Know How Multi-Dollar Businesses Use Data Science and Machine Learning
How we interact with content has changed over the past few decades. This is because of the fast growth in OTT platforms, the adoption of media streaming services, and the steady rise of the high-speed net and smartphone users. You can now access content whenever and wherever you like.
The content has changed the way consumers see or opt for any services. For example, there were days when we had to use cable or a broadcast to watch movies or episodes, but the OTT platform has changed the viewpoint completely.
The development of OTT platforms has resulted in significant growth worldwide. In India, the number of internet users is expected to rise to around 900 million by 2025.
But it also brings more competition and the need to utilize advanced data science. Users now have many options when choosing a streaming service. Leveraging big data streaming analytics can help the new and improved OTT platforms climb the ladder.
We'll go through the importance of data science to an OTT platform, why you should invest in data science for these platforms, and how Netflix utilizes big data analytics to market properly to its customers.
What is OTT?
Over-the-top (OTT) is a growing service in the entertainment and media industries, where video and movies are streamed as a single product to consumers. OTT is believed to be the only future of broadcasting in multimedia.
The traditional set-top box had some constraints, which have been overcome with the utilization of an OTT platform. OTT services deliver content on demand. Customers now have better control over what type of content they want to watch, making the whole procedure interactive.
Importance of Data Analytics and Data Science in OTT Platforms
As previously mentioned, customers drive the way businesses are conducted these days. It's important to understand how customers engage with the services on OTT platforms. This type of insight into the customer's experience helps service providers modify their approach to be more personalized and relevant.
The market is populated with OTT services. Consumers now have access to a variety of streaming services. If an OTT platform wants to retain customers, it must utilize data science and analytics. Big data streaming analytics comes in handy in these situations.
If not applied properly, it may lead to customer churn.
What is customer churn?
Although this is not much relevant to our prime topic, still for a better understanding of the use of data analytics in the OTT platforms, I would like to explain the same a bit. When customers aren't getting the desired experience using a service provider, they can choose another provider that enhances their viewing experience. Focusing the most on this factor makes the Netflix model so effective, as they have prioritized the viewer experience for a long time.
The OTT platform can lower the customer churn rate significantly by providing personalized content. As a result, it helps them retain customers longer, solidifying their market value.
Big data streaming analytics creates a detailed customer viewpoint. It develops a precise model using consumer data, both existing and past, and identifies those more likely to convert. OTT services can then use this data to create strategies to retain their customers.
How does data science help OTT platforms enhance their businesses?
1. Analyze Their Market Value
Analyzing the data helps in gaining information about online activities and their position in the market. It can help them know their value in the market and what areas they should focus on improving. Data analytics and tools analyze the existing data and provide valuable insights into the current state of the OTT platform.
- These include identifying new subscribers gained over a specific period. Additionally, finding the trend in the number of new subscribers gained, i.e., rising, falling, or reaching saturation.
- You can know the percentage of converted subscribers, i.e., how many subscribers transformed from free to paid customers and vice versa.
- OTT platforms can keep an eye on the number of canceled subscriptions over a period.
This calculative information may help OTT services analyze their growth rate. In addition, through the information received, they may calculate their position within the market and find defects in the strategies. This will help in implementing new and better approaches to increase customer retention.
2. Know the Audience
Data Analytics can bring a much better understanding of the audience. Among getting information regarding customer demographics, one can also get a detailed overview of a customer's activities. It can help target specific customers accordingly.
These are the activities one can perform on the customer's data:
- Segmenting customers on common traits such as demographics, geography, etc.
- Classifying audiences into groups like people who like comedy series.
- And finally, establish different models based on this data using Data analytics tools. E.g., a customer retention model that prioritizes clients that are more inclined to switch service providers.
3. High Customer Engagement
Data analytics helps OTT providers market their services based on customer preference. This is achieved by identifying the content your customers like and using it to engage with them.
Data science and data analytics can give suggestions to customers like
- Choose the content that your viewers like.
- Suggest content that the viewer would like to view.
