How to use data to make a hit TV show | Sebastian Wernicke

133,036 views ・ 2016-01-27

TED


Please double-click on the English subtitles below to play the video.

00:12
Roy Price is a man that most of you have probably never heard about,
0
12820
4276
00:17
even though he may have been responsible
1
17120
2496
00:19
for 22 somewhat mediocre minutes of your life on April 19, 2013.
2
19640
6896
00:26
He may have also been responsible for 22 very entertaining minutes,
3
26560
3176
00:29
but not very many of you.
4
29760
2256
00:32
And all of that goes back to a decision
5
32040
1896
00:33
that Roy had to make about three years ago.
6
33960
2000
00:35
So you see, Roy Price is a senior executive with Amazon Studios.
7
35984
4832
00:40
That's the TV production company of Amazon.
8
40840
3016
00:43
He's 47 years old, slim, spiky hair,
9
43880
3256
00:47
describes himself on Twitter as "movies, TV, technology, tacos."
10
47160
4816
00:52
And Roy Price has a very responsible job, because it's his responsibility
11
52000
5176
00:57
to pick the shows, the original content that Amazon is going to make.
12
57200
4056
01:01
And of course that's a highly competitive space.
13
61280
2336
01:03
I mean, there are so many TV shows already out there,
14
63640
2736
01:06
that Roy can't just choose any show.
15
66400
2176
01:08
He has to find shows that are really, really great.
16
68600
4096
01:12
So in other words, he has to find shows
17
72720
2816
01:15
that are on the very right end of this curve here.
18
75560
2376
01:17
So this curve here is the rating distribution
19
77960
2656
01:20
of about 2,500 TV shows on the website IMDB,
20
80640
4376
01:25
and the rating goes from one to 10,
21
85040
2896
01:27
and the height here shows you how many shows get that rating.
22
87960
2976
01:30
So if your show gets a rating of nine points or higher, that's a winner.
23
90960
4696
01:35
Then you have a top two percent show.
24
95680
1816
01:37
That's shows like "Breaking Bad," "Game of Thrones," "The Wire,"
25
97520
3896
01:41
so all of these shows that are addictive,
26
101440
2296
01:43
whereafter you've watched a season, your brain is basically like,
27
103760
3056
01:46
"Where can I get more of these episodes?"
28
106840
2176
01:49
That kind of show.
29
109040
1200
01:50
On the left side, just for clarity, here on that end,
30
110920
2496
01:53
you have a show called "Toddlers and Tiaras" --
31
113440
3176
01:56
(Laughter)
32
116640
2656
01:59
-- which should tell you enough
33
119320
1536
02:00
about what's going on on that end of the curve.
34
120880
2191
02:03
Now, Roy Price is not worried about getting on the left end of the curve,
35
123095
4161
02:07
because I think you would have to have some serious brainpower
36
127280
2936
02:10
to undercut "Toddlers and Tiaras."
37
130240
1696
02:11
So what he's worried about is this middle bulge here,
38
131960
3936
02:15
the bulge of average TV,
39
135920
1816
02:17
you know, those shows that aren't really good or really bad,
40
137760
2856
02:20
they don't really get you excited.
41
140639
1656
02:22
So he needs to make sure that he's really on the right end of this.
42
142320
4856
02:27
So the pressure is on,
43
147200
1576
02:28
and of course it's also the first time
44
148800
2176
02:31
that Amazon is even doing something like this,
45
151000
2176
02:33
so Roy Price does not want to take any chances.
46
153200
3336
02:36
He wants to engineer success.
47
156560
2456
02:39
He needs a guaranteed success,
48
159040
1776
02:40
and so what he does is, he holds a competition.
49
160840
2576
02:43
So he takes a bunch of ideas for TV shows,
50
163440
3136
02:46
and from those ideas, through an evaluation,
51
166600
2296
02:48
they select eight candidates for TV shows,
52
168920
4096
02:53
and then he just makes the first episode of each one of these shows
53
173040
3216
02:56
and puts them online for free for everyone to watch.
54
176280
3136
02:59
And so when Amazon is giving out free stuff,
55
179440
2256
03:01
you're going to take it, right?
56
181720
1536
03:03
So millions of viewers are watching those episodes.
57
183280
5136
03:08
What they don't realize is that, while they're watching their shows,
58
188440
3216
03:11
actually, they are being watched.
59
191680
2296
03:14
They are being watched by Roy Price and his team,
60
194000
2336
03:16
who record everything.
