Today we are not just seeing companies utilizing Big Data to make the most of their business opportunities or to cut out waste, we are seeing it as a key protagonist in the success of consumer entertainment platforms and technologies.
A prime example is in the key battleground between Apple Music and Spotify; playlisting.
To combat Apple’s apparent advantage in this area with thousands of curated lists, Spotify created their ‘Discover Weekly’ playlists to help users find the kind of music that they are likely to appreciate. To do this, Spotify take information from previous listening habits and create a playlist of 30 songs every week that that specific user will probably like. Apple, however, have taken the approach of curating lists based on explicitly selected content, but given the breadth of their user base, this is likely to become more data driven in the future.
These are not just nice to have aspects of online entertainment platforms today though, it is an absolute necessity.
One of the biggest changes in the way that companies provide entertainment content is that rather than specializing in a small number of items, they now provide a huge number of titles for their consumers. Netflix for instance has over 36,000 unique titles and this number is constantly growing. The ability to find the perfect film or TV show for any occasion should theoretically be easy, but the reality is that with the individual tastes of people, it is not as simple as searching as the database is so vast.
This has made the suggestion engines on Netflix one of the most important elements to not only making the site useful, but fully useable. The difficulty of trying to find a specific film for a specific time and mood is then made considerably easier and the vast database of films becomes much more navigable.
It is not only true of films and TV shows, where you will be entertained for several hours through films and series, we have seen in music streaming aspects that this needs to be done within the space of around 3 minutes, meaning that the next song being chosen needs to have been found after searching through your listening history. The longer your history, the better your recommendations, which is why Apple Music (less than 3 months old) is still lagging behind when it comes to data driven suggestions, something that they are likely to significantly improve in the future.
All of this entertainment being shown to us based on our previous consumption has also meant that we watch and listen to more of it.
Where I used to have a cassette with one album on, I would have needed to listen to that album through, then change the cassette to another album, which I would have previously bought. The same is true with any number of physical media types such as CDs, DVDs, Blurays and even records. Today by paying the same amount as one film or one album, I have access to millions of songs and tens of thousands of movies and TV shows. It means that I get get bored far easier and the more I consume, the more difficult it becomes to find anything new.
Through predictive algorithms it is possible to identify new and previously unheard/unwatched content, allowing further content to be consumed.
Without this use of data within these entertainment platforms, it would be almost impossible to use them effectively and the way that most people use them today would be vastly different. In fact, when you look at the recent redesign of the Netflix site, you can see the importance of these suggestion algorithms - before I get to any kind of broad category I have 54 suggestions spread across 9 rows of films, similarly, Spotify prominently place the discover weekly playlist for their users, making it almost impossible to miss.
We have now reached a point where the size of the libraries has become almost meaningless as they are all as vast as each other, the key battlegrounds have now become how the content is filtered and shown, something that data has a pivotal role in.