Netflix’s success is not by luck or chance. Rather it is by the firm’s ability to innovate and personalize its users’ experiences. In today’s world innovation is not a luxury rather it’s a necessity. Firms who ignore innovation like blockbuster have a hard time competing in such a VUCA world. In today’s day and age, people expect digital platforms to tailor experiences for their exact preferences and likings. Which is what Netflix actually does. Each time you get a movie that matches your personality, it is actually due to data analytics in action. Data analytics constitutes machine learning, predictive analytics, and algorithms that actually recommend movies for you and tailor these recommendations. In this blog we will go through how Netflix uses data analytics for recommendations, how did the platform evolve to get so personalized, and exactly how certain methods such as filtering and predictive analytics allow the platform to tailor hyper-focused experiences.
How Netflix Uses Data Analytics For Recommendations to Keep You Watching?
Collaborative Filtering
This technique is one of the foundational techniques that the platform uses to recommend shows and movies to watch. This method relies on using consumer’s tastes and preferences to recommend similar shows and films for you. Think of it as a club of movie enthusiasts that have similar taste in what you like to watch, Netflix recommends similar films as it thinks you might be interested in joining the club. An example of this, you watch film X and enjoy it, another user watches the film x too, and then Y, the platform recommends Y as it assumes that you too do have similar taste.
Neural Networks & Deep Learning
Neural networks and deep learning are robust methodologies that Netflix uses to identify patterns in user behavior, content and based on this create recommendations that are more precise. In fact, Neural networks and deep learning are inspired by the human brain. This is by using layers of nodes that are connected to one another ( neurons ) to aid in data processing and pattern recognition. To explain, deep learning is the subset of machine learning that uses neural networks with more than 3 hidden layers typically to solve intricate problems such as image recognition and Natural Language Processing. Think of Netflix using neural networks and deep learning as using a computer program that thinks like a human brain to learn your likings, so it can tailor the perfect recommendation for you.
Usage of NLP ( Natural Language Processing )
The Netflix team actually deeply analyzes the plot’s summary, subtitles, and reviews of the content of the film or series to get an in-depth understanding on the show or film. Consequently leading to the platform using data analytics not just for recommendations based on the genre or the actor that the viewer might like. But, also for the theme and the movie mood of content. The exact method that Netflix leverages NLP could be referred to as content-based filtering. Apart from the summary and subtitle, it uses even the language or director metadata to recommend for you media that you are more likely to consume.
Reinforcement Learning
As previously mentioned, Netflix uses a branch of machine learning where the system learns trial and error. Think of it as someone who takes notes of what you like or what you don’t. For example, if a movie pops up recommended for you to watch and you actually decide to watch it, Netflix take notes, also when you ignore the content, they take notes again and the following step is as someone who knows you so well, they recommend what you might like as they already know from your previous actions. This loop going back and forth allows the platform to adjust recommendations in a way that you feel they understand you so well as if they were your best friend.
Latent Factors
One of the advanced techniques that the platform deploys is that it breaks down the user preferences and item attributes into hidden features. To explain, instead of assuming that you are fond of action films, this advanced technique might detect that your preference constitutes action comedy, certain action movie actors, or even movies that are not so lengthy in time.
How Netflix Leverages Data Analytics For Global Reach & Personalization ?
Netflix’s data analytics strategy is not limited to the global reach. They actually deploy data analytics efficiently to operate on a global scale. That is why if you travel to Egypt you will find entirely different entertainment recommendations than if you travel to Japan for instance. The platform tailors recommendations not only to the individual preference but also the nations preferences. The platform uses data analytics to analyze cultural nuances, the viewing habits and holidays within the region to tailor content that does resonate with the audience’s diversity and preferences.
Why is Using Data Analytics For Personalization Useful ?
It has been recorded that a large chunk of sales, 35% of the notorious platform Amazon, is due to the recommendation methods they use. Netflix who uses a similar engine actually benefits from subscribers sky rocketing and even people ignoring traditional TV and shifting entirely to online platforms. A simple explanation is that imagine you are someone who views regular TV and as you sift through, nothing actually captures your attention and then suddenly something does, you watch but only the following day you face the same dilemma and you feel overwhelmed. Leveraging data analytics and AI efficiently solves this dilemma by monitoring your actions closely so that you won’t have to spend processing power to find out the next movie or series to watch. I guess you’ve already got a lot on your plate!
Netflix’s Plan Of Using Data Analytics For The Future
As to no one’s surprise, Netflix is committed to provide the best user experience for its users. Data analytics is a core part of the platform’s methodology. The platform actually does invest plenty of their resources in research and development processes. As for now, it has been unveiled that the firm is on the outlook for ways to explore more ways to personalize artwork of content and even interactive storytelling. As the platform advances, the viewing experience becomes more engaging and tailored to your unique preferences. Additionally, as you can see we are moving into a future where it will be AI and human and this opens a sea of opportunities especially for firms that rely heavily on tailoring their user experience so that they can actually enable customer retention. In fact, the firm is exploring solutions that are voice-controlled to actually personalize and tailor the experience for its users.
Netflix’s Rise
In 2009, Netflix decided that it wanted to build its recommendation system based on data analysis like other tech companies at that time, such as Google, Amazon, YouTube, and many others. So, they asked 480,000 users to rate 17,770 movies, and they got 100,480,507 ratings. So, they started to analyze the data, make patterns, and understand what the user may watch based on their history and the movies and shows they like and follow. Nowadays, Netflix’s recommendation system analyzes over 30 million daily “plays,” as well as over 4 million subscriber ratings and 3 million searches, allowing them to make successful bets on producing widely-acclaimed hits such as “House of Cards.”
Why Your Business Should Start Using Data Analytics?
Why Your Business Should Start Using Data Analytics
| # | Reason |
|---|---|
| 1 | It gives a better understanding of your target audience’s needs and wants. |
| 2 | Create customized content and advertisements. |
| 3 | Boost your sales. |
| 4 | Cost reduction. |
The Outcomes Of Netflix’s Investment In Data Analysis
Netflix saves $1 billion annually thanks to its recommendation system. This recommendation system influences 80% of the watched content on Netflix, according to Inside Big Data. Now, we want you to imagine what you can achieve if you’re a few steps ahead of your competitors. Or imagine that you have a cheat sheet for market reactions and perceptions.
Marketeers has developed its platform based on the same prediction concept; we call it Smart Value™ which will guide you by giving a full view of the market, including the gaps, the opportunities, the performance, and analyzing historic data to predict the reaction of the customers to the different decisions to help make precise decisions for growth.