61
196360
1376
03:17
They record when somebody presses play, when somebody presses pause,
62
197760
3376
03:21
what parts they skip, what parts they watch again.
63
201160
2536
03:23
So they collect millions of data points,
64
203720
2256
03:26
because they want to have those data points
65
206000
2096
03:28
to then decide which show they should make.
66
208120
2696
03:30
And sure enough, so they collect all the data,
67
210840
2176
03:33
they do all the data crunching, and an answer emerges,
68
213040
2576
03:35
and the answer is,
69
215640
1216
03:36
"Amazon should do a sitcom about four Republican US Senators."
70
216880
5536
03:42
They did that show.
71
222440
1216
03:43
So does anyone know the name of the show?
72
223680
2160
03:46
(Audience: "Alpha House.")
73
226720
1296
03:48
Yes, "Alpha House,"
74
228040
1456
03:49
but it seems like not too many of you here remember that show, actually,
75
229520
4096
03:53
because it didn't turn out that great.
76
233640
1856
03:55
It's actually just an average show,
77
235520
1856
03:57
actually -- literally, in fact, because the average of this curve here is at 7.4,
78
237400
4576
04:02
and "Alpha House" lands at 7.5,
79
242000
2416
04:04
so a slightly above average show,
80
244440
2016
04:06
but certainly not what Roy Price and his team were aiming for.
81
246480
2920
04:10
Meanwhile, however, at about the same time,
82
250320
2856
04:13
at another company,
83
253200
1576
04:14
another executive did manage to land a top show using data analysis,
84
254800
4216
04:19
and his name is Ted,
85
259040
1576
04:20
Ted Sarandos, who is the Chief Content Officer of Netflix,
86
260640
3416
04:24
and just like Roy, he's on a constant mission
87
264080
2136
04:26
to find that great TV show,
88
266240
1496
04:27
and he uses data as well to do that,
89
267760
2016
04:29
except he does it a little bit differently.
90
269800
2015
04:31
So instead of holding a competition, what he did -- and his team of course --
91
271839
3737
04:35
was they looked at all the data they already had about Netflix viewers,
92
275600
3536
04:39
you know, the ratings they give their shows,
93
279160
2096
04:41
the viewing histories, what shows people like, and so on.
94
281280
2696
04:44
And then they use that data to discover
95
284000
1896
04:45
all of these little bits and pieces about the audience:
96
285920
2616
04:48
what kinds of shows they like,
97
288560
1456
04:50
what kind of producers, what kind of actors.
98
290040
2096
04:52
And once they had all of these pieces together,
99
292160
2576
04:54
they took a leap of faith,
100
294760
1656
04:56
and they decided to license
101
296440
2096
04:58
not a sitcom about four Senators
102
298560
2456
05:01
but a drama series about a single Senator.
103
301040
2880
05:04
You guys know the show?
104
304760
1656
05:06
(Laughter)
105
306440
1296
05:07
Yes, "House of Cards," and Netflix of course, nailed it with that show,
106
307760
3736
05:11
at least for the first two seasons.
107
311520
2136
05:13
(Laughter) (Applause)
108
313680
3976
05:17
"House of Cards" gets a 9.1 rating on this curve,
109
317680
3176
05:20
so it's exactly where they wanted it to be.
110
320880
3176
05:24
Now, the question of course is, what happened here?
111
324080
2416
05:26
So you have two very competitive, data-savvy companies.
112
326520
2656
05:29
They connect all of these millions of data points,
113
329200
2856
05:32
and then it works beautifully for one of them,
114
332080
2376
05:34
and it doesn't work for the other one.
115
334480
1856
05:36
So why?
116
336360
1216
05:37
Because logic kind of tells you that this should be working all the time.
117
337600
3456
05:41
I mean, if you're collecting millions of data points
118
341080
2456
05:43
on a decision you're going to make,
119
343560
1736
05:45
then you should be able to make a pretty good decision.
120
345320
2616
05:47
You have 200 years of statistics to rely on.
121
347960
2216
05:50
You're amplifying it with very powerful computers.
122
350200
3016
05:53
The least you could expect is good TV, right?
123
353240
3280
05:57
And if data analysis does not work that way,
124
357880
2720
06:01
then it actually gets a little scary,
125
361520
2056
06:03
because we live in a time where we're turning to data more and more
126
363600
3816
06:07
to make very serious decisions that go far beyond TV.
127
367440
4480
06:12
Does anyone here know the company Multi-Health Systems?
128
372760
3240
06:17
No one. OK, that's good actually.
