Review Monitor

Give it a watchlist of hotels, local businesses, apps or SaaS products (their pages on Booking.com, Agoda, Trip.com, Tripadvisor, Google Maps, Google Hotels, Yelp, Google Play, the App Store, Capterra or TrustRadius) and every run returns what changed since the last one: an alert row per new 1★ or 2★ review and a summary row per entity with rating drift, 30-day vs lifetime average, owner-reply rate and lag, review velocity and, for apps, per-version rating regressions. $12 per 1,000 entity checks and $2 per 1,000 alerts; you pay only for rows you get.

$12.00 per 1,000 entities checked

Why this actor

• 11 review sources, one feed and one row shape, deduplicated.
• Flags, not raw dumps. flags lists what needs attention (new_negative, rating_drop, unanswered_negative, velocity_spike and more).
• Cheap first run. The first run per entity is a baseline at the lower baseline price; alerts start with the second.
• No personal data. No reviewer names or avatars; failed URLs come back as free error rows with a code.

Drop-in input

Drop-in input. A startUrls list written for compass/google-maps-reviews-scraper, maxcopell/tripadvisor-reviews or voyager/booking-reviews-scraper works as it is; their review caps (maxReviews, maxItemsPerQuery, maxReviewsPerHotel) become the per-entity depth.

As stated in the actor’s README. The full list of accepted input names is on the Store page.

What you get

Summary row, one per entity and run:

Alert row, one per new negative review:

changeType: "edited" marks a review you already knew that its author edited down into the alert band.

Sample row (from the README example; long text shortened, lists cut to two items):

{
  "rowType": "summary",
  "status": "changed",
  "platform": "appstore",
  "entityId": "618783545-us",
  "input": "https://apps.apple.com/us/app/slack/id618783545",
  "name": "Slack",
  "url": "https://apps.apple.com/us/app/slack/id618783545",
  "checkedAt": "2026-10-21T22:59:28.653Z",
  "previousCheckAt": "2026-10-21T22:54:50.343Z",
  "rating": 4.14,
  "previousRating": 4.14,
  "ratingChange": 0,
  "reviewCount": 54143,
  "previousReviewCount": 54115,
  "reviewCountChange": 28,
  "newReviews": 6,
  "editedReviews": 0,
  "newNegativeReviews": 1,
  "newReviewsAvgRating": 4.5,
  "avgRating30d": 3.29,
  "previousAvgRating30d": 3.29,
  "reviews7d": 55,
  "reviews30d": 100,
  "reviewsPerWeek": 23.3,
  "avgRatingLifetime": 3.29,
  "lifetimeSource": "sample",
  "rating30dVsLifetime": 0,
  "ownerReplyRate90d": 0.05,
  "medianReplyLagDays": 0,
  "unansweredNegative30d": 35,
  "appVersions": [],
  "versionRegression": null,
  "windowComplete30d": false,
  "historyReviews": 100,
  "historySince": "2026-10-08",
  "stopReason": "onlyNew",
  "flags": [
    "new_negative",
    "unanswered_negative"
  ]
}

Price

Live Apify price on 2026-10-31, per 1,000 rows, by Apify plan. You pay only for rows delivered; error and summary rows marked free in the README cost nothing. Plus $0.001 per GB of memory when a run starts.

Use cases

• Hotel groups and franchises: one feed for every location on Booking, Tripadvisor and Yelp; reply to the new 1★ reviews first, watch ownerReplyRate90d per location.
• Agencies: a weekly reputation report per client from the summary rows (ratingChange, rating30dVsLifetime, reviewCountChange).
• App teams: versionRegression shows when a release averages clearly lower than the one before it; alerts carry the appVersion.
• SaaS product marketing: watch your Capterra and TrustRadius pages and three competitors' in one watchlist.

Run it from code or an AI agent

Actor ID arcadium/reputation-monitor. Set APIFY_TOKEN to your Apify API token. The input below is the actor’s prefilled example; see the guide for every field.

curl -X POST "https://api.apify.com/v2/acts/arcadium~reputation-monitor/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"startUrls":["https://www.booking.com/hotel/gb/the-savoy.html","https://www.yelp.com/biz/katzs-delicatessen-new-york","https://apps.apple.com/us/app/slack/id618783545"],"maxReviewsPerEntity":50}'

// npm install apify-client
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('arcadium/reputation-monitor').call({
  "startUrls": [
    "https://www.booking.com/hotel/gb/the-savoy.html",
    "https://www.yelp.com/biz/katzs-delicatessen-new-york",
    "https://apps.apple.com/us/app/slack/id618783545"
  ],
  "maxReviewsPerEntity": 50
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

# pip install apify-client
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("arcadium/reputation-monitor").call(run_input={
    "startUrls": ["https://www.booking.com/hotel/gb/the-savoy.html", "https://www.yelp.com/biz/katzs-delicatessen-new-york", "https://apps.apple.com/us/app/slack/id618783545"],
    "maxReviewsPerEntity": 50,
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=arcadium/reputation-monitor",
      "headers": {
        "Authorization": "Bearer <APIFY_TOKEN>"
      }
    }
  }
}

