+ adult households. 2,499,000 11,465,000 10,671,000

56. Estimating Adults 35+ 60 MINUTES 1. Tuned Households 2. With 35+ adult resident? 3. One person household? 4. Estimate probability of 35+ adult viewing in remaining 35+ adult households. 2,499,000 11,465,000 10,671,000 VPVH 1.24 =10,173,000 Modeled

57. The demo VPVH estimate for 2+ member households with an Adult 35+ in residence...

58. ... uses peoplemeter data from the B third of the sample.

59. Estimating Adults 35+ 60 MINUTES 1. Tuned Households 2. With 35+ adult resident? 3. One person household? 4. Estimate probability of 35+ adult viewing in remaining 35+ adult households. 2,499,000 11,465,000 10,671,000 VPVH 1.24 =10,173,000 5. Sum total 35+ adult viewers. 12,672,000

60. Estimating Adults 35+ 60 MINUTES 1. Tuned Households 2. With 35+ adult resident? 3. One person household? 4. Estimate probability of 35+ adult viewing in remaining 35+ adult households. 2,499,000 11,465,000 10,671,000 VPVH 1.24 =10,173,000 5. Sum total 35+ adult viewers. 12,672,000 + =

61. Estimating Adults 35+ 60 MINUTES 1. Tuned Households 2. With 35+ adult resident? 3. One person household? 4. Estimate probability of Male 18-49 viewing in remaining 35+ adult households. 2,499,000 11,465,000 10,671,000 VPVH 1.24 =10,173,000 5. Sum total 35+ adult viewers. 12,672,000 Modeled VPVH = 1.11

62. To validate the model, this estimate based on the A and B thirds of the NTI sample...

63. Is compared to the peoplemeter estimate produced by the C third of the NTI sample.

64. The difference is 2%.

65. Modeled 1.11 Peoplemeter 1.09 Difference +2% VPVH

66. A second comparison puts a 2% difference into perspective.

67. Two new random half- samples produce peoplemeter VPVH estimates of 1.06 and 1.12.

68. A difference of 6%.

69. Peoplemeter A 1.06 Peoplemeter B 1.12 Difference +6% VPVH. Two Half Samples

70. Here are similar comparisons for another five randomly selected prime time programs.

71. The Practice (A18-49) modeled VPVH: 0.69 actual VPVH: 0.68 new split: 0.72 vs. 0.66 1% vs. 8%

72. modeled VPVH: 0.42 actual VPVH: 0.38 new split: 0.37 vs. 0.43 11% vs. 16% Mon. Night FB (M18-49)

73. modeled VPVH: 0.48 actual VPVH: 0.48 new split: 0.43 vs. 0.47 0% vs. 9% Ally McBeal (W18-49)

74. modeled VPVH: 0.56 actual VPVH: 0.64 new split: 0.59 vs. 0.67 14% vs. 14% West Wing (A25-54)

75. modeled VPVH: 0.58 actual VPVH: 0.64 new split: 0.61 vs. 0.56 10% vs. 9% Buffy (P12-34)

76. The modeled Viewer estimates are well within the sampling error range of a 2,500 household panel...

77. And are statistically indistinguishable from measured data.

78. The next step is to apply these modeling techniques to local demo audiences.

79. But here the smaller local samples will produce...

80. ... more variation in VPVH than the modeling will.

81. CONCLUSION

82. Large set meter panels together with viewer modeling...

83. ... promise to produce better ratings for the dollars spent than the current system.

84. Larger set meter panels can augment people- meter panels for national ratings.

85. In local set-metered markets, viewer modeling can replace diaries...

86. ... which will produce better data and solve the “sweeps problem.”

87. Because of costs and response problems, a peoplemeter panel can barely measure television.

88. A set-meter panel and viewer modeling can do it better and for less.