So audience data when you're talking about original productions can be a blessing and a curse. We don't need big data to tell us that the top four favorite subjects of Netflix users are marriage, royalty, parenthood, and reunited lovers. We've been telling stories about those things for hundreds - if not thousands - of years.
The data can also be misleading, because sometimes what audiences want isn't what's good for dramatic effect. Both Dexter and Homeland suffered from audiences reacting positively to the main character, which caused Showtime to stop the writers from making creative decisions that yes, would have alienated the audience initially, but on the other hand would have made for shows that would have in their later seasons been better received.
Another example: I'm pretty sure that if you looked at the Netflix data it would show that people liked to watch movies and TV shows that either had happy endings or twist endings. The former makes you feel good, the latter makes you appreciate the writing. What people really don't like are ambiguous endings. If we based creative decisions purely on audience data a show like The Sopranos wouldn't have the ending it does. The upshot of all of this is that many writers end up 'cheating' the data to get their shows made. Take Orange is the New Black, where the creator Jenji Kohan has gone on record saying she basically used the main white, engaged protagonist as a trojan horse to get the show commissioned. What I'm basically trying to say in this rant is that using data to inform what shows to commission is one thing, but using it to direct the shows as they progress is another (and something I'm against).
> We don't need big data to tell us that the top four favorite subjects of Netflix users are marriage, royalty, parenthood, and reunited lovers. We've been telling stories about those things for hundreds - if not thousands - of years.
I'm really irritated at this attitude. Things that are obvious to you aren't obvious to other people. Confirming things using hard data is useful.
You're right that audience data is going to be full of the obvious. But it's also good for watching trends, before they go bust. A number of genres (e.g. superheroes) have cycles of growing, being massively popular, then dying off for a couple decades.
If you're in the business of making content, you want to join the trend early and get out before everyone realizes it's a fad. A truly great movie can buck trends, or even change them, but there are few truly great movies. Or you could just ignore this all and make the perennial favorites, generic romance or action movies.
Actually their recommendation engine is a little too accurate. It drives as much as 70% of their views. They have to purposely randomize it. For those curious how it works: See the brilliant talk from the guy who heads it up himself (this was a great talk to attend in person) http://www.mlconf.com/mlconf-2013/mlconf-2013-agenda/xavier-...
The data can also be misleading, because sometimes what audiences want isn't what's good for dramatic effect. Both Dexter and Homeland suffered from audiences reacting positively to the main character, which caused Showtime to stop the writers from making creative decisions that yes, would have alienated the audience initially, but on the other hand would have made for shows that would have in their later seasons been better received.
Another example: I'm pretty sure that if you looked at the Netflix data it would show that people liked to watch movies and TV shows that either had happy endings or twist endings. The former makes you feel good, the latter makes you appreciate the writing. What people really don't like are ambiguous endings. If we based creative decisions purely on audience data a show like The Sopranos wouldn't have the ending it does. The upshot of all of this is that many writers end up 'cheating' the data to get their shows made. Take Orange is the New Black, where the creator Jenji Kohan has gone on record saying she basically used the main white, engaged protagonist as a trojan horse to get the show commissioned. What I'm basically trying to say in this rant is that using data to inform what shows to commission is one thing, but using it to direct the shows as they progress is another (and something I'm against).