How Netflix Scaled Its Marketing Game Using Machine Learning

Netflix started out in search for a solution to their long-standing problem. That problem was – “how do we make recommendations better for customers?”. They also had another problem at hand, which was related to new content development. They wanted to learn more about how consumers interact with content online and what they want next. That’s what makes Netflix so good as an online content company. They’re able to provide top-notch content across various mediums and genres.

With over 117M subscribers, and 50% of them coming from outside the US, Netflix needed technology now more than ever. With a global audience with unique tastes, it needed unique programming that catered to their diverse audiences. Netflix didn’t want to bucket itself into only being specialized in one genre. It wanted to make sure that it was exploring all options before claiming one was better.

Straying Away from One-Size Fits All

Netflix used to rely on having a singular experience across all platforms. This included how they ran their servers, showed customers content on mobile v/s TVs, as well as how cell-phone service was treated across all parts of the country. This made things increasingly tough as there were certain regions that didn’t have access to faster speeds or didn’t want to watch older classics.

There were different people with different tastes and Netflix didn’t want to be inflexible in their approach. They were able to leverage statistical modeling and data sciences to optimize the speed at which they would play their content. That’s why it looks so good on all screens. Netflix used machine learning to make streaming that much smoother.

Stability During Streaming

One of the biggest applications of machine learning has been in the space of streaming stability. Netflix tries to predict the kind of internet speeds you’re getting by going back 15 minutes and checking on all the speeds registered. If you’re in an area that gets bad coverage, it can optimize the streaming experience so that you don’t get any interruptions.

It can send some data packets at different speeds or relay information at stretches to boost efficiencies. Through this process, the machine learning code tries to predict network speeds, streaming quality and many other factors in the streaming line. They combine temporal pattern recognition with contextual indicators to have more accurate representations of network speed. This helps them optimize their own servers, thereby giving them more cost-efficiencies.

Buffering vs. Streaming Control

Buffering is one of the biggest issues that is concerning the streaming world right now. Netflix is no different from the myriad of streaming services that are trying to avoid buffering as much as possible. That’s where machine learning comes in and tries to solve the problem of distributed streaming.

Movies and popular shows are encoded at various qualities so that different networks can stream them effortlessly. There are specific adaptive streaming algos that are responsible for making each video quality appear in front of the appropriate screens. That’s why when users playback a certain video, they see a different quality at first and then the streaming stabilizes into more fuller experiences. That’s because streaming is prioritized over the quality of the stream. Meaning that you get uninterrupted viewing with little to no lag.

Rebuffering is another core area of contention. When users told Netflix about the pain of rebuffering, they were able to determine the root cause and improve their brand equity. They were able to spot the holes in their tech and fix it to retain quality customers.

Predictive Caching

Predictive Caching has made Netflix’s marketing that much stronger. Netflix uses machine learning to be able to predict what you want to watch next. This has made their viewing experience that much more fluid and enjoyable. Fans go online talking about how Netflix knows exactly what they want to watch, and they love it.

Netflix also does this for series that you’re following so that you can binge without any delays. The next episode in the series loads up that much faster and you’re able to watch the next one within seconds. This helps in increasing retention and boosting marketing to another level.

Data Analysis and Marketing

With all this data available about what consumers watch and what they prefer, marketing has skyrocketed within the last few years for Netflix. The company has a dedicated marketing insights team that collects data on a regular basis and applies simple machine learning code to uncover new insights. They introduce these insights in new ad campaigns, brand promotion activities, and new series launches.

Since they understand what emotions reflect best at what imagery, they understand how to captivate our attention. Machine learning has given them the ability to analyze large chunks of data so that they can create more memorable marketing campaigns. Even when it comes to online display advertising, they’re able to collate their insights with the existing ones they have at the content hub within Netflix.

The consistently learning model in the Netflix’s marketing arsenal aims to enhance all aspects of data analysis. Netflix has scaled it’s marketing successfully using this very strategy over and over again. For years now, they’ve been able to structure effective narratives in their marketing campaigns and their show offerings.

The scale has been huge for Netflix, which is now able to understand how different markets consumer marketing content differently.

Conclusion

Netflix has been one of the most successful companies in the world, in due part because of their focus on technology. They employ machine learning and artificial intelligence to give audiences what they want. The algorithm is always learning, and implementation is key. There can’t be any space for error for Netflix because of its brand equity and staggering user-growth.  

Netflix is one of the premier content channels online, as they’re one step ahead of the recommendations and “view more” game than anybody else. This increases watch-time and promotes longer uninterrupted viewing for paying customers.