How the Spotify Algorithm Works and What Artists Can Actually Influence
By Written by Peter McVeigh
There is a lot of mythology around the Spotify algorithm. This article sticks to the data, the facts, and the things that are actually within your control.
There is no shortage of people willing to sell you advice about the Spotify algorithm. Some of it is useful. A lot of it is myth dressed up in data language.
This article tries to do something different. It sticks to what is actually documented, what Spotify's own engineering team has published, and what the data shows about how the recommendation systems work. And then it looks honestly at what you, as an artist, can actually influence.
Because the honest answer is: you can influence some of it. And the parts you can influence matter a great deal.
What the Algorithm Actually Is
The Spotify algorithm is not one thing. It is a collection of systems working together to decide what to play to each listener.
The most significant of these is a collaborative filtering system, which analyses listening behaviour across Spotify's user base, over 600 million users as of 2026, to identify patterns. If listeners who play your music tend to also play certain other artists, the algorithm infers a connection and may recommend your music to fans of those artists. This is the same basic principle used by Netflix, Amazon, and most major recommendation platforms.
Alongside this, Spotify uses Natural Language Processing (NLP) to analyse text about your music, blog posts, reviews, playlist descriptions, and editorial copy, to build a sense of what genre, mood, and context your music belongs to. This was described by former Spotify data scientist Glenn McDonald, who also created the genre-mapping project everynoise.com, as a way of anchoring artists within a sonic and cultural landscape.
The third major component is audio analysis. Spotify's systems analyse the audio signal of your tracks directly, measuring tempo, key, energy, danceability, acousticness, and other sonic properties. This data feeds into recommendations based on sound rather than behaviour alone.
You can see some of this data for your own tracks via the Spotify for Artists dashboard, where each release is assigned values for energy, tempo, and similar characteristics.
How Discover Weekly and Radio Actually Work
Discover Weekly is the playlist most artists hope to appear in, and understanding how it is built matters.
According to Spotify's own engineering blog, Discover Weekly is generated by collaborative filtering combined with an analysis of your listening history. It builds a model of your taste and then identifies tracks that listeners with similar taste enjoy, that you have not heard before.
For an artist to appear in Discover Weekly, your music needs to be saved and engaged with by listeners whose profiles share characteristics with the intended recipient. This is why genuine engagement, saves, playlist adds, and repeat plays, matters so much more than raw stream counts.
Spotify Radio and the autoplay that follows playlists or albums work similarly, drawing on both behavioural patterns and audio analysis to continue a listening session with music that matches what the listener was already playing.
The Release Radar playlist, which surfaces new music from artists a listener already follows, is triggered simply by following the artist and releasing new music. This is one of the most reliable ways to get in front of existing fans and it costs nothing beyond the follower relationship.
What the Data Says About Stream Behaviour
Spotify counts a stream only after thirty seconds of playback. This is the threshold for royalty calculation and it is also relevant to how the algorithm reads engagement signals.
The skip rate on a track matters. A song that is skipped consistently in the first thirty seconds is sending a clear negative signal to the recommendation systems. Spotify has confirmed that skip behaviour is a factor in how tracks are surfaced. A 2019 report from MusicWatch found that over half of all music on streaming platforms is skipped within the first five seconds, which underlines how critical the opening of a track is.
The save rate, how often a listener saves a track to their library or adds it to a playlist, is one of the strongest positive signals you can generate. Saves tell the algorithm that the listener found the music meaningful enough to want it again, which is a much stronger signal than a passive play.
Playlist adds by listeners, as distinct from editorial playlist placements, are also significant. When a real user adds your track to a personal playlist, it creates a data point that links your music to the mood, context, or activity that playlist represents.
Editorial Playlists: The Gatekeepers
Editorial playlists, the ones curated by Spotify's in-house team like New Music Friday, Peaceful Piano, or any of the mood and genre playlists, are not generated by the algorithm. They are selected by human editors.
