Yeah, Netflix would even recommend the next movie or the series to watch based on your interests! No doubt they’ve managed to gather 220Mn+ paid subscribers!
Even Zomato has figured out what food are you going to order today, tomorrow and for eternity! They’ve also smartly used the data of our behaviour and patterns of ordering food, and now they just tempt you to order every single time they strike a conversation.
One thing they’ve nailed is understanding customer expectations. And they’ve gone way too far with that.
However, with brands taking the bar so high on customer expectations, other brands also need to match the same expectations & bring something unique to the table!
But how will brands jump to this level of proactivity? How will they anticipate needs, trends & behaviours? Let’s learn from Netflix predictive analytics case study!
What is predictive analytics?
Predictive analytics analyzes current & historical data to make predictions using various statistical techniques- usually data mining, predictive modeling, and machine learning. Historically, it has helped brands understand customers and is also used to identify risks and opportunities and guides in the decision-making process.
Companies today are swamped with data stored and collected from various mediums and sources. To gain insights from this data, data scientists use deep learning and machine learning algorithms and make predictions about future events, and plan necessary strategies. Learnings gained through predictive analytics can be used further within prescriptive analytics to drive actions based on predictive insights.
87% of customers require active communication from companies. ~ inContact.
What does predictive analytics offer?
Predictive analytics allows businesses to look into the future with more accurate and reliable insights. At a macro-level, predictive analytics provides a lens into consumer behavior and purchase patterns, but businesses use it at a micro-level as well.
For example, retailers use predictive analytics to forecast inventory requirements and manage shipping schedules. Airlines use predictive analytics to set ticket prices based on past ticket trends. Marketing departments used predictive analytics to optimize product development, advertising, distribution and retailing, or marketing research. Predictive analytics can help attract, retain, and nurture customers at the most opportune moments.
It can also be used as a preventative measure. For example, in her interview with Engati CX, Tyler Cohen Wood mentioned that predictive analytics detects and halts malicious activities and criminal behavior in cyberspaces. When the model notices an unusual behavior pattern from the cybercriminal attempting to infiltrate a system, it fires an alert to cybersecurity teams immediately to resolve the issue.
But, what’s the difference between Predictive Analytics & Data analytics?
Data Analytics is the process of finding the logical patterns by applying various filters & models on the raw data.
On the other hand, Predictive Analytics is about making predictions about the future outcomes by understanding the past & the current data trends.
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What data does Netflix capture?
For one of the shows, House of cards, Netflix captured
30 million “plays”
4 million ratings
3 million searches
Take a minute to digest that.
Now, let’s look at the data points that Netflix collects:
Customer interactions on the app
Responsiveness to shows/movies
Date, time, location & the device being used to watch
When & where you paused / resumed
How many shows you complete / leave midway
How many minutes / hours / days / weeks you take to complete a series / movie
How many times you search before choosing the show / movie
Queries you use to search your shows / movies
Shows preferred by men / women / children / teenagers
Feedback & ratings of subscribers
Scrolling behaviour
And many more…
Based on the research conducted by Netflix, their personalized recommendations have turned out accurate for 75% of subscribers!
How Netflix uses data analytics?
Netflix uses AI-powered algorithms to make predictions based on the user’s watch history, search history, demographics, ratings, and preferences. These predictions shows with 80% accuracy what the user might be interested in seeing next.
1. Personalized recommendation engine
We’ve already seen the data points that Netflix captures in the above section. A series of algorithms are applied on this data & based on the subscriber’s viewing preferences, Netflix is able to predict what you’re likely to watch next!
Some examples of the algorithms that Netflix uses are Personalized Video Ranking, Trending Now Ranker, Continue Watching Ranker, etc.
2. Content Development Analytics
Based on the shows / movies that are performing really well and are picking up with the audience, Netflix uses a projection model to select which projects to invest and work on!
Stranger Things, Squid Game, and other shows that touched the sky - have all gone through the grind (and marketed phenomenally)!
3. Operations Optimization
Netflix uses data analytics to optimize the ground logistics for the shoots of the shows / movies. Just like projecting the shows that’d turn out to be successful, they have built algorithms that help them project the costs of filming in one location v/s another location.
Even the post-production activities are analysed using data, and performed with best productivity.
4. Customized marketing
For the show - House of Cards, Netflix created more than 10 versions for the show’s trailer. If you watched a lot of shows centered around women, you’ll be shown the trailer that’s focussed on female characters, and so on.
Mind blown, right?
5. Artwork & imagery selection
Netlfix uses the AVA tool (Aesthetics Visuals Analysis) that checks the entire video & identifies the frames that can be used as artworks.
Now that you know how to create predictive analytic models, here’s how they can drive business performance.
