Rankings
Observable competitive position.
- Category and overall rank by country
- Free, paid and grossing chart position
- Keyword rank for a tracked set
- Rank movement over time
- Chart entry and exit detection
Observed store data, with modelled revenue estimates left out.
Most app intelligence products lead with revenue estimates. Those are models built on sampled panels, frequently wrong by large margins, and presented as data. We deliver what the stores actually publish and let you model on top of it yourself.
Free pilot on your own sources, returned in 48 hours. No card, no trial clock — and you keep the sample data either way.
Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.
App store data scraping is the automated collection of what mobile app stores publish publicly: rankings, ratings, prices, in-app purchase tiers, version history, release notes and store listing metadata, per country and per store.
The market convention in app intelligence is to lead with download and revenue estimates. We do not provide those, and the reason is worth stating clearly.
If your work needs revenue estimates, licensed app intelligence vendors provide them and are transparent that they are estimates. We would rather point you there than blend a model into a dataset labelled as observed data.
Rank and ASO tracking are the largest use cases. IAP pricing by country is the most under-collected.
Observable competitive position.
Quality signal, without reviewer personal data.
The structure competitors rarely discuss.
Development velocity, fully observable.
What competitors are testing.
Who is behind the app.
A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.
Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 100+ and is agreed during scoping.
| Field | Type | What it captures | Refresh |
|---|---|---|---|
app_key / bundle_id |
string | Cross-store app identity plus the store's own bundle or package identifier | Every run |
store / country |
enum | Store and storefront country, both first-class since data differs by both | Every run |
title / subtitle |
string | Listing title and subtitle, which are primary ASO fields | Daily |
category_rank |
object | Rank keyed by chart name, including category and overall charts | Daily |
keyword_ranks |
array | Position for a tracked keyword set, per country | Daily |
rating / rating_count |
decimal / int | Rating average and count, tracked over time for trajectory analysis | Daily |
price / has_iap |
decimal / boolean | App price and whether in-app purchases are offered | Daily |
iap_tiers |
array | In-app purchase tier names and prices in local currency | Daily |
version / released_at / release_notes |
string / date | Current version, its release date and the published notes | Daily |
releases_90d |
int | Release count over a trailing window, as a development velocity signal | Daily |
screenshot_count / has_video |
int / boolean | Creative asset counts, with change detection for ASO testing analysis | Daily |
Reviewer names and profiles are not part of the deliverable. Review text and its associated app version are, since those are product feedback rather than a personal dossier.
Rank and pricing differ by storefront country, so country is a first-class dimension rather than a setting.
Alternative app stores can be added where their listings are publicly accessible. Store terms restrict automated access, and we state that plainly rather than implying the question is settled. Request a source we don't list →
We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.
| Market | Why demand concentrates here |
|---|---|
| United States | The largest storefront by revenue concentration and the standard reference market for rank benchmarking. |
| United Kingdom, Germany & France | Key European storefronts with distinct keyword sets and localised listing content. |
| Japan & South Korea | High-value storefronts with substantially different chart dynamics and localisation requirements. |
| India, Brazil & Indonesia | High-volume storefronts with aggressive purchasing-power-adjusted IAP pricing worth tracking separately. |
We run production collection across 40+ countries. Coverage depth varies by market and by source, so we confirm what is actually available for your specific markets during scoping rather than claiming uniform global coverage. Ask about a market we don't list →
ASO and product teams dominate, with agencies and investors close behind.
Keyword rank and metadata changes across countries need systematic tracking, and competitor ASO testing is invisible without change detection.
Keyword and category rank by country plus competitor metadata change detection on titles, subtitles and creative.
Organic installs
Competitor release cadence and feature direction is only visible through version history and release notes.
Release cadence with notes captured and feature mentions extracted, plus rating response to releases.
Feature velocity
Competitor IAP pricing and regional price structures are hard to observe and rarely collected properly.
IAP tier names and prices by country with change detection, revealing pricing strategy and regional decisions.
ARPU
Client reporting needs defensible rank and metadata data across portfolios and countries.
Consistent rank, rating and metadata panels across client and competitor apps, delivered on a reporting schedule.
Client retention
App diligence needs observable rank, rating velocity and monetisation structure rather than proprietary estimates.
Longitudinal rank, rating velocity, release cadence and IAP structure panels, with modelled estimates deliberately excluded.
Diligence confidence
Tracking dozens of competitor apps across countries manually is not sustainable.
Full competitor portfolio monitoring with rank, metadata, IAP and release change alerting across tracked countries.
Response time
Four patterns, with the outcome each is judged on.