- Work on a marketing campaign that has to be based on data analytics results.
4. Grow with Advertising Revenue
Advertisements have established a new definition by utilizing data analytics. The usability of data, data management, and measurement in this new era has increased ROI from advertisements broadcasted on OTT platforms. In addition, through personalization, advertisers can establish a meaningful relationship with their customers.
Companies use insights gained from live as well as historical data to create an accurate customer profile. This helps them identify consumers, know how to target, and produce targeted advertisements.
5. Data Clean Rooms (PII)
Data clean rooms in OTT media companies help share customer data with the advertising team and maintain consumer data privacy. Data clean rooms can facilitate real-time data, doing personal-level marketing without sharing personally identifiable information (PII). The data matching proprietary data and other data sources help identify lapses of customers and high-value targets and create chances to generate additional sales.
A great example of how data science powers OTT platforms is Netflix. Netflix has been using data analytics to collect and analyze the data gained from their massive amounts of subscribers and use it to suggest or recommend a TV show, movie, or other types of content.
Use of Data Analytics in Netflix?
Most user activity on Netflix is determined by the personalized recommendations Netflix provides its users. It uses data analytics tools to create a detailed customer profile for millions of its users and multiple customer interaction data points and responses to the content.
Data is extracted from viewers on a particular show, such as
- Date and time on which the user watched the show.
- After pausing for a while, does the user resume viewing a show?
- Did they watch the entire show?
- How long did it take to finish the show?
- Which scenes did the user view again?
- The search numbers and ratings for the show.
This data is generated to enhance the user experience on the OTT platform. For example, Netflix utilizes Big Data and advanced analytics to provide viewers with individualized movie and TV program suggestions and predict a show's popularity before it is approved. Personalizing marketing material and maximizing production planning to improve technical, commercial, and general decision-making is a big part of its strategy.
How Netflix Earns Billions Through Data Analytics?
1. Personalized Recommendation Engine
Netflix's data collection suggests a set of algorithms. It can predict what viewers will watch and even arrange selections in rows based on the subscriber's preference.
- Platform searches, i.e., keywords and number of searches.
- Know if the viewer has paused, rewatched, rewound, or forwarded. (It even saves when the show was paused, when the user left it, or which scene the viewer repeats).
- Content rate and desertion time.
- Subscribers' scrolling and browsing behavior.
Anyone with a subscription to Netflix can see the upper left side of its interface suggests a list of programs you would like to watch and a recommendation engine that 80% of the content streamed is based on its recommendation engine.
A few examples of algorithms that Netflix uses to enhance its recommendation engine are:
PVR (personalized video ranking)- This filters the catalog to a certain extent by criteria (for example - true American crime, buddy comedy, psychological thriller, etc.)
Trending now ranker - Temporary trends like current events that can shape viewing habits are ranked to increase engagement.
Continue watching ranker - Analysis of content that the user has partially viewed will be displayed so that they can resume watching at any moment.
2. Content Development Analytics
Different audiences have different reactions to a content piece. For example, ‘Orange is the new black,’ might be loved by some customers only.
Netflix runs a projection model to ensure the project is renewed and fits specific parameters. Nonetheless, Netflix believes more in people making the final decisions.
3. Operation Optimization
Netflix can analyze and optimize filmmaking from a financial and operational standpoint. It can explore almost everything from its viewer experience to the shoots.
For example, Netflix has developed an algorithm that predicts the estimated cost of filming in one particular location to another. They even use analytics to increase film efficiency and post-production activity like editing by reducing streamlined workflows and bottlenecks.
The rise of OTT platforms is inevitable. To retain user engagement in the face of increased competition, they must incorporate data science and analytics into their business models. The viewer's likes and dislikes highly influence the entertainment industry. You can learn data science and machine learning to develop algorithms or build models for the OTT industry. Succeeding in these types of projects requires extensive knowledge of data science.
You can enroll in an Advanced Data Science Program and learn about utilizing Data Science and Machine Learning in various industries.