Spotify Artist Analytics & Monthly Listeners Tracker

Read the numbers on any Spotify artist page — monthly listeners, followers, the five cities with the most listeners — and, because Spotify publishes no history at all, remember them so the next run can tell you which way they moved.

Built for artist managers, A&R, promoters and PR teams who need growth evidence rather than a screenshot: schedule it daily or weekly across a watchlist and each run answers how fast, in which direction, in which cities.

No Spotify account, no Spotify for Artists login, no API key, no browser.

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What you get per artist

Field	Notes
monthlyListeners	Unique listeners in the last 28 days
followers	Accounts following the artist
topCities	Five cities with city, country, region and listener counts
listenersPerFollower	Reach that has not converted into a following yet
monthlyListenersChange + …Percent, …PerDay, trend	Movement since the previous run
topTracks	Ten most-played tracks with lifetime play counts
discoveredOnPlaylists	The playlists actually feeding the artist new listeners
featuredInPlaylists, relatedArtists	Editorial placement and Spotify's similarity graph
upcomingConcerts	Dates, venues, cities, festival flag
socialLinks	Facebook, Instagram, Twitter, TikTok, YouTube, Wikipedia
albumCount, singleCount, latestReleaseName, latestReleaseDate	Catalogue at a glance
biography, avatarImage, headerImage	Press-kit material

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Quick start

{
  "artistUrls": ["https://open.spotify.com/artist/0gxyHStUsqpMadRV0Di1Qt"],
  "artistNames": ["Tulus", "NIKI"],
  "trackChanges": true,
  "exportFormats": ["csv"]
}

Artists can be given as links or plain names — a name costs one extra search request, and the run log prints which artist it matched so you can spot an ambiguous one.

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How the growth tracking works

Spotify shows today's number and nothing else. To turn that into a trend, each run writes its readings to a named key-value store (trackingStoreName, default spotify-artist-analytics) and compares the next run against it.

• The first run has nothing to compare against: rows are marked isFirstReading: true. That is the baseline, not a failure.
• Every later run adds monthlyListenersChange, …ChangePercent, …ChangePerDay, followersChange, topTrackPlaycountChange, trend, previousReadingAt and daysSincePreviousReading.
• …ChangePerDay is what makes a weekly run comparable with a daily one.
• Use a named store, not the run's own — a run-scoped store vanishes with the run and every run would look like the first. Point several scheduled runs at the same name to build one history; use different names to keep separate watchlists apart.

Schedule it daily for a launch campaign, weekly for a roster.

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Cost

One request per artist, plus one to pick up an anonymous token. A 200-artist watchlist is 201 requests. Looking an artist up by name adds one request each.

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Limits worth knowing

• Monthly listeners are a rolling 28-day figure Spotify refreshes on its own schedule, so two runs hours apart will often show no change. Daily is the most granular cadence that means anything.
• worldRank is null for almost everyone. Spotify sends 0 for artists outside its published global ranking; that is normalised to null here so it cannot be misread as first place.
• Top cities are always five, and they are listener counts, not stream counts.
• discoveredOnPlaylists arrives padded with placeholder entries that resolve to nothing. They are dropped, so the count is of real playlists.
• This reads the public artist page. It is not Spotify for Artists and does not expose stream-by-stream, demographic or save data.

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Output

Every run writes to the Apify dataset. Set exportFormats to also drop a ready-made spotify-artist-analytics.csv, .xlsx, .json or .ndjson into the run's key-value store. Nested fields flatten poorly into CSV, so switch on emitTopTrackRows if you want the top tracks as their own spreadsheet rows.

See CRAWLING_METHOD.md for how the data is obtained.