Rank is collected per store per country for category charts and a tracked keyword set, so rank movement is attributable to specific markets rather than blended into a global figure.
Outcome: ASO decisions made per storefront rather than against a global average that describes no market.
Titles, subtitles, descriptions, screenshots and videos are monitored with change detection, revealing what competitors are testing and when.
Outcome: Competitor ASO experiments observed as they run instead of inferred after the fact.
In-app purchase tiers are captured with names and prices per country, with change detection, exposing how competitors structure and regionalise monetisation.
Outcome: Pricing and packaging decisions informed by observed competitor structures across markets.
Version history, release dates and notes are collected, producing release frequency over trailing windows plus extracted feature mentions.
Outcome: Competitor roadmap direction inferred from observable release behaviour rather than from announcements.
Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.
Rank reporting blended storefronts into one figure, so a sustained decline in two high-value countries was masked by growth elsewhere.
Rank collection per store per country with category and keyword position tracked separately, plus rating trend by storefront.
Country-level rank movement became visible and ASO effort was redirected to the affected storefronts.
Client reporting described competitor positioning but could not show what competitors were actively testing in their listings.
Daily metadata change detection on titles, subtitles, descriptions, screenshots and preview video across competitor sets.
Competitor listing experiments were observed while running, with dates, and fed into client test planning.
Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →
Before you commit to anything, we run this service against your own sources and send you the output. If the coverage isn't there, the sample will show you that too — which is the point. We would rather lose the deal at the pilot than at month three.
Same collection pipeline and same QA underneath. The difference is who holds the schedule and how the data reaches you.
We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.
Best fit: Teams who need the data, not the infrastructure.
The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.
Best fit: Product and engineering teams building on live data.
A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.
Best fit: Research, strategy and diligence work with a deadline.
Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.
Country-by-country rank collection multiplies volume fast, and store layout changes are frequent.
| Consideration | In-house scraping team | Generic proxy / DIY tool | Actowiz managed feed |
|---|---|---|---|
| Time to first usable data | 6–12 weeks of engineering before anything is trustworthy | Days, but output needs manual cleanup before use | Free pilot in 48 hours, production in 5–10 business days |
| Who fixes it when a source changes | Your engineers, at the cost of their roadmap | You do — tools report failures, they don't resolve them | We do, same business day, inside the retainer |
| Data quality assurance | Whatever your team has time to build | None beyond HTTP success | Schema validation plus sampled human QA on every run |
| Compliance documentation | Rarely produced, then requested urgently by legal | Not provided; terms risk sits with you | Sources, method and lawful basis documented for review |
| Accountability | Distributed across a team with other priorities | A support ticket queue | A named engineer and an account owner |
| True annual cost | Engineer salaries, proxies, hosting, ongoing maintenance | Low licence fee plus significant hidden analyst time | One fixed monthly retainer, quoted after scoping |
App store data is often collected for a single storefront and treated as global. In a category where storefronts differ on rank, price and even listing content, that produces conclusions that hold nowhere.
Because country is a dimension, volume scales with countries multiplied by keywords multiplied by apps multiplied by frequency. Four multipliers compound quickly, and this is where app store engagements get expensive.
We scope the country and keyword set with you deliberately. A well-chosen ten countries covering your actual revenue concentration usually answers more than forty countries collected thinly, and costs a quarter as much. That conversation happens before we quote.
Among all app store fields, release notes are the most consistently overlooked and among the most informative. They are published by the developer, describe actual shipped changes, and carry a date.
Version, release date and full release note text are captured on every run, with release counts over trailing windows computed and feature mentions extracted where notes are specific enough to support it.
Some publishers write generic notes — "bug fixes and improvements" — which carry no feature information. We do not infer content from generic notes; the release event is recorded and the feature extraction is empty rather than speculative. Generic notes are themselves mildly informative, since publishers investing in communication usually say what shipped.
For sentiment analysis on the review side, see our review and ratings service, which applies the same personal-data boundary.
App set, countries and keyword sets are scoped first, since those three multipliers determine cost more than anything else.
You send us target sites, regions, SKUs or keywords. We return a field-level schema proposal, coverage estimate and refresh recommendation — usually within two working days.
We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.
Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.
Feeds run at your chosen cadence and land in the warehouse or bucket you already use. Schema changes are versioned and announced before they ship.
We watch coverage drift, fill rates and source changes daily. A named engineer owns your account, and layout breaks are fixed by us — not queued for you.
JSON, JSONL, CSV, Parquet or XLSX, delivered to Amazon S3, Google Cloud Storage, Azure Blob, SFTP, Snowflake, BigQuery, Databricks or a REST/GraphQL endpoint. Webhooks fire on completion, and every batch ships with a manifest containing row counts, schema version and QA results so your pipeline can fail loudly instead of silently ingesting a bad file.
