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

翻译人员: Chia Shimin 校对人员: Yolanda Zhang

Roy Price is a man that most of you have probably never heard about,

Roy Price这个人， 在座的绝大多数可能都没听说过，

即使他曾经在2013年4月19日这一天

even though he may have been responsible

for 22 somewhat mediocre minutes of your life on April 19, 2013.

占用了你们生命中普通的22分钟。

He may have also been responsible for 22 very entertaining minutes,

他也许曾经带给了 各位非常欢乐的22分钟，

but not very many of you.

但对你们当中很多人来说 可能并不是这样。

而这一切全部要追溯到

And all of that goes back to a decision

that Roy had to make about three years ago.

Roy在三年前的一个决定。

So you see, Roy Price is a senior executive with Amazon Studios.

实际上，Roy Price是 亚马逊广播公司的一位资深决策者。

That's the TV production company of Amazon.

这是亚马逊旗下的一家 电视节目制作公司。

He's 47 years old, slim, spiky hair,

他47岁，身材不错， 短发梳得很有型，

describes himself on Twitter as "movies, TV, technology, tacos."

他在Twitter上形容自己是 "电影、电视、科技、墨西哥卷饼（爱好者）"。

Roy Price有一个 责任非常重大的工作，

And Roy Price has a very responsible job, because it's his responsibility

to pick the shows, the original content that Amazon is going to make.

因为他要负责帮亚马逊挑选 即将制作的原创节目。

And of course that's a highly competitive space.

当然，这个领域的竞争非常激烈。

I mean, there are so many TV shows already out there,

我是说，其他公司已经有 那么多的电视节目，

that Roy can't just choose any show.

Roy不能只是随便乱挑一个节目。

He has to find shows that are really, really great.

他必须找出真正会走紅的节目。

So in other words, he has to find shows

换句话说，他挑选的节目

必须落在这条曲线的右侧。

that are on the very right end of this curve here.

So this curve here is the rating distribution

这条曲线是IMDB （译注：网络电影资料库）里

of about 2,500 TV shows on the website IMDB,

2500个电视节目的 客户评分曲线图，

and the rating goes from one to 10,

评分从1到10，

and the height here shows you how many shows get that rating.

纵轴表明有多少节目达到这个评分。

So if your show gets a rating of nine points or higher, that's a winner.

所以如果你的节目达到9分或更高， 你就是赢家，

Then you have a top two percent show.

你就拥有那2%的顶尖节目。

That's shows like "Breaking Bad," "Game of Thrones," "The Wire,"

例如像是“绝命毒师”、 “权力的游戏”、“火线重案组”，

so all of these shows that are addictive,

全部都是会让人上瘾的节目，

whereafter you've watched a season, your brain is basically like,

看完一季之后， 你基本马上就会想，

"Where can I get more of these episodes?"

“我要去哪里找到剩下的剧集？”

That kind of show.

基本就是这类的节目。

On the left side, just for clarity, here on that end,

曲线左边，不妨选个最靠边， 比较明显的点，

you have a show called "Toddlers and Tiaras" --

这儿有个叫“选美小天后" （译注：儿童选秀类）的节目——

(Laughter)

（笑声）

-- which should tell you enough

——足够让你明白

about what's going on on that end of the curve.

曲线最左端代表了什么。

Now, Roy Price is not worried about getting on the left end of the curve,

现在，Roy Price并不担心 会选个落在曲线最左边的节目，

because I think you would have to have some serious brainpower

因为我认为你们都具备 严肃的判断力

to undercut "Toddlers and Tiaras."

来给 "选美小天后" 打个低分 。

So what he's worried about is this middle bulge here,

他担心的是 中间多数的这些节目，

the bulge of average TV,

多到爆的这些一般的电视节目，

you know, those shows that aren't really good or really bad,

这些节目不算好，但也不是很烂，

they don't really get you excited.

它们不会真正地让你感兴趣。

So he needs to make sure that he's really on the right end of this.

所以他要确保他看好的节目 是落在最右端这里。

So the pressure is on,

那么压力就来了，

and of course it's also the first time

当然，这也是亚马逊第一次

想要做这类事情，

that Amazon is even doing something like this,

so Roy Price does not want to take any chances.

