1 00:00:02,040 --> 00:00:09,760 Speaker 1: Welcome to brain Stuff from How Stuff Works. Hey, brain Stuff, 2 00:00:09,800 --> 00:00:14,000 Speaker 1: it's me Christian Seger. We have all heard about TV ratings. 3 00:00:14,040 --> 00:00:16,640 Speaker 1: They're just an estimate of how many people are watching 4 00:00:16,640 --> 00:00:19,920 Speaker 1: a particular show at a given time, and they are 5 00:00:19,960 --> 00:00:23,640 Speaker 1: a big deal. As of people in the US watched 6 00:00:23,680 --> 00:00:28,200 Speaker 1: about thirty four hours of TV each week, Billions of 7 00:00:28,240 --> 00:00:30,520 Speaker 1: AD dollars hang in the balance, and if a show 8 00:00:30,560 --> 00:00:35,199 Speaker 1: doesn't perform, then it risks getting axed. We're all familiar 9 00:00:35,240 --> 00:00:37,560 Speaker 1: with it. More than a few fans have been disappointed 10 00:00:37,560 --> 00:00:41,839 Speaker 1: when low ratings doom their favorite shows to cancelation. And 11 00:00:41,880 --> 00:00:44,159 Speaker 1: without naming any names, it's fair to say that some 12 00:00:44,240 --> 00:00:48,720 Speaker 1: viewers are surprised when shows they hate continue through season 13 00:00:48,880 --> 00:00:53,400 Speaker 1: after season after season due to high ratings. But what 14 00:00:53,479 --> 00:00:56,639 Speaker 1: are these ratings anyways, where do they come from, and 15 00:00:56,680 --> 00:00:59,680 Speaker 1: why are they so important? Well, in the United States 16 00:00:59,680 --> 00:01:03,880 Speaker 1: and Canada, TV ratings are synonymous with one company, Nielsen, 17 00:01:03,960 --> 00:01:07,040 Speaker 1: which was founded in nineteen twenty three by an engineer 18 00:01:07,120 --> 00:01:12,559 Speaker 1: named Arthur Nielsen. Originally, he wanted to sell engineering performance surveys, 19 00:01:12,640 --> 00:01:16,880 Speaker 1: a way to measure the efficiency and quality of engineering operations. 20 00:01:17,160 --> 00:01:21,679 Speaker 1: By ninety two, Nielsen expanded, creating a retail index that 21 00:01:21,800 --> 00:01:25,200 Speaker 1: tracked purchases in the food and drug markets. This was 22 00:01:25,240 --> 00:01:28,240 Speaker 1: the first successful attempt to measure these markets on a 23 00:01:28,240 --> 00:01:31,959 Speaker 1: wide scale, and by nineteen fifty the company applied this 24 00:01:32,040 --> 00:01:36,960 Speaker 1: technique to a little industry called television. Today, Nielsen measures 25 00:01:37,000 --> 00:01:40,000 Speaker 1: the number of people watching television shows and makes its 26 00:01:40,080 --> 00:01:44,080 Speaker 1: data available to cable networks as well as advertisers and 27 00:01:44,120 --> 00:01:49,120 Speaker 1: the media. The company uses a technique called statistical sampling 28 00:01:49,160 --> 00:01:52,000 Speaker 1: to rate the shows. This is the same technique that 29 00:01:52,080 --> 00:01:56,440 Speaker 1: polsters used to predict the outcomes of elections. Nielsen creates 30 00:01:56,480 --> 00:01:59,240 Speaker 1: a sample audience and counts how many people in that 31 00:01:59,320 --> 00:02:03,040 Speaker 1: audience view you each program. They extrapolate from the sample 32 00:02:03,240 --> 00:02:06,360 Speaker 1: and estimate the number of viewers in the entire population 33 00:02:06,560 --> 00:02:10,000 Speaker 1: watching the show to find out who's watching what. The 34 00:02:10,040 --> 00:02:13,040 Speaker 1: company gets thousands of households to become part of the 35 00:02:13,160 --> 00:02:18,679 Speaker 1: representative sample for the national ratings estimates. These participants are 36 00:02:18,760 --> 00:02:22,280 Speaker 1: randomly selected, and they're paid a little bit but not 37 00:02:22,520 --> 00:02:25,360 Speaker 1: near enough to you know, quit their day jobs and 38 00:02:25,400 --> 00:02:29,280 Speaker 1: watch TV full time. Every US household with a TV 39 00:02:29,520 --> 00:02:32,320 Speaker 1: theoretically has a chance to be a part of the sample, 40 00:02:32,639 --> 00:02:35,720 Speaker 1: but the sample itself is not very large. I mean, 41 00:02:36,040 --> 00:02:41,080 Speaker 1: that's just a few thousand households extrapolated to represent millions 42 00:02:41,200 --> 00:02:46,200 Speaker 1: right well. To make up for this, the company measures TVs, homes, programs, 43 00:02:46,240 --> 00:02:49,160 Speaker 1: and people in a variety of ways. The data is 44 00:02:49,200 --> 00:02:52,639 Speaker 1: broken down by demographic, type of stream, and so on. 45 00:02:53,000 --> 00:02:56,760 Speaker 1: This representative sample is compared to the general population, and 46 00:02:56,880 --> 00:03:00,799 Speaker 1: Nielsen also calls thousands of household to see if their 47 00:03:00,800 --> 00:03:04,080 Speaker 1: TV sets are on and who is watching. But the 48 00:03:04,080 --> 00:03:07,919 Speaker 1: phone survey could happen to anyone fitting the criteria, and 49 00:03:08,080 --> 00:03:10,800 Speaker 1: it could also be a one time thing. So what 50 00:03:10,919 --> 00:03:14,240 Speaker 1: about the genuine Nielsen families, you know, the one Nielsen 51 00:03:14,320 --> 00:03:18,200 Speaker 1: monitors continually. Well. To find out what these people are watching, 52 00:03:18,240 --> 00:03:21,680 Speaker 1: the company installs a black box on the TVs in 53 00:03:21,720 --> 00:03:24,480 Speaker 1: a home. This isn't the same as a black box 54 00:03:24,520 --> 00:03:27,840 Speaker 1: on a plane. No, it's just a computer and a modem. 