Compare an App's App Store Reviews Across Countries

An app’s rating and its complaints can differ a lot from one App Store country to the next. Each country’s App Store keeps its own reviews and its own rating, so a US-only view tells you nothing about why India, Germany or Brazil feels different. Checking a dozen storefronts by hand, in several languages, is the kind of job that never gets done.

Try it live: App Store Reviews Scraper: iOS Reviews from 174 Countries. Pay per result delivered. Failed and empty results are never charged.

The App Store Reviews Scraper reads as many App Store countries as you list, or all 174, in one run. Every row says which country it came from and carries that country’s own rating, which makes a side by side comparison a short script.

What you get and who it is for

Field	Role in the comparison
country	The App Store the review was written in, upper case
app_rating, app_ratings_count	That country’s average and number of ratings
rating	Stars on this review, 1 to 5
title, text	The complaint or praise, in the language it was written in
reviewed_at, app_version	When, and on which build
app_id, app_name	The app, with the name that country’s store uses
sort	Which list the review came from, including the featured fallback

Product teams use it to find market-specific bugs, localization teams to spot translation and payment complaints, and analysts to size a competitor’s weak markets before entering them.

The input

{
  "apps": ["310633997", "324684580"],
  "countries": ["us", "gb", "in", "de", "br", "jp"],
  "maxReviewsPerCountry": 500,
  "sort": "mostRecent"
}

• apps takes numeric App Store ids or App Store links; these are WhatsApp and Spotify. The country inside a link is ignored.
• countries is the list of storefronts to read, as two letter codes. Set allCountries to true instead to read all 174.
• maxReviewsPerCountry at 500 takes the whole most recent list Apple’s feed offers in each country. For more history, set sort to both and raise it to 1,000.
• Leave onlyNewReviews off for this job. A comparison wants a full sample every time, not just what changed.

What the output looks like

Two rows, trimmed, from our run on October 7, 2026 for WhatsApp and Spotify in the US and GB. Apple’s recent feed came back empty on that run, so the actor delivered each country’s 10 featured reviews, marked featured. Reviewer names are masked and long texts cut.

[
  {
    "app_id": "310633997",
    "app_name": "WhatsApp Messenger",
    "country": "US",
    "review_id": "12389964257",
    "user_name": "B**************",
    "rating": 3,
    "title": "Requires update but won’t download",
    "text": "Over the weekend, the app began showing a message that required me to update in order to keep using the app. ...",
    "reviewed_at": "2025-03-06T21:36:14.000Z",
    "app_rating": 4.68,
    "app_ratings_count": 18723283,
    "sort": "featured",
    "position": 6,
    "app_url": "https://apps.apple.com/us/app/id310633997"
  },
  {
    "app_id": "310633997",
    "app_name": "WhatsApp Messenger",
    "country": "GB",
    "review_id": "12601429956",
    "user_name": "L*******",
    "rating": 2,
    "title": "Becoming unreliable",
    "text": "The last few months every now and then it just stops working and says that I have gone against some terms, ...",
    "reviewed_at": "2025-04-30T02:52:48.000Z",
    "app_rating": 4.7,
    "app_ratings_count": 4198565,
    "sort": "featured",
    "position": 3,
    "app_url": "https://apps.apple.com/gb/app/id310633997"
  }
]

The same run, summed up per app and country:

App	Country	app_rating	app_ratings_count	Featured 10, average stars	Featured 10 with 1 or 2 stars
WhatsApp Messenger	US	4.68	18,723,283	2.8	4
WhatsApp Messenger	GB	4.7	4,198,565	2.9	5
Spotify	US	4.77	42,454,826	4.5	0
Spotify	GB	4.74	6,992,827	3.8	3

The store averages sit within 0.03 of each other for each app, yet the reviews a visitor reads on the page differ: Spotify’s featured US reviews average 4.5 stars, its GB ones 3.8. Recent reviews split further. In our US run later that day, 41 of WhatsApp’s 100 newest reviews had 1 or 2 stars, and the keyword pass below put 9 of them under bans and login, 6 under updates and design, and 4 under spam, scams and hacking. Spotify’s 20 low-star US reviews leaned on ads: 10 matched ads and premium, 5 crashes and playback. In WhatsApp’s 20 newest GB reviews that morning, 6 had 1 or 2 stars, and 4 of those were about updates and design.

