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Service · App store data

App Store Data Scraping Services

Observed store data, with modelled revenue estimates left out.

App store data scraping is the automated collection of publicly visible mobile app store data — category and keyword rankings, ratings and review counts, in-app purchase tiers, regional pricing, release cadence, and ASO metadata such as titles, subtitles and screenshots. Modelled download and revenue estimates are excluded because they are not observations.

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.

Observed store data only No modelled revenue estimates Free pilot sample in 48 hours
app_store_data_2026-08-05.jsonl LIVE FEED
{"app_key":"aw-app-441802", "store":"app_store","country":"GB", "bundle_id":"com.example.fittrack", "title":"FitTrack: Habit & Workout Log", "subtitle":"Build streaks that stick", "developer":"Example Labs Ltd", "category_rank":{"Health & Fitness":28, "Overall Free":412}, "keyword_ranks":[{"kw":"habit tracker","rank":4}], "rating":4.7,"rating_count":28412, "price":0.00,"has_iap":true, "iap_tiers":[{"name":"Pro Monthly","price":4.99}, {"name":"Pro Annual","price":34.99}], "version":"7.4.1","released_at":"2026-07-29", "releases_90d":11, "screenshot_count":8,"has_video":true} {"app_key":"aw-app-441802", "store":"play_store","country":"IN", "iap_tiers":[{"name":"Pro Monthly","price":199.00, "currency":"INR"}],"rating":4.4}
2 of 1,884,200 app-store-country rows · run 2026-08-05T06:00ZIAP tiers captured 91.3% · schema v4.0
Our Data Powers
B2C Marketplace
amazon
D2C + Marketplace
NYKAA
D2C + Marketplace
Walmart
FMCG Marketplace
udaan
Food Delivery
Uber Eats
Quick Commerce
blinkit
Taxi Aggregator
Uber
E-Commerce
Tmall

Key facts at a glance

What it is
Managed collection of publicly visible app store data across iOS and Android stores
Rankings
Category and overall rank plus keyword rank for a tracked keyword set, by country
Ratings
Rating average and count with distribution where published, tracked over time
Monetisation
In-app purchase tier names and prices by country, plus app price where paid
Release cadence
Version history, release dates and release notes, giving a development velocity signal
ASO metadata
Title, subtitle, description, screenshots and video presence for optimisation analysis
Explicit exclusion
No modelled download or revenue estimates, because those are models rather than observations
Who it's for
App publishers, ASO teams, product managers, agencies and investors
2 storesiOS and Android40+ countries
91.3%IAP tier capture ratewhere publicly shown
Release cadenceas a velocity signalversion-level
Zeromodelled revenue estimatesobservations only

Key takeaways

  • What it is: Managed collection of publicly visible app store data across iOS and Android stores
  • Rankings: Category and overall rank plus keyword rank for a tracked keyword set, by country
  • Ratings: Rating average and count with distribution where published, tracked over time
  • Monetisation: In-app purchase tier names and prices by country, plus app price where paid
  • Release cadence: Version history, release dates and release notes, giving a development velocity signal
  • ASO metadata: Title, subtitle, description, screenshots and video presence for optimisation analysis

Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.

Definition

What is app store data scraping, and why do we exclude revenue estimates?

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.

Why modelled estimates are not data

  • They are models, not observations. Estimates are inferred from rank position, sampled panels and proprietary assumptions. No store publishes downloads or revenue publicly.
  • Accuracy varies enormously. Comparisons against publisher-reported figures have repeatedly shown large errors, particularly outside top-grossing charts, in smaller countries and for subscription apps.
  • The method is undisclosed. You cannot audit a figure whose derivation is proprietary, which makes it unusable in anything that gets reviewed.
  • They crowd out better inputs. Teams anchor on an estimate instead of using observable signals they could actually defend.

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.

