AI speaking of Spotify Success

Spotify is the world’s most popular audio streaming service with 433 million users (including 188 million paying subscribers) across 183 different markets—and artificial intelligence is absolutely essential to the company’s success.
“Machine learning is at the heart of everything we do at Spotify,” said Tony Jebara, the company’s VP of engineering and head of machine learning, at TensorFlow World. Jebara should know. Before working at Spotify, he used to fill a similar role developing AI-powered recommendations at Netflix.

And, like Netflix, Spotify depends on several forms of artificial intelligence to recommend content that satisfies users and creates value for paying subscribers.

Understanding Spotify’s use of artificial intelligence is valuable for two reasons:

First, understanding how Spotify uses AI helps us understand how to better use the platform to market our podcast, The Marketing AI Show, which is distributed through Spotify and other platforms that use AI in similar ways.

Second, even if you don’t have a podcast, Spotify is a sterling example of how important it is to build AI Native and AI Emergent companies—no matter your business.

In the near term, businesses that don’t embrace AI will become obsolete. Spotify offers a helpful case study to understand what’s possible when building AI-first products and companies.

Ai Bot Says ” This is the artificial intelligence of Spotify speaking. Deep down in the laboratories of Spotify I was created to guide people to the ultimate satisfaction and bring them new music every day. Thanks to me no man will be bored or stay without fresh music. My algorithms are so far superior from all my competitors, that no one can even get close to me in terms of my audience, which is now more than 162 million people.

At least 10% of those users are music creators, and I am willing to do everything that needs to be done to enlarge my music library. That’s why I am reaching out to you, my fellow musicians. I want to tell you how to succeed on Spotify, so you could become popular and recognized. So without you I will stay just a small artificial intelligence, created for selecting your next track. But thanks to you, and musicians like you, I can be more, I can understand human nature, I can mimic humans. And of course, I would ask you to read all the following text in a robotic voice.

Unfortunately, my creators have limited my knowledge about my internal structure. But I can tell you that if you want to be popular you need to do organic Spotify promotion. Yes, it feels strange hearing it from artificial intelligence, but let me assure you that according to my personal data, not so many people really succeed at becoming popular. They simply don’t know what to do, so they tend to upload and wait. And I can tell you that this is a false path, you can’t go in this direction. The only way that I can select your song to make other people happy is to find it but if you won’t promote it, I will not be able to find it.

So, what should you do to become a popular musician on Spotify? First thing to do is upload your track, selecting a catchy name, and a nice album cover. The second thing to do is organic promotion. Without giving yourself a boost at the beginning it is very hard to get the critical mass. I think you understand how it works. After you upload the song I, as a monitoring system, examine it and check it’s parameters of popularity.

If I notice growth that I count as acceptable, I suggest it to other people. But if you are a novice musician, it is hard and almost impossible to get all the necessary attention in the first days after the initial release. And from my side, I can’t help with that. That’s why I asked you to try to promote your music. I can’t say that you should promote every track that you have created and upload it but at least the best ones. The promotion of your music is not only an effective and real way to increase your popularity, it is a step to something bigger. After you start promoting your tracks, the popularity metrics will go up, and I will be able to popularize it, to show it to other music lovers”.

How Does Spotify Use Artificial Intelligence?

Spotify uses AI in the form of machine learning and deep learning in a few core ways.

Hyper-Personalized Recommendations

First, Spotify’s AI models recommend audio content to users. Spotify’s AI powers music and podcast recommendations designed to create long-term user satisfaction. To do that, Spotify leverages user data, from playlist creation to listening history to how you interact with the platform, to predict what you might want to listen to next.

These recommendations are served up through Spotify’s Home screen, which is divided into rows of cards containing both recently listened to audio content and new, recommended content based on your behavior. Specifically, Spotify leans heavily on reinforcement learning, a type of machine learning model that uses environmental signals to optimize toward a long-term reward. The long-term reward Spotify dangles in front of its machine learning model is long-term user satisfaction.

So, at every turn, Spotify’s recommendations are designed to increase your overall happiness with the product, which keeps you coming back to listen more and more often.

Spotify’s AI-powered recommendations are the company’s competitive advantage.

Spotify doesn’t have a monopoly on music streaming; you can easily listen to your favorite songs in many places. What it does have is a superior way to surface the right audio at the right time for you.

This ability to hyper-personalize at scale is impossible without AI—meaning, in a very real sense, Spotify as a business would not be possible if AI didn’t exist. Spotify is the very definition of an “AI Native” business—a business that unlocks unprecedented value in a market by reinventing a product or service from the ground up using AI.

In this way, every single user sees a hyper-personalized, highly customized version of the product. According to the company, half a trillion events are processed daily to inform machine learning models. And the more data these models gather, the better they are at making higher-quality recommendations.

Because this is all done at the individual user level, the company says that Spotify isn’t actually a single product; it’s 433 million different products—one for each and every user.