How to use data to make a hit TV show
- 1. Analysis of TED talk “How to use data to make a hit TV show?” by Sebastian Wernicke
- 2. Sebastian Wernicke Chief Data Scientist, One Logic Munich, Germany Bioinformatics roots | Now data, strategy and growth with startups and Fortune 500 companies | Also TED Speaker, Udacity instructor, photographer, fan of hippos and flightless birds
- 4. Example #1 Roy Price Senior executive with Amazon Studios
- 5. Problem statement: 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. And of course that's a highly competitive space. So he takes a bunch of ideas for TV shows, and from those ideas, through an evaluation, they select eight candidates for TV shows, and then he just makes the first episode of each one of these shows and puts them online for free for everyone to watch. Collect all the data, they do all the data crunching, and an answer emerges, and the answer is, "Amazon should do a sitcom about four Republican US Senators Solution:
- 6. The average of this curve here is at 7.4, and "Alpha House" lands at 7.5, so a slightly above average show, but certainly not what Roy Price and his team were aiming for.
- 7. Example #2 Ted Sarandos Chief Content Officer of Netflix
- 8. Ted Sarandos is on a similar constant mission to find a great TV show Problem statement: Solution: 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. And then 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. And once they had all of these pieces together, they took a leap of faith, and they decided to license not a sitcom about four Senators but a drama series about a single Senator.
- 9. "House of Cards" gets a 9.1 rating on this curve, so it's exactly where they wanted it to be
- 10. Conclusion: So you have two very competitive, data-savvy companies. They connect all of these millions of data points, and then it works beautifully for one of them, and it doesn't work for the other one. So why? “Even the most data- savvy companies get it wrong. Yes, even Google gets it wrong sometimes.”
- 11. Despite all those failures, data is moving rapidly into real-life decision-making -- into the workplace, law enforcement, medicine. So we should better make sure that data is helping.
- 12. Complex Problem Take that problem apart into its bits and pieces so that you can deeply analyze those bits and pieces Put all of these bits and pieces back together again to come to your conclusion Tool Data and Data Analysis Tool Brain
- 13. Why Netflix won to AmazonStudios? Netflix was so successful, because they used data and brains where they belong in the process. They use data to first understand lots of pieces about their audience that they otherwise wouldn't have been able to understand at that depth, but then the decision to take all these bits and pieces and put them back together again and make a show like "House of Cards," that was nowhere in the data. Amazon, on the other hand, they did it the wrong way around. They used data all the way to drive their decision-making, first when they held their competition of TV ideas, then when they selected "Alpha House" to make as a show.
- 15. In the end, it's not data, it's risks that will land you on the right end of the curve.
- 16. THANK YOU- Vaibhav Srivastav B.I.E.T. Jhansi