What observable store data supports

  • Rank movement by category and keyword, per country, which is the observable competitive signal.
  • Rating trajectory and review velocity, which respond to releases and are strong quality indicators.
  • Monetisation structure. IAP tier names and prices by country reveal pricing strategy and regional pricing decisions directly.
  • Release cadence. Version frequency and release note content is a genuine development velocity signal, and it is fully observable.
  • ASO metadata changes. Title, subtitle and screenshot changes show what competitors are testing.
What we collect

Six categories of app store data

Rank and ASO tracking are the largest use cases. IAP pricing by country is the most under-collected.

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

Ratings & reviews

Quality signal, without reviewer personal data.

  • Rating average and count
  • Rating distribution where published
  • Review velocity over time
  • Review text and version where public
  • Rating response to releases

Pricing & monetisation

The structure competitors rarely discuss.

  • App price where paid
  • In-app purchase tier names and prices
  • Subscription period and trial mentions
  • Regional price variation by country
  • Price and tier change detection

Releases & versions

Development velocity, fully observable.

  • Version numbers and release dates
  • Release note content
  • Release frequency over trailing periods
  • Minimum OS and size where published
  • Feature mentions in release notes

ASO metadata

What competitors are testing.

  • Title, subtitle and short description
  • Long description and keyword usage
  • Screenshot count and ordering
  • Preview video presence
  • Icon and creative change detection

Developer & portfolio

Who is behind the app.

  • Developer name and portfolio
  • Portfolio size and category spread
  • New app launch detection
  • App removal and delisting
  • Cross-store presence
Service scope

What the ecommerce data scraping service includes

A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.

✓ Included in every engagement

  • Country as a first-class dimension on every record, not a collection setting
  • In-app purchase tiers with prices in local currency and change detection
  • Release notes captured in full with trailing release counts computed
  • ASO metadata change detection across titles, subtitles and creative
  • Deliberate exclusion of modelled estimates from the deliverable
  • Source discovery, scoping and a written collection plan
  • Free pilot on your own sources before any commitment
  • Full pipeline build, hosting and proxy infrastructure
  • Schema design, validation and sampled human QA on every run
  • Ongoing maintenance when source layouts change — our cost, not yours
  • Delivery to your warehouse, bucket, SFTP or API endpoint
  • Documented methodology and compliance notes for your legal review

× Not included — stated upfront

  • Modelled download or revenue estimates of any kind
  • Developer console, store analytics or any authenticated store surface
  • Reviewer names, profiles or review history
  • Feature inference from generic release notes
  • Anything behind a login, paywall or credentialed session
  • Personal data beyond a documented lawful basis
  • Licensed third-party datasets we do not hold rights to
  • Guarantees about fields a source simply does not publish
Schema

App store data fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 100+ and is agreed during scoping.

Deliverable schema — v4.0 core fields (full dictionary: 100+ fields)
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.

Coverage

Stores, countries and surfaces we collect from

Rank and pricing differ by storefront country, so country is a first-class dimension rather than a setting.

Apple App StoreGoogle Play StoreCategory chartsOverall chartsFree, paid and grossing chartsSearch results for tracked keywordsProduct listing pagesIn-app purchase listings where publicVersion history where publishedDeveloper pages40+ storefront countriesEditorial and featured placements where visible

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 →

Markets served

Countries and markets where this service is in highest demand

We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.

Highest-demand markets for this service, and why 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.

North America

United StatesCanadaMexico

United Kingdom & Ireland

United KingdomIreland

Western Europe

GermanyFranceNetherlandsBelgiumSpainItalySwitzerlandAustria

Nordics

SwedenNorwayDenmarkFinland

Middle East

United Arab EmiratesSaudi ArabiaQatarKuwaitIsrael

Asia Pacific

SingaporeAustraliaNew ZealandJapanSouth KoreaMalaysiaIndonesiaThailandVietnamPhilippines

South Asia

IndiaBangladeshSri LankaPakistan

LATAM

BrazilArgentinaChileColombia

Africa

South AfricaNigeriaKenyaEgypt

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 →

Who buys this data

Which teams buy app store data

ASO and product teams dominate, with agencies and investors close behind.

