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

譯者: 易帆 余 審譯者: Ernie Hsieh

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

Roy Price這個人， 各位可能都未曾聽過，

even though he may have been responsible

即使他曾負責過 你生命中平凡無奇的22分鐘，

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

在2013年4月19日這一天。

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

他也許也曾負責帶給 各位非常歡樂的22分鐘，

but not very many of you.

但你們其中也許很多人並沒有。

And all of that goes back to a decision

而這一切全部要回到

that Roy had to make about three years ago.

Roy在三年前的一個決定。

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

所以，你明白，Roy Price是 Amazon廣播公司的一位資深決策者。

That's the TV production company of Amazon.

這是Amazon旗下的一家 電視節目製作公司。

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

他47歲，身材不錯，尖頭髮，

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

在Twitter上形容自己是 “電影、電視、科技、墨西哥捲餅 。”

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

Roy Price有一個 責任非常重大的工作，

因為他要負責幫Amazon挑選 即將製作的原創內容節目。

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

And of course that's a highly competitive space.

當然，這是高度競爭的領域。

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

我的意思是， 外面已經有那麼多的電視節目，

that Roy can't just choose any show.

Roy不能隨便亂挑一個節目。

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

他必須找出真正、 真正很讚的節目。

So in other words, he has to find shows

換句話說，

他必須從這條曲線上的右邊挑選節目。

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

So this curve here is the rating distribution

這條曲線是 IMDB網路電影資料庫裡

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

2500個電視節目的 客戶評分分布圖，

and the rating goes from one to 10,

評分從 1到10，

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

最高的地方代表 有多少節目達到這個評分。

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

所以如果你的節目達到 9分或更高， 你就是贏家。

Then you have a top two percent show.

你就是那百分之二的頂尖節目。

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

例如像是" 絕命毒師 、 權力遊戲、火線重案組 "

so all of these shows that are addictive,

全部都是會讓你上癮的節目，

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

看完一季之後，你的大腦基本上像是 ...

"Where can I get more of these episodes?"

" 我要去哪裡找到更多這部片的影集？ "

That kind of show.

等等這類的節目。

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

左邊末端，很明顯地，

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

你們有個叫" 小小姐與后冠 "的節目

(Laughter)

（笑聲）

-- which should tell you enough

一個足夠讓你明白

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

為什麼它會在曲線末端的節目。

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

現在，Roy Price不擔心 在曲線左邊末端的節目。

because I think you would have to have some serious brainpower

因為我認為你們都會想 有一些嚴肅的判斷力

to undercut "Toddlers and Tiaras."

來降低" 小小姐與后冠 "的評分 。

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

所以，他擔心的是中間多數的這些節目，

the bulge of average TV,

多到爆的這些一般性電視節目，

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

你知道，這些節目 既不是很好也不是很壞，

they don't really get you excited.

它們不會真正地讓你興奮。

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

所以他要確保他真的 是在右邊的末端這裡，

So the pressure is on,

所以，壓力就來了，

and of course it's also the first time

所以當然，這也是第一次 Amazon

that Amazon is even doing something like this,

也想要做類似這樣的事情，

so Roy Price does not want to take any chances.

Roy Price不想冒風險，

He wants to engineer success.

他想要建造成功，

He needs a guaranteed success,

他要一個保證的成功，

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

所以他就舉辦一個比賽。

So he takes a bunch of ideas for TV shows,

他為電視節目帶來了很多想法，

and from those ideas, through an evaluation,

並且透過一個評估，形塑這些想法，

they select eight candidates for TV shows,

他們為電視節目挑選了八個候選名單，

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

然後他製作每一個節目的第一集，

and puts them online for free for everyone to watch.

然後把他們放到網路上， 讓每個人免費觀看。

And so when Amazon is giving out free stuff,

所以當Amazon要給你免費的東西時，

you're going to take it, right?

你就會拿，對吧？

So millions of viewers are watching those episodes.

所以上百萬人在看這些影集，

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

而這些人不明白的是， 當他們在觀看節目的時候，

actually, they are being watched.

實際上他們也正被觀查中。

They are being watched by Roy Price and his team,

他們被Roy Price及他的團隊觀查，

who record everything.

