{"id":23885,"date":"2020-06-18T11:00:39","date_gmt":"2020-06-18T10:00:39","guid":{"rendered":"https:\/\/www.hult.edu\/blog\/?p=23885"},"modified":"2021-10-11T12:17:48","modified_gmt":"2021-10-11T11:17:48","slug":"netflix-leverages-learning-systems-to-get-ahead","status":"publish","type":"post","link":"https:\/\/www.hult.edu\/blog\/netflix-leverages-learning-systems-to-get-ahead\/","title":{"rendered":"Netflix leverages learning systems to get ahead"},"content":{"rendered":"

In the first quarter of 2020, the world-leading stock indexes crashed, sometimes <\/span>losing over 12% in a single day<\/a>, due to the global pandemic. <\/span>In the same period, <\/span>Netflix’s stocks grew by almost 14%<\/a><\/span>. As global lockdowns keep people home, it\u2019s no surprise that Netflix’s <\/span>viewership skyrocketed<\/span>. The question is: <\/span>how has Netflix positioned itself as the go-to place for streaming videos?<\/span> As I outline in my <\/span>latest book<\/a><\/span>, this is largely thanks to their mastery of data analytics and learning systems.<\/span><\/p>\n

\u00a0<\/span>The increasing digitization of business models is leading our systems of production into a\u00a0Fourth<\/span> Industrial Revolution<\/span>, an age of rapid change in the way companies succeed. To help companies navigate this new industrial revolution, I\u2019ve embarked on a three-year long research project of over seventy case studies worldwide. The result is a framework called\u00a0<\/span>C.L.E.V.E.R.<\/span><\/b><\/span>, which is the acronym for six drivers that determine strategic success during this new industrial revolution. <\/span><\/p>\n

These strategic drivers are: <\/span>Collaborative Intelligence<\/span>, Learning Systems, Exponential Technologies, Value Facilitation, Ethical Championship and Responsive Decision-Making.<\/span><\/p>\n

“The C.L.E.V.E.R. framework by Alessandro Lanteri, opened our eyes by giving us a clear view of the drivers that could shape our future.”<\/strong><\/p>\n

Giuseppe Giachetti, <\/span>CEO of Ludocards<\/span><\/h5>\n<\/blockquote>\n

What\u2019s Netflix doing?<\/span><\/h3>\n

Netflix tracks the content its viewers watch and like, the movies they stop watching, the moments when they fast-forward or rewind\u2014this is all<\/span> data. <\/span>Netflix gathers and analyzes it to extract information about the best <\/span>content for you. <\/span>With such information, Netflix\u2019s algorithms make automatic decisions such as recommending other content that you are likely to watch. They have become so accurate that <\/span>80% of the content streamed on the platform comes from recommendations<\/a><\/span>.<\/span><\/p>\n

Most people refer to algorithms that perform automatic actions as <\/span>Artificial Intelligence (AI)<\/span>. AI are algorithms that <\/span>process<\/span> data to generate outputs that <\/span>appear<\/span><\/i> to be intelligent by human standards. For example, they can quickly calculate the fastest route from A to B, accounting for realtime traffic information. It would be an impressive feat of intelligence for a human to do the same. Yet, the very same algorithms can sometimes fail drivers, <\/span>leading them into a muddy field<\/a><\/span>. As said, algorithms usually <\/span>appear<\/span><\/i> to be intelligent, but they <\/span>aren\u2019t <\/span><\/i>really. This is why I don\u2019t like to talk about AI, and I prefer Learning Systems.<\/span><\/p>\n

\u00a0<\/span><\/p>\n

What are learning systems?<\/span><\/h3>\n

Algorithms can autonomously identify the features of certain objects and recognize which features these objects have in common. For example, image recognition algorithms learn to associate your photos with your name. Whenever you upload a new photo the algorithms identify the features and automatically associate it with your name. Each time algorithms analyze a new photo they get better. That\u2019s learning.<\/span><\/p>\n

 <\/p>\n

How do these systems learn?<\/span><\/h3>\n

The main approaches to learning systems are: supervised learning, unsupervised learning, and reinforcement learning.<\/span><\/p>\n