Many people nowadays like streaming movies online or connecting them with their smart home tv to stream. Interestingly, AI, Machine Learning and Data Science are taking up every industry and the entertainment industry is not left behind. AI is becoming Ubiquitioas in the entertainment industry like in Netflix. These activities are behind the scenes and users are not aware of what is taking place. These AI solutions are changing the business model by solving different problems. These solutions are improving over time and improving user experience and many businesses. Sometimes AI gives positive results and sometimes it has negative effects.
Why could Netflix end up applying AI, Machine learning or data science?
One is to recommend movies to different users. If a user watches one movie of action he is likely to watch another related movie. This helps to create artificial intelligence algorithms to which analysis users’ history and come up with different tastes for different users and recommend what you may be interested to watch and keep subscribing to watch more. This is making you get hooked to their platform and spend more.
Autonomous generation of thumbnails. Netflix collects images and ranks them to help in the recommendation of movies. This depends on other users who are similar to you and what they have clicked. Users who like certain movies are likely to click certain actors or movies. This helps to create intelligent algorithms that help to sort out these calculations.
Movie production sites search. Netflix has collected a lot of data that they use to analyze to get the best scene for shooting their movies. This helps in allocating budget and setting and scheduling of actors and production resources. They can even preset the scene on a computer and decide whether to shoot at night or during the day. This is more of a data science field and not likely machine learning techniques because it uses recorded data to predict scenarios.
Editing movies to come up with high-quality pictures. Quality control is not an easy task, especially in movie production. Technology helps in this problem by helping to sync movie pictures with sound. This reduces the time spent if it was done manually which is a labor-intensive process.
Determining what quality to stream with technology is easy and it has helped Netflix to know how much bandwidth usage for different regional servers.
These applications have helped Netflix serve its millions of users effectively with different user experiences. This is just the beginning of these AI technology applications and it will continue to develop over time.
Netflix has also applied this AI technology solutions in their business model. Business forms the core factor of this whole business and without a business idea, these cases would be of no use and would remain just an unviable idea.
What we have discussed above is trying to solve a business goal, need or hypothesis. Many product managers spend a
lot of time trying to apply data science or ML without easily concluding what problem they are solving or business needs which leads to wastage of resources.
For AI to be productive there must be a business model and a problem its solving. You should also consider when making new algorithms if they will be implementable. For example,
- Netflix has to consider if these users who click to what related users are subscribing
- They should consider other related movies if they will be affected.
- Sometimes algorithms can lead to false results which may be negative or positive.
- Also, they should consider if the images users are clicking are related to the movies they want.
Netflix is a good example of how you can increase revenue with AI. It also teaches the need for research in AI to connect AI with problem-solving and AI.