129
377080
1656
06:18
OK, so Multi-Health Systems is a software company,
130
378760
3216
06:22
and I hope that nobody here in this room
131
382000
2816
06:24
ever comes into contact with that software,
132
384840
3176
06:28
because if you do, it means you're in prison.
133
388040
2096
06:30
(Laughter)
134
390160
1176
06:31
If someone here in the US is in prison, and they apply for parole,
135
391360
3536
06:34
then it's very likely that data analysis software from that company
136
394920
4296
06:39
will be used in determining whether to grant that parole.
137
399240
3616
06:42
So it's the same principle as Amazon and Netflix,
138
402880
2576
06:45
but now instead of deciding whether a TV show is going to be good or bad,
139
405480
4616
06:50
you're deciding whether a person is going to be good or bad.
140
410120
2896
06:53
And mediocre TV, 22 minutes, that can be pretty bad,
141
413040
5496
06:58
but more years in prison, I guess, even worse.
142
418560
2640
07:02
And unfortunately, there is actually some evidence that this data analysis,
143
422360
4136
07:06
despite having lots of data, does not always produce optimum results.
144
426520
4216
07:10
And that's not because a company like Multi-Health Systems
145
430760
2722
07:13
doesn't know what to do with data.
146
433506
1627
07:15
Even the most data-savvy companies get it wrong.
147
435158
2298
07:17
Yes, even Google gets it wrong sometimes.
148
437480
2400
07:20
In 2009, Google announced that they were able, with data analysis,
149
440680
4496
07:25
to predict outbreaks of influenza, the nasty kind of flu,
150
445200
4136
07:29
by doing data analysis on their Google searches.
151
449360
3776
07:33
And it worked beautifully, and it made a big splash in the news,
152
453160
3856
07:37
including the pinnacle of scientific success:
153
457040
2136
07:39
a publication in the journal "Nature."
154
459200
2456
07:41
It worked beautifully for year after year after year,
155
461680
3616
07:45
until one year it failed.
156
465320
1656
07:47
And nobody could even tell exactly why.
157
467000
2256
07:49
It just didn't work that year,
158
469280
1696
07:51
and of course that again made big news,
159
471000
1936
07:52
including now a retraction
160
472960
1616
07:54
of a publication from the journal "Nature."
161
474600
2840
07:58
So even the most data-savvy companies, Amazon and Google,
162
478480
3336
08:01
they sometimes get it wrong.
163
481840
2136
08:04
And despite all those failures,
164
484000
2936
08:06
data is moving rapidly into real-life decision-making --
165
486960
3856
08:10
into the workplace,
166
490840
1816
08:12
law enforcement,
167
492680
1816
08:14
medicine.
168
494520
1200
08:16
So we should better make sure that data is helping.
169
496400
3336
08:19
Now, personally I've seen a lot of this struggle with data myself,
170
499760
3136
08:22
because I work in computational genetics,
171
502920
1976
08:24
which is also a field where lots of very smart people
172
504920
2496
08:27
are using unimaginable amounts of data to make pretty serious decisions
173
507440
3656
08:31
like deciding on a cancer therapy or developing a drug.
174
511120
3560
08:35
And over the years, I've noticed a sort of pattern
175
515520
2376
08:37
or kind of rule, if you will, about the difference
176
517920
2456
08:40
between successful decision-making with data
177
520400
2696
08:43
and unsuccessful decision-making,
178
523120
1616
08:44
and I find this a pattern worth sharing, and it goes something like this.
179
524760
3880
08:50
So whenever you're solving a complex problem,
180
530520
2135
08:52
you're doing essentially two things.
181
532679
1737
08:54
The first one is, you take that problem apart into its bits and pieces
182
534440
3296
08:57
so that you can deeply analyze those bits and pieces,
183
537760
2496
09:00
and then of course you do the second part.
184
540280
2016
09:02
You put all of these bits and pieces back together again
185
542320
2656
09:05
to come to your conclusion.
186
545000
1336
09:06
And sometimes you have to do it over again,
187
546360
2336
09:08
but it's always those two things:
188
548720
1656
09:10
taking apart and putting back together again.
189
550400
2320
09:14
And now the crucial thing is
190
554280
1616
09:15
that data and data analysis
191
555920
2896
09:18
is only good for the first part.
192
558840
2496
09:21
Data and data analysis, no matter how powerful,
193
561360
2216
09:23
can only help you taking a problem apart and understanding its pieces.
194
563600
4456
09:28
It's not suited to put those pieces back together again
195
568080
3496
09:31
and then to come to a conclusion.