# pip install langchain-apify   (reads APIFY_API_TOKEN from the environment)
from langchain_apify import ApifyActorsTool

tool = ApifyActorsTool("arcadium/reputation-monitor")
rows = tool.invoke({"run_input": {
    "startUrls": ["https://www.booking.com/hotel/gb/the-savoy.html", "https://www.yelp.com/biz/katzs-delicatessen-new-york", "https://apps.apple.com/us/app/slack/id618783545"],
    "maxReviewsPerEntity": 50,
}})
# or give `tool` to an agent: create_react_agent(llm, [tool])

# pip install llama-index-readers-apify
import os
from llama_index.core import Document, VectorStoreIndex
from llama_index.readers.apify import ApifyActor

reader = ApifyActor(os.environ["APIFY_TOKEN"])
documents = reader.load_data(
    actor_id="arcadium/reputation-monitor",
    run_input={
        "startUrls": ["https://www.booking.com/hotel/gb/the-savoy.html", "https://www.yelp.com/biz/katzs-delicatessen-new-york", "https://apps.apple.com/us/app/slack/id618783545"],
        "maxReviewsPerEntity": 50,
    },
    dataset_mapping_function=lambda item: Document(text=str(item.get("text") or ""), metadata={"url": item.get("url")}),
)
index = VectorStoreIndex.from_documents(documents)

# pip install crewai crewai-tools langchain-apify   (reads APIFY_API_TOKEN from the environment)
from crewai import Agent
from crewai_tools import ApifyActorsTool

tool = ApifyActorsTool(actor_name="arcadium/reputation-monitor")
researcher = Agent(
    role="Research analyst",
    goal="Answer with data collected by the tool",
    backstory="Uses public web data.",
    tools=[tool],
)

{
  "nodes": [
    {
      "name": "Run arcadium/reputation-monitor",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4,
      "position": [
        0,
        0
      ],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/arcadium~reputation-monitor/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\"startUrls\":[\"https://www.booking.com/hotel/gb/the-savoy.html\",\"https://www.yelp.com/biz/katzs-delicatessen-new-york\",\"https://apps.apple.com/us/app/slack/id618783545\"],\"maxReviewsPerEntity\":50}",
        "options": {
          "timeout": 300000
        }
      }
    }
  ],
  "connections": {}
}

// Extensions > Apps Script. Store your token once: Project Settings > Script properties > APIFY_TOKEN.
function importLastRun() {
  const token = PropertiesService.getScriptProperties().getProperty('APIFY_TOKEN');
  const url = 'https://api.apify.com/v2/acts/arcadium~reputation-monitor/runs/last/dataset/items?status=SUCCEEDED&clean=true&format=json';
  const res = UrlFetchApp.fetch(url, { headers: { Authorization: 'Bearer ' + token } });
  const rows = JSON.parse(res.getContentText());
  if (!rows.length) return;
  const keys = Object.keys(rows[0]);
  const values = rows.map((r) => keys.map((k) => (typeof r[k] === 'object' ? JSON.stringify(r[k]) : r[k])));
  const sheet = SpreadsheetApp.getActiveSheet();
  sheet.clearContents();
  sheet.getRange(1, 1, 1, keys.length).setValues([keys]);
  sheet.getRange(2, 1, values.length, keys.length).setValues(values);
}

Limitations

• Public review pages only. Nothing behind a login.
• Supported: Booking.com, Agoda and Trip.com hotels (Agoda: its own reviews, not the Booking.com ones it also shows), Tripadvisor places, Google Hotels hotels and Google Maps places (Google's own reviews; dates are approximate because Google shows "3 months ago"), Yelp businesses, Google Play and App Store apps, Capterra and TrustRadius products. Other URLs return a free invalid_input error row.
• The 30-day, reply and velocity figures come from the reviews the actor has read. For entities with more reviews per month than maxReviewsPerEntity they describe the newest reviews read (windowComplete30d: false) until the history fills up over later runs.
• App stores state the average of all star ratings, most of them without text, so avgRatingLifetime there is the average of the written reviews read (lifetimeSource: "sample").
• Reply lag needs a reply date: Tripadvisor, Agoda, Trip.com, Yelp and the app stores have one; Booking and Capterra show the reply only; TrustRadius has no owner replies.
• App versions: Google Play states the version on every review. The App Store does so only on its RSS feed, which the actor uses as a fallback, so App Store version data is usually empty.
• TrustRadius is read through residential proxies; the actor reads at most 3 pages (30 reviews) per TrustRadius product and run.
• A review that is deleted on the site is not reported.
• Entities are read one after another, about 5 seconds each (7 on the first run), so 700 entities take about an hour. The default run timeout is 4 hours; split bigger watchlists into several stateKeys run in parallel.

FAQ

Why did the first run return no alerts? It is the baseline: there is nothing to compare with yet. It records the newest reviews per entity and returns one summary row each at the baseline price. Run it again tomorrow (or put it on a schedule) and the alerts come.

How do I start over? Use a new stateKey. The old records stay in the reputation-monitor-state and reputation-monitor-seen stores until you delete them.

Can I get every new review, not only the negative ones? Set alertMaxRating to 4 for everything below 5 stars, or use the per-site review actors with "only new reviews" for full review rows.

Something looks wrong in the output. Who do I tell? Open an issue on the actor page with the run link. We read them all.

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