Sebastian Wernicke: How to use data to make a hit TV show 如何运用数据做出一个爆红的电视节目
Roy Price is a senior executive with Amazon Studios. his responsibility to pick the shows, the original content that Amazon is going to make that's a highly competitive space. He has to find shows that are really, really great.
So this curve here is the rating distribution of about 2,500 TV shows on the website IMDB, and the rating goes from one to 10, and the height here shows you how many shows get that rating. if your show gets a rating of nine points or higher, that's a winner. Then you have a top two percent show. So in other words, he has to find shows that are on the very right end of this curve here.
"Breaking Bad," "Game of Thrones," "The Wire,"“绝命毒师”、 “权力的游戏”、“火线重案组”
Roy Price does not want to take any chances. He wants to engineer success. He needs a guaranteed success, Roy Price不想只是碰运气。 他想要打造成功。 他要一个万无一失的成功
he takes a bunch of ideas for TV shows, and from those ideas, through an evaluation, they select eight candidates for TV shows, Amazon is giving out free stuff, They record when somebody presses play, when somebody presses pause, what parts they skip, what parts they watch again. So they collect millions of data points, they want to have those data points to then decide which show they should make.
do all the data crunching, and an answer emerges 处理过后得到了一个答案 "Alpha House." lands at 7.5,
Meanwhile， Ted Sarandos, who is the Chief Content Officer of Netflix, instead of holding a competition, what he did -- and his team of course -- was they looked at all the data they already had about Netflix viewers, you know, the ratings they give their shows, the viewing histories, what shows people like, and so on.
they use that data to discover all of these little bits and pieces about the audience: what kinds of shows they like, what kind of producers, what kind of actors.
took a leap of faith，信心满满地
not a sitcom about four Senators but a drama series about a single Senator. 不是四个参议员的喜剧， 而是一系列有关一位 单身参议员的电视剧
House of Cards,“纸牌屋” gets a 9.1 rating on this curve,
logic kind of tells you that this should be working all the time.
despite having lots of data, does not always produce optimum results.
the pinnacle of scientific success:达到了一个科学界的顶峰 It worked beautifully for year after year after year, until one year it failed.
even the most data-savvy companies, Amazon and Google, they sometimes get it wrong. into the workplace, law enforcement, medicine.进入工作场所、 执法过程、 医药领域。
the difference between successful decision-making with data and unsuccessful decision-making,
So whenever you're solving a complex problem, you're doing essentially two things. taking apart and putting back together again. The first one is, you take that problem apart into its bits and pieces so that you can deeply analyze those bits and pieces, and then of course you do the second part. You put all of these bits and pieces back together again to come to your conclusion. 首先，你会把问题拆分得非常细， 这样你就可以深度地分析这些细节， 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， 来得出你要的结论。
now the crucial thing is that data and data analysis is only good for the first part. Data and data analysis, no matter how powerful, can only help you taking a problem apart and understanding its pieces. It's not suited to put those pieces back together again and then to come to a conclusion. There's another tool that can do that, and we all have it, and that tool is the brain.
Ted Sarandos and his team made that decision to license that show, "House of Cards,"
I believe it's still on us to make the decisions if we want to achieve something extraordinary, still pays off ，会有很大的收获