Digital music streaming has never been the same until Spotify intervened and developed a new model of operating. Everyday people would gravitate to this app, allowing them to create unique playlists, tap into new tracks and albums that were trending, and explore podcasts and audio stories that spoke to their interests.
Swedish founders Daniel Ek and Martin Lorentzon began this journey in 2006, going on to create an $8 billion enterprise with 300 million users around the globe. It would be an invention that was geared towards an on-demand music service, empowering customers to download, browse and share their favorite artists, tracks, albums and podcasts in a secure format.
Although they have been the target of criticism from Radiohead front man Thom Yorke and pop superstar Taylor Swift for their financial distribution model towards artists, their empire continues to grow. Amid a number of factors that play a key role with their success, it is the intervention of research data and analytics that informs decision making at all levels of the business.
A clever method that Spotify would embrace was allowing their hub of artists to study the metrics for themselves. This is where managers and musicians would be able to trace what types of music was trending, which albums were successful and how much of a particular song would be consumed from start to finish. Although this would not directly influence the artistic process, it would allow these professionals to highlight certain tracks and market themselves in a fashion that was appealing to the wider online music community.
One of the essential connections that drives Spotify and their relationship to research data is customizing the user experience. Sometimes the individual does not want to listen back the same playlist that they created last month. They want something new – they want something fresh. The algorithm will take into account what has been played, what has been repeated and what has been searched, informing these unique packages that are identified on the home screen.
A benefit that Spotify realizes early in the piece is that customization allows them to push new singles and albums from artists that followers love to listen to. This is where the ‘Discover’ and ‘Release Radar’ features come into their own, connecting a customer with a new piece of material they might otherwise miss. That cycle to promote new material increases sales and the incentive to do business with Spotify as an artist.
It has been a challenge that exists for streaming services across the globe, whether they are in the music or television business – how to receive user data on mass rather than just those who pay for the service outright. In order for Spotify to take advantage of that model, they had to engage a free user platform that gave them more information to work with. A free download and free product with certain restrictions would give the Swedish app that level of access.
Targeted advertisements is where Spotify really leverages their research data capabilities for commercial gain. Businesses don’t want to simply throw money at an app that is popular because they desire cut-through with their campaigns. In this setting, they could utilise intrinsic sets of data that detailed consumer interests and behaviors that targeted select groups of users.
Spotify might have had conservative estimations and humble objectives early in the piece, but they have since utilized their research capabilities to open new doors that are taking their organization to new heights. The acquisition of blockchain startup Mediachain Lans with the AI service Niland indicate an aggressive push to enhance the experience for the user, the artist and commercial affiliates alike.
