Google Maps Review Intelligence Monitor

Google Maps Review Intelligence Monitor

Turn raw local-business reviews into complaint themes, evidence quotes, urgency scores, owner-response drafts, and client-report bullets. This Actor analyzes Google Maps review records collected by another Apify Actor or supplied inline; it does not scrape Google Maps directly.

Instead of returning another spreadsheet of review text, it gives agencies and operators a clean action dataset: what customers complain about, what they praise, which reviews need attention, and what to do next.

Workflow Hub

See the public review intelligence workflow for the scraper dataset -> analyzer path, demo story, and links across the review-intelligence Actors. For the first run, use the Google Maps review tutorial or the local review response queue use case. The proof GIF shows reviews becoming a local response queue.

What You Learn

• Which reviews are positive, negative, or mixed
• Which themes keep appearing: service, wait time, price, quality, delivery, availability, and experience
• Which complaints deserve urgent follow-up
• Which positive reviews can become local landing-page or ad proof
• Which exact quote explains the theme and priority score
• Which reply draft or workflow bucket to use
• What action to take for each review

Use Cases

• Weekly reputation monitoring for local SEO clients
• Multi-location complaint triage for franchises
• Owner-response prioritization for agencies
• Local landing-page proof mining from positive reviews
• Review exports to Google Sheets, Slack, dashboards, or reporting decks

Input

You can provide reviews inline or pass an Apify datasetId from another review scraper.

{
  "businessName": "Northstar Dental",
  "sourceName": "Google Maps",
  "reviews": [
    {
      "rating": 1,
      "text": "The staff was rude and the wait was terrible.",
      "authorName": "Example Customer",
      "date": "2026-05-01"
    }
  ],
  "maxReviews": 100,
  "includeRawReview": true
}

Output

Each dataset item is one analyzed review:

{
  "status": "succeeded",
  "recordIndex": 1,
  "billingEventName": "google-maps-review-analyzed",
  "businessName": "Northstar Dental",
  "sourceName": "Google Maps",
  "rating": 1,
  "sentimentLabel": "negative",
  "sentimentScore": -100,
  "detectedThemes": ["service", "wait_time"],
  "complaintThemes": ["service", "wait_time"],
  "urgencyScore": 95,
  "priorityReason": "Low-star public review needs fast owner response.",
  "workflowCategory": "urgent_owner_response",
  "proofQuote": "The staff was rude and the wait was terrible.",
  "replyNeeded": true,
  "ownerResponseDraft": "Thank you for sharing this. We are sorry the service did not meet expectations at Northstar Dental...",
  "recommendedAction": "Audit staff response patterns for Northstar Dental; service complaints are driving visible review risk.",
  "analyzedAt": "2026-05-12T12:00:00+00:00"
}

The run also writes a SUMMARY key-value-store record with analyzed counts, sentiment counts, top complaint themes, top urgent reviews, praise quotes, client-report bullets, and the charge event name.

FAQ

Does this scrape Google Maps?

No. It analyzes Google Maps review records you provide inline or through an Apify dataset from another Actor. It is an intelligence layer, not a Google Maps scraper.

What input do I need for the first run?

Use the Store example with one or more review records. At minimum, each record should include review text, and ratings improve sentiment and urgency scoring.

What do I get back?

One dataset item per analyzed review, including sentiment, complaint themes, urgency, proof quote, owner-response draft, and recommended next action. The run also writes a SUMMARY record.

Can I use data from another review scraper?

Yes. Pass a datasetId from another Actor or paste inline records. The analyzer recognizes common review fields such as text, reviewText, rating, stars, authorName, date, reviewUrl, locationName, and placeId.

How much does it cost?

The configured paid event is google-maps-review-analyzed at $0.003 per successfully analyzed review.

Pricing

Default monetization model: pay per event.

Recommended chargeable event:

• Event name: google-maps-review-analyzed
• Event meaning: one successfully analyzed review
• Store price: $0.003 per analyzed review
• Pricing status: active from 2026-05-13T19:03:43Z; verified by private and public paid smokes

Successful rows are pushed only after the charge path allows the event. If Actor.charge fails, the Actor fails closed before returning paid output.

Limitations

• This MVP analyzes review records; it does not scrape Google Maps directly.
• Review source schemas vary. The Actor recognizes common fields such as text, reviewText, rating, stars, authorName, date, reviewUrl, locationName, and placeId.
• Theme detection is deterministic and intentionally explainable. It is built for reliable monitoring, not black-box sentiment theater.

Automation And Agent Use

• Run a review scraper first, then pass its dataset ID to this Actor.
• Schedule weekly analysis for each client or location group.
• Send negative high-urgency reviews to Slack or a ticketing queue.
• Append sentimentLabel, detectedThemes, and recommendedAction to Google Sheets for client reporting.

Local Development

python3 -m pip install -r requirements.txt
ACTOR_TEST_PAY_PER_EVENT=true apify run --purge --input-file examples/smoke-input.json