1. Highly-personalized marketing
Imagine using a model that can monitor customer behavior at both a micro and macro level. Customers expect this kind of service as it makes the experiences more convenient and enjoyable. Predictive analytics enables you to carry this out. Personalization can only be effective when it’s based on quality data. Use this data and these insights to deliver hyper-personalized messages to the right customer at the right time and place.
2. Forecasting needs
Building on this, predictive analytics can anticipate the needs of your customers before your customer does. Predictive analytics makes it possible for businesses to forecast customer needs based on purchase history, search history, interests, demography, and more. This is what makes Netflix so successful.
3. Reduces churn
As we’ve mentioned above, predictive analytics is marvelous at identifying malware and abnormal, risky behavior. But this mechanism can also be applied to flighty customers. Leaders can use analytics to predict when a once-promoter may turn into a detractor before your agents can. Once the abnormal behavior is detected, the model can alert your customer service leaders to pay extra attention to these customers. It enables businesses to take a proactive approach to reduce churn and customer attrition.
Predictive analytics can significantly improve internal operations efficiency to enhance the customer experience. The smoother the operation, the faster the service. Having efficient internal operations can help ensure that the customer receives quality service without any fuss. The model can help staff within the contact center by forecasting inventory needs, for example.
By introducing a predictive analytics model, you can further boost employee productivity to give you an edge over your competitors.
5. Pre-emptive support
The model can predict significant events in the customer life cycle to increase revenue in these critical times. For example, an insurance company will send out alerts for car insurance or driver’s tests when they’re aware of the family’s child coming of age. Providing recommendations at these turning points of a customer’s life can give you an edge.
6. Handling feedback
As the model becomes more sophisticated with the data being fed into it, it can act on real-time feedback to deliver ultra-personalized recommendations. The customer’s actions, such as jumping from one category to another on an e-commerce website, immediately impact the model and will affect the following recommendations they receive. These trends can easily be identified and acted on by the model.
7. Developing pricing models
Insurance companies typically use predictive models to determine the optimal pricing model for their clients. There’s a telematics program called Snapshot that uses in-car sensors to determine to price. The data from the model personalizes the rate for each customer based on their skill level when it comes to driving. Someone who drives less often and stays close to home is likely to have a lower rate than someone who’s always on the road and likes to speed.
8. Providing a lens into the future
We’ve spoken about predictive intelligence at a fairly micro-level, but its scope is endless. Organizations can use it to track and predict customer behavior trends to create an experience like never before. The current trends suggest that because of the forced digital transformation and migration into our devices, customers demand speed and agility, and care. Customer experience is going to dominate each industry. So, how can you get started?
Impact of data & analytics?
Well, Netflix managed to get a 93% retention rate. That’s beyond imagination.
How Netflix uses data analytics? Netflix uses AI-powered algorithms to make predictions based on the user's watch history, search history, demographics, ratings, and preferences. These predictions shows with 80% accuracy what the user might be interested in seeing next.
How Netflix uses data analytics? Netflix uses AI-powered algorithms to make predictions based on the user's watch history, search history, demographics, ratings, and preferences. These predictions shows with 80% accuracy what the user might be interested in seeing next.
Netflix's AI considers your viewing habits and hobbies to provide Netflix recommendations. Users can take charge of their multimedia streaming and customize their interactions owing to the system's ability to compile and recommend content based on their preferences.
Predictive analytics is a kind of data mining that uses gathered data to make predictions based on the actions of individuals. Netflix benefits from predictive analytics by using it to predict its users' viewing habits. For example, it uses data collected from its users to determine what movies they'll watch next.
Netflix has saved $1 billion by using machine learning algorithms that analyze what its customers are watching, then recommend other movies and TV shows they'll like based on this data.
Through their analytics, Netflix may know how much content users need to watch in order to be less likely to cancel. For instance, maybe they know “If we can get each user to watch at least 15 hours of content each month, they are 75% less likely to cancel.
Big data and analytics. So, how does Netflix use data analytics? By collecting data from their 151 million subscribers, and implementing data analytics models to discover customer behaviour and buying patterns. Then, using that information to recommend movies and TV shows based on their subscribers' preferences.
We estimate the likelihood that you will watch a particular title in our catalog based on a number of factors including: your interactions with our service (such as your viewing history and how you rated other titles), other members with similar tastes and preferences on our service, and.
Netflix has approximately 692.10 million viewers globally, a 6% increase from 2022 or about 37 million more in viewership. According to projections, there will be an additional 32.9 million viewers in 2024, a 5% increase over 2023. Key Stats: There were approximately 544 million users of Netflix worldwide in 2020.
How does Netflix go about generating this list? They can mine the data from other users who have similar tastes and see what other content similar users liked. This information is then used to develop tailored content for each user. This approach to data mining and developing recommendations is not unique to Netflix.
Their strategy is to licenses content, distribute that content over their own platform, that content gets consumed, such that it can access and use consumer data to drive success.
