Originally Published on: QuantzigRecommended For You: Data Analytics in Movie Recommendation Engine

The landscape of consumer media engagement has undergone a significant transformation. Departing from traditional methods such as watching TV, going to theaters, or relying on recommendations from friends or store owners, people now prefer streaming movies online through platforms like Netflix, Amazon Prime Video, or Hulu. While watching a movie is one part of the experience, receiving personalized movie recommendations through a movie recommendation engine adds another dimension to the overall enjoyment. The effectiveness of data analytics algorithms is evident as a vast majority of users often end up watching movies and shows suggested by these engines.

What Data and User Behavior are Analyzed? Developing an accurate and personalized movie recommendation engine requires collecting extensive volumes and types of data. This data is sourced from millions of customers who are members of streaming services. Since all content is consumed digitally, user behavior-related data is easily gathered, allowing companies to create user personas. Here are some metrics or user-behavior data 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 and reviews
• Browsing and scrolling behavior
• Instances of pausing, rewinding, or fast-forwarding

How Data Analytics Shapes a Movie Recommendation Engine When users sign up for streaming services like Netflix or Hulu, they provide details about their interests, preferred genres, and rate movies in the platform's database. The algorithm relies on this information, as well as external sources, to suggest movies that align with user preferences. Streaming services invest significant resources in improving the accuracy of their recommendation algorithms. Netflix, for instance, stated that its recommendation algorithm drives 75% of viewer activity. By analyzing massive amounts of data, Netflix identified patterns such as users who liked "The Social Network" and the British version of "House of Cards" also enjoying movies with Kevin Spacey and those directed by David Fincher. This led to the creation of an American version of "House of Cards" featuring both Spacey and Fincher.

Beyond the Movie Recommendation Engine Data analytics can be applied beyond movie recommendations in the streaming industry. Companies can use these principles to analyze user preferences and behavior for customizing personalized trailers, promoting subscription plans, offering discounts, and deciding which movies to license. Metrics like 'maximum entertainment gained per dollar spent' help determine which titles are worth licensing.

Notable Movie Recommendation Engines:

• Rotten Tomatoes
• IMDb
• Netflix
• Movielens
• Hulu
• Nanocrowd
• Jinni
• Taste Kid
• Metacritic
• Amazon Prime

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