他們紀錄了每一件事。

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

他們紀錄了，那些人按了撥放， 那些人按了暫停，

what parts they skip, what parts they watch again.

那些部分他們跳過， 那些部分他們又重看一遍。

So they collect millions of data points,

所以他們收集了上百萬的數據資料，

because they want to have those data points

因為他們想要用這些數據資料來決定

to then decide which show they should make.

要做甚麼樣的節目。

And sure enough, so they collect all the data,

確定好後，他們收集所有的數據，

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

他們做完所有數據處理後， 得到一個答案，

and the answer is,

而答案就是，

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

" Amazon需要製作一個有關 美國共和黨參議員的喜劇 "。

They did that show.

他們做了，

So does anyone know the name of the show?

有人知道這個節目嗎？

(Audience: "Alpha House.")

（觀眾：" 艾爾發屋 "）

Yes, "Alpha House,"

是的，" 艾爾發屋 "

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

但實際上，你們大部人 應該不記得有這部片子，

because it didn't turn out that great.

因為這部片並不那麼賣座。

It's actually just an average show,

它實際上僅是一般的節目，

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

實際上，一般的節目差不多 坐落在曲線上的 7.4分，

and "Alpha House" lands at 7.5,

而" 艾爾發房屋 "落在7.5分，

so a slightly above average show,

所以比一般的節目高一點點，

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

但絕對不是Roy Price與 他的團隊所要達到的目標。

Meanwhile, however, at about the same time,

這時，然而，同一時間，

另一家公司的另一個決策者，

at another company,

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

用同樣的數據分析做了一個頂尖的節目，

and his name is Ted,

他的名字是 Ted，

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

Ted Sarandos是Netflix的 首席節目內容決策者，

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

就跟 Roy一樣，他也要不停的找

to find that great TV show,

最棒的節目，

and he uses data as well to do that,

而他也使用數據來這樣做，

except he does it a little bit differently.

但他的做法，有點不太一樣。

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

不是舉辦比賽，當然，他和他的團隊

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

也有觀察Netflix已經有的觀眾數據，

you know, the ratings they give their shows,

觀眾對節目的評分、觀看紀錄、

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

那些節目是人們喜歡的等等，

And then they use that data to discover

他們也使用數據去發掘

all of these little bits and pieces about the audience:

觀眾所有的小細節：

what kinds of shows they like,

他們喜歡甚麼類型的節目、

what kind of producers, what kind of actors.

甚麼類型的製作人、甚麼類型的演員，

And once they had all of these pieces together,

一旦他們收集全部的細節後，

they took a leap of faith,

他們很有信心地

and they decided to license

決定要製作一部，

not a sitcom about four Senators

不是四個參議員的喜劇，

but a drama series about a single Senator.

而是一系列有關一位 單身參議員的戲劇。

You guys know the show?

各位知道那個節目嗎？

(Laughter)

（笑聲）

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

是的，" 纸牌屋 "，Netflix ，當然，

至少頭二季，用這節目盯住那個分數。

at least for the first two seasons.

(Laughter) (Applause)

（笑聲）（掌聲）

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

" 纸牌屋 "在這曲線上拿到 9.1分，

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

這當然是他們想要的。

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

現在，當然問題就是 這到底是怎麼一回事？

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

你有兩個非常有競爭力、 精通數據資料的公司。

They connect all of these millions of data points,

他們連結了所有的數據資料，

and then it works beautifully for one of them,

然後，其中一個做的很漂亮，

and it doesn't work for the other one.

而另一個卻沒有，

So why?

為什麼？

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

因為邏輯上告訴你， 這應該每次都有效啊，

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

我的意思是， 如果你收集了所有的數據資料

on a decision you're going to make,

來決定一個決策，

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

那你應該可以得到一個 相當不錯的決策。

You have 200 years of statistics to rely on.

你有 200年的統計數據做後盾，

You're amplifying it with very powerful computers.

你用很強大的電腦去增強它，

The least you could expect is good TV, right?

至少你可以期待到一個 好的電視節目，對吧？

And if data analysis does not work that way,

但如果數據分析 並沒有想像中的有效，

then it actually gets a little scary,

那，這真的有點恐怖，

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

因為我們正轉向一個 數據越來越多的時代，

to make very serious decisions that go far beyond TV.