Descriptive analysis, which identifies what has already happened. Diagnostic analysis, which focuses on understanding why something has happened. Predictive analysis, which identifies future trends based on historical data. Prescriptive analysis, which allows you to make recommendations for the ...
Netflix said that the drop in subscribers was caused due to the streaming giant's decision to withdraw from Russia over Ukraine war, which resulted in a loss of 700,000 subscribers this year, according to its quarterly earnings report released on Tuesday.
Still, when it comes to retaining users, no competing service comes close to Netflix. On average, two-thirds of the company's monthly-paying customers are still subscribed 12 months after first signing up.
“They are losing subscribers in the US and Europe because of competition, recession, inflation, and general fears about the economy.” said Michael Pachter, an analyst for Wedbush Securities. He said that Netflix will continue to grow as people cut the cable cord and as they offer a cheaper ad-supported option.
This decline can be attributed to some surrounding factors. The first is the increasing price of Netflix through the years. Their competitors are offering cheap prices in their services in comparison to Netflix, with an equally good quality of content roster.
Netflix's marketing strategy for segmentation, targeting and positioning is mostly focused on low-cost moment motion films and TV shows, with the largest audience focusing on the mass business sector. In contrast to other competitors, Netflix provides a viewing experience free of commercial interruptions.
As consumers understand and prefer Netflix content, more new members join to view only Netflix content, thus helping the company with brand recognition. Netflix also benefits from economies of scale compared to new competitors in the market.
Their well-known business model: subscribers enjoyed unlimited rentals, without the added worry of late fees or shipping & handling. Netflix quickly developed a reputation for revolutionizing the movie rental market. As a result, Netflix dominated the market and enjoyed minimal direct competition.
The company invests heavily in creating its shows and movies, spending billions of dollars on production and development each year. This strategy allows Netflix to differentiate itself from its competitors and offer exclusive content that is not available on any other streaming platform.
It's machine learning, AI, and the creativity behind the scenes that guess what will make a user pick a particular show to watch. Machine learning and data science help Netflix personalize the experience for you based on your history of picking shows to watch.
Your ratings are also crucial to Netflix's recommendation algorithm. If you rate a show or use the double thumbs-up feature on Netflix to rate some movies higher than others, you'll likely see more similar TV shows or movies on your recommendations.
Explanation of Recommendations through Matrix Factorization
To improve users' experience, Netflix has developed a sophisticated recommendation system that suggests movies based on your past viewing history, ratings, and preferences.
A highly data-driven company, Netflix uses descriptive analytics to see what genres and TV shows interest their subscribers most. These insights inform decision-making in areas from new content creation to marketing campaigns, and even which production companies they work with.
Big data has helped Netflix massively in their mission to become the king of stream. Big data helps Netflix decide which programs will be of interest to you and the recommendation system actually influences 80% of the content we watch on Netflix.
Netflix uses customer data analytics to get content recommendations because it knows which movies its customers like to watch. For example, if a Netflix user likes Rocky, it will also offer them sports documentaries.
Most Netflix subscribers are based in the EMEA region (Europe, Middle East, and Africa), accounting for over 77 million of Netflix's total global subscriber base.
Netflix is a leading subscription-based streaming service with over 232 million paid subscribers worldwide. In 2022, Netflix produced 891 original productions, generating $31.61 billion in revenue and $4.49 billion in net income.
Netflix Inc. lost more than one million users in Spain in the first three months of 2023 according to market research group Kantar, a sign that the streaming giant's crackdown on password-sharing could backfire.
In total, 62% of U.S. video-on-demand users say they like Netflix. However, in actuality, among the 94% of U.S. respondents who know Netflix, 66% of people like the brand.
Unstructured data is therefore much more difficult to interpret and often a case for Data Scientists. One confusion that is often made is between Big Data and unstructured data. Big Data is not necessarily unstructured, but can also be in structured form (e.g. streaming data at Netflix).
Discriminant analysis is one of the most powerful classification techniques in data mining. The discriminant analysis utilizes variable measurements on different groups of items to underline points that distinguish the groups. These measurements are used to classify new items.
Netflix has blamed their subscribers loss on competition with other companies. A lot of companies, such as Disney and NBC, created their own streaming services, which drew a lot of attention away from Netflix. With the launch of Disney Plus in 2019 and Peaco*ck in 2020, the popularity of Netflix was bound to decline.
Netflix's biggest subscriber loss came from its biggest market, the United States and Canada, where the streamer said it lost 1.3 million users in the second quarter. But that was offset by increased subscriptions elsewhere.
Netflix loses 200,000 subscribers in its first quarter, warns of bigger losses to come. Netflix says it lost 200,000 subscribers in its first quarter, a first in its decline in more than a decade. It projects to lose more than 2 million more subscribers in the second quarter as competition for streaming heats up.