所以Roy Price不想只是碰运气。

He wants to engineer success.

他想要打造成功。

He needs a guaranteed success,

他要一个万无一失的成功，

and so what he does is, he holds a competition.

于是，他举办了一个竞赛。

So he takes a bunch of ideas for TV shows,

他带来了很多关于电视节目的想法，

and from those ideas, through an evaluation,

通过一个评估，

they select eight candidates for TV shows,

他们挑了八个候选的电视节目，

and then he just makes the first episode of each one of these shows

然后他为每一个节目制作了第一集，

and puts them online for free for everyone to watch.

再把它们放到网上， 让每个人免费观看。

And so when Amazon is giving out free stuff,

当亚马逊要给你免费的东西时，

you're going to take it, right?

你就会拿，对吧？

So millions of viewers are watching those episodes.

所以几百万人在看这些剧集，

What they don't realize is that, while they're watching their shows,

而这些人不知道的是， 当他们在观看节目的时候，

actually, they are being watched.

实际上他们也正被观察着。

他们被Roy Price及他的团队观察，

They are being watched by Roy Price and his team,

who record everything.

他们纪录了所有的一切。

They record when somebody presses play, when somebody presses pause,

他们纪录了哪些人按了拨放， 哪些人按了暂停，

what parts they skip, what parts they watch again.

哪些部分他们跳过了， 哪些部分他们又重看了一遍。

So they collect millions of data points,

他们收集了几百万个数据，

因为他们想要用这些数据来决定

because they want to have those data points

to then decide which show they should make.

要做什么样的节目。

And sure enough, so they collect all the data,

理所当然，他们收集了所有的数据，

处理过后得到了一个答案，

they do all the data crunching, and an answer emerges,

and the answer is,

而答案就是，

"Amazon should do a sitcom about four Republican US Senators."

“亚马逊需要制作一个有关 四个美国共和党参议员的喜剧”。

They did that show.

他们真的做了。

So does anyone know the name of the show?

有人知道这个节目吗？

(Audience: "Alpha House.")

（观众：" 阿尔法屋。"）

是的，就是"阿尔法屋"。

Yes, "Alpha House,"

but it seems like not too many of you here remember that show, actually,

但看起来你们大部人都 不记得有这部片子，

because it didn't turn out that great.

因为这部片子收视率并不太好。

It's actually just an average show,

它其实只是个一般的节目，

actually -- literally, in fact, because the average of this curve here is at 7.4,

实际上，一般的节目差不多 对应曲线上大概7.4分的位置，

而 “阿尔法屋” 落在7.5分，

and "Alpha House" lands at 7.5,

so a slightly above average show,

所以比一般的节目高一点点，

but certainly not what Roy Price and his team were aiming for.

但绝对不是Roy Price和 他的团队想要达到的目标。

Meanwhile, however, at about the same time,

但在差不多同一时间，

at another company,

另一家公司的另一个决策者，

another executive did manage to land a top show using data analysis,

同样用数据分析 却做出了一个顶尖的节目，

他的名字是 Ted，

and his name is Ted,

Ted Sarandos, who is the Chief Content Officer of Netflix,

Ted Sarandos是Netflix的 首席内容官，

and just like Roy, he's on a constant mission

就跟 Roy一样，他也要不停地寻找

to find that great TV show,

最棒的节目，

and he uses data as well to do that,

而他也使用了数据分析，

except he does it a little bit differently.

但他的做法有点不太一样。

So instead of holding a competition, what he did -- and his team of course --

不是举办竞赛，他和他的团队

was they looked at all the data they already had about Netflix viewers,

观察了Netflix已有的所有观众数据，

you know, the ratings they give their shows,

比如观众对节目的评分、

观看纪录、 哪些节目最受欢迎等等。

the viewing histories, what shows people like, and so on.

他们用这些数据去挖掘

And then they use that data to discover

all of these little bits and pieces about the audience:

观众的所有小细节：

what kinds of shows they like,

他们喜欢什么类型的节目、

什么类型的制作人、 什么类型的演员。

what kind of producers, what kind of actors.

And once they had all of these pieces together,

就在他们收集到全部的细节后，

they took a leap of faith,

他们信心满满地

and they decided to license

决定要制作一部，

not a sitcom about four Senators

不是四个参议员的喜剧，

but a drama series about a single Senator.