來做出遠比電視節目 還要嚴肅的決策。

Does anyone here know the company Multi-Health Systems?

你們當中有人知道" MHS "這家公司嗎？

沒人？好，這樣很好，

No one. OK, that's good actually.

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

好的，MHS是一家軟體公司，

and I hope that nobody here in this room

而我希望在座的各位，

ever comes into contact with that software,

沒有人與這個軟體有牽連，

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

因為如果你有，代表你在監獄中

(Laughter)

（笑聲）

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

在美國這裡如果有人被判入監， 然後要申請假釋，

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

很有可能那家公司的數據分析軟體

will be used in determining whether to grant that parole.

會被用來判定是否能獲得假釋。

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

所以，它也是採用 Amazon 和 Netflix 公司相同的原則，

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

但不同的是， 他們是用來決定電視節目將來的好壞，

你是用來決定一個人將來的好壞，

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

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

表現普通22分鐘的電視節目，很糟糕，

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

但，我猜，要做更多年的牢，更糟糕。

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

但不幸的是，實際上已經有證據顯示， 該數據分析除了擁有龐大的數據外，

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

它並不總是跑出適當的結果。

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

但並不只有像是MHS這樣的軟體公司

doesn't know what to do with data.

不明白數據怎麼了，

Even the most data-savvy companies get it wrong.

甚至最頂尖的數據公司也會出錯，

Yes, even Google gets it wrong sometimes.

是的，甚至Google有時也會出錯。

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

2009年，Google宣布他們可以用數據分析，

來預測流行性感冒，討人厭的流感，

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

經由他們的Google搜尋引擎來做數據分析。

by doing data analysis on their Google searches.

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

而且它準確無比，當時造成一股新聞的轟動，

including the pinnacle of scientific success:

包含一個科學界成功的高峰：

a publication in the journal "Nature."

在 "自然期刊"上發表文章。

It worked beautifully for year after year after year,

之後的每一年，它都預測地很漂亮，

until one year it failed.

直到有一年它失敗了。

And nobody could even tell exactly why.

沒有人能正確地說明到底甚麼原因。

It just didn't work that year,

那一年它就是不準了，

and of course that again made big news,

當然，又造成了一次大新聞，

including now a retraction

包含現在

of a publication from the journal "Nature."

被" 自然期刊 "撤銷發表的文章

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

所以，即使是最頂尖的數據分析公司， Amazon和Google，

they sometimes get it wrong.

他們有時也會出錯。

And despite all those failures,

但儘管有這些失敗，

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

數據正快速地進入我們 實際生活上的決策、

into the workplace,

進入工作職場、

law enforcement,

法律執行、

medicine.

醫藥界。

So we should better make sure that data is helping.

所以，我們應該確保數據是有幫助的。

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

我個人已經經歷過很多 自己在數據上的掙扎，

because I work in computational genetics,

因為我在計算遺傳學界工作，

which is also a field where lots of very smart people

這個領域有很多非常聰明的人

are using unimaginable amounts of data to make pretty serious decisions

使用多到難以想像的數據 來制定相當嚴肅的決策，

like deciding on a cancer therapy or developing a drug.

像是癌症治療決策或藥物開發。

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

經過這幾年，我已經注意到一種模式

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

或者規則，如果你要這麼說也行，

between successful decision-making with data

就是有關於用數據做出

and unsuccessful decision-making,

成功決策和不成功決策，

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

我發現這個模式值得分享， 它是這樣的......

當你要解決一個複雜問題時，

So whenever you're solving a complex problem,

you're doing essentially two things.

本質上你會做兩件事，

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

第一件事是，你會把問題拆分得很仔細，

so that you can deeply analyze those bits and pieces,

所以你可以深度地分析這些細節，

and then of course you do the second part.

當然你的第二件事就是，

You put all of these bits and pieces back together again

你會再把這些細節拿回來整合一起，

to come to your conclusion.

來得出你要的結論。

And sometimes you have to do it over again,

有時候你必須一做再做，

but it's always those two things:

就這兩件事：

taking apart and putting back together again.

拆分、再合併一起。

但，關鍵是

And now the crucial thing is

that data and data analysis

數據與數據分析

is only good for the first part.