Run it in Apify Console

1. Open https://apify.com/themineworks/app-store-reviews-scraper and click Try for free.
2. In Apps, enter one app id or App Store link per line.
3. In Countries, enter one code per line, or switch on Read every App Store country.
4. Set Max reviews per app in each country to 500 and Sort to Most recent first.
5. Click Start and watch the Output tab fill country by country.
6. Click Export and download CSV, JSON or Excel, then pivot on country.

Run it from Python

pip install apify-client, then:

import csv
from collections import Counter, defaultdict
from statistics import mean
from apify_client import ApifyClient

THEMES = {
    "bans and login": ["bann", "ban ", "restrict", "deactivat", "log in", "login", "appeal"],
    "ads and premium": [" ads", "adds", "commercial", "premium"],
    "updates and design": ["update", "bubble", "layout", "design"],
    "spam, scams, hacking": ["spam", "scam", "hack"],
    "crashes and playback": ["crash", "freez", "lag", "restart", "shuffle", "not working"],
}

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("themineworks/app-store-reviews-scraper").call(run_input={
    "apps": ["310633997", "324684580"],
    "countries": ["us", "gb", "in", "de", "br", "jp"],
    "maxReviewsPerCountry": 500,
    "sort": "mostRecent",
})

groups = defaultdict(list)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item.get("_type") == "info":
        continue  # the run summary row, never billed
    groups[(item["app_id"], item["country"])].append(item)

summary = []
for (app_id, country), reviews in sorted(groups.items()):
    low = [r for r in reviews if r["rating"] <= 2]
    themes = Counter()
    for r in low:
        blob = f" {r.get('title', '')} {r.get('text', '')} ".lower()
        for theme, words in THEMES.items():
            if any(w in blob for w in words):
                themes[theme] += 1
    summary.append({
        "app_id": app_id,
        "app_name": reviews[0]["app_name"],
        "country": country,
        "store_rating": reviews[0].get("app_rating"),
        "store_ratings_count": reviews[0].get("app_ratings_count"),
        "reviews_read": len(reviews),
        "avg_stars_read": round(mean(r["rating"] for r in reviews), 2),
        "low_star_reviews": len(low),
        "top_complaints": "; ".join(f"{t} ({n})" for t, n in themes.most_common(3)),
        "sorts": ",".join(sorted({r["sort"] for r in reviews})),
    })

with open("reviews_by_country.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.DictWriter(f, fieldnames=list(summary[0]))
    writer.writeheader()
    writer.writerows(summary)

for row in summary:
    print(row["app_name"], row["country"], row["store_rating"],
          row["avg_stars_read"], row["low_star_reviews"], row["top_complaints"])

The result is one row per app and country with the store’s own rating, the average of the reviews read, the low-star count and the three most common complaint themes. The sorts column shows featured wherever the fallback kicked in, so you never compare a featured sample against a recent one without noticing. The theme lists are a starting point: replace them with the problems your app actually has. For something stronger than keyword matching, scoring the text with a sentiment pipeline follows the same shape, with reviews in place of Reddit posts.

Repeat it every month

Country gaps move with releases and pricing changes, so a monthly snapshot is a good rhythm. In Apify Console, save the input as a task, open Schedules, create a schedule with 0 7 1 * * (07:00 on the first of each month) and pick the task. Keep onlyNewReviews off so each month is a complete sample. Apify’s Google Sheets integration can add each run to a sheet, or a webhook on a successful run can trigger your own copy of the script above.

What it costs

$0.10 per 1,000 reviews on the Bronze plan, and the same $0.10 on Silver, Gold and the higher plans, plus a flat $0.005 per run. Two apps in six countries at up to 500 reviews each is at most 6,000 reviews: $0.60 plus $0.005. Our every country test run read WhatsApp in all 174 countries at 20 reviews each and returned 3,342 reviews from 173 of them, about $0.33 plus $0.005. Countries where the app is not sold, and countries with no written reviews, cost nothing.

Limits worth knowing

• Apple’s feed holds 500 reviews per country for each sort order, about 1,000 with both.
• Small markets return little. In the every country run, a few countries had only 1 to 7 reviews, so keep the review count next to every average.
• app_rating covers every star rating in the country, and the reviews are only the latest written ones. Compare countries on the same measure.
• Keyword themes only catch the language they are written in. For jp, de or br, add local terms or translate the text before matching.
• When Apple’s feed stays empty, you get the 10 featured reviews instead: older, longer and without app_version or votes.

Related

• Monitor New App Store Reviews for Your App Every Day, the companion guide for daily alerts
• Mining 1-star reviews to validate an idea, a method for turning complaints into decisions
• Amazon Reviews Scraper in Python, the same review analysis for physical products
• App Store Reviews Scraper on The Mine Works