# App Review Competitive Report — Head-to-Head Comparison (`nexgenwatch/app-review-competitive-report`) Actor

Stop paying $50–100+/month for a review-intelligence subscription to compare your app against the competition.

- **URL**: https://apify.com/nexgenwatch/app-review-competitive-report.md
- **Developed by:** [NexGen Watch](https://apify.com/nexgenwatch) (community)
- **Categories:** Business, Marketing
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $16,750.00 / 1,000 competitive reports

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## App Review Competitive Report — Head-to-Head Sentiment, Themes, Complaints

**Stop paying $50–100+/month for a review-intelligence subscription to compare your app against the competition.**
This actor delivers a full **head-to-head competitive read across up to 5 apps** — the same deep per-app analysis *plus* the positioning layer — **per report, on demand, with no subscription.** Apps in, one competitive intelligence report out.

| | Typical review-intelligence SaaS | App Review Competitive Report |
|---|---|---|
| Pricing | **$50–100+/month**, every month | **$25.00 / report** (FREE tier), pay only when you run |
| Scope | One seat, rate-limited comparisons | **Up to 5 apps compared head-to-head, per report** |
| Commitment | Annual/monthly lock-in | None — per report |
| Break-even | — | Run a competitive read **every week (~$100/mo at FREE, less on volume tiers)** — and only in the weeks you actually need it |

If a PM, ASO consultant, or agency pulls **one competitive read per week**, that's ~$100/month at the FREE tier (and less on BRONZE/SILVER/GOLD) — versus $50–100+/month for an always-on seat you rarely max out, per analyst. One report already covers a 5-app landscape that a single-app tool would bill you five runs for.

### What you get in one report

Feed it **2 to 5 apps** (each `app_id` + `store`, optional `country`/`company_label`). For **every app** it fetches up to **1,000 public reviews** (logged-out, public data only) and runs the full **ngd-lexicon-v1** deep analysis — then adds the head-to-head layer:

**Per app (same depth as the single-app deep report):**

- **Full sentiment breakdown** — positive / neutral / negative split, average sentiment score, average star rating
- **Theme clustering** — the terms and phrases reviewers actually use, ranked by mentions
- **Complaint ranking** — negative-review clusters ranked by frequency, each with its share of negatives and a real example quote
- **Feature-request extraction** — reviews that ask for something, clustered into the most-requested asks
- **Trend vs prior period** — automatic median split, with sentiment and rating deltas

**Head-to-head positioning (across all apps):**

- **Relative sentiment leaderboard** — apps ranked by sentiment score, positive %, negative %, and average rating
- **Shared vs unique themes** — what reviewers raise across the whole category vs what's specific to one app
- **Shared vs unique complaints** — the pain everyone shares vs the pain that's yours (or your competitor's) alone
- **Winner by dimension** — who leads on sentiment, rating, fewest complaints, developer responsiveness, and raised demand
- **Overall composite positioning** — a single ranked standing across the leaderboard dimensions
- **Rendered markdown competitive brief** — the whole comparison as a readable report (a dataset field *and* a key-value-store record), ready to paste into a deck, battlecard, or ticket

### Output

- One **`competitive_summary`** dataset record — the full head-to-head report (leaderboard, shared/unique diffs, winners, composite ranking, roster) plus the rendered markdown brief.
- One **`app_report`** dataset record per compared app — the full deep analysis for that app.
- Markdown briefs are also written to the key-value store (`COMPETITIVE_<id>` for the comparison, `REPORT_<store>_<app_id>_<id>` per app).

### Input

| Field | Required | Description |
|---|---|---|
| `targets` | yes | Array of **2 to 5** apps. Each: `app_id` (Apple numeric id or Google Play package), `store` (`apple` or `google_play`), optional `country` (default `us`), `language` (Google Play, default `en`), `company_label` (display name) |
| `max_reviews` | no | Cap on public reviews fetched **per app** (default/max 1000) |
| `max_themes` | no | Max theme and complaint clusters per app (default 12) |

### Method — deterministic, transparent, model-included

Analysis runs the **ngd-lexicon-v1** engine: a transparent lexicon plus a rating prior — the identical engine used across the review-intelligence fleet, so scores are comparable app-to-app. It is fully **deterministic** — the same reviews always produce the same competitive report. **No external LLM, no API key to bring, no "AI" black box.** The model is included in the price.

### Pricing

| Event | FREE | BRONZE | SILVER | GOLD+ |
|---|---|---|---|---|
| Actor Start (`apify-actor-start`) | $0.05 | $0.05 | $0.05 | $0.05 |
| Competitive report (`competitive_report`) | $25.00 | $22.50 | $20.00 | $16.75 |

Prices are the filed pay-per-event amounts per plan tier (PLATINUM/DIAMOND match GOLD). Blocked and refused runs do not intentionally charge value events.

### Notes

- Public, logged-out data only. No login, no private endpoints.
- Cost-bounded: memory/time caps, a bounded per-page request-attempt budget, and a hard per-app review/output cap.
- An app that returns no public reviews is skipped and listed under `skipped_targets`; the report still runs as long as at least 2 apps have data.

# Actor input Schema

## `targets` (type: `array`):

The apps to compare head-to-head. Provide between 2 and 5 targets. Each target is an object with app\_id and store, plus optional country, language and company\_label.

## `max_reviews` (type: `integer`):

Hard cap on public reviews fetched for EACH app (max 1000).

## `max_themes` (type: `integer`):

Maximum theme and complaint clusters returned per app.

## `proxy_configuration` (type: `object`):

Optional Apify proxy settings; a fresh session is requested for every retry.

## Actor input object example

```json
{
  "targets": [
    {
      "app_id": "284882215",
      "store": "apple",
      "country": "us",
      "company_label": "Facebook"
    },
    {
      "app_id": "389801252",
      "store": "apple",
      "country": "us",
      "company_label": "Instagram"
    }
  ],
  "max_reviews": 1000,
  "max_themes": 12,
  "proxy_configuration": {
    "useApifyProxy": true
  }
}
```

# Actor output Schema

## `results` (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 = {
    "targets": [
        {
            "app_id": "284882215",
            "store": "apple",
            "country": "us",
            "company_label": "Facebook"
        },
        {
            "app_id": "389801252",
            "store": "apple",
            "country": "us",
            "company_label": "Instagram"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("nexgenwatch/app-review-competitive-report").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 = { "targets": [
        {
            "app_id": "284882215",
            "store": "apple",
            "country": "us",
            "company_label": "Facebook",
        },
        {
            "app_id": "389801252",
            "store": "apple",
            "country": "us",
            "company_label": "Instagram",
        },
    ] }

# Run the Actor and wait for it to finish
run = client.actor("nexgenwatch/app-review-competitive-report").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 '{
  "targets": [
    {
      "app_id": "284882215",
      "store": "apple",
      "country": "us",
      "company_label": "Facebook"
    },
    {
      "app_id": "389801252",
      "store": "apple",
      "country": "us",
      "company_label": "Instagram"
    }
  ]
}' |
apify call nexgenwatch/app-review-competitive-report --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/hmrODqDgbR3yRKAyI/builds/75MdQfOxKM0ylv5u9/openapi.json