tools / revenue

revenue

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Modeled revenue estimate

Roughly what does this app gross — said with honest error bars?

Apple publishes no revenue numbers for arbitrary apps. What it publishes is the top-grossing chart — and grossing rank maps to spend through a power law. revenue fits that curve (calibrated against published rank–revenue datapoints) and returns gross consumer spend bands in USD, daily and monthly, low/mid/high, scaled per storefront and per genre chart.

The tool is built to be quoted honestly. Bands span mid ÷ K to mid × K (K = 2 from the overall chart, 2.5 from a genre chart); apps on no consulted chart get only upperBoundDaily — they earn less than a rank-100 app, possibly far less. Every response carries the note that these are modeled estimates, that grossing reflects App Store billing only, and that developer proceeds run ~70–85% of gross.

This is the difference from panel-based market intelligence: not a confident-looking point estimate from an opaque pipeline, but a public-signal model that shows its input (topGrossing, via charts), its basis, and its spread.

See the live US top-grossing chart the model reads →

worked example

Model ChatGPT's US gross from its grossing rank.

tools/call → revenue

{
  "name": "revenue",
  "arguments": {
    "appId": 6448311069,
    "countries": [
      "US"
    ]
  }
}

revenue → structuredContent (excerpt)

{
  "appId": 6448311069,
  "chartSize": 100,
  "currency": "USD",
  "storefronts": [
    {
      "country": "US",
      "appName": "ChatGPT",
      "genreName": "Productivity",
      "topGrossing": 1,
      "genreGrossing": 1,
      "estimate": {
        "basis": "overall-chart",
        "rankUsed": 1,
        "daily":   { "low": 2000000,  "mid": 4000000,   "high": 8000000 },
        "monthly": { "low": 61000000, "mid": 120000000, "high": 240000000 },
        "upperBoundDaily": null,
        "bandMultiplier": 2
      }
    }
  ],
  "note": "figures are MODELED estimates, not Apple data: …"
}

real response, captured 2026-07-29 — trimmed only where marked with …

Read it as “#1 grossing US ≈ $2–8M/day gross consumer spend, center $4M” — an order of magnitude with stated spread, not a fake-precise point.
basis tells you which chart fed the model; genre-chart estimates carry a wider band (bandMultiplier: 2.5) because genre spend-share scaling adds its own uncertainty.
Gross ≠ developer revenue (Apple's cut) and store billing only — apps monetizing outside IAP earn beyond what their rank shows. The response says all of this itself.

when to use it

Sizing a category: model the top 10 grossing apps in a genre and you have the addressable spend, bands and all.
Competitive context without a $70k market-intelligence contract — when order-of-magnitude answers the actual question (“is this a $10k/day or $100k/day competitor?”).
Tracking monetization movement over time by re-modeling as grossing rank shifts (the hosted service archives daily grossing snapshots).
Not for apps monetizing off-store (ads, external subscriptions) and not audited financials — agents are instructed to present it as an estimate, never fact.

description — as served to agents

Order-of-magnitude revenue estimate for an app, per storefront, MODELED from its public top-grossing chart rank (overall chart, falling back to the app's genre chart) — Apple publishes no revenue data for arbitrary apps, and grossing rank is the only public monetization signal. daily/monthly are gross consumer spend bands in USD (low/mid/high; developer proceeds ~70-85% of gross): a power-law fit calibrated on published rank-revenue datapoints, scaled per storefront and genre. Apps on no consulted chart (top 100) get only upperBoundDaily — they earn less than a rank-100 app, possibly far less. Treat bands as honest uncertainty, not precision: same-rank apps differ several-x when revenue is whale-driven, and subscription renewals move grossing rank without downloads. Costs one lookup plus up to two chart fetches per storefront (throttled ~1/s). Confirm chart movement over time with charts.

input

appId *

integer

App Store trackId of the app

countries

string[]

2-letter storefront codes, e.g. ["US", "GB", "JP"]. Each storefront is queried separately; at most 25 per call.(default ["US"])

output

appId *

integer

chartSize *

integer

currency *

"USD"

storefronts *

object[]

country *

string

available *

boolean

appName *

string | null

genreId *

integer | null

Apple primary genre id, e.g. 6014 = Games

genreName *

string | null

topGrossing *

integer | null

overall top-grossing position, null = not in top 100

genreGrossing *

integer | null

position on the app's own genre grossing chart, null = not in top 100 / no genre

estimate *

object | null

null when the app is not available in this storefront

basis *

"overall-chart" | "genre-chart" | "not-charted"

which rank fed the model; not-charted = upper bound only

rankUsed *

integer | null

the grossing rank the estimate is computed from

daily *

object | null

daily gross consumer spend band, USD; null when not-charted

low *

number

mid *

number

high *

number

monthly *

object | null

monthly gross consumer spend band, USD; null when not-charted

low *

number

mid *

number

high *

number

upperBoundDaily *

number | null

not-charted only: the rank-100 value of the narrowest consulted chart, padded to the band's high edge (bandMultiplier x the model midpoint) so "earns less than this" stays honest — possibly far less

countryMultiplier *

number

storefront scale vs US = 1.0

genreMultiplier *

number | null

genre-chart scale vs the overall chart; null unless a genre chart was consulted (it scales the genre-chart estimate, or the not-charted ceiling)

bandMultiplier *

number

band half-width K (low = mid/K, high = mid*K): 2 for overall-chart estimates, 2.5 when a genre multiplier is in play (it adds uncertainty)

note *

string

pairs with

chartsthe grossing rank that feeds the model›appprice, genre and availability context›competitorspick whose revenue to model›

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