55 00:03:27,880 --> 00:03:30,600 Speaker 1: The box keeps track of when the TV is on 56 00:03:30,840 --> 00:03:34,240 Speaker 1: and what it's tuned to. Every night, the box gathers 57 00:03:34,320 --> 00:03:37,160 Speaker 1: up the households viewing data and sends all of this 58 00:03:37,280 --> 00:03:41,320 Speaker 1: information to the company's central computer. By monitoring what is 59 00:03:41,360 --> 00:03:44,600 Speaker 1: on TV at any given time, the company is able 60 00:03:44,640 --> 00:03:48,440 Speaker 1: to keep track of how many people watch, which program 61 00:03:48,480 --> 00:03:51,240 Speaker 1: that seems fine, but how do we know who is 62 00:03:51,280 --> 00:03:55,480 Speaker 1: watching what? Well, after all, not everyone in a household 63 00:03:55,600 --> 00:03:58,680 Speaker 1: is going to love the same shows. That's where the 64 00:03:58,840 --> 00:04:02,440 Speaker 1: people meters come in. These are small boxes placed near 65 00:04:02,480 --> 00:04:05,520 Speaker 1: the TV sets of those in the national sample. They 66 00:04:05,560 --> 00:04:08,200 Speaker 1: measure who is watching by giving each member of the 67 00:04:08,240 --> 00:04:11,200 Speaker 1: household a button to turn on and off to show 68 00:04:11,240 --> 00:04:15,520 Speaker 1: when he or she begins and ends viewing. This information 69 00:04:15,600 --> 00:04:19,520 Speaker 1: is also collected each night. The national TV ratings have 70 00:04:19,600 --> 00:04:24,200 Speaker 1: relied on these meters for years. To ensure reasonably accurate results, 71 00:04:24,480 --> 00:04:28,840 Speaker 1: the company uses audits and quality checks. They also regularly 72 00:04:29,120 --> 00:04:33,640 Speaker 1: compare the ratings they get from different samples and measurement methods. So, 73 00:04:33,880 --> 00:04:37,880 Speaker 1: for example, a one point oh Nielsen rating indicates that 74 00:04:37,960 --> 00:04:40,560 Speaker 1: one percent of the one hundred and fifteen point nine 75 00:04:40,640 --> 00:04:45,719 Speaker 1: million estimated TV watching households tuned into a program. The 76 00:04:45,839 --> 00:04:49,240 Speaker 1: data is also broken off into different demographic ratings, the 77 00:04:49,279 --> 00:04:53,040 Speaker 1: most important being people ages eighteen to thirty four. Now, 78 00:04:53,080 --> 00:04:57,120 Speaker 1: make no mistake, this research is worth billions of dollars. 79 00:04:57,320 --> 00:05:00,760 Speaker 1: Advertising rates are based on Nielsen's data. That's why a 80 00:05:00,839 --> 00:05:04,120 Speaker 1: thirty second commercial on one show might cost twice as 81 00:05:04,200 --> 00:05:07,560 Speaker 1: much as a commercial on a low rated show programmers 82 00:05:07,600 --> 00:05:11,239 Speaker 1: also use Nielsen's data to decide which shows to keep 83 00:05:11,360 --> 00:05:15,000 Speaker 1: and which to cancel. A show that has several million 84 00:05:15,080 --> 00:05:18,640 Speaker 1: viewers may seem popular to us, but a network may 85 00:05:18,640 --> 00:05:21,640 Speaker 1: need millions more watching that program to make it a 86 00:05:21,680 --> 00:05:26,400 Speaker 1: financial success. That's why some shows with loyal following still 87 00:05:26,440 --> 00:05:30,240 Speaker 1: get canceled. Sorry, Firefly, and there's an elephant in the 88 00:05:30,320 --> 00:05:34,200 Speaker 1: room here too. The way people watch TV is changing. 89 00:05:34,440 --> 00:05:38,600 Speaker 1: With DVRs, Netflix and other streaming services, TV viewers are 90 00:05:38,680 --> 00:05:42,880 Speaker 1: more likely to customize their viewing habits, watching stuff when 91 00:05:42,920 --> 00:05:45,279 Speaker 1: they want to see it, rather than when it happens 92 00:05:45,320 --> 00:05:48,640 Speaker 1: to be on Nielsen has ways of measuring some of this, 93 00:05:48,839 --> 00:05:52,039 Speaker 1: but not all of it. As viewing habits continue to 94 00:05:52,120 --> 00:05:57,920 Speaker 1: fragment across different platforms, advertisers, content creators, and audience members 95 00:05:57,920 --> 00:06:02,279 Speaker 1: alike are right to ask how accurate these ratings actually are. 96 00:06:07,240 --> 00:06:09,480 Speaker 1: Check out the brainstuff channel on YouTube, and for more 97 00:06:09,520 --> 00:06:12,080 Speaker 1: on this and thousands of other topics, visit how stuff 98 00:06:12,080 --> 00:06:26,680 Speaker 1: works dot com.