Competitor Review Analyzer: Brand Comparison

Compare customer-feedback themes supported by at least two explicitly labeled brands. Findings are hypotheses to check against actual product capabilities, not verified competitor weaknesses.

First run

Preview the output: run the input below with no records, dataset IDs or app IDs. It uses labeled synthetic records with no report-event fee. After the run, open Markdown report for the readable result or JSON report for integration.

{
  "demoMode": true
}

The preview uses example.com source URLs and fictional feedback. It is a format demonstration, not customer or competitor research.

Analyze your own data

Include brand on every record and supply evidence about the same topic for at least two brands. Two brands discussing unrelated topics do not create a comparison. Include text and original sourceUrl so every comparison remains reviewable.

For an existing dataset, select it with the dataset picker in the input form. The API equivalent is below; replace the placeholder before running:

{
  "demoMode": false,
  "sourceType": "dataset",
  "datasetIds": [
    "YOUR_LABELED_COMPETITOR_REVIEW_DATASET_ID"
  ],
  "maxRecords": 1000,
  "maxInsights": 20
}

You can instead paste objects into records. Each record should contain text and its original sourceUrl; optional fields include id, platform, brand, location, rating, date, likes and title. Supplied sources override synthetic demo mode. Dataset reads do not rerun the collector. Any separate upstream collection workflow is outside this report price.

What comes back

One report row in the default dataset contains an insights array, analyzed-record count and finding count. The output links also provide REPORT.md and REPORT.json in the key-value store. Use JSON to preserve nested evidence; use the Markdown report for review.

Comparisons include per-brand sample counts, excerpts and complaint-supported hypotheses within shared topics. Unlabeled records or topics without at least two brands cannot establish a cross-brand comparison.

Record limits and sample comparison

maxRecords caps raw current records examined at 1–1,000 before deduplication and filtering. maxInsights caps findings at 1–30. A small or unmatched batch can return fewer findings, including zero. The fee is per delivered report, not per finding.

previousDatasetId remains in the input schema for compatibility. Prior-sample comparisons are implemented only in App Store Review Feature Roadmap; leave that field blank in this workflow.

Pricing

$0.25 per delivered report, covering at most 1,000 current raw records. This includes analysis of inline records, existing datasets or native Apple reviews. The actor uses one report-delivered event and no additional dataset-row event. See the Pricing tab for the current platform price.

Synthetic previews on the imported-data workflows have no report-event fee. The App Store live sample uses actual reviews and the normal $0.25 report price.

Method and recurring use

Analysis uses deterministic English phrase/rating rules and text grouping; no generative AI model is called. Scores and draft actions are research aids. Sarcasm, negation, uncommon phrasing and multilingual text can be misclassified. Inspect supporting evidence before acting. Topics can overlap, so their percentages must not be added together. Samples do not establish market-wide demand, product capabilities or future business results.

Use the Actor API or Apify MCP with the same input schema. Imported datasets are snapshots; provide fresh source data for repeated analysis. This actor does not directly scrape YouTube, Reddit or Google Maps or start third-party collectors. No external model key is required.

Local run

Requires Node.js 22 or newer. Run npm ci, then npm start. The Dockerfile uses apify/actor-node:22.

Input example

{
  "demoMode": true
}

All fields are described in the Input tab, and the same JSON works in the API and for AI agents.

Use Competitor Review Analyzer with AI agents (MCP)

Give Claude, ChatGPT, Cursor, VS Code or any MCP client this Actor as a tool through the hosted Apify MCP server. The agent sees the input fields with descriptions and example values, runs the Actor, and reads the results.

MCP server URL (this Actor only):

https://mcp.apify.com/?tools=technicaldost/competitor-review-gap-analyzer

Claude Code:

$claude mcp add --transport http apify "https://mcp.apify.com/?tools=technicaldost/competitor-review-gap-analyzer" --header "Authorization: Bearer YOUR_APIFY_TOKEN"

Cursor, VS Code, Claude Desktop and other clients (mcp.json or the client's MCP settings):

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=technicaldost/competitor-review-gap-analyzer",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}

Clients that support sign-in (such as Claude.ai connectors) can use the URL without a token and log in to Apify instead. Get your token in Apify Console → Settings → API & Integrations, or generate a ready config for your client at mcp.apify.com. In the agent the tool is named technicaldost--competitor-review-gap-analyzer; ask it, for example, to "run Competitor Review Analyzer with {"demoMode": true}".

AI agents without an Apify account can also run and pay for this Actor per request with x402 agentic payments.

Run Competitor Review Analyzer via API

Run the Actor and get its results in one call (replace YOUR_APIFY_TOKEN):

curl -X POST "https://api.apify.com/v2/acts/technicaldost~competitor-review-gap-analyzer/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"demoMode": true}'

Python (pip install apify-client):

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("technicaldost/competitor-review-gap-analyzer").call(run_input={"demoMode": true})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

JavaScript / TypeScript (npm install apify-client):

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('technicaldost/competitor-review-gap-analyzer').call({"demoMode": true});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Long runs: start them with POST /v2/acts/technicaldost~competitor-review-gap-analyzer/runs and fetch the dataset when the run finishes, or receive a webhook.

Related Actors

• App Store Review Analyzer: Feature Roadmap — Collect public Apple App Store review feeds or import reviews, then group feature requests, usability complain
• Google Maps Review Analyzer: Service Gaps — Analyze exported Google Maps review datasets or inline records for service-gap themes, recurring complaints an
• App Store Review Scraper (Apple iOS) — Scrape Apple App Store reviews and app metadata: rating, title, text, author, version, plus average rating and
• B2B SaaS Review Buyer Intent Monitor — Extract review-site complaints, alternatives, and buyer-intent signals for SaaS competitors

FAQ

Can I use Competitor Review Analyzer with AI agents like Claude or ChatGPT?

Yes. Connect the Apify MCP server with ?tools=technicaldost/competitor-review-gap-analyzer (see above) and the agent can run the Actor and read its results on its own.

Can I call Competitor Review Analyzer from my own code?

Yes. Use the REST API, or the official Python and JavaScript clients shown above. Results are available as JSON, CSV, Excel, XML or HTML.

How do I run it on a schedule?

Save your input as a task in Apify Console and add a schedule (for example, daily). Use webhooks or integrations to send each run's results onward.

Is it legal to use this Actor?

It works with publicly available data. Make sure your use complies with the source website's terms and with data protection laws such as GDPR, especially for personal data.

Something doesn't work or I need a feature. What should I do?

Open an issue in the Issues tab on this Actor's page with an example input, and we'll look into it.