Google Play Ratings by Country: Compare App Ratings Across Markets

Compares the star rating Google Play shows in each country for the apps you list, with star shares, price, spread and change since the last run, one report per app. Not affiliated with Google.

What it does

For every app you list, the actor opens its Google Play page once per country you choose and saves one report per app: the average rating Google Play shows in each country, the share of each star level, the local price, the change since your previous run, and a cross-country comparison (mean rating, best and worst country, spread). The pages are read live at run time.

Google Play computes the rating per storefront: on 6 November 2026, for example, com.booking read 4.7957 in the US, 4.7269 in Germany and 4.6715 in India. This actor puts those figures side by side in one row per app. It returns aggregate figures only: no review texts, reviewer names or developer details. It is not affiliated with or endorsed by Google.

Who it is for

• App store optimisation (ASO) and app marketing teams who watch how their app is rated in each market and spot a country where it slips.
• Product managers launching in new markets who check how the app, and its competitors, are rated there before and after the launch.
• Analysts who benchmark travel apps, games or any other apps against competitors country by country.

How to use it

1. Open the actor on Apify Store and go to its Input tab.
2. Fill in apps; every other field has a default (see Input below).
3. Click Start. The run ends at the limits below, or earlier at the "Maximum cost per run" you set.
4. When the run ends, open the Output tab: view the results as a table, or export them as JSON, CSV or Excel.

Input

Example input:

{
  "apps": [
    "com.booking"
  ],
  "countries": [
    "US",
    "DE",
    "IN"
  ],
  "maxApps": 1
}

For a weekly comparison in the 20 default markets, leave countries out and run the actor on an Apify schedule once a week. With compareWithPrevious on (the default), every report then shows the change since the week before:

{
  "apps": ["com.booking", "com.tripadvisor.tripadvisor", "com.expedia.bookings"]
}

Output

Each result is one dataset item:

Each entry of countries holds country, listed, rating, starShares (oneStar to fiveStar), price, currency and ratingChange; crossCountry holds countriesRated, meanRating, bestCountry, bestRating, worstCountry, worstRating and ratingSpread.

Example output (real items from a test run):

[
  {
    "url": "https://play.google.com/store/apps/details?id=com.booking",
    "appId": "com.booking",
    "title": "Booking.com: Hotels & Travel",
    "found": true,
    "category": "TRAVEL_AND_LOCAL",
    "ratingCount": 6525332,
    "minInstalls": 500000000,
    "countries": [
      {
        "country": "US",
        "listed": true,
        "rating": 4.7957,
        "starShares": {
          "oneStar": 1.8,
          "twoStar": 0.4,
          "threeStar": 1.6,
          "fourStar": 8.9,
          "fiveStar": 87.3
        },
        "price": 0,
        "currency": "USD",
        "ratingChange": 0
      },
      {
        "country": "DE",
        "listed": true,
        "rating": 4.7269,
        "starShares": {
          "oneStar": 2,
          "twoStar": 0.5,
          "threeStar": 1.8,
          "fourStar": 14.2,
          "fiveStar": 81.5
        },
        "price": 0,
        "currency": "EUR",
        "ratingChange": 0
      },
      {
        "country": "IN",
        "listed": true,
        "rating": 4.6715,
        "starShares": {
          "oneStar": 3.6,
          "twoStar": 0.9,
          "threeStar": 2.4,
          "fourStar": 11,
          "fiveStar": 82.1
        },
        "price": 0,
        "currency": "INR",
        "ratingChange": 0
      }
    ],
    "crossCountry": {
      "countriesRated": 3,
      "meanRating": 4.7314,
      "bestCountry": "US",
      "bestRating": 4.7957,
      "worstCountry": "IN",
      "worstRating": 4.6715,
      "ratingSpread": 0.1242
    },
    "previousFetchedAt": "2026-11-06T19:36:29.182Z",
    "fetchedAt": "2026-11-06T19:37:56.844Z"
  }
]

Download the dataset as JSON, CSV or Excel, or read it through the Apify API.