We collect publicly visible store listing, chart and search data without accounts or credentials. Reviewer names and profile data are not part of the deliverable. We do not provide modelled download or revenue estimates, and we do not access developer console or analytics data. Store terms restrict automated access and methodology is documented per store.
These are contractual, not marketing copy. They appear in the engagement document.
| Commitment | What we hold ourselves to |
|---|---|
| Pilot turnaround | A real sample from your own sources within 48 hours of scoping, at no cost. |
| Go-live | Production collection running within 5–10 business days of sign-off. |
| Delivery punctuality | 99.5% on-schedule delivery, measured monthly and reported to you. |
| Breakage response | Source layout changes triaged same business day; critical sources inside 4 hours. |
| Data quality | Schema validation on every run plus sampled human QA before any delivery leaves us. |
| Escalation | A named engineer and an account owner, not a shared ticket queue. |
| Change requests | Field additions and source changes handled inside the retainer, not re-quoted. |
| Exit | Your historical data exported in full on request. No lock-in, no export fee. |
Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.
What ASO, product and investment teams ask during evaluation.
No, deliberately. No store publishes downloads or revenue publicly, so every figure in the market is modelled from rank position, sampled panels and proprietary assumptions — frequently wrong by large margins, especially outside top charts and in smaller countries.
Presenting a model inside a dataset labelled as observed data is misleading, and an undisclosed derivation cannot be audited. If you need estimates, licensed app intelligence vendors provide them and are clear they are estimates. We deliver observations and let you model on top.
Because rank, IAP pricing, listing content, ratings and even availability all differ by storefront. An app in the top 20 in one country may not chart in another, and subscription tiers are priced per country at very different levels.
Country is therefore a dimension on every record, not a collection setting. The cost consequence is real: volume scales with countries times keywords times apps times frequency, which is why we scope the country and keyword set with you before quoting.
Yes, for a tracked keyword set per store per country. Keyword sets need to be language and market specific, since a single global set is inadequate for anything beyond English-speaking storefronts.
Keyword count is one of the four cost multipliers, so we recommend starting with the keywords that actually drive your installs rather than an exhaustive set. That usually means dozens rather than hundreds, and it produces a more useful dataset.
Yes, where publicly shown on the store listing — tier names and prices in local currency, per country, with change detection. Our capture rate is about 91%; some apps expose tiers only inside the app rather than on the listing.
This is one of the more under-collected fields in the market and one of the most useful, because IAP structure and regional pricing reveal monetisation strategy directly rather than through inference.
Review text and the app version it relates to, yes, where public. Reviewer names, profiles and review history are not part of the deliverable.
Review text is product feedback about an app; a reviewer's profile and history is a personal dossier. We collect the former and decline the latter, in this service and in every other. The same boundary applies in our review and ratings service.
Store terms do restrict automated access, and we say so rather than implying otherwise. Our practice is to collect only publicly visible listing, chart and search data, without accounts or credentials, at low request rates.
You receive a written methodology document per store describing exactly what is accessed and how, plus a DPA before signature, so your counsel can assess your specific use case. Where an official API covers part of your requirement, we will tell you and use it rather than scraping the same data.
Yes — titles, subtitles, descriptions, screenshot count and ordering, icon and preview video presence, all with change detection.
This is one of the highest-value outputs because competitor ASO testing is otherwise invisible. Seeing a competitor rotate screenshots or revise a subtitle, with the date, tells you what they are experimenting with while the experiment is running.
Daily for rank, since chart positions move daily and the movement is the signal. Daily also for ratings and IAP pricing, which change without notice. Metadata can be daily or weekly depending on how closely you track competitor testing.
Sub-daily rank collection is rarely worth its cost, since stores update charts on their own cadence rather than continuously.
We quote individually, and here the quote is driven by four multipliers: app count, country count, keyword count and refresh frequency. They compound, which is why scoping matters more than in most categories.
A focused competitor set across ten revenue-concentrated countries with a targeted keyword list at daily refresh sits at the lighter end. Broad app coverage across forty countries with large keyword sets sits considerably higher. One scoping call, a free pilot on your own app set within 48 hours, then a fixed monthly quote. Request a quote.
Send us your apps, competitors and target countries. We return rank, ratings, IAP tiers and metadata within 48 hours.
Free pilot, no card, no obligation. No modelled revenue estimates, by design.Our web scraping expertise is relied on by 4,000+ global enterprises including Zomato, Tata Consumer, Subway, and Expedia — helping them turn web data into growth.
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