而是一系列有关一位 单身参议员的电视剧。

You guys know the show?

各位知道那个节目吗？

(Laughter)

（笑声）

Yes, "House of Cards," and Netflix of course, nailed it with that show,

是的，“纸牌屋”， 当然，Netflix至少在头两季

at least for the first two seasons.

在这个节目上赚到了极高的收视率。

(Laughter) (Applause)

（笑声）（掌声）

"House of Cards" gets a 9.1 rating on this curve,

“纸牌屋” 在这个曲线上拿到了 9.1分，

so it's exactly where they wanted it to be.

他们绝对实现了最初的目标。

Now, the question of course is, what happened here?

很显然，问题来了， 这到底是怎么回事？

So you have two very competitive, data-savvy companies.

有两个非常有竞争力、 精通数据分析的公司。

They connect all of these millions of data points,

他们整合了所有的数据，

and then it works beautifully for one of them,

然后，其中一个干的很漂亮，

and it doesn't work for the other one.

而另一个却没有，

So why?

这是为什么呢？

Because logic kind of tells you that this should be working all the time.

毕竟逻辑分析会告诉你， 这种方法应该每次都有效啊，

I mean, if you're collecting millions of data points

我是说， 如果你收集了所有的数据

on a decision you're going to make,

来制定一个决策，

then you should be able to make a pretty good decision.

那你应该可以得到一个 相当不错的决策。

You have 200 years of statistics to rely on.

你有200年的统计方法做后盾。

You're amplifying it with very powerful computers.

你用高性能的计算机 去增强它的效果。

The least you could expect is good TV, right?

至少你可以期待得到一个 还不错的电视节目，对吧？

And if data analysis does not work that way,

但如果数据分析 并没有想像中的有效，

then it actually gets a little scary,

这就有点吓人了，

because we live in a time where we're turning to data more and more

因为我们生活在一个 越来越依赖数据的时代，

to make very serious decisions that go far beyond TV.

我们要用数据做出远比电视节目 还要严肃重要的决策。

Does anyone here know the company Multi-Health Systems?

你们当中有人知道 "MHS" 这家公司吗？

没人？好，这就好。

No one. OK, that's good actually.

OK, so Multi-Health Systems is a software company,

好的，MHS是一家软件公司，

而我希望在座的各位

and I hope that nobody here in this room

ever comes into contact with that software,

没人与他们的软件有任何关系，

因为如果你有， 就表示你犯了罪被判刑了。

because if you do, it means you're in prison.

(Laughter)

（笑声）

If someone here in the US is in prison, and they apply for parole,

如果有人在美国被判入狱， 要申请假释，

then it's very likely that data analysis software from that company

很有可能那家公司的数据分析软件

will be used in determining whether to grant that parole.

就会被用来判定你是否能获得假释。

So it's the same principle as Amazon and Netflix,

它也是采用跟 亚马逊和Netflix公司相同的原则，

but now instead of deciding whether a TV show is going to be good or bad,

但并不是要决定 某个电视节目收视率的好坏，

you're deciding whether a person is going to be good or bad.

而是用来决定 一个人将来的行为是好是坏。

And mediocre TV, 22 minutes, that can be pretty bad,

一个22分钟的普通电视节目 可以很糟糕，

but more years in prison, I guess, even worse.

但我觉得要坐很多年的牢，更糟糕。

And unfortunately, there is actually some evidence that this data analysis,

但不幸的是，实际上已经有证据显示， 这项数据分析尽管可以依靠

despite having lots of data, does not always produce optimum results.

庞大的数据资料， 它并不总能得出最优的结果。

And that's not because a company like Multi-Health Systems

但并不只有像MHS这样的软件公司

doesn't know what to do with data.

不确定到底怎么分析数据，

就连最顶尖的数据公司也会出错。

Even the most data-savvy companies get it wrong.

Yes, even Google gets it wrong sometimes.

是的，甚至谷歌有时也会出错。

In 2009, Google announced that they were able, with data analysis,

2009年，谷歌宣布 他们可以用数据分析来

to predict outbreaks of influenza, the nasty kind of flu,

预测流行性感冒何时爆发， 就是那种讨人厌的流感，

by doing data analysis on their Google searches.

