Spotify, Facebook, and Strong Triadic Closure
ITunes has a new competitor emerging: Spotify. Free by invitation, you can stream from a vast database of music, no more paying for individual songs. As an added bonus, you can post the songs you listen to on Facebook, allowing you to share your favorite tunes and get recommendations from your friends.
Spotify’s partnership with Facebook is essential to its growth in the United States. After its launch this summer, Facebook users are used to seeing “news” updates about different song choices from their friends. The more people hear about other Facebook friends using a given program, the more benefit people tend to see in the program, and are more likely to try it. Up-to-date Facebook statuses are one of the best ways to quickly spread the word about any product through friend networks.
Once the user has been convinced to try Spotify, they must be convinced to continue using the program. If Spotify is successful in keeping a customer, that same customer will also post statuses about Spotify and spread its popularity. When considering how Spotify will keep its customers, we can look at the idea of strong triadic closure used in class: if there are two strong bonds between nodes in a triangle, the third edge must be at least a weak bond. This idea can be used in music tastes. Between Facebook and Spotify, the companies can identify friendships on Facebook, as well as individual people’s music preferences. Let’s consider friends that have 50% commonality in songs listened to as friends with strong ties of music preferences. Let’s also say that friends with 20% commonality in music tastes have a weak music ties, and anyone under 20% commonality in their music lists have no music ties. Say user A has a 60% commonality with user B, and user C has 55% commonality with user B. Strong triadic closure says that user A and user C will have at least a 20% commonality in music lists. Spotify can use this information to suggest songs. Instead of only suggesting music to A that B likes, it can also suggest songs to A that C likes, expanding A’s music library and tastes.
If instead of just considering a group of three friends and we consider the entire network, full of many friends that have music tastes, Spotify can recommend music with even more success. Chances are a given user will have strong music taste ties with 10 or more friends, and such a vast list of music will include several songs that the original user isn’t familiar with, but most likely will if it’s on say 5 or so different lists. The overall idea of comparing music lists is common throughout the music industry, but using friends, who have more in common than random users to compare to, Spotify will be much more successful.
Overall, Spotify’s use of Facebook is essential to its development as a threat to an already-established music program such as iTunes. The vast improvements that can outdo iTunes are due to strong triadic closure.