Pricing

Pay per event: a small fee per run plus a fee per result saved. Platform usage is included in these prices.

Example (lowest plan price): a run that saves 100 results costs $0.016 + 100 × $0.04 = $4.016. That is $40.00 per 1,000 results plus $0.016 per run. You pay only for results saved to the dataset; set "Maximum cost per run" in the run options and the actor stops there.

One report covers every country you ask for (up to 50), so the price per app does not grow with the number of countries. Apps that Google Play does not know are saved with found: false and are not charged. Examples: 10 apps in 20 countries cost $0.02 + 10 × $0.05 = $0.52 per run; the same run on a weekly schedule comes to four or five runs a month, $2.08 to $2.60.

Limits

• One run saves at most 200 results and loads at most 11,000 pages. Larger jobs: split the start URLs over several runs.

• Default memory 512 MB; Apify datacenter proxy.

• When a limit is reached, the run ends normally and keeps every result saved so far; the status message says which limit it was.

• One run reads up to 200 apps in up to 50 countries, so at most 10,000 pages; the page limit above leaves room for retries.

• The actor reads at most 2 pages per second and 3 at a time to keep its load on Google Play low, so a run of 50 apps in 20 countries (1,000 pages) takes at least 8 minutes.

• Aggregate figures only: no review texts, reviewer names, developer details or descriptions.

• Google Play scales every country's star bars to the global number of ratings, so the report gives each star level as a share in percent, not as a count. ratingCount and minInstalls are global figures.

• A country where Google Play has no page for the app answers 404; that country shows listed: false with empty figures.

• If Google answers with a CAPTCHA, an unusual-traffic page or a sign-in page, or keeps answering "too many requests", the run stops reading and says so in its status message; the reports saved before stay in the dataset.

• Not affiliated with or endorsed by Google. Google Play is a trademark of Google LLC.

Use with AI agents

AI agents can call this actor as a tool through the Apify MCP server. Add it to an MCP client (Claude Desktop, Cursor, VS Code and others):

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=saffo/play-ratings-by-country"
    }
  }
}

Or call it over HTTP and get the results in the response:

curl -X POST "https://api.apify.com/v2/acts/saffo~play-ratings-by-country/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" -H "Content-Type: application/json" \
  -d '{"apps":["com.booking"],"countries":["US","DE","IN"],"maxApps":1}'

An agent can answer "Where is this app rated lowest?" by calling the actor with apps and countries and reading crossCountry.worstCountry and crossCountry.worstRating from the report.

FAQ

How much does it cost?

You pay per event: from $0.04 per app rating report plus $0.016 per run. The Pricing section lists the price on every Apify plan. Platform usage is included, and the run stops at the maximum cost per run you set.

Can I use it from an AI agent or my own code?

Yes. Add it to any MCP client through the Apify MCP server, or call the Apify API; the examples under Use with AI agents show both. Results come back as JSON.

How many results can one run return?

Up to 200 results from at most 11,000 pages. For more, split the input over several runs.

Does it collect personal data?

No. It saves only the fields listed under Output and leaves out names, e-mail addresses, phone numbers and profiles of private persons.

Why does the rating differ by country?

Google Play computes the average rating per storefront from the ratings given in that country, so the same app can show 4.8 in one country and 4.6 in another on the same day. The actor reads the page of each country (the gl parameter) and reports each figure as Google Play shows it.

Why is ratingCount the same in every country?

Google Play reports the global total number of ratings on every storefront. For the same reason the star bars of each country add up to that global total, which is why the report turns them into shares.

Support

Open an issue on the actor's Issues tab.

Changelog

0.1 (2026-11-06)

• First release: one report per Google Play app comparing the rating, star shares and price across up to 50 countries, with mean, best and worst country, spread and the change since the previous run.