他们用自己的搜寻引擎 来做数据分析。

And it worked beautifully, and it made a big splash in the news,

结果证明它准确无比， 引得各路新闻报道铺天盖地，

甚至还达到了一个科学界的顶峰：

including the pinnacle of scientific success:

a publication in the journal "Nature."

在 “自然” 期刊上发表了文章。

It worked beautifully for year after year after year,

之后的每一年，它都预测得准确无误，

until one year it failed.

直到有一年，它失败了。

没有人知道到底是什么原因。

And nobody could even tell exactly why.

It just didn't work that year,

那一年它就是不准了，

当然，这又成了一个大新闻，

and of course that again made big news,

including now a retraction

包括现在

of a publication from the journal "Nature."

被 "自然” 期刊撤稿。

So even the most data-savvy companies, Amazon and Google,

所以，即使是最顶尖的数据分析公司， 亚马逊和谷歌，

they sometimes get it wrong.

他们有时也会出错。

但尽管出现了这些失败，

And despite all those failures,

data is moving rapidly into real-life decision-making --

数据仍然在马不停蹄地渗透进我们 实际生活中的决策——

into the workplace,

进入工作场所、

law enforcement,

执法过程、

medicine.

医药领域。

So we should better make sure that data is helping.

所以，我们应该确保数据是 能够帮助我们解决问题的。

Now, personally I've seen a lot of this struggle with data myself,

我个人也曾经多次 被数据分析搞的焦头烂额，

because I work in computational genetics,

因为我在计算遗传学领域工作，

which is also a field where lots of very smart people

这个领域有很多非常聪明的人

are using unimaginable amounts of data to make pretty serious decisions

在用多到难以想像的数据 来制定相当严肃的决策，

like deciding on a cancer therapy or developing a drug.

比如癌症治疗，或者药物开发。

And over the years, I've noticed a sort of pattern

经过这几年，我已经注意到一种模式

or kind of rule, if you will, about the difference

或者规则，你们也可以这么理解，

between successful decision-making with data

就是有关于用数据做出

and unsuccessful decision-making,

成功决策和不成功决策，

and I find this a pattern worth sharing, and it goes something like this.

我觉得这个模式值得分享， 大概是这样的。

So whenever you're solving a complex problem,

当你要解决一个复杂问题时，

you're doing essentially two things.

你通常必然会做两件事。

The first one is, you take that problem apart into its bits and pieces

首先，你会把问题拆分得非常细，

so that you can deeply analyze those bits and pieces,

这样你就可以深度地分析这些细节，

and then of course you do the second part.

当然你要做的第二件事就是，

再把这些细节重新整合在一起，

You put all of these bits and pieces back together again

来得出你要的结论。

to come to your conclusion.

And sometimes you have to do it over again,

有时候你必须重复几次，

but it's always those two things:

但基本都是围绕这两件事：

taking apart and putting back together again.

拆分、再整合。

And now the crucial thing is

那么关键的问题在于，

that data and data analysis

数据和数据分析

is only good for the first part.

只适用于第一步，

Data and data analysis, no matter how powerful,

无论数据和数据分析多么强大，

can only help you taking a problem apart and understanding its pieces.

它都只能帮助你拆分问题和了解细节，

It's not suited to put those pieces back together again

它不适用于把细节重新整合在一起

and then to come to a conclusion.

来得出一个结论。

There's another tool that can do that, and we all have it,

有一个工具可以实现第二步， 我们每个人都有，

那就是大脑。

and that tool is the brain.

If there's one thing a brain is good at,

如果要说大脑很擅长某一件事，

it's taking bits and pieces back together again,

那就是，它很会把琐碎的细节 重新整合在一起，

even when you have incomplete information,

即使你拥有的信息并不完整，

and coming to a good conclusion,

也能得到一个好的结论，

especially if it's the brain of an expert.

特别是专家的大脑。

And that's why I believe that Netflix was so successful,

而这也是我相信 Netflix会这么成功的原因，

because they used data and brains where they belong in the process.

