App Review Intelligence API | App Store + Google Play Summary

Scrape user reviews from both the Apple App Store and Google Play simultaneously to extract critical product feedback and mobile search visibility data. Instead of running separate workflows for iOS and Android, this app review scraper unifies both storefronts into a single executive summary row. It is designed for App Store Optimization (ASO) specialists, product managers, and growth marketers who need to schedule recurring state checks on their mobile applications. Extract highly structured data directly from the stores to track overall sentiment, analyze recurring complaint themes, and catch sudden rating drift before it impacts your organic rankings.

The web crawler bypasses the manual work of stitching two massive datasets together. Every time you run the scraper, you get a clean, actionable summary that clusters user feedback into distinct themes and provides immediate recommended actions based on real user comments. You can easily schedule daily or weekly scrapes to monitor your app's performance over time, keeping a close eye on competitor updates or new feature rollouts. The extracted details include cross-platform rating averages, flagged negative reviews, theme clusters, and direct platform warnings. Stop wasting time manually browsing store pages and instantly gather the vital user feedback you need to improve your mobile visibility.

Store Quickstart

• Start with store-input.example.json for the lowest-friction first run.
• Check sample-output.example.json for the current starter proof and the exact summary row shape buyers see first.
• Check live-proof.example.json for the latest live canary + contract capture on the public actor surface.
• Then use store-input.templates.json as the familiar Wave 1 ladder:
  • Quickstart Baseline (Cross-Platform Summary) — smallest useful dataset run for one product
  • Recurring Baseline (Scheduled Summary Checks) — same summary workflow tuned for repeat monitoring
  • Webhook Routed Summary (Cross-Platform Alerts) — send the same payload into Slack, Zapier, or an internal monitor once dataset output already fits
  • App Store Only Baseline / Google Play Only Baseline — actor-specific fallback lanes when only one storefront matters right now

What first success looks like

A good first run gives you:

1. meta.executiveSummary — one buyer-ready brief with status, cadence, reviewed platforms, and the top complaint themes.
2. results[] — one shareable product row with rating drift, recurring complaints, praise themes, and recommended actions.
3. reviewCoverage, warnings, and sourceFailures — explicit honesty about shallow Google Play review depth or any missing storefront signal.

Key Features

• 📱 Cross-platform baseline — Merge Apple App Store and Google Play public signals into one product row
• 🧠 Theme clustering — Group recurring complaint and praise themes into stable buckets
• 🚨 Rating drift detection — Flag sudden softness between long-term store ratings and current public review samples
• ⚠️ Honest source handling — Preserve warnings and per-platform failures instead of hiding degraded coverage
• 📡 Dataset or webhook delivery — Feed dashboards, Slack alerts, CRM tasks, or internal monitors

Use Cases

Who	Why
Product teams	Track recurring complaint clusters after releases without stitching two stores together manually
ASO teams	Compare rating softness, review themes, and storefront changes across iOS and Android
Support / QA	Turn public review pain points into triage queues or webhook alerts
Market researchers	Build consistent competitor baselines from both major app stores

Input

Field	Type	Default	Description
productName	string	prefilled	Optional label for the monitored product. If empty, the actor uses the best store title it finds.
country	string	"us"	Default ISO country code for both stores. App Store lookups and review feeds use lowercase values like us, gb, jp.
language	string	"en_US"	Google Play language code for the public page request, for example en_US.
appleApp	object	prefilled	Optional App Store target. Provide {"appId":"570060128"} or {"url":"https://apps.apple.com/us/app/id570060128"}.
googlePlayApp	object	prefilled	Optional Google Play target. Provide {"appId":"com.duolingo"} or a full play.google.com URL.
reviewLimitPerPlatform	integer	20	Maximum reviews to analyze per platform. Google Play usually exposes about 20 public review cards without deeper paginat
lookbackDays	integer	180	Prefer reviews inside this window. If a source has no matches inside the window, the actor falls back to the public samp
minThemeMentions	integer	2	Minimum complaint mentions before a theme is treated as a real cluster instead of one-off noise.

Input Example

{
  "productName": "Duolingo",
  "country": "us",
  "language": "en_US",
  "appleApp": {
    "appId": "570060128",
    "country": "us"
  },
  "googlePlayApp": {
    "appId": "com.duolingo",
    "country": "us",
    "language": "en_US"
  },
  "reviewLimitPerPlatform": 20,
  "lookbackDays": 180,
  "minThemeMentions": 2,
  "delivery": "dataset",
  "dryRun": false
}

Output

Field	Type	Description
meta	object
results	array
results[].productKey	string
results[].productName	string
results[].storesCovered	array
results[].status	string
results[].summary	string
results[].recommendedCadence	string
results[].reviewsAnalyzed	number
results[].reviewCoverage	object
results[].ratingDrift	object
results[].recommendedActions	array
results[].topThemes	array
results[].praiseThemes	array
results[].recurringComplaints	array
results[].platforms	object

Output Example

{
  "meta": {
    "generatedAt": "2026-04-04T09:30:00.000Z",
    "warnings": [
      "Google Play public HTML exposed 4 review cards in the fixture sample; deeper pagination is intentionally disabled to keep maintenance low."
    ],
    "sourceFailures": [],
    "totals": {
      "products": 1,
      "platformsRequested": 2,
      "platformsSucceeded": 2,
      "platformsFailed": 0,
      "reviewsAnalyzed": 7,
      "recurringComplaints": 2,
      "complaintThemes": 4
    },
    "executiveSummary": {
      "overallStatus": "urgent",
      "brief": "Duolingo is showing a softer public review sample than its long-term store ratings, with monetization and stability complaints persisting across platforms.",
      "recommendedCadence": "daily",
      "reviewedPlatforms": 2,
      "totalReviewsAnalyzed": 7,
      "recurringComplaintCount": 2,
      "topComplaintThemes": [
        {
          "id": "monetization",
          "label": "Pricing, ads & paywall",
          "complaintMentions": 2,
          "platforms": [
            "appStore",
            "googlePlay"
          ]
        },
        {
          "id": "stability",
          "label": "Crashes, freezes & lost progress",
          "complaintMentions": 2,
          "platforms": [
            "appStore",
            "googlePlay"

