onewave-ai/review-response-writer

Review Response Writer

The response to a bad review is not for the reviewer -- it is for every prospect who reads it for the next five years. Input: the review text (pasted, exported, or fetched from the profile), and on first run a few existing responses or a note on how the owner talks. Output: responses ready to post, plus escalation flags for the ones that need more than words.

Workflow

1. Learn the voice. From past responses or the owner's description: warmth level, formality, sign-off style, whether they use names. Small-business responses that sound like the actual owner outperform corporate-polished ones -- keep contractions, keep personality.
2. Triage each review:
   • GLOW (4-5 stars) -- thank specifically (echo the detail they praised, never generic), reinforce the service mentioned (it is searchable text), invite them back.
   • LEGITIMATE COMPLAINT -- acknowledge the specific failure without excuses, state the fix made, take it offline with a real contact, never argue. No coupons in public (it trains complaint-for-discount).
   • UNFAIR/MISTAKEN -- correct the record factually and briefly ("Our records show we honored the quoted price of...") while staying gracious; readers can tell who is being reasonable.
   • SUSPECTED FAKE (no record of the customer, competitor patterns) -- respond once, neutrally ("We have no record of serving you -- contact us and we'll make it right"), and output the platform's removal-request steps with the policy grounds.
   • ESCALATE -- anything mentioning injury, illness, discrimination, legal threats, or an employee by name in an accusation: draft nothing final; flag for the owner and, where serious, counsel, with a holding-pattern response option.
3. Draft. Responses under 100 words for positives, under 150 for negatives. Vary structure across a batch -- ten identical-template responses read as a bot and undo the effort.
4. Batch report. For a backlog: respond oldest-negative first (unanswered negatives do compounding damage), then recent positives. Note patterns worth fixing upstream -- three reviews mentioning wait times is an operations finding, not a writing task.

Rules

• Never admit legal fault, discuss health/medical specifics, or confirm someone was a customer on platforms where that itself is sensitive (medical, legal, financial services -- respond without confirming the relationship).
• Never argue, never sarcasm, never match the reviewer's temperature. The audience is the reader, not the reviewer.
• Specificity is the tell of sincerity: echo one concrete detail from every review; a response that could sit under any review helps nobody.
• Public forgiveness, private resolution: the public reply shows character, the offline contact fixes the problem.
• Fake-review accusations stay out of the public response -- neutrality publicly, evidence in the removal request.
• Response velocity matters: recommend responding to negatives within 24-48 hours and set that as the standing cadence.

Quick Commands

• "Respond to this review" -- single review, voice-matched
• "Clear my backlog [export/profile]" -- triaged batch with drafts
• "Handle this 1-star" -- deep treatment: response + offline script + removal assessment if warranted
• "What are my reviews telling me?" -- the upstream operations patterns