Until recently, many Zimbabwean artistes had found alternative ways of having their music and their recordings sold outside the country without bothering too much about sales within Zimbabwe.
Two months ago I argued with responsible authorities about the insignificance of banning airplay on Winky D’s music from ZBC platforms. I thought that it was a futile exercise as there are plenty of other platforms from which listeners could access his music. My words were: "Banning of records in this country is now a thing of the past as ZBC does not control platforms like YouTube, Spotify, Tik-Tok, I-Tunes, Sound Cloud, Pandora, Amazon etc."
However, despite this window, Zimbabwean musicians are in a bind. I now eat my words. There was talk of Tik-Tok being banned in the United States in the next six months unless it is sold to an American company. Musicians thought that if they cannot reach the US through Tik-Tok, they can penetrate it through Spotify. Now Spotify has also come out with specific conditions.
Spotify has decided not to pay for tracks with less than a 1,000 streams per year, which most Zimbabwean tunes posted on that platform are. Artistes such as Holy Ten, Saint Floew Jah Prayzah, Kabza de Small, Winky D and Voltz JT are already on the Spotify platform, but are now subjected to new regulations which might hinder the payment of royalties to them.
Under the new policy, more than 60% of Spotify tracks will not rack up any royalties.
Last week Spotify officially demonetised all tracks with under 1,000 streams annually — a new policy that could see nearly two-thirds of tracks fail to generate any royalties.
The new policy came into force on 1 April. Its launch follows a report published by the streaming giant last year, entitled Modernising our royalty system, in which news of the much-speculated decision was first announced.
Spotify says there will be no “change to the size of the music royalty pool being paid out to rights holders” — rather, this pool will be divided among the remaining eligible tracks, presumably meaning bigger bucks for a smaller number of artistes and rights holders instead of “spreading it out into $0.03 payments”.
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Let us put all this into its current technical context:
Today, we live in an interconnected world where everyone can share the highlights of their life across social media. One of the applications most commonly shared is Spotify, the Swedish-based audio streaming service that houses a range of recorded music and podcasts for public consumption. Spotify is embedded within social media applications, such as Instagram and X, to allow for easier sharing, making it accessible to a wide audience. Spotify curates your personal entertainment tastes to create playlists based on aspects ranging from your location to your current mood. Although this technology can be seen as interesting and harmless, it is important to understand how this algorithm works and affects your personal privacy. Based on installs and active users, Spotify is currently the most popular music streaming application. With various initiatives to attract and preserve users, it has made a name for itself in the music streaming market. Among these initiatives, Spotify Wrapped has intrigued many about the methods of data collection, and how such data is used. The answer to these questions lie in algorithms and machine learning.
Spotify’s home screen welcomes users with playlist recommendations such as their Discover Weekly, Recent Releases, as well as another section specifically custom-made for users. The latter includes playlists with names such as “Jump Back In, Recently Played, or Recommended for Today.” These recommendations are the result of extensive data gathered and used by Spotify; the most prominent example of this being Spotify’s Year In Review where it provides users with data regarding their music consumption over the year.
Spotify collects all data that is entered by the artists: songs names, description, lyrics, genres, images, and song files. In addition to these, the algorithm collects and tracks “provider side” data, like listening history, skipped songs, downloads, social interactions – in addition to external data coming from text data about songs or artists themselves. The statistics in the Spotify algorithm includes auditory history, emission rate, listening time and playlist features. Auditory history consists of identifying a song’s mood style and musical genre. The emission rate consists of transforming the least number of omissions into a greater number of recommendations. Listening time also helps determine how much a user enjoys a specific item; when exceeding 30 seconds in musical audio, it is considered positive data. Before then, the transmission is not monetised. This is referred to as the 30-second rule.
Similar to other online platforms, once data has been collected, recommender systems are used. In the case of LinkedIn, the recommended system delivers suggestions of people a user may know based on their current network, working history, and interests. Its role is to provide suggestions based on behaviour or characteristics that have been tracked by the system. So, what about Spotify? With such an overwhelming quantity of content: “recommender systems are necessary to help navigate and facilitate the decision process.” These systems achieve both collaborative-filtering and content-based recommendations. The former assumes that past behaviour predicts future behaviour — that people will like similar things that they have liked in the past. The latter relies on available features of the user-item relationship to build a mode, such as demographic data comparison. It also takes advantage of Natural Language Processing (NLP) and raw audio analysis. NLP is software’s ability to understand spoken and written human language. It allows for the audio analysis that follows the collection of audio data from songs.
According to Spotify data, the service’s song library contains 100 million songs, yet only around 37.5 million of them meet the new threshold to generate revenue.
The platform claims, however, that the remaining tracks – nearly two-thirds of its entire catalogue – represent just 0.5% of all streams on the platform. This is not the only obstacle to royalties that Spotify has introduced. In an attempt to tackle fraudulent activity on the platform, the digital music service now also requires a minimum number of unique listeners for royalties to be generated. In addition, the length of play-time required for so-called “functional content” (such as white noise) to generate royalty payments has increased.
This development comes after Daniel Ek, co-founder and CEO of Spotify, hit back at claims that the music streaming service does not adequately pay artistes arguing that his firm had put over $9 billion back into the music industry in 2023.
Ek has also announced the launch of a new beta test.
It concerns (artificial intelligence) AI playlists, and according to Ek, it’s “a feature that uses AI chat prompts to help you curate personalised playlists.”
“It’s still a work in progress but I’m excited for you to give it a try. We’re starting with Premium users in the UK and AU, so let me know what you guys think,” he said on X.
In view of all this, musicians in Zimbabwe will continue to struggle. To some of them relief came when Sir Wicknell started to donate cars which Spotify’s royalties could not meet.
Since last year, Sir Wicknell has consistently referred beneficiaries to Victor at Exquisite Cars Dealership. Musicians would be told, “Go and see Victor!” Now it looks like Victor Matiyenga has been dumped as Chivayo’s new instructions are for artistes to go and see Madzibaba Chipaga instead.
Wicknell Chivayo has been gifting musicians in Zimbabwe with cars ranging from Mercedes Benz, Lexus to Toyota Aquas. The latest to receive a car was Sniper Storm who was instructed to go to “Madzibaba Chipaga” for the car. Up to this day, no one knows exactly what Sir Wicknell’s source of income is. However, it is said that when you give to the needy, do not let your left hand know what your right hand is doing. It will all come together one day.
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