The streaming pioneer has been reeling under strained consumer spending, rising costs of financing production and increased competition from Disney+ and Amazon Prime. It had pinned its hopes on the launch of the ad-supported tier, but analysts say they have not seen a burst of subscriptions.
Elon Musk weighed in on Netflix's subscriber loss in the first quarter of 2022 by claiming “the woke mind virus” is making the streaming platform “unwatchable.” Netflix announced Tuesday that it had lost 200,000 subscribers, marking the first time in 10 years the streamer has reported a decrease in subscribers.
Why Netflix Suffered Subscriber Losses? Netflix revealed that it had lost 1 million members for the first time in two decades; it claimed that it was a result of rising inflation and increasing competition in the streaming market. Price increases are also the reason behind the loss of Netflix subscribers.
According to Vulture, Netflix works with that “28-day viewership rule” we mentioned above. That rule is what supposedly decides the life or death of any series that sits on the streaming servers you use to watch every episode of The Recruit or Kaleidoscope.
Netflix is successful thanks to big data and analytics.
Their success can be attributed to their impressive customer retention rate, which is 93% compared to Hulu's 64% and Amazon Prime's 75%. However, it's not just their ability to retain most of their 151 million subscribers that have made them successful.
Netflix has not reported a monthly churn rate since 2011, when it was 4.9%. Antenna estimates that Netflix's domestic churn rate was 1.9% and 2.0% in 2020 and 2021, respectively, and has popped up to 3.3% so far in 2022.
Netflix collects non-viewing data from your interactions with its service and from third parties. This data can include which devices you use, any devices on your local network, IP address, interactions with advertising, rough location at each login, and more.
A highly data-driven company, Netflix uses descriptive analytics to see what genres and TV shows interest their subscribers most. These insights inform decision-making in areas from new content creation to marketing campaigns, and even which production companies they work with.
Netflix marketing is based on integrating marketing strategies. In order to provide a seamless experience, Netflix follows a customer-centric model. The platform does data analytics by using content marketing wisely. The customer-centric approach taken by Netflix creates a strong connection with customers.
Spotify uses reinforcement learning to recommend just the right songs to its user. The user behavior while playing a particular song is analyzed to make predictions and deduce sustainable, diverse, and fulfilling recommendations for the users.
The massive streaming service employs mathematical predictions in their software to recommend things that a viewer will like after they've been watching over time. This method has also been one of the most integral customer retention strategies of the modern day.
The personalization algorithm resets itself every 24 hours, optimizing the content so that users will keep discovering from Netflix's most updated catalog. Such features keep customers glued to their screens and ensure customer retention for the long term.
So, How Does Netflix Leverage Big Data and Analytics? Netflix has digitized its interactions with its 151 million subscribers. It collects data from each of its users and with the help of data analytics understands the behavior of subscribers and their watching patterns.
Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities.
In the U.S., Netflix will subscribe to Nielsen's National TV measurement data and Streaming Platform Ratings. In Mexico and Poland, Netflix will subscribe to cross-platform audience insights which are derived from streaming panels in each respective market.
Netflix does not do performance reviews per se in the sense of a formal process where a manager retrospectively evaluates an employee's work performance. Netflix ditched the annual performance reviews and replaced them with 360 feedback reviews.
Their core USP has always remained the same: Deliver couch potatoes the best selection of TV shows and movies possible, in the most convenient way possible. Even though the company has changed dramatically, the USP has stayed the same. It's the specifics that've evolved.
On February 22, 2023, Netflix announced a new pricing strategy that will significantly cut subscription prices in over 100 countries. The move is an attempt to stay competitive in the increasingly crowded streaming market, where rivals such as Disney+, Amazon Prime Video, and HBO Max are gaining ground.
Netflix's positioning strategy is simple. Its primary goal is to establish itself as a leading subscription-based streaming platform in the global mass media and technology & entertainment industry.
It provides users with personalized movie suggestions. To accomplish this, Netflix employs ML/AI/Data to analyze a specific user's watch history and compare it to the movie preferences of others with similar movie tastes. As a result, Netflix has the best selection of shows and movies that you might enjoy watching.
The Spotify algorithm uses machine learning to analyze this data and create personalized playlists and recommendations for each user. For example, if a user frequently listens to pop music, the algorithm may recommend similar pop songs or artists that the user may like.
Streams are calculated by how many times people listen to an artist's song; monthly listeners are how many people listen to an artist in 28 days. For example, let's say you release a new album, and one of your fans listens to it nonstop for the following two weeks, while another fan listens to it just once.
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Introduction: My name is Virgilio Hermann JD, I am a fine, gifted, beautiful, encouraging, kind, talented, zealous person who loves writing and wants to share my knowledge and understanding with you.
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