How often is the data updated?

Every day. The main batch runs between 02:00 and 07:00 UTC, and a subset of high-velocity apps is re-checked every 4 to 6 hours so ranking-board movements don't fall behind the live store.

Where does the data come from?

From the app stores themselves (Apple App Store and Google Play), search engine index signals (Google, Bing), ad platforms (Meta, Google Ads, TikTok Ads), SEO intelligence tools (Semrush, Ahrefs), Apple Search Ads, and AppsFlyer. No single source is treated as ground truth — they get cross-checked by our model.

Why does this app show "<$3k" or "<3k"?

It means the signals available for that app only confirmed the value is below our display threshold (revenue under ~$3k/month, or fewer than 3k downloads/month). Rather than invent a precise figure, we render the bucket label so the uncertainty is visible.

Why does RPD show "-" for some apps?

We only compute revenue per download when revenue itself is above the floor. Otherwise the numerator is unknown and any ratio would look like signal without being one. "-" means "we don't know yet," not "zero."

How accurate are the revenue estimates?

Our in-house ML model — built and trained on the cross-source signals listed above — produces estimates with roughly 70% accuracy against ground-truth comparisons we run periodically. Where confidence is too low, we fall back to bucket labels instead of fake precision.

Can I export the data?

Pro subscribers can export filtered app lists to CSV or Excel from the trending apps page. A programmatic API is on the roadmap for the next phase.

Methodology

How we collect, validate, and estimate app data.

Q: Why does this app show "<$3k" or "<3k"?

It means the signals available for that app only confirmed the value is below our display threshold (revenue under ~$3k/month, or fewer than 3k downloads/month). Rather than invent a precise figure, we render the bucket label so the uncertainty is visible.

Q: Why does RPD show "-" for some apps?

We only compute revenue per download when revenue itself is above the floor. Otherwise the numerator is unknown and any ratio would look like signal without being one. "-" means "we don't know yet," not "zero."

Q: How accurate are the revenue estimates?

Our in-house ML model — built and trained on the cross-source signals listed above — produces estimates with roughly 70% accuracy against ground-truth comparisons we run periodically. Where confidence is too low, we fall back to bucket labels instead of fake precision.

Q: Can I export the data?

Pro subscribers can export filtered app lists to CSV or Excel from the trending apps page. A programmatic API is on the roadmap for the next phase.

Where the numbers come from, how we cross-check them, and where they fall short. No black boxes.

1. Data sources

Many noisy signals, aggregated on purpose.

We pull from app stores, search engines, ad platforms, SEO tooling, and attribution providers. No single source is treated as ground truth — they get cross-checked against each other.

App stores (official)

— Apple App Store rankings, listings, and structured metadata
— Google Play rankings, listings, and structured metadata

Search engines

— Google Search index signals (visibility, organic reach)
— Bing Search index signals

Paid search and visibility

— Google Ads inventory and bid landscape signals
— Apple Search Ads visibility and competitive signals

Paid social

— Meta Ads (Facebook + Instagram) creative and reach signals
— TikTok Ads creative and reach signals

SEO intelligence

— Semrush traffic, keyword, and backlink signals
— Ahrefs traffic, keyword, and referring-domain signals

Attribution and lifecycle

— AppsFlyer install attribution and post-install lifecycle signals

2. Validation and estimation

An in-house ML model does the cross-check.

Cross-validate signals

A single source — one ad platform's reach number, one ranking snapshot — is noisy in isolation. The model reconciles signals across every source listed above so a metric only ships when at least one other source agrees.

Estimate revenue when no source publishes it

Stores don't publish revenue directly. We trained the model to map the combination of download signals, ad spend indicators, in-app purchase visibility, and ranking trajectory into a revenue estimate.

~70%

Honest about accuracy

Against the ground-truth comparisons we run periodically, the model lands at roughly 70% accuracy. Where confidence is too low to report a real number, we fall back to bucket labels (

<$3k

<3k

) rather than fake precision.

3. Refresh cadence

Daily, with hot apps re-checked more often.

Main batch runs daily between 02:00 and 07:00 UTC.
A subset of high-velocity apps is re-checked every 4–6 hours so ranking moves don't fall behind the live store.
The sitemap <lastmod> on every detail page reflects the latest model output time, not the last render.

4. Coverage today

Where the dataset reaches right now.

iOS App Store and Google Play, US storefront.
Roughly 10,000 ranked apps in the sitemap and ~13,000 in the underlying dataset.
More countries on the roadmap, not done yet.

5. Known limits

Where the numbers fall short — documented.

Floor effects on weak signals

When source signals only confirm “less than the threshold,” we render

<$3k

for revenue and

<3k

for downloads, instead of guessing a precise figure.

Revenue per download (RPD) suppression

When revenue sits at the floor (i.e. renders as

<$3k

), we suppress the RPD value to

−

. Dividing by an unknown numerator produces noise that looks like signal, and we'd rather show nothing than mislead.

Refresh lag for some metrics

A subset of metrics can lag the live store state by up to ~24 hours, especially during high-traffic events (launches, holidays).

US storefront only, for now

We currently cover the US App Store and Google Play storefronts. Multi-country expansion (EU, Asia) is on the roadmap, not done.

6. FAQ

Common questions about the numbers.

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