因为他们在分析过程中同时 使用了数据和大脑。

They use data to first understand lots of pieces about their audience

他们利用数据， 首先去了解观众的若干细节，

that they otherwise wouldn't have been able to understand at that depth,

没有这些数据， 他们不可能进行这么透彻的分析，

but then the decision to take all these bits and pieces

但在之后，要做出重新整合，

制作像"纸牌屋"这样的节目的决策，

and put them back together again and make a show like "House of Cards,"

that was nowhere in the data.

就无法依赖数据了。

是Ted Sarandos和他的团队（通过思考） 做出了批准该节目的这个决策，

Ted Sarandos and his team made that decision to license that show,

which also meant, by the way, that they were taking

这也就意味着，

a pretty big personal risk with that decision.

他们在做出决策的当下， 也正在承担很大的个人风险。

And Amazon, on the other hand, they did it the wrong way around.

而另一方面，亚马逊把事情搞砸了。

他们全程依赖数据来制定决策，

They used data all the way to drive their decision-making,

首先，举办了关于节目创意的竞赛，

first when they held their competition of TV ideas,

then when they selected "Alpha House" to make as a show.

然后他们决定选择制作 "阿尔法屋"。

Which of course was a very safe decision for them,

当然，对他们而言， 这是一个非常安全的决策，

because they could always point at the data, saying,

因为他们总是可以指着数据说，

"This is what the data tells us."

“这是数据告诉我们的。”

但数据并没有带给他们 满意的结果。

But it didn't lead to the exceptional results that they were hoping for.

当然，数据依然是做决策时的 一个强大的工具，

So data is of course a massively useful tool to make better decisions,

但我相信，当数据开始 主导这些决策时，

but I believe that things go wrong

when data is starting to drive those decisions.

并不能保证万无一失。

No matter how powerful, data is just a tool,

不管它有多么的强大， 数据都仅仅是一个工具，

and to keep that in mind, I find this device here quite useful.

记住这句话之后， 我发现这个装置相当有用。

Many of you will ...

你们很多人就会......

(Laughter)

（笑声）

Before there was data,

在有数据之前，

这就是用来做决策的工具。

this was the decision-making device to use.

(Laughter)

（笑声）

Many of you will know this.

你们很多人应该知道这个玩意儿。

This toy here is called the Magic 8 Ball,

这个玩具称做“魔术8号球”，

and it's really amazing,

它真的很奇妙，

because if you have a decision to make, a yes or no question,

因为如果你要做一个 “是” 或 “不是” 的决策时，

你只要摇一摇这颗球， 就可以得到答案了——

all you have to do is you shake the ball, and then you get an answer --

"Most Likely" -- right here in this window in real time.

“很有可能是”—— 在这个视窗里，马上就可以看到。

I'll have it out later for tech demos.

我回头会带它去做技术示范。

(Laughter)

（笑声）

Now, the thing is, of course -- so I've made some decisions in my life

事实上，当然—— 我已经在我人生中做出了一些决定，

where, in hindsight, I should have just listened to the ball.

虽然事后证明， 我当初应该直接用这颗球。

But, you know, of course, if you have the data available,

但，当然，如果你手里有数据，

you want to replace this with something much more sophisticated,

你就会想用更尖端的方式 来取代这颗球，

like data analysis to come to a better decision.

比方说，用数据分析来得到更好的决策。

But that does not change the basic setup.

但这无法改变基本的设定。

So the ball may get smarter and smarter and smarter,

这球可能会变得越来越智能，

but I believe it's still on us to make the decisions

但我相信，如果我们想达成某些 像曲线最右端那样

if we want to achieve something extraordinary,

出色的成就，最后的决定权

on the right end of the curve.

还是应该落在我们身上。

And I find that a very encouraging message, in fact,

事实上，我还发现了 一件非常鼓舞人心的事，

that even in the face of huge amounts of data,

即使面对庞大的数据，

it still pays off to make decisions,

当你要做出决定，

想要变成一位该领域的专家 并承担风险时，

to be an expert in what you're doing

and take risks.

你仍然会有很大的收获。

Because in the end, it's not data,

因为到最后，不是数据，

而是风险，会把你引到曲线的最右端。

it's risks that will land you on the right end of the curve.

Thank you.

谢谢各位。

(Applause)

（掌声）