Head of ASO / Growth

App publishers
The problem

Keyword rank and metadata changes across countries need systematic tracking, and competitor ASO testing is invisible without change detection.

What we deliver

Keyword and category rank by country plus competitor metadata change detection on titles, subtitles and creative.

Metric that moves

Organic installs

Product Manager

App publishers
The problem

Competitor release cadence and feature direction is only visible through version history and release notes.

What we deliver

Release cadence with notes captured and feature mentions extracted, plus rating response to releases.

Metric that moves

Feature velocity

Monetisation Lead

App publishers
The problem

Competitor IAP pricing and regional price structures are hard to observe and rarely collected properly.

What we deliver

IAP tier names and prices by country with change detection, revealing pricing strategy and regional decisions.

Metric that moves

ARPU

Account / Strategy Lead

ASO and mobile agencies
The problem

Client reporting needs defensible rank and metadata data across portfolios and countries.

What we deliver

Consistent rank, rating and metadata panels across client and competitor apps, delivered on a reporting schedule.

Metric that moves

Client retention

Investment Analyst

Mobile and consumer funds
The problem

App diligence needs observable rank, rating velocity and monetisation structure rather than proprietary estimates.

What we deliver

Longitudinal rank, rating velocity, release cadence and IAP structure panels, with modelled estimates deliberately excluded.

Metric that moves

Diligence confidence

Competitive Intelligence Lead

Larger publishers
The problem

Tracking dozens of competitor apps across countries manually is not sustainable.

What we deliver

Full competitor portfolio monitoring with rank, metadata, IAP and release change alerting across tracked countries.

Metric that moves

Response time

Use cases

How app store data gets used in practice

Four patterns, with the outcome each is judged on.

Keyword and category rank tracking by country

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.

Competitor ASO change detection

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.

IAP pricing and regional strategy analysis

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.

Release cadence as a development velocity signal

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.

Engagement examples

Two engagements, anonymised

Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.

App publisher · US

Global rank averages hid country-level collapse

Situation

Rank reporting blended storefronts into one figure, so a sustained decline in two high-value countries was masked by growth elsewhere.

What we ran

Rank collection per store per country with category and keyword position tracked separately, plus rating trend by storefront.

Result

Country-level rank movement became visible and ASO effort was redirected to the affected storefronts.

Mobile agency · EU

Competitor ASO experiments were invisible

Situation

Client reporting described competitor positioning but could not show what competitors were actively testing in their listings.

What we ran

Daily metadata change detection on titles, subtitles, descriptions, screenshots and preview video across competitor sets.

Result

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 →

The 48-hour sample — run on your sources, not ours

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.

  • Real extraction from your actual sources
  • Returned inside two business days
  • Coverage and QA note included
  • You keep the data either way
  • No card, no trial clock
  • Named engineer on the call
Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.
How we engage

Three ways to engage us for this work

Same collection pipeline and same QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

  • Dedicated engineer assigned to your account
  • Site changes fixed by us, not reported to you
  • Scheduled delivery to your warehouse or S3
  • Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

  • On-demand and scheduled endpoints
  • Rate limits agreed to your load profile
  • Sandbox keys for integration testing
  • Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

  • Fixed scope agreed in writing upfront
  • Single delivery with full QA report
  • Methodology documented for your records
  • Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

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.

Build vs buy

Should you build app store collection in-house or hire it as a service?

Country-by-country rank collection multiplies volume fast, and store layout changes are frequent.

In-house build vs self-serve tool vs Actowiz managed service
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

Why country is a dimension and not a setting

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.

