# Mobile App Review Intelligence (`glowing_glove/mobile-app-review-intelligence`) Actor

Turn recent public Apple App Store reviews into compact product intelligence with complaint themes, praise themes, version signals, and recommended actions.

- **URL**: https://apify.com/glowing\_glove/mobile-app-review-intelligence.md
- **Developed by:** [Ushba Khan](https://apify.com/glowing_glove) (community)
- **Categories:** E-commerce, Social media, Automation
- **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$24.00 / 1,000 analyzed apps

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Mobile App Review Intelligence

Mobile App Review Intelligence turns recent public Apple App Store reviews into product, support, ASO, and competitor-research signals.

Instead of exporting a wall of raw review text, the actor analyzes the latest public reviews for each app and returns one compact row per app. The row highlights what users are complaining about, what they praise, which app versions are mentioned, and what product or support teams should investigate next.

### What You Can Use It For

- Monitor competitor app review pain points before planning your own roadmap.
- Find recurring complaints after an app update.
- Identify support, login, billing, crash, performance, and trust issues from recent reviews.
- Build ASO and product research dashboards from structured review intelligence.
- Track whether negative reviews cluster around specific app versions.
- Summarize public customer language for product marketing, support macros, and release planning.

### Input

Provide Apple App Store app URLs or numeric app IDs.

```json
{
  "appUrlsOrIds": [
    "284882215",
    "https://apps.apple.com/us/app/x/id333903271"
  ],
  "country": "us",
  "maxReviews": 30,
  "maxReviewPages": 2
}
```

### Output

Each successful dataset row is focused on buyer-facing app intelligence:

- `appName`
- `appId`
- `appStoreUrl`
- `currentVersion`
- `sellerName`
- `primaryGenre`
- `storeAverageRating`
- `storeUserRatingCount`
- `reviewsAnalyzed`
- `sampleAverageRating`
- `negativeReviewRate`
- `latestReviewDate`
- `recentReleaseNotes`
- `versionSignals`
- `complaintThemes`
- `praiseThemes`
- `productOpportunities`
- `reviewEvidence`

### How The Actor Works

The actor uses public Apple sources:

- iTunes Lookup API for app metadata.
- Apple App Store customer review feeds where Apple returns recent public review entries.
- Public App Store page review highlights and version history as a fallback when review feeds are sparse for a country or app.

It then classifies review text into practical themes such as crashes, login problems, subscription issues, performance, update bugs, support, notifications, privacy, and trust. The output keeps only short evidence snippets and summary signals, so the dataset is useful in a spreadsheet or automation workflow without cleanup.

### Practical Workflow

1. Add competitor or owned app IDs.
2. Start with 20-30 reviews per app for a quick check.
3. Review complaint themes and negative review rate.
4. Compare version signals to detect update-related regressions.
5. Export results to your product dashboard, support workflow, or ASO research sheet.

### Notes

- This actor analyzes public Apple App Store data only.
- It does not access private developer dashboards, user accounts, or non-public review data.
- Review availability can vary by country and app.
- The actor is designed for product research and monitoring, not for legal, medical, financial, or compliance conclusions.

# Actor input Schema

## `appUrlsOrIds` (type: `array`):

Apple App Store app URLs or numeric app IDs.

## `country` (type: `string`):

Two-letter App Store country code used for reviews and metadata.

## `maxReviews` (type: `integer`):

Maximum recent public reviews to analyze for each app.

## `maxReviewPages` (type: `integer`):

How many App Store RSS review pages to request per app.

## `requestTimeoutSecs` (type: `integer`):

Maximum time to wait for each Apple public endpoint request.

## `maxConcurrency` (type: `integer`):

How many apps to analyze at the same time.

## `proxyConfiguration` (type: `object`):

Optional proxy configuration. Most App Store review runs work without proxy.

## Actor input object example

```json
{
  "appUrlsOrIds": [
    "284882215",
    "333903271"
  ],
  "country": "us",
  "maxReviews": 30,
  "maxReviewPages": 2,
  "requestTimeoutSecs": 25,
  "maxConcurrency": 3,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# Actor output Schema

## `results` (type: `string`):

No description

## `summary` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "appUrlsOrIds": [
        "284882215",
        "333903271"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("glowing_glove/mobile-app-review-intelligence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "appUrlsOrIds": [
        "284882215",
        "333903271",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("glowing_glove/mobile-app-review-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "appUrlsOrIds": [
    "284882215",
    "333903271"
  ]
}' |
apify call glowing_glove/mobile-app-review-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=glowing_glove/mobile-app-review-intelligence",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/XfXaM4cOi6h4C5GeJ/builds/qH0aMzLG1AkP2U0mP/openapi.json