We exist in a world where purchasing music has become a thing of the past and streaming tracks is the new popular trend, which seems to be here to stay. This has led to streaming platforms rising like a phoenix, from Apple Music to Pandora, Songza, and of course, the well-known Spotify.
All these music streaming platforms have been adopting data gained through user interactions as an attempt to enhance their algorithms, boost their user experiences, target potential audiences via ads and to improve their business approaches and decisions. One factor in which Spotify is a pro in is in having knowledge of its customers. The platform incorporates proprietary algorithms for comprehending the music taste of the user and to steer them towards fresh genres, songs, and artists.
The time back when we would pay to get to download music is long gone. From Songza which incorporated a team of “music experts” who would compile the playlists as per their preferences to Pandora which manually labeled a song’s characteristics allowing users to select the tags and narrow them down to create the playlists they preferred. And then comes Spotify, a music streaming platform that incorporates artificial intelligence, machine learning techniques as well as big data for the purpose of serving a customized and exclusive listening experience.
This proves that Spotify is largely a company propelled by data and it adopts data in each of its functions to determine decisions. By acquiring data points, the platform is making use of that information for preparing algorithms and machines to listen to music and generate insights that can benefit their business and play a role in enhancing the experience of their customers.
You can also take a look at How Spotify uses machine learning models
A crucial approach Spotify applies to adopt the data generated by their users is to use it for developing content that every user will regard to be exclusive to their unique tastes. The goal is to ensure that a satisfactory experience is provided to the users so that they become long-lasting customers. This has been achieved by adopting various Artificial Intelligence and Machine Learning algorithms.
For instance, an integral role in Spotify’s data collection is played by the platform’s “Discover” feature, which had initially been introduced in 2012. This feature emerged as a playlist of the songs released by the beloved artists of the user but slowly went on to develop into a type of recommendation engine, which suggested a collection of tracks as the user’s playlist completed, which were aligned along the lines of the songs which the playlist contained.
Presently the “Discover Weekly” has emerged as one of Spotify’s biggest trump cards, compiled fully through a machine learning algorithm, it generates a personalized playlist that is exclusive to the listening activity of the user. The algorithm examines the playlists of other users to determine the similarities among tracks and then adopts that data for developing a fresh playlist that aligns with the prevailing track preferences of the user. Additionally, every user has a personal “taste profile” made of microgenres which plays a role in personalizing these playlists. You can learn about the process in detail here.
For the purpose of being able to personalize these playlists, a great deal of attention had to be paid by the platform to both the tracks which the users stream as well as how they generally interact with every track.
For example, if a track has been played by the subscriber but skipped in less than the initial 30 seconds, it is perceived by Spotify as an un-enthusiastic reaction and the song’s information is not incorporated while computing playlists. Yet when a song has been added by the user to their library or playlist and has been listened to fully, this is perceived by the platform as a positive reaction, giving them the confirmation that the song has agreed with the taste of the user, which in turn aids the algorithm in further developing the user’s overall taste profile.
Recommended blog - YouTube is using Artificial Intelligence
Why is Spotify making use of Big Data?
The daily taste profile of the listener is also incorporated in Spotify’s playlists named “Daily Mixes”. These playlists are different from the music genres which the user normally incline towards and are generally composed of songs which the user has added to their playlists or saved, or which have been created by the artists which the user has included in their present playlists or any fresh artists or albums which the user is unfamiliar with.
These playlists are vast and dynamic, though they may have more accustomed music compared to the “Discover Weekly” playlists, Spotify can still add a few intriguing songs which the user is unfamiliar with as an effort to make the playlist more lively.
Another example is that of the “Release Radar” playlist. It’s a weekly playlist that incorporates various fresh releases by the artists that every user follows, which is likewise in format to the main “Discover” playlist. If the listeners follow their beloved artists on Spotify the algorithm is able to generate a precise playlist with fresh song suggestions by that artist. The algorithm can also affix some additional fresh songs, making the playlist compelling.
While enhancing the experience of customers, Spotify has also been able to adopt a humongous section of data generated through its users for the purpose of updating their ad campaigns and targeting their customers in a more compelling manner.
This is basically carried out by the platform examining the knowledge they have gained regarding their listeners and then adopting those insights to create ads that trickily aim at the platform’s target audience.