You can pitch for editorial consideration through Spotify for Artists. The pitch must be submitted at least seven days before the release date and should include a completed profile for the track, including mood, style, instrumentation, and any relevant context.
Spotify has stated publicly that editorial placements are considered independently of streaming numbers. A genuinely great track from an unsigned artist has the same chance of being pitched as one from a major label, in theory. In practice, tracks with professional metadata, a complete artist profile, and previous engagement tend to receive more consideration.
Being placed on a significant editorial playlist can generate hundreds of thousands of streams and a substantial jump in followers. But it is not something you can engineer through tactics. The best approach is to pitch honestly, keep the information complete and accurate, and let the track speak for itself.
What You Can Actually Influence
This is the part of most algorithm guides that gets vague. So here is a specific list of what is genuinely within your control.
Your release metadata is the first thing. Fill in every field available to you on Spotify for Artists. Genre, mood, instruments, language, and the pitch information for editorial consideration. Incomplete metadata makes it harder for the algorithm to categorise and surface your music correctly.
Your artist profile matters more than most artists treat it. An up-to-date bio, a quality profile image, active use of Spotify Canvas looping videos, and regular Clips all signal to Spotify that this is an active artist profile, which is a factor in how prominently you are surfaced.
Your release cadence matters. Spotify's systems favour active artists. Releasing regularly, whether singles, EPs, or albums, keeps your profile current and keeps you appearing in Release Radar for your followers. A gap of many months between releases allows your profile to go cold in recommendation systems.
Encouraging genuine saves and playlist adds is one of the highest-value actions you can take. Ask your audience directly to save your tracks. Not to stream them, to save them. The language matters because the behaviour it produces is different.
Growing your follower count on Spotify is more valuable than growing your monthly listener count. Followers receive your music in Release Radar automatically. Monthly listeners include people who heard you once via a playlist and may never listen again. Followers are your reliable audience.
What You Cannot Influence and Should Stop Trying To
Stream farming, using bots or click farms to inflate your stream counts artificially, is against Spotify's terms of service and is actively detected. Spotify routinely removes fraudulent streams, which can result in your royalty payments being adjusted downward and your account being flagged. The MBW reported in 2023 that Spotify had removed billions of streams from its platform as part of its ongoing fraud detection efforts.
Buying playlist placements from unofficial sources carries similar risks. Paid placements on low-quality playlists with no real listeners generate streams with no genuine engagement, which can hurt your algorithmic standing more than help it.
The number of tracks on your album does not reliably influence your algorithmic performance. There was a period where artists released albums with a large number of short tracks to game stream counts. Spotify's royalty model is moving toward a minimum play threshold approach that makes this less effective, and the engagement signals from that kind of release are typically poor.
The Honest Summary
The Spotify algorithm rewards music that real people genuinely engage with. That is a simplification, but it is not a wrong one.
The tactics that work, complete metadata, regular releases, genuine saves, a growing follower base, pitching editorial properly, all of them are just good practice dressed up in algorithm language. None of them will save a track that is not connecting with listeners. All of them help a track that is.
Focus on making music that earns the engagement, and then use the tools available to you to make sure it reaches the right people.
Sources and Further Reading
Spotify Engineering Blog: research.atspotify.com covers the technical architecture of Spotify's recommendation systems in detail.
Spotify for Artists: artists.spotify.com is the official source for editorial pitching and profile management tools.
Glenn McDonald / Every Noise at Once: everynoise.com is the work of a former Spotify data scientist mapping the genre landscape the algorithm uses.
Music Business Worldwide: musicbusinessworldwide.com has covered Spotify's fraud detection, royalty changes, and algorithm updates extensively.
MusicWatch research on skip rates: musicwatchinc.com publishes regular reports on streaming behaviour.
Spotify Loud and Clear: loudandclear.byspotify.com provides Spotify's own published data on how royalties are distributed.
Disclaimer
This article is based on publicly available information and data current at time of writing. Spotify's systems change regularly and some specifics may have evolved. Always refer to Spotify for Artists directly for the most current guidance.