What varies by storefront

  • Rank. An app in the top 20 in one country may not chart in another. Rank is computed per storefront.
  • IAP pricing. Subscription tiers are priced by country, often at very different purchasing-power-adjusted levels.
  • Listing content. Titles, subtitles and screenshots are frequently localised, and localisation quality varies.
  • Ratings. Rating averages differ by country, sometimes substantially, which is itself a product quality signal by market.
  • Availability. Apps and specific IAP tiers are not available in every storefront.
  • Keyword sets. Relevant keywords differ by language and market, so a single tracked set is inadequate.

Cost implications, stated plainly

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.

Release notes are underrated, and they are fully observable

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.

What release history reveals

  • Development velocity. Release frequency over a trailing window is a real signal about team capacity and investment.
  • Feature direction. Notes name shipped features, which reveals roadmap direction months before marketing does.
  • Platform priority. Divergent iOS and Android release cadence shows where a publisher is investing.
  • Quality trajectory. A run of releases dominated by bug fixes reads differently from one shipping features.
  • Rating response. Joining rating trajectory to release dates shows which releases damaged or improved sentiment.

How we deliver it

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.

How it works

How an app store data engagement goes live in 5 to 10 business days

App set, countries and keyword sets are scoped first, since those three multipliers determine cost more than anything else.

Scope the sources and fields

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.

Pilot sample, free

We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.

Production build and QA harness

Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.

Scheduled delivery into your stack

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.

Ongoing monitoring and SLA support

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.

Formats & destinations

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.

Compliance & data ethics

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.

Service commitments

What we commit to, in writing

These are contractual, not marketing copy. They appear in the engagement document.

Service level commitments written into every managed engagement
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.

Why teams pick Actowiz for this work

  • Engineers, not a dashboard. You get people who fix breakages, not a self-serve tool you maintain yourself.
  • We tell you what we can't do. Scope limits and coverage gaps are stated before you sign, not discovered in month three.
  • QA is part of the service. Schema validation and sampled human review run before delivery, every run.
  • Compliance is documented. Sources, method and lawful basis written down so your legal team can review them.
  • Fixed monthly cost. No per-request metering, no surprise overage on a month when a competitor adds SKUs.
  • Six years, 40+ countries. Long-running production pipelines across retail, travel, mobility and finance.
Definitions

Terms used on this page

Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.

Storefront country
The country-specific version of an app store. Rank, in-app purchase pricing, listing content and availability all differ by storefront, which makes country a dimension rather than a setting.
Modelled estimate
A download or revenue figure inferred from rank position and sampled panels rather than observed. No store publishes these, so all such figures are models, and we exclude them from deliverables.
Release cadence
The frequency of published app versions over a trailing window. It is a genuine and fully observable development velocity signal, unlike modelled revenue.
FAQ

App store data: frequently asked questions

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.

See real app store data for your own app set

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.
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Advanced Computer Vision: Fine-grained object detection for precise product classification using text and image embeddings.
GPT-Based Analytics Layer: Natural language query-based reporting and visualization for business intelligence.
Human-in-the-Loop AI: Continuous feedback loop to improve AI model accuracy over time.
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Latest Insights & Resources

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Blog

EU AI Act for Data Teams: What Scrapers Must Change in 2026

The EU AI Act impact on web scraping & AI training data GPAI transparency, copyright reservations, prohibited practices & a compliance checklist from Actowiz.

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Case Study

B2B Supplier Automates Government Tender Discovery from GeM & eProcure

How a B2B supplier replaced manual tender-portal checking with an automated, filtered feed of relevant government tenders from GeM and CPP/eProcure never missing a bid deadline again.

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Report

FIFA World Cup 2026 Aftermath: Hotel & Airfare Normalization in Host Cities (Data Study)

Actowiz Solutions tracks post–World Cup 2026 travel pricing — hotel ADR & airfare normalization across host cities, event-premium decay data & lessons for travel teams.

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Free 500-row sample · No credit card · Response within 2 hours