For instance, one display ad which first ran in Williamsburg, New York set off an extended prevailing marketing campaign for Spotify where the platform adopted used listening history for developing humorous, targeted advertisements.
The first ad on which was written, “Sorry, Not Sorry Williamsburg, Bieber’s hit trended highest in this zip code” had been popular, engaging, and amusing among the local audience which helped Spotify measure the possible impression adopting the data of the listeners for developing such personalized advertisement campaigns could create on the platform’s user engagement and their sales.
This experience set off the trend of strategic and well-responsive advertisements which are adopted to publicize the platform even now. A few of the well-known campaigns include a series of holiday advertisements, as well as a series of meme-inspired advertisements. In 2019, Spotify started operating a global market campaign on the basis of the listening history of its users. In this campaign, humorous and meme-like advertisements were created for ascertaining potential customers. In our prevailing world of meme-culture, there has been an enthusiastic response by Spotify’s potential and target customers.
Recommended blog - Review based Recommendations system
In early 2018, the streaming platform had stated that their free users would no longer be required to solely shuffle through music on their application. Rather, their users were now allowed the liberty of exploring 15 of the platform’s well-known playlists which included the platform’s popular “Discover Weekly” as well as “RapCaviar”.
The primary intention behind the platform’s decision was propelled by data. The access shift allows the platform to produce the data of an additional hundred million-plus users, which is largely useful with the company focused upon advancing its suggestion algorithms to serve its users with a satisfactory personalized experience.
As an endeavor to make their massive amount of data available for their musicians as well as their managers, the platform introduced a Spotify for Artists application in which access to analytics is provided such as which playlists have been helping to generate new users and the number of streams they are receiving overall.
The mobile application permits the musicians to gain access to the information via their tour bus and the geographic streaming data can be useful for musicians and their teams to sketch out tours more efficiently. It also allows the artists to gain more control over their Spotify presence such as for choosing the “artist’s pick” and also for tasks like updating their bios or posting playlists.
Recommended blog - Top 10 Machine Learning Algorithms
This is Spotify’s year-end report, a tradition that supplies each user of the platform with an excuse to share their music taste on social media without any hesitation. Spotify Wrapped’s application of data is not just about simple analytics.
The company has a custom fit their listening platform to incorporate ingrained ego-boosters for its users. Around the completion of the year, a report will be received by the users informing them if they belong in the leading 1% of say, a band’s most loyal followers or among the leading non-mainstream song listeners.
Through Spotify Wrapped, the platform is basically serving its users with data on a silver platter, presenting it in a manner that would intrigue and entice them. And it definitely works. The artfully presented data succeeds in making the users feel recognized and validated and sparks their enthusiasm. Through this data, the platform is developing an experience as if narrating a tale using music data instead of words.
“Seeing top songs on Spotify Wrapped is like seeing an old best friend that you lost touch with.”
- Haley Weiss in The Atlantic
This form of natural engagement becomes an integral reason why Spotify has kept Spotify Wrapped to serve as a useful weapon in their long-term marketing design. The platform’s listeners fill their social media accounts with screenshots of their Spotify profile as well as linking their playlists, to let their friends and family know where they stand on the platform.
In the modern world, where streaming now dominates over purchased music, the industry has been compelled to steer its fixation from record sales towards accumulating such data with the goal of unraveling the impression a particular song, artist or album is creating on the public. As the data also supplies a more profound insight into listening trends, audience markets, and other such sections, it presents an unceasing revolution for the people in the industry.
Being a social and sharing experience, and through combining its application of data with a robust user experience custom made for social media, Spotify becomes an inadvertently self-marketable platform since the users promote their engagement on their own accord.
6 Major Branches of Artificial Intelligence (AI)READ MORE
Reliance Jio and JioMart: Marketing Strategy, SWOT Analysis, and Working EcosystemREAD MORE
Top 10 Big Data TechnologiesREAD MORE
8 Most Popular Business Analysis Techniques used by Business AnalystREAD MORE
Deep Learning - Overview, Practical Examples, Popular AlgorithmsREAD MORE
7 Types of Activation Functions in Neural NetworkREAD MORE
What Are Recommendation Systems in Machine Learning?READ MORE
7 types of regression techniques you should know in Machine LearningREAD MORE
Introduction to Time Series Analysis in Machine learningREAD MORE
How Does Linear And Logistic Regression Work In Machine Learning?READ MORE