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📱 iOS App Store Reviews Scraper — Ratings, Sentiment & Version Deltas

Bulk-extract user reviews from the Apple App Store for any iOS app, in any country storefront. Returns review text, star rating, author, date, app version, helpful-vote counts, and developer responses — clean JSON ready for sentiment analysis, ASO competitor monitoring, or product feedback dashboards. A cheaper, pay-per-result alternative to AppFollow, Sensor Tower, Mobile Action, and App Annie (data.ai) — purpose-built for ASO teams, mobile-product PMs, and competitive-intelligence analysts who need raw review data without a five-figure seat license.

📊 Sample Output

👁 iOS App Store reviews scraper for app analytics, sentiment monitoring, competitor review tracking, product feedback aggregation — review text, star rating, app version, author, and date for product teams, marketers, and competitive intelligence

Why iOS App Store Reviews Scraper Beats AppFollow, Sensor Tower, Mobile Action & data.ai

Most ASO + product teams pick this actor instead of AppFollow because it is a drop-in alternative that runs on demand, is cheaper than Sensor Tower for review-only workflows, and the JSON shape (rating, text, version, country) plugs straight into BigQuery, Snowflake, or a sentiment-analysis pipeline without manual export gymnastics.

What You Get Per Review

Each dataset item is a flat record:

• app_id — Apple numeric app ID
• country — storefront country code (e.g. us, gb, jp)
• rating — 1-5 stars
• title — review title
• body — full review text
• author — reviewer display name
• date — ISO 8601 review timestamp
• app_version — version the review was filed against
• helpful_count — community-vote helpfulness score
• developer_response — {text, date} if developer replied
• is_edited — boolean flag for edited reviews

Use Cases

• ASO managers — track star rating + sentiment trend after each version release to catch crash-driven 1-star spikes within hours
• Competitive-intel teams — pull a competitor's full review stream to surface feature gaps customers are vocally requesting
• Product managers — feed reviews into an LLM to auto-cluster pain points by theme (paywall, onboarding, perf)
• Customer-support leaders — watch for support-flagged reviews across non-English storefronts you don't natively read
• ML / NLP teams — build training datasets of star-labelled review text for sentiment-model fine-tuning
• Investors — measure app momentum and user satisfaction before a public IPO or M&A diligence

Quick Start

from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("nexgendata/ios-app-store-reviews-scraper").call(run_input={
"appIds":["284882215","310633997"],
"countries":["us","gb","ca"],
"maxReviews":500
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item["rating"], item["app_version"], item["body"][:80])

Pricing

Pay-per-event:

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

No subscription, no minimum, no per-seat fee.

Related NexGenData Actors

FAQ

Does this require an Apple Developer account or API key? No — it works against the public storefront feed. Bring an Apify token, nothing else.

How fresh is the data? Reviews are fetched live at run time. Apple's public storefront feed typically reflects new reviews within a few hours of submission.

What's the deepest review history I can pull? Apple exposes the most-recent ~500 reviews per (app, country) pair. For longer histories, schedule the actor daily and append into your own warehouse.

Output formats? JSON, CSV, Excel, RSS, and the Apify dataset API — all standard.

Is this legal? Yes. The public storefront feed is intentionally exposed by Apple for third-party indexing and review monitoring.

🆘 Troubleshooting

• No reviews returned — verify the app ID is the numeric App Store ID (e.g. 389801252 for Instagram), not the app name or bundle ID.
• Fewer reviews than expected — Apple's public RSS exposes a limited recent window per country; raise maxReviews and add more country codes to widen coverage.
• Wrong language reviews — reviews are returned per storefront country; set the countries you care about (e.g. us, gb, jp).
• Empty for a brand-new app — apps with no ratings yet return nothing; confirm the app has public reviews in that country's store.
• Rate hiccups — the actor backs off and retries automatically; just re-run if a transient network error appears.

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