Trustpilot Review Intelligence Monitor

Trustpilot Review Intelligence Monitor

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

Instead of returning another spreadsheet of review text, it gives brand, ecommerce, and agency teams 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 and links across the review-intelligence Actors. For the first run, use the Trustpilot review tutorial or the Trustpilot Review Intelligence alternatives page. The proof GIF shows reviews becoming a reputation queue.

What You Learn

• Which reviews are positive, negative, or mixed
• Which themes keep appearing: support, refunds, delivery, pricing, product quality, trust, and experience
• Which complaints deserve urgent follow-up
• Which positive reviews can become Trustpilot widget copy, landing-page proof, 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 Trustpilot reputation monitoring for brands and agencies
• Refund, delivery, and support complaint triage
• Public-response prioritization for reputation teams
• Brand-proof mining from positive Trustpilot 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 Software",
  "sourceName": "Trustpilot",
  "reviews": [
    {
      "rating": 1,
      "text": "Support ignored my refund request after a delayed delivery.",
      "authorName": "Example Customer",
      "date": "2026-05-01"
    }
  ],
  "maxReviews": 100,
  "includeRawReview": true
}

Output

Each dataset item is one analyzed review:

{
  "status": "succeeded",
  "recordIndex": 1,
  "billingEventName": "trustpilot-review-analyzed",
  "businessName": "Northstar Software",
  "sourceName": "Trustpilot",
  "rating": 1,
  "sentimentLabel": "negative",
  "sentimentScore": -100,
  "detectedThemes": ["support", "refunds", "delivery"],
  "complaintThemes": ["support", "refunds", "delivery"],
  "urgencyScore": 95,
  "priorityReason": "Money-related complaint is likely to escalate without follow-up.",
  "workflowCategory": "urgent_public_response",
  "proofQuote": "Support ignored my refund request after a delayed delivery.",
  "replyNeeded": true,
  "ownerResponseDraft": "Thank you for raising this. We are sorry the refunds experience with Northstar Software fell short...",
  "recommendedAction": "Escalate refund and cancellation complaints for Northstar Software; these reviews can become visible trust blockers.",
  "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 Trustpilot?

No. It analyzes Trustpilot review records you provide inline or through an Apify dataset from another Actor. It is an intelligence layer, not a Trustpilot 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, public-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, companyName, and companyDomain.

How much does it cost?

The configured paid event is trustpilot-review-analyzed at $0.004 per successfully analyzed review.

Pricing

Default monetization model: pay per event.

Recommended chargeable event:

• Event name: trustpilot-review-analyzed
• Event meaning: one successfully analyzed review
• Store price: $0.004 per analyzed review
• Pricing status: active from 2026-05-13T19:04:19Z; 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 Trustpilot directly.
• Review source schemas vary. The Actor recognizes common fields such as text, reviewText, rating, stars, authorName, date, reviewUrl, companyName, and companyDomain.
• 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 brand, product line, or Trustpilot domain.
• 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