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Documentation Index

Fetch the complete documentation index at: https://docs.patchline.ai/llms.txt

Use this file to discover all available pages before exploring further.

Audience intelligence answers “who’s actually listening?” — per artist, per release, per market. The fan graph extends that across your full roster + storefront to show how fans connect: who overlaps between which artists, where momentum is building, which audience segments are responding to what.   Full audience + fan-graph features. Pro shows per-artist audience without cross-roster fan-graph view.

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Two layers

LayerWhat it answersTier
Audience (per artist)“Who is listening to X?” — demographics, geography, social channelsPro+
Fan graph (cross-roster)“Who is listening to multiple artists in my roster?” — overlap, momentum, segmentsScale+

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Audience — per artist

What you see for one artist:
SectionWhat it containsSource
DemographicsAge (binned), gender (estimated), languageLicensed audience data
GeographyTop cities, top countries, geographic shareLicensed audience data
Listener tierCasual / engaged / fan classificationPatchline-derived from listening behavior
Social audienceIG, TikTok, YouTube, X overlap and growthLicensed audience data
Fan momentum30-day growth across streaming + socialLicensed audience data
Streaming behaviorRepeat-listen %, playlist save %, completion rateAggregated platform signals where available

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Where to find it

Web dashboard: sidebar → Artists → click an artist → Audience tab. Via Aria:

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Fan graph — cross-roster

The fan graph is the cross-artist view. If you manage Mira, Tessa, and Cleo Bell, the fan graph shows:
  • Overlap matrix — how many fans listen to ≥ 2 of your artists
  • Bridge artists — which roster artist drives the most cross-discovery
  • Geographic momentum — where the roster collectively is gaining
  • Cluster identification — natural fan segments (dreampop heads, bedroom-pop seekers, etc.)
  • Storefront-fan overlap — fans who bought from your storefront and also stream multiple roster artists

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Example fan graph output

You: Show me the fan graph across my roster
Aria: 5 artists, ~32k unique fans:

       Mira    Tessa   Cleo Bell   Run Hot   Velvet Bird
Mira       —    14%       18%         3%         9%
Tessa     14%     —       22%         2%         11%
CleoB     18%   22%        —          1%         8%
RunHot     3%    2%        1%         —          15%
VelvB      9%   11%        8%        15%         —

Cluster 1 (dreampop, 56% of unique fans):
  Mira + Tessa + Cleo Bell + Velvet Bird (slightly)
  Top markets: LA, NYC, London, Stockholm

Cluster 2 (electronic, 18%):
  Run Hot + Velvet Bird (slightly)
  Top markets: Berlin, Detroit, Tokyo

Cluster 3 (crossover, 11%):
  Velvet Bird connects both clusters.
  3,500 fans listen to ≥ 2 artists across clusters.

Bridge artist: Velvet Bird. Pushing her releases benefits both
clusters disproportionately.

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Where the fan graph lives

  • Web: sidebar → Artists → Fan graph tab (Scale+).
  • Via Aria: “show me the fan graph” or “where’s my roster overlap.”

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Streaming behavior

For each artist’s top track, Patchline surfaces:
  • Completion rate — % of listeners who finish the track (high = strong hook + retention).
  • Repeat-listen % — % who play multiple times in a session.
  • Playlist-save % — % of casual listeners who save to their own playlist.
These signals are stronger predictors of long-term success than raw stream counts.

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Examples

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Where is my growth coming from?

Aria: Mira's top growth markets (last 30d):
  1. Stockholm, Sweden — +320 monthly listeners (+47% mo/mo)
  2. Brooklyn, USA      — +280 (+18%)
  3. Berlin, Germany    — +210 (+24%)
  4. London, UK         — +195 (+11%)
  5. Los Angeles, USA   — +180 (+8%)

  Notable: Stockholm growth correlates with a "Lush Sounds" playlist
  add 23 days ago. Worth a Swedish-language email blast to fans there.

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Cross-roster discovery

Aria: Velvet Bird. 15% of her fans also listen to Run Hot, 9% to
Mira, 8% to Cleo Bell. She's the bridge artist between your dreampop
and electronic clusters.

Releasing her next single in a way that points to both clusters could
unlock unique cross-streams.

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Privacy

  • Audience data is aggregated, not individually identifiable.
  • No personally-identifiable fan information (names, emails, etc.) surfaces in audience or fan-graph views.
  • Demographics are estimated from listening behavior, not declared identity.

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Pricing

TierAudience (per artist)Fan graph (cross-roster)
——
——
✅—
✅✅
✅ + API✅ + API
Queries are cached for 24h. Force-refreshing costs 2 credits per query.

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FAQ

A network view of fans across your roster + storefront. It’s an aggregate, not individual-fan tracking — we never expose identifiable fans. The graph surfaces overlap percentages, cluster identification, and bridge artists.
Data quality varies by artist. Smaller artists (under ~5k monthly listeners) often have limited demographic data.
Yes for individual artists (CSV via audience tab). Cross-roster fan-graph export is Enterprise-only.
Daily refresh where provider coverage is available. Force-refresh on demand (2 credits).
Yes — any artist you’ve added to roster gets audience data. Scout agent returns audience data for discovery candidates too.