This will be the first of many blog pots where I examine a company from a technical and economical perspective. I will attempt to break down difficult one or more technical concepts into simple terms and how the product translates into a business.
Music is common among all cultures. When the iPod came out in the 2000s, if you wanted music you had to buy each track. Now you can imagine if you listened to 100s of songs this adds up. This led to illegally downloading music from online and cutting into the profits of music producers.
Streaming music sought to solve this problem by getting the licenses to the music and offering a platform to listen to any music of your choice for a monthly cost.
Spotify was one of the first companies in this space and have successfully acquired a large market share. Apple came late to the market but had an advantage since it owned a large of market share of the mobile phones.
Both companies have grown their user base on the years. Despite Apple music, Spotify has been widening the gap. Let’s look into one reason that might be.
You will find more infographics at Statista
First let’s understand what it takes to build a music streaming. At its core, you need the music. Once you have the music, any company can build a streaming service. Besides the interface and sound quality which are similar between services, the key differentiator is the recommendation engine.
At recommendation engine, recommends you new products, services or data base on your personal data. For example, Amazon will recommend you new products base on your search or order history. This powerful for business because it drives more revenue.
In case for streaming service, it is even more important because users would like to find new music but that’s hard when there are millions of songs.
How a recommendation engine works?
A recommendation engine needs data. The first technique is called collaborative filtering. Spotify uses other users playlist to find your new music. If there’s a playlist of 10 songs and you have listened to 9 of those songs then it is very likely it will recommend you that song. Spotify weights playlist on a number of factors such as number of followers.
The second way is identifying what specific genre you like to listen.
With a good recommendation engine, it is one factor that music streaming companies can offer to their new customers and continue to retain their existing customer base. As users play more music, the engine will have more data to continuously recommend better and better music. As a result it becomes a high cost for user to switch to a new music service since it has to learn about the user all over again.
Resources
https://qz.com/571007/the-magic-that-makes-spotifys-discover-weekly-playlists-so-damn-good/
Tech By The Bay 