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🤖 Google Play Store Scraper — App Metadata, Ratings, Reviews & Rankings

Bulk-extract Android app data from the Google Play Store: app title, developer, category, install range, rating histogram, review count, screenshots, description, version history, content rating, in-app purchase tiers, contains-ads flag, last-updated date, and category chart ranking. A pay-per-result alternative to Sensor Tower, AppFollow, Mobile Action, and data.ai (App Annie) — built for ASO managers tracking competitor app pages, mobile-product analysts sizing categories, ad-tech buyers measuring competitor reach, and ML teams building app-domain models.

Why Google Play Scraper Beats Sensor Tower, AppFollow, Mobile Action & data.ai

Most ASO + competitive-intel teams pick this actor instead of Sensor Tower because they only need raw Play Store data, not the full intelligence-platform suite — drop-in alternative to AppFollow for cataloging competitor apps, cheaper than Mobile Action for one-off pulls, and the JSON shape plugs straight into BigQuery without seat-license overhead.

What You Get Per App

Each dataset item is a flat record:

• app_id (package name, e.g. com.spotify.music)
• country — Play Store locale
• title, summary, description
• developer — {name, id, website, email, address}
• category, genre
• installs_range — e.g. "1B+", "500M-1B"
• installs_exact — when displayed
• rating, review_count
• rating_histogram — {1: n, 2: n, 3: n, 4: n, 5: n}
• price, currency, in_app_product_price_range
• contains_ads, contains_iap
• content_rating, target_audience
• screenshots[], icon_url, video_url
• version, version_history[] — {version, released_at, recent_changes}
• min_android_version, size_mb
• last_updated, released_at
• chart_rank_overall, chart_rank_category — by country
• reviews_sample[] — first 100 reviews with {rating, text, author, date, version}
• similar_apps[] — Play Store's "Similar apps" suggestions
• developer_apps[] — other apps by same developer
• permissions[] — declared Android permissions

Use Cases

• ASO managers — track competitor app pages weekly for description-keyword changes and rating drift
• Mobile-product analysts — size a category by summing installs_range across top-100 apps in country
• Ad-tech buyers — measure competitor app reach by contains_ads + install volume
• VC analysts — find breakout apps by tracking week-over-week rating + install range deltas
• ML / NLP teams — build app-domain training corpora from descriptions and review samples
• Security researchers — flag apps with anomalous permission lists in sensitive categories

Quick Start

from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("nexgendata/google-play-store-scraper").call(run_input={
"appIds":["com.spotify.music","com.instagram.android"],
"countries":["us","gb","br","in"],
"includeReviews":True,
"reviewsPerApp":100
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item["title"], item["rating"], item["installs_range"])

Pricing

Pay-per-event:

• Actor Start: small fixed charge per run (memory-scaled)
• Per app: $1 per 1,000 apps returned

No subscription, no minimum.

Related NexGenData Actors

FAQ

Are reviews always sampled? Only when you set includeReviews: true. Reviews count toward the per-result event but typically multiplies by your reviewsPerApp setting.

How are install ranges resolved? Play Store displays only ranges (e.g. "1M+"). The installs_exact field is populated when Google shows a more precise value on the app page.

Can I scrape paid + region-locked apps? Yes — pass the country corresponding to the storefront the app is available in.

Output formats? JSON, CSV, Excel, and the Apify dataset API.

Is this legal? Yes — Google Play storefront pages are public web pages.

About NexGenData

NexGenData publishes 260+ buyer-intent actors covering SEC filings, YC alumni, lead generation, competitive intelligence, stock fundamentals across 30+ exchanges, and more. All pay-per-result. Browse the full catalog at https://apify.com/nexgendata?fpr=2ayu9b

How NexGenData Pricing Works

Every NexGenData actor uses pay-per-event pricing — you only pay for results that actually land in your dataset. No monthly minimum, no seat fees, no surprise overage bills.

• Actor Start: a single-event charge each time you spin the actor up (scaled to memory size)
• Result / item: charged per item written to the default dataset
• No charge for retries, internal proxy rotation, or failed sub-requests — those are absorbed by the platform

Apify Platform Bonus

New to Apify? Sign up with the NexGenData referral link — you get free platform credits on signup (enough for several thousand free results) and you help fund the maintenance of this actor fleet.

Integration Surface

Every actor in the NexGenData catalog can be triggered from:

• Apify console — point-and-click run
• Apify API — REST + webhooks
• Apify Python / JS SDKs — programmatic batch
• Zapier, Make.com, n8n — official integrations
• MCP — many actors are exposed as MCP tools for Claude / ChatGPT / Cursor agents
• Schedules — built-in cron for daily / weekly / monthly runs
• Webhooks — POST results to any HTTPS endpoint on dataset write

Support

NexGenData maintains 260+ Apify actors and ships updates regularly. Bug reports via the Apify console issues tab get a response within 24 hours. Roadmap requests are welcome — high-demand features ship in the next version.

Home: thenextgennexus.com Full catalog: apify.com/nexgendata