HOW NETFLIX USES DATA ANALYTICS: A CASE STUDY

 Netflix is a media service provider that is based out of America. It provides movie streaming through a subscription model. It includes television shows and in-house produced content along with movies.

Netflix is currently renowned as the most valued company/media company in the world and transcends even Disney. So much so the current valuation of the Netflix company is $234 billion. The company and numerous case studies attribute their great success to the customer retention that they have been able to achieve.

Customer retention may be defined as the process of engaging the customers and appealing to them to use the service or buy the product.

Now, this may seem like a simple tactic at first glance, but keep in mind that it is also considered by many to be the most important tactic used by any media company. And Netflix has used it so cleverly that its customer retention rate is extremely emotional and continues to increase over time.

None of us know exactly the actual number of subscribers to this huge platform but studies revealed that as of December 2020, Netflix subscribers ("paid subscription") amounted to 203.66 million. This is an excellent achievement for Netflix, as it has passed the 200 million mark for the first time.
In the end, Netflix has taken on an active role as a media company . Netflix has taken on an active role as a media company .

Well now, how Netflix uses data and big data analytics

Netflix’s model has changed from renting/selling DVDs to global streaming in a year. Unlike cable TV, internet TV is all about choice between a wide range of items available on the catalog of internet TV with pieces from different genres, from different demographics to appeal to people of different tastes. When Netflix turned into a streaming service, they had huge access to the activity data of its members. This includes their details associated with the device, the time of the day, the day of the week and the frequency of watching. As the number of people subscribing and watching Netflix grew, the task became a big data project.

For any company or association, data collection is essential. Netflix with its 203 million subscribers what you do is use the information collected, convert it into insights, results or views, and recommend TV programmes and images based on users' preferences and interests.

Diving deeper into the individualized recommendations and the results several data points have been collected and a detailed profile of each subscriber has been generated, so that each profile of each subscriber is much more detailed than the information or preferences handed by the subscriber at the moment of their Netflix operation.

Still, data collected by Netflix is substantially about client commerce on the operation or webpage and responsiveness to shows or pictures. To put it simply, if you ’re watching any Television show or movie on Netflix, it knows the date, position, and device being used to watch, as well as the time of your watching. On top of that, Netflix also knows about how and when you left and renew your shows and pictures. They also take into consideration if you're completing the show or not, how numerous hours, days, or weeks to complete the occasion or a season or a movie.

Eventually, it tracks every action taken by the user on Netflix and considers it as a data point.

In the same way, Netflix captures screenshots of scenes that observers watch constantly and it categorizes you as per the standing. It keeps track of how numerous times you search before choosing to watch a show and indeed what keywords you have used in your hunt.
After all this data collection, it is analysed, decoded and a buzz word is applied to generate what are known as "recommendation algorithms".

At this point in time, you might have understood the reason behind the success of Netflix’s tremendous ability to collect, process, and use data.

Netflix’s ability to collect and use the data is the reason behind its success. It results in better customer retention per year. The study says the rate of customer retention is increasing on Netflix because 80% of users follow the recommendation, and the recommended show or movie is streamed.

What I have discussed above is intrinsically related to the concept of "green light for original content". Green light means having permission to make commodities. So, original content with a green light is claimed or qualified as happily approved on the basis of the colour touch points taken from the user database.

Big data and certain forms of analytics are used for personalised marketing, say, for example, to promote a TV show or movie that Netflix releases (which might have colourful or campy promos). Still, the viewer will get a selection of Christmas films or films of a certain genre, if that same viewer watches content that is more focused on the Christmas mood or on a specific genre.

However, the same applies to numerous aspects like someone watches pictures of certain directors only or certain actors or actresses only. This by and out study or report of each client reduces the time spent to exploration on marketing strategies because Netflix formerly knows the interests and sensitive likes or dislikes of their subscribers.

Conclusions

In conclusion, using data techniques enables Netflix to segment its clients and identify user clusters in order to observe and offer personalized content. In this sense, these techniques have helped to set up its own production brand adapted, largely, to subscriber interests and demands.
Through the use of data mining and content recommendation techniques we have been able to see how they manage to also know what their users want or need, and, as a result, lower the number of service cancellations.

In this way, we could analyze, evaluate, and identify trends that will help to continue identifying why big data is considered to be a strategic axis in the on demand audiovisual content distribution industry. Netflix knowledge of their clients is the cornerstone of their modus operandi
Likewise, we consider that research on big data applied to the audiovisual content on demand distribution and consumption business calls for new research paths that can understand the relationship of new technologies with users and content generators.