Big Data Case Study: What makes Spotify successful

Introduction

We live in a world where buying music has become obsolete, and streaming tunes is the new trendy trend that appears to be here to stay. As a result, streaming systems such as Apple Music, Pandora, Songza, and, of course, the well-known Spotify have risen like phoenixes.

Big data and data analytics have had a significant impact on enhancing user experience. The primary element has been Spotify's "Explore" function, which debuted in 2012 and provides users with fresh listening options. Ultimately, this evolved into a "Discover Weekly" function that sends users a tailored playlist of music they haven't heard before that should match their tastes. Spotify users spent over 2.3 billion hours playing "Discover Weekly" playlists in the first five years. This has not only added value for customers by helping them to find new music, but it has also enabled numerous musicians to break into foreign markets.

"Wrapped" is one of Spotify's most entertaining features. Every December, "Wrapped" compiles a list of users' favourite or most-listened-to songs/artists from the previous year. "Wrapped" also informs users whether they are in the top 1% of an artist's most devoted fans. This information is provided to all users in the form of a customised tale utilising data visualisation. By awarding badges to users who participate with "Wrapped," Spotify creatively fosters user engagement. A Tastemaker badge, for example, might be granted if a user's playlists garnered a large number of new followers. A Pioneer badge is awarded to a person who listens to hit music before anybody else.

Spotify and Big Data - The Lesser-Known Bond

All of these music streaming services have used data gathered from user interactions to better their algorithms, improve user experiences, target potential audiences through adverts, and improve their business approaches and decisions. One area where Spotify excels is in its understanding of its clients. The platform uses proprietary algorithms to understand the user's music preferences and direct them to new genres, songs, and artists.

The days of having to pay to download music are long gone. From Songza, which employed a staff of "music experts" to assemble playlists based on their tastes, to Pandora, which manually identified a song's qualities and allowed users to choose and filter the categories to make the playlists they desired. Then there's Spotify, a music streaming site that uses artificial intelligence, machine learning algorithms, and big data to provide a personalised and unique listening experience.

This demonstrates that Spotify is essentially a data-driven firm that uses data to make choices in all of its tasks. By accumulating data points, the platform is making use of that information for building algorithms and machines to listen to music and provide insights that may assist their business and play a part in enriching the experience of their clients.

Why does Spotify employ Big Data?

Spotify employs Big Data in the following ways:

Creating Customized Content - One critical method Spotify takes to adopting data supplied by its users is to utilise it to create material that each user will perceive as special to their individual likes. The objective is to guarantee that consumers have a positive experience so that they become repeat clients. This was accomplished through the use of several Artificial Intelligence and Machine Learning techniques. For example, Spotify's "Explore" function, which was first introduced in 2012, plays an important part in data collecting. This feature began as a playlist of music published by the user's favourite artists, but gradually evolved into a form of recommendation engine, which recommended a collection of tracks when the user's playlist completed, which were aligned along the lines of the songs in the playlist.

Now, "Discover Weekly" has emerged as one of Spotify's most powerful trump cards; built entirely using a machine learning algorithm, it provides a tailored playlist that is unique to the user's listening activities. The algorithm evaluates other users' playlists to find track similarities, and then uses that information to create a new playlist that fits with the user's current track choices. Furthermore, each user has a distinct "taste profile" made up of microgenres that helps to personalise these playlists.

Digitizing the user's taste - The listener's daily taste profile is also used in Spotify's "Daily Mixes" playlists. These playlists differ from the music genres that the user normally prefers and are typically made up of songs that the user has saved or added to their playlists, or that have been created by artists that the user has included in their current playlists, or any new artists or albums that the user is unfamiliar with. These playlists are large and dynamic; while they may contain more recognisable music than the "Discover Weekly" playlists, Spotify might still include a few fascinating tracks that the listener is unfamiliar with in order to make the playlist more active.

The "Release Radar" playlist is another example. It's a weekly playlist that includes various new releases by the artists that each user follows, in the same structure as the main "Discover" playlist. If users follow their favourite musicians on Spotify, the algorithm can create a customised playlist with new song choices from that artist. The algorithm can also add some new songs to the playlist, making it more interesting.

For Improved Marketing through targeted advertisements - While boosting the experience of consumers, Spotify has also been able to adopt a massive portion of data collected by its users for the purpose of upgrading their ad campaigns and targeting their customers in a more attractive manner. This is accomplished mostly by the platform studying the facts they have collected about its listeners and then using those insights to build adverts that subtly target the platform's intended demographic.

For example, a display ad that originally appeared in Williamsburg, New York, triggered an extensive global marketing effort for Spotify in which the site leveraged listening data to produce amusing, tailored commercials.

Continuously updating its system - In early 2018, the streaming platform had said that its free users will no longer be compelled to just shuffle through music on their application. Rather, its customers were now permitted the liberty of browsing 15 of the platform's well-known playlists which included the platform's renowned "Discover Weekly" as well as "RapCaviar". The fundamental motivation for the platform's choice was driven by data. The access change allows the platform to generate data from an additional hundred million or more users, which is particularly beneficial given that the firm is focusing on improving its suggestion algorithms in order to provide its consumers with a satisfying tailored experience.

In an effort to make their huge quantity of data available to its musicians and management, Spotify launched the Spotify for Musicians programme, which provides access to information such as which playlists have been helping to attract new users and the number of streams they are receiving overall.

The mobile application allows artists to access information through their tour bus, and the geographic streaming data can help musicians and their teams plan tours more effectively. It also gives artists more control over their Spotify presence, such as selecting the "artist's pick" and doing chores such as updating their profiles or publishing playlists.

Spotify Wrapped - This is Spotify's year-end report, a tradition that gives every platform user an occasion to freely share their music tastes on social media. The use of data by Spotify Wrapped goes beyond mere statistics. The corporation has tailored its listening platform to include embedded ego-boosters for its customers. By the end of the year, customers will receive a report notifying them if they are among the top 1% of, say, a band's most devoted fans or among the top non-mainstream song listeners.

Spotify Wrapped essentially serves data to its consumers on a silver platter, presenting it in a way that will interest and captivate them. And it certainly works. The visually appealing data succeeds in making people feel noticed and validated, and it piques their interest. With this data, the platform is creating an experience that is similar to narrating a story using music data instead of words.

Conclusion

In today's environment, when streaming has surpassed bought music, the music business has been forced to shift its focus from record sales to amassing such data in order to decipher the impact of a certain song, artist, or album on the general population. Because the data also provides a more in-depth understanding of listening habits, audience markets, and other similar areas, it represents an ongoing revolution for those in the business.

Spotify, as a social and sharing experience, and by combining its data application with a strong user experience tailored to social media, becomes an accidentally self-marketable platform since users promote their participation on their own.

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