只適用於第一步驟，

Data and data analysis, no matter how powerful,

無論數據與數據分析多麼地強大，

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

它只能幫助你拆分問題及了解細節，

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

它不適用於把細節 拿回來放在一起再整合，

and then to come to a conclusion.

來得出一個結論。

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

有一個工具可以這麼做， 而我們都擁有它，

and that tool is the brain.

那工具就是大腦。

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

如果要說大腦有一項能力很強，

it's taking bits and pieces back together again,

那就是，它很會把事情 拆分細節後再整合一起，

even when you have incomplete information,

即使當你有的只是不完整的資訊，

and coming to a good conclusion,

也能得到一個好的決策，

especially if it's the brain of an expert.

特別是專家的大腦。

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

而這也是為什麼我相信 Netflix會這麼成功的原因，

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

因為他們在過程中使用數據與大腦。

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

他們利用數據， 首先了解很多觀眾的細節，

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

否則沒有這些數據， 他們沒有能力可以了解這麼深，

but then the decision to take all these bits and pieces

但做出拆分、整合

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

及製作" 紙牌屋 "的

這兩個決策，是數據中無法幫你決定的。

that was nowhere in the data.

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

Ted Sarandos和他的團隊做出 許可該節目的這個決策，

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

總之，意思就是，

他們在做出決策當下， 也正在承擔很大的個人風險。

a pretty big personal risk with that decision.

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

而另一方面，Amazon他們把它搞砸了。

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

他們全程依賴數據來制定決策，

first when they held their competition of TV ideas,

首先，他們舉辦節目想法的競賽，

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

然後當他們選擇" 艾爾發屋 "來作為節目，

Which of course was a very safe decision for them,

當然啦，對他們而言， 這是一個非常安全的決策，

because they could always point at the data, saying,

因為他們總是可以指著數據說，

"This is what the data tells us."

＂這是數據告訴我們的＂

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

但這並沒有帶領他們到 他們所希望的傑出結果。

所以，數據當然是做決策時的 一個強大的工具，

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

but I believe that things go wrong

但我相信，當數據開始主導這些決策時，

when data is starting to drive those decisions.

事情也會開始出錯。

No matter how powerful, data is just a tool,

不管它有多麼的強大， 數據僅是一個工具，

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

並把這個記在腦裡， 我發現這個裝置相當有用。

Many of you will ...

你們很多人將會 ...

(Laughter)

（笑聲）

Before there was data,

在有數據之前，

this was the decision-making device to use.

這就是用來做決策的工具

(Laughter)

（笑聲）

Many of you will know this.

你們很多人應該知道這個玩意。

This toy here is called the Magic 8 Ball,

這個玩具在這裡稱做＂魔術 8號球＂，

and it's really amazing,

它真的很奇妙，

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

因為如果你要做一個 ＂是或不是＂的決策時，

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

你只要搖一搖這顆球， 然後你就可以得到答案了--

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

＂很有可能是＂-- 就在這視窗裡及時顯現給你看，

I'll have it out later for tech demos.

我會帶它去做技術示範。

(Laughter)

（笑聲）

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

事情是，當然啦 -- 我已經在我人生中做出一些決定，

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

但早知道，我就應該聽這顆球的話。

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

但，當然，如果你有有效的數據，

you want to replace this with something much more sophisticated,

你想要用超複雜的方式來取代這顆球，

like data analysis to come to a better decision.

例如，用數據分析來得到更好的決策。

But that does not change the basic setup.

但這無法改變基本的設定，

So the ball may get smarter and smarter and smarter,

所以這球會越來越聰明，

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

但我相信，如果我們想達成某些 曲線右邊末端的非凡成就，

if we want to achieve something extraordinary,

最後我們自己還是得做出決定，

on the right end of the curve.

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

事實上，我發現 一個非常激勵人心的訊息，

that even in the face of huge amounts of data,

即使面對龐大的數據， 你仍會有很大的收穫，

it still pays off to make decisions,

在你做出決策、 變成一位該領域的專家

to be an expert in what you're doing

並承擔風險時。

and take risks.

因為，最後，不是數據，

Because in the end, it's not data,

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

是風險會帶你來到曲線的右邊末端。

Thank you.

謝謝各位。

(Applause)

（掌聲）