“Behind every successful man, there is a Smart Woman.” If Netflix were a man, Data Science would be the Smart Woman behind its success. Do you wonder how Netflix recommendations perfectly match your preferences? Using Data Science, Netflix has surpassed its competition and now has over 100 million users globally. Data science helps Netflix keep track of all your likes and dislikes to make sure you’re satisfied.
The Concept
Data science is a combination of tools, algorithms, and machine learning principles that help users gain functional and beneficial patterns from raw data. A Data Scientist can identify future occurrences of an event by using Advanced Machine Learning Algorithms. The IoT (Internet of Things) has given rise to the fundamentals of Data Science, making it the most valuable resource for all companies today.
The Aim
Netflix has always strived to improve User Interface at all levels. Their primary goal is to add Contextual Awareness to their recommendations. It means that the proposals should have high logical reasoning behind them. As per DataFlair, two types of contextual classes are relevant to Netflix.
1. Explicit
- Location
- Language
- Time of the Day
- Device
2. Inferred
- Binging Patterns
- Companion
The Application
Netflix has used Data Science to ensure that users enjoy Value for money. With the help of various Analytical Tools, the Streaming Giant identifies the liking and proclivity of users and directs them towards similar options. A study suggests that recommendations influence more than 80% of all streamed content on Netflix.
Netflix does not use the conventional Hadoop warehouse. It instead uses an upgraded Data Storage System, Amazon’s S3. It allows it to spin more Hadoop clusters for work bases accessing the same Data. It uses Hive for Ad hoc queries and Analytics/PIG for ETL (Extract, transform, load)
The Data
To begin their Analysis, Netflix gathers Raw Fata, from which it plans to extract resourceful information using Data Science Algorithms. A combination of these algorithms transforms plain numbers to a detailed Recommendation Plan. For every 5 minutes a user spends on scrolling, Netflix can predict more than 40% of their relative selection patterns. There are several fields on Netflix, where Data is collected, captured, and stored.
- Time: The primary step is to understand and store the Time and Date when users stream content. It helps them identify your Sunday night-horror movie plans or your Afternoon-thriller preferences.
- Searches: All Search Titles are automatically stored to re-direct further recommendations towards these searches. Let’s say you search “John Wick,” watch the movie and close Netflix. The next time you switch the application back on, you will undoubtedly find more Action movies or more Keanu Reeves starters.
- Browsing and scrolling behavior: Netflix also uses Advanced Analytical programs to identify which Movie/TV show you decided to stop and read about. It helps them showcase more similar content to catch your eye and get you interested again.
- Pause/Fast-forward: Using Data Science, Netflix catches the exact durations where a user starts Pausing or Fast-forwarding while streaming content. It helps it identify what kind of scenes are preferred over others. If you skip an action movie’s emotional scene, it develops the algorithm to avoid passionate movies in future recommendations. But if you re-watch an emotional scene, it will adapt accordingly.
- A device used: If you use separate mechanisms to stream different content, this differentiation is stored permanently. For example, Children watching cartoons on the home-TV will not be recommended movies watched by their parents on the iPad, despite using the same account.
The Project
Netflix uses Data at all levels possible. From the time it a user logs in to log out, it stores all possible information it needs. It then channels these Data to bring out actionable information. The most famous story of Netflix’s marketing is how they purchased the “House of Cards” series. The series, starred by Kevin Spaced and directed by David Fincher, was one of the biggest blockbuster hits. More than a hundred million dollars was incurred to purchase this TV series, for several reasons.
- Netflix identified a vast fan base for Actor Kevin Spacey, who has acted in movies such as 21 and American Beauty.
- It also did a background check about Trending and Popular movies on their platform. Movies like Fight Club and The Social Network were highly rated and viewed by their audience, all directed by the renowned David Fincher.
- Netflix also viewed the statistics of the British version of the series, that was earlier released. The UK version received due appreciation by its target audience, which boosted its stance.
- The Political Drama Genre was one of their most active genres, with movies like Elizabeth I: The Virgin Queen and Winnie Mandela, doing rounds on their website.
Using programmable algorithms, all factors were linked to a pattern, making Netflix spend the big bucks on House of Cards. The series then became a massive hit and climbed to the #1 position on their trending charts, making it a successive and profitable Analysis.
The Benefits
Why would a company like Netflix, having a Market Monopoly, spend their time on Data Science? The answer is Consumer Retention. It is crucial to attracting new customers while retaining the current batch. Using Data Analysis tools, users of Netflix have preferred its platform over other service providers such as Hotstar and Amazon Prime. Netflix has beautifully driven millions of users towards its platform, achieving 20 Billion Dollars in revenue in 2019.
The Outcome
Netflix gained more than 3.1 Million followers on its platform after the release of House of Cards; this addition was majorly gained from the US streamers. It helped Netflix in plenty of ways.
- Revenue: Newly subscribed users added more than 72.5 Million Dollars in Revenue for Netflix. It was more than 75% of the combined investment Netflix made to air both seasons of the show.
- Word of Mouth: Adding high users and tending to their needs using Data Science helped Netflix gain even more popularity globally. It also led to the sequential addition of users through referrals, expanding, and creating further growth opportunities.
