App Store Reviews

App Store Reviews

Scrape iOS App Store and Google Play reviews to extract pain points, praise, and feature requests from real mobile users. iTunes is API-only (no browser); Google Play needs an interactive browser session.

Output directory: ~/Desktop/{slug}_reviews_{YYYY-MM-DD}/

What It Extracts

Review Data

• Rating (1-5 stars)
• Review title and body
• Username and date
• Version reviewed
• Developer response (if any)

Aggregated Signals

• Rating distribution (1★ vs 5★ %)
• Most common words in negative reviews
• Feature requests extracted from reviews
• Competitor mentions ("switched from X", "better than Y")
• Churn signals ("cancelling", "uninstalling", "requesting refund")

Data Sources

Pipeline

Step 1: Find App IDs

# iOS — search iTunes API
APP_NAME="{APP_NAME}"
curl -s "https://itunes.apple.com/search?term=${APP_NAME// /+}&entity=software&limit=5" \
  | node -e "
    const r = JSON.parse(require('fs').readFileSync('/dev/stdin','utf8'));
    r.results.forEach(a => console.log(a.trackId, a.trackName, a.averageUserRating));
  "

# iOS — get app details
APP_ID="{IOS_APP_ID}"
curl -s "https://itunes.apple.com/lookup?id=${APP_ID}" \
  | node -e "
    const r = JSON.parse(require('fs').readFileSync('/dev/stdin','utf8'));
    const a = r.results[0];
    console.log(a.trackName, a.averageUserRating, a.userRatingCount);
  "

Step 2: Fetch App Store Reviews (RSS Feed)

# iOS App Store RSS — up to 500 most recent reviews
COUNTRY="us"
curl -s "https://itunes.apple.com/${COUNTRY}/rss/customerreviews/page=1/id=${APP_ID}/sortby=mostrecent/json" \
  | node -e "
    const d = JSON.parse(require('fs').readFileSync('/dev/stdin','utf8'));
    const reviews = d.feed?.entry || [];
    reviews.forEach(r => {
      if (!r.id || !r.content) return;
      console.log(JSON.stringify({
        rating: r['im:rating']?.label,
        title: r.title?.label,
        body: r.content?.label,
        author: r.author?.name?.label,
        date: r.updated?.label,
        version: r['im:version']?.label
      }));
    });
  "

# Get pages 2-5 (each page has ~50 reviews)
for PAGE in 2 3 4 5; do
  curl -s "https://itunes.apple.com/${COUNTRY}/rss/customerreviews/page=${PAGE}/id=${APP_ID}/sortby=mostrecent/json"
  sleep 1
done

Step 3: Google Play Reviews

Goal-driven cloud browser task. The agent describes what to extract and the tool runs the multi-step flow (open page, sort, scroll to load more, capture):

composio execute BROWSER_TOOL_CREATE_TASK -d '{
  "task":     "Sort reviews by newest, then scroll until at least 100 reviews are loaded. For each review extract: rating (1-5), title, body, username, date, and version. Return as a JSON array. Stop when 100 are captured or the page stops loading more.  Stop when: 100 reviews captured OR no new reviews load for 3 consecutive scrolls.",
  "startUrl": "https://play.google.com/store/apps/details?id={PACKAGE_NAME}&showAllReviews=true"
}'

# Capture watch_task_id from the response, then poll:
composio execute BROWSER_TOOL_WATCH_TASK -d '{ "taskId": "<watch_task_id>" }'
# Read response.data.output once response.data.status == "finished" and is_success == true.

Step 4: Analyze Reviews

Filter by rating:

• 1-2 star: pain points, churn reasons, bugs
• 3 star: mixed signals, missing features
• 4-5 star: love signals, what to preserve

Extract:

# Keyword frequency analysis from reviews
node -e "
  const reviews = require('./reviews.json');
  const keywords = ['crash', 'slow', 'bug', 'love', 'great', 'expensive', 'switched', 'missing', 'wish', 'please add'];
  const counts = {};
  keywords.forEach(k => counts[k] = reviews.filter(r => r.body.toLowerCase().includes(k)).length);
  console.log(JSON.stringify(counts, null, 2));
"

Output Format

---
app_name: {Name}
ios_app_id: {ID}
play_package: {com.example.app}
ios_rating: {N}/5 ({N} ratings)
play_rating: {N}/5 ({N} ratings)
reviews_analyzed: {N}
researched_at: {ISO date}
---

## Rating Distribution
| Stars | iOS | Google Play |
|-------|-----|-------------|
| ★★★★★ | {N}% | {N}% |
| ★★★★☆ | {N}% | {N}% |
| ★★★☆☆ | {N}% | {N}% |
| ★★☆☆☆ | {N}% | {N}% |
| ★☆☆☆☆ | {N}% | {N}% |

## Top Pain Points (1-2★ Reviews)
- "{review excerpt}" — {date}, {platform}
- "{review excerpt}" — {date}, {platform}

## Top Praise (4-5★ Reviews)
- "{review excerpt}" — {date}, {platform}

## Feature Requests (from reviews)
| Feature | Mentions | Example Quote |
|---------|----------|---------------|
| {feature} | {N} | "..." |

## Competitor Mentions
- {App name}: mentioned {N} times (switched to/from)

## Churn Signals
- "Uninstalling because..." — {N} mentions
- "Cancelling subscription because..." — {N} mentions

Key Use Cases

1. PMF research — what do users of competing apps actually want?
2. Competitive intelligence — where do competitor users get frustrated?
3. Feature validation — are users asking for what you're building?
4. Churn analysis — why do people uninstall or cancel?
5. Copy research — what language do real users use to describe the problem?