196
571600
1896
09:33
There's another tool that can do that, and we all have it,
197
573520
2736
09:36
and that tool is the brain.
198
576280
1296
09:37
If there's one thing a brain is good at,
199
577600
1936
09:39
it's taking bits and pieces back together again,
200
579560
2256
09:41
even when you have incomplete information,
201
581840
2016
09:43
and coming to a good conclusion,
202
583880
1576
09:45
especially if it's the brain of an expert.
203
585480
2936
09:48
And that's why I believe that Netflix was so successful,
204
588440
2656
09:51
because they used data and brains where they belong in the process.
205
591120
3576
09:54
They use data to first understand lots of pieces about their audience
206
594720
3536
09:58
that they otherwise wouldn't have been able to understand at that depth,
207
598280
3416
10:01
but then the decision to take all these bits and pieces
208
601720
2616
10:04
and put them back together again and make a show like "House of Cards,"
209
604360
3336
10:07
that was nowhere in the data.
210
607720
1416
10:09
Ted Sarandos and his team made that decision to license that show,
211
609160
3976
10:13
which also meant, by the way, that they were taking
212
613160
2381
10:15
a pretty big personal risk with that decision.
213
615565
2851
10:18
And Amazon, on the other hand, they did it the wrong way around.
214
618440
3016
10:21
They used data all the way to drive their decision-making,
215
621480
2736
10:24
first when they held their competition of TV ideas,
216
624240
2416
10:26
then when they selected "Alpha House" to make as a show.
217
626680
3696
10:30
Which of course was a very safe decision for them,
218
630400
2496
10:32
because they could always point at the data, saying,
219
632920
2456
10:35
"This is what the data tells us."
220
635400
1696
10:37
But it didn't lead to the exceptional results that they were hoping for.
221
637120
4240
10:42
So data is of course a massively useful tool to make better decisions,
222
642120
4976
10:47
but I believe that things go wrong
223
647120
2376
10:49
when data is starting to drive those decisions.
224
649520
2576
10:52
No matter how powerful, data is just a tool,
225
652120
3776
10:55
and to keep that in mind, I find this device here quite useful.
226
655920
3336
10:59
Many of you will ...
227
659280
1216
11:00
(Laughter)
228
660520
1216
11:01
Before there was data,
229
661760
1216
11:03
this was the decision-making device to use.
230
663000
2856
11:05
(Laughter)
231
665880
1256
11:07
Many of you will know this.
232
667160
1336
11:08
This toy here is called the Magic 8 Ball,
233
668520
1953
11:10
and it's really amazing,
234
670497
1199
11:11
because if you have a decision to make, a yes or no question,
235
671720
2896
11:14
all you have to do is you shake the ball, and then you get an answer --
236
674640
3736
11:18
"Most Likely" -- right here in this window in real time.
237
678400
2816
11:21
I'll have it out later for tech demos.
238
681240
2096
11:23
(Laughter)
239
683360
1216
11:24
Now, the thing is, of course -- so I've made some decisions in my life
240
684600
3576
11:28
where, in hindsight, I should have just listened to the ball.
241
688200
2896
11:31
But, you know, of course, if you have the data available,
242
691120
3336
11:34
you want to replace this with something much more sophisticated,
243
694480
3056
11:37
like data analysis to come to a better decision.
244
697560
3616
11:41
But that does not change the basic setup.
245
701200
2616
11:43
So the ball may get smarter and smarter and smarter,
246
703840
3176
11:47
but I believe it's still on us to make the decisions
247
707040
2816
11:49
if we want to achieve something extraordinary,
248
709880
3016
11:52
on the right end of the curve.
249
712920
1936
11:54
And I find that a very encouraging message, in fact,
250
714880
4496
11:59
that even in the face of huge amounts of data,
251
719400
3976
12:03
it still pays off to make decisions,
252
723400
4096
12:07
to be an expert in what you're doing
253
727520
2656
12:10
and take risks.
254
730200
2096
12:12
Because in the end, it's not data,
255
732320
2776
12:15
it's risks that will land you on the right end of the curve.
256
735120
3960
12:19
Thank you.
257
739840
1216
12:21
(Applause)
258
741080
3680
About this website

This site will introduce you to YouTube videos that are useful for learning English. You will see English lessons taught by top-notch teachers from around the world. Double-click on the English subtitles displayed on each video page to play the video from there. The subtitles scroll in sync with the video playback. If you have any comments or requests, please contact us using this contact form.

https://forms.gle/WvT1wiN1qDtmnspy7