The Display
Every section on Netflix’s home page is unique to its user’s account. Each chapter is displayed based on a vast set of Data collected, combined to produce the most relevant recommendations.
1. Trending:
The Trending section is formatted according to the Location and preferences of the user. Chris Hemsworth’s Extraction was on the top of the Trending list in India, just after its release. Every user in India who had viewed action-based content or Chris Hemsworth’s movies was recommended Extraction.
2. Continue Watching
This section is a set of collective content that a User has begun streaming, but has left unfinished. Pause durations are stored to start streaming the content on the exact scene on which it has been paused/terminated before.
3. Genre Content
If the user frequently indulges in viewing Action movies, A section will be separately created named “Violent Movies.” This section will contain all popular Action Movies that have plenty of Violent scenes. If a user watches shows like Money Heist (A top-rated show dealing with thieves in Spain), they will find an additional section named “Risk-Taker and Rule-Breaker TV” on their Home Page.
4. Because You Watched
There is also a combination section, where all other Data is factored in. Suppose a User watched the Movie Polar, a new part called “Because you watched Polar” will be created, containing other movies of the Same genre, Actors, Directors, and Producers.
Netflix aims at making people wonder how it always has a ready-made list that will entertain them. Every Pause, Scroll, and Log-in time is used to enhance User Interface in the best way possible.
The Testing
Netflix always conducts Background Testing at scale to understand the functionality of their Data analysis-driven recommendations. The Results and Statistics from these Tests determine whether a set of algorithms should be widely introduced in their platforms globally.
Personalization Based on Interleaving
Netflix conventionally followed the A/B testing policy, where two sets of reduced algorithms were tested on two different sets of samples. The results of these tests were based on how accurately the recommendation section appealed to the target samples. This method was subsequently scrapped because of its implausibility.
Netflix adopted a new method of Testing. In this testing method, Netflix decided to infuse Interleaving of Algorithms to decide on the best Page Ranking Algorithm for improving User Interface. This method benefited the American Media Service Provider in many ways.
- Cost-friendly: Interleaving involves blending, which means Netflix carried out two tests for the price of one. Background testing involves a significant amount of Cost, which was saved using this method.
- Time-saving: Combining two testing methods into one saves time to work on other matters and quickly gives out the results. We all know that Time is Money; hence, this is considered as a more suitable and profitable choice of Testing.
The Importance
As the world moves into the future, digitization has been normalized by all. The inflow of Users on the Internet is continually growing in large numbers. It has created a heated environment filled with intense competition among Media Service Providers like Netflix and Amazon Prime.
1. Engagement:
Data Science helps Netflix to increase the participation of users powerfully and creatively. Using Analytics, a virtual rapport between the user and the Service provider, is created. Netflix aims at exploiting this rapport with their Market Share advantage.
2. Solution:
Netflix aims at using Data Science as a go-to for problem-solving. There are plenty of problems that Data Science can help with.
- Low reach: Recommendations on Netflix can improve the view count on overlooked content. It helps Netflix to keep its audience engaged on its platform.
- Feedback and Ratings: Analytical programs and Probability models help Netflix average a cluster of User Ratings to categorize content, based on its ability to impress.
- Policy Control: Netflix has a strict policy that discourages the sharing of a single account by multiple people. Netflix allows up-to five Individual Profiles to access the website using one account. Using Data Science governs the Devices used for log-ins from the same accounts to avoid a breach.
- Innovation and Efficiency: The critical quality of Data Science is that it never runs out of fashion. Machine learning continually adapts to the present, uses previously-stored Data available at present to predict future outcomes. Efficiency for Netflix would mean to deliver the right content to the right user.
- Decision making: Gathering Data to make decisions is not the mantra to success. The mantra lies in mastering Analytics to use the Data and channel it in the right direction. Netflix has used Data Science to identify the appropriate opportunities and paths available.
- Personalization: In a commercial market where the physical sale is conducted, a consumer can ask for personalized products, test it, and purchase it. Data Science has helped Netflix stretch its range to meet all the customized demands of the public.
For a consumer, a sense of satisfaction is met when the correct product is available at the right time and place, for the right price. Netflix has made its users’ lives more convenient by providing high-quality, relevant content at their fingertips.
The Conclusion
It all comes down to one question:
Based on the historical actions taken by a user and the data available, what is the most probable video a user will play right now?
The list of recommendations can be prepared within seconds using Probability Models and Analytical Programs. Data science has become an integral part of the growing world. It has built the foundation on which companies like Netflix and more will develop their future. Netflix has minimized its scope for errors, enhanced User Interface, and boosted User Engagement.
Once in action, decision-making seems like an easy task. But it requires creative workers, using high-end tools to create solutions adaptable across all verticals. Netflix holds a dominating market share and is crowned as “HBO of Internet Tv.”The success of any platform on the World Wide Web can’t come without a strong foundation. Without Data Science, companies would be stuck with unfiltered clusters of Databases, with no clue how they will proceed further.
Every person must ask themselves whether Data Analytics will improve their business or not? Netflix did it, so should you.