Proof buyers can inspect

Buyer question	Proof asset	What it proves
"Will the first run already be usable in a product or growth review?"	store-input.example.json + sample-output.example.json	The quickstart returns one summary-first row with executive summary, rating drift, theme clusters, and recommended actions.
"Is the public actor still working live on Apify?"	live-proof.example.json	The latest canary + contract capture passed on the public actor surface.
"What is the safest way to expand without changing the output shape?"	store-input.templates.json	The quickstart, recurring, webhook, and single-store fallback templates keep the same summary-first lane while widening scope.

API Usage

Run this actor programmatically using the Apify API. Replace YOUR_API_TOKEN with your token from Apify Console → Settings → Integrations.

cURL

curl -X POST "https://api.apify.com/v2/acts/taroyamada~app-review-cross-platform-intelligence/run-sync-get-dataset-items?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{ "productName": "Duolingo", "country": "us", "language": "en_US", "appleApp": { "appId": "570060128", "country": "us" }, "googlePlayApp": { "appId": "com.duolingo", "country": "us", "language": "en_US" }, "reviewLimitPerPlatform": 20, "lookbackDays": 180, "minThemeMentions": 2, "delivery": "dataset", "dryRun": false }'

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("taroyamada/app-review-cross-platform-intelligence").call(run_input={
  "productName": "Duolingo",
  "country": "us",
  "language": "en_US",
  "appleApp": {
    "appId": "570060128",
    "country": "us"
  },
  "googlePlayApp": {
    "appId": "com.duolingo",
    "country": "us",
    "language": "en_US"
  },
  "reviewLimitPerPlatform": 20,
  "lookbackDays": 180,
  "minThemeMentions": 2,
  "delivery": "dataset",
  "dryRun": false
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

JavaScript / Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });
const run = await client.actor('taroyamada/app-review-cross-platform-intelligence').call({
  "productName": "Duolingo",
  "country": "us",
  "language": "en_US",
  "appleApp": {
    "appId": "570060128",
    "country": "us"
  },
  "googlePlayApp": {
    "appId": "com.duolingo",
    "country": "us",
    "language": "en_US"
  },
  "reviewLimitPerPlatform": 20,
  "lookbackDays": 180,
  "minThemeMentions": 2,
  "delivery": "dataset",
  "dryRun": false
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Tips & Limitations

• Run weekly for trend tracking; daily for competitive launch monitoring.
• Use webhook delivery to push insights into your BI tools or marketing Slack.
• Archive results in Apify Dataset for your own historical trend analysis.
• Combine with ai-visibility-monitor-actor for AI-era brand surface coverage.
• Start with a small watchlist, iterate on keyword precision before scaling.

FAQ

What platforms are covered?

This actor covers public Apple App Store and Google Play storefront signals only. If you want single-store runs, use the dedicated sibling actors.

Can I run only one platform?

Yes. Provide only appleApp or only googlePlayApp and the actor still returns a valid single-platform summary row.

How complete are the review samples?

They are intentionally public-surface only. Apple can expose more review rows through RSS than Google Play, while Google Play may fall back to aggregate-only coverage.

Can I send the summary somewhere else?

Yes. Keep delivery: "dataset" for inspection or switch to delivery: "webhook" to push the same summary payload into Slack, Zapier, n8n, or your own endpoint.

Related Actors

Use this actor first when you need one buyer-ready summary across iOS and Android. Add the adjacent actor only when you need the deeper layer below:

• Apple App Store Intelligence API — Add after the cross-platform baseline when the iOS team needs deeper App Store review coverage, pricing checks, or storefront-specific QA.
• Google Play Intelligence API — Add when Android launches, geo tests, or Play-only warning handling need a dedicated package / country / language watchlist.
• Shopify Store Intelligence API | Catalog & Merch Signals — Add when the same brand also sells through Shopify and you want to connect app complaints with catalog, merchandising, or launch-page changes.
• Shopify App Store Review Intelligence — Add when the company also sells software to Shopify merchants and you want to compare consumer app complaints with merchant onboarding, billing, or support complaints.
• Domain Security Audit API | SSL Expiry, DMARC & Security Headers — Add when review pain points may stem from login, billing, or checkout trust issues on the brand's owned domains.

Pricing & Cost Control

Apify Store pricing is usage-based. This actor keeps start friction low and charges the buyer-ready app summary row:

• apify-actor-start: $0.00005
• apify-default-dataset-item: $0.015 per app summary row

The current implementation emits one dataset row per monitored app. Raw review-row pricing is reserved for a future explicit PPE event if raw review export is added.

• Start with the Quickstart Baseline on 1 product in dataset mode while you validate summary quality.
• Keep reviewLimitPerPlatform modest for the Recurring Baseline; raise it only when deeper complaint analysis changes decisions.
• Use the single-store variants when only one storefront matters, then move to Webhook Routed Summary once the downstream route is stable.
• Use dryRun: true before scheduled runs or webhook delivery.

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