We’re all TV show addicts , most of our weekends are spent binge watching our favorite shows , When Reed Hastings and Marc Randolph realized this , They came up with the ides of an online streaming site called NETFLIX in 1997 .
Analytics at Netflix
The core job of analytics is to help companies gain insight into their customers. Then, the companies can optimize their marketing and deliver a better product. (Without analytics, companies are in the dark about their customers.) Analytics gives businesses the quantitative data they need to make better, more informed decisions and improve their services.
If you’re watching a series like Arrested Development, Netflix is able to see (on a large scale) the “completion rate” (for lack of a better term) of users. For example, the people at Netflix could ask themselves “How many users who started Arrested Development (from season 1) finished it to the end of season 3?” Then they get an answer. Let’s say it’s 70%.
Then they ask “Where was the common cut off point for users? What did the other 30% of users do? How big of a ‘time gap’ was there between when consumers watched one episode and when they watched the next? We need to get a good idea of the overall engagement of this show.”
They then gather this data and see user trends to understand engagement at a deep level. If Netflix saw that 70% of users watched all seasons available of a cancelled show, that may provoke some interest in restarting Arrested Development. They know there’s a good chance users will watch the new season.
But the data gets deeper than that. Here’s a look at some of the “events” Netflix tracks:
- When you pause, rewind, or fast forward
- What day you watch content (Netflix has found people watch TV shows during the week and movies during the weekend.)
- The date you watch
- What time you watch content
- Where you watch (zip code)
- What device you use to watch (Do you like to use your tablet for TV shows and your Roku for movies? Do people access the Just for Kids feature more on their iPads, etc.?)
- When you pause and leave content (and if you ever come back)
- The ratings given (about 4 million per day)
- Searches (about 3 million per day)
- Browsing and scrolling behavior
- Netflix also looks at data within movies. They take various “screen shots” to look at “in the moment” characteristics. Netflix has confirmed they know when the credits start rolling; but there’s far more to it than just that. some have figured these characteristics may be the volume, colors, and scenery that help Netflix find out what users like.
In 2006 Netflix announced the Netflix Prize, a competition for creating an algorithm that would “substantially improve the accuracy of predictions about how much someone is going to enjoy a movie based on their movie preferences.” There was a winner, which improved the algorithm by 10%. However, Netflix never did implement the algorithm, saying: “We evaluated some of the new methods offline but the additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment.” But Netflix didn’t shun all algorithm and data efforts.
Now you see how Netflix makes informed decisions based on data. Clearly, data cannot make every decision; there are some situations where intuition has to take over. For instance, data could not predict that a show like Breaking Bad would be a success. The creator was a former writer on The X-Files, and dramas are 50/50. In these cases, decisions are heavily based on the people and team behind the idea of the show. Whether Netflix can make a successful show like this (one with little to no data) is yet to be seen.What analytics and data can do is give you insight so you can run a better business and offer a superior product. People with data have an advantage over those who run on intuition !
Article by Shelly Saksena.