How Data Analytics Shapes Playlist Creation  

These algorithms combine and analyze various data sets to create curated playlists that keep users locked in. Here is how these streaming giants curate playlists for users.

The Big Data Advantage

In the age where physical media consumption was the order of the day, label owners had limited data on which to base their marketing decisions. For instance, they could only access single and album sales, ticket sales, and word-of-mouth analytics, which needed to be improved to offer a detailed understanding of customers. However, with the rise of streaming, the industry can access an abundance of information that, when analyzed with machine learning algorithms, offers detailed insights of customers. 

These algorithms do not just consider the number of streams per song but combine hundreds of metrics from listeners’ experiences. Spotify, for instance, uses multiple metrics like what songs are being listened to, the time of the day, which songs are being added to playlists, etc, to understand users’ preferences. By using these metrics, streaming platforms can then create playlists that appeal to users’ preferences.

Certainly, other online businesses, like online casinos, stand to benefit from this big data advantage. For instance, a Zambian casino can use different metrics to recommend game selections, like online slots with themes that appeal to the players’ preferences. That way, a platform minimizes the hassle of selecting a game from the multiple options, fostering a strong connection with players.  

The Perfect Playlist through Analytics

Streaming giants are now offering various categories of personalized playlists for users to keep them involved. Spotify’s Discover Weekly playlist is a prime example of how streaming platforms leverage analytics to engage users. This playlist is usually unique to every listener, providing a weekly customized song selection. The ZipDo survey suggested that the Discover Weekly playlist exposed over 48.6 million songs to users in 2023. The report further indicated that over 60% of Spotify streaming happens through playlists. 

Source: Pexels

Streaming platforms employ three major machine learning (ML) models to curate playlists. In collaborative filtering, user behavior is contrasted to the behavior of other listeners to make recommendations. Platforms that do not have a star rating feature can also use this filtering to examine indirect feedback, like the number of times a song is played. Combined with NLP, these algorithms can analyze textual opinions across online forums, giving more accurate suggestions.

Streaming platforms use raw audio models to ensure that new songs from underground artists do not go unnoticed. These models use convolutional neural networks to identify critical characteristics of raw audio and use this information to suggest the songs to the users that might enjoy them.  

In summary, technological advancements have brought incredible shifts in how online businesses relate with consumers. In the music industry, for instance, AI & ML algorithms have allowed streaming platforms to personalize playlists by understanding users’ preferences, thus creating more robust connections with customers.  


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