The media consumption habit of consumers has primarily shifted from what it used to be. Instead of sitting in front of the TV and watching movies aired by the favorite channels, going to the movie theatre, or renting movie titles recommended by a friend or a store owner, people are watching movies online on apps like Netflix, Amazon Prime Videos, or Hulu. Watching a movie in such apps is only one part of the experience; getting movie recommendations that largely suit user preferences using a movie recommendation engine is the truly remarkable another part of the overall experience. The data analytics algorithms are so impressive that the vast majority of the users end up watching movies and shows from the recommended movies section.
What Kind of Data and User Behavior are Captured?
To create a movie recommendation engine that is accurate and personalized, enormous volumes and varieties of data should be collected. The data comes from millions and millions of customers that are members of such movie streaming services. Since all content is consumed in the digital medium, all user-behavior related data can be easily collected. As a result, companies can create personas of the users of what their average customers look like. Here are some metrics or user-behavior data that is collected by a movie recommendation engine:
- Movie or show completion rate (how many users completed the full movie)
- Date and time when the content was consumed
- Geographic location and zip code of the user
- Device used to view the movie or show
- Ratings
- Reviews
- Browsing and scrolling behavior
- When a movie is paused, rewind, or fast-forwarded
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How Data Analytics is Used to Create a Movie Recommendation Engine
When you sign-up for movie streaming services like Netflix or Hulu, apart from asking your general details, they also ask for their interests, movie genres, and rate some of the movies in their database. It’s built on the premise that if the algorithm cannot suggest a movie that the customers would like to watch, they will eventually cancel their subscription. So, to keep them interested, they rely on your personal preferences and outside sources to make sure the algorithm suggests a movie that you end up watching. But is the movie recommendation engine efficient? Netflix recently tweeted that the recommendation algorithm drives 75% of its viewer activity. Movie streaming services have spent a considerable amount of resources on improving the accuracy of their recommendation algorithm, by hosting contests, hiring experts, and investing in infrastructure. For instance, by gathering massive amounts of data Netflix realized that a lot of people liked “The Social Network” directed by David Fincher, and the British version of “House of Cards” had a large audience base, and finally those who watched the British version also watching movies with Kevin Spacey and those directed by David Fincher. By factoring in these three data points, Netflix created an American version of House of Cards with both David Fincher and Kevin Spacey.
To know more about how movie-streaming services companies use data analytics to create a movie recommendation engine, content personalization, movie suggestions, and content management system, request a demo.
Going Beyond the Movie Recommendation Engine
The movie recommendation engine is only one aspect where movie-streaming companies can use data analytics. The underlying principles and data analytics technology can be used to analyze user preferences and behavior to customize personalized trailers, promote subscription plans, and provide discounts. Additionally, companies can also decide on which movies to license since renting movies from studios is very expensive. They use metrics which is close to ‘maximum entertainment gained per dollar spent’ to decide which titles to license.
Notable Movie Recommendation Engines
- Rotten Tomatoes
- IMDb
- Netflix
- Movielens
- Hulu
- Nanocrowd
- Jinni
- Taste Kid
- Metacritic
- Amazon Prime
