Guest and customer review responses

---
title: "Guest and customer review responses: AI Use Case"
description: "Collect new public reviews in one place, draft a specific, on-brand reply to each, flag the ones that need a manager, and share recurring themes with the team that can fix them."
url: https://lidvizion.ai/use-cases/guest-review-responses/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Use cases](https://lidvizion.ai/use-cases/) · Customer support

# Guest and customer review responses

Collect new public reviews in one place, draft a specific, on-brand reply to each, flag the ones that need a manager, and share recurring themes with the team that can fix them.

[Talk through this workflow](https://lidvizion.ai/contact/?need=ai-workflow&from=%2Fuse-cases%2Fguest-review-responses%2F#write) · [Automate the repeatable](https://lidvizion.ai/services/automation/)

Where it starts

## The trigger.

A new review appears on a booking site, map listing or review platform.

How it runs today

- Reviews go unanswered for days, or get the same generic reply.
- Serious complaints are handled the same way as minor ones.
- Recurring problems mentioned in reviews never reach the team that could fix them.

The workflow

## Four bounded steps.

Each step has an owner and a stop rule.

1. 01 / Collect
   Bring new reviews from each platform into one queue.
2. 02 / Draft
   Draft a specific reply that addresses what the reviewer actually said, in the approved tone.
3. 03 / Flag
   Send serious complaints, safety issues and legal threats to a manager before any reply.
4. 04 / Learn
   Summarize recurring themes each month for the people who can fix them.

Where people stay in control

## Approval points.

- A person approves every public reply before it is posted.
- Serious complaints, safety issues and refund requests always go to a manager.

How we would measure it

## Against a baseline.

Measured before and after, on the same work.

### Time from review to reply.

### Share of reviews answered.

### Recurring issues fixed after being raised in the monthly summary.

Related

## Go deeper.

- [Automate the repeatable](https://lidvizion.ai/services/automation/): Give recurring work an owner, a trigger, and a stop rule.
- [Embeddings and feature vectors](https://lidvizion.ai/resources/basics/embeddings-feature-vectors/): How models turn text, images and records into numbers that can be compared.
- [Reply to every review without sounding generic](https://lidvizion.ai/guides/reply-to-every-review-without-sounding-generic/): A four-step routine to gather reviews in one queue, draft specific replies in your voice, route serious complaints to a manager, and turn recurring themes into fixes.
- [What Is Human-in-the-Loop AI?](https://lidvizion.ai/answers/what-is-human-in-the-loop-ai/): What does human-in-the-loop mean for AI in a business?

More use cases

## Other places to start.

[All use cases](https://lidvizion.ai/use-cases/)

- [Support triage and reply drafts](https://lidvizion.ai/use-cases/support-triage/): Customer support Classify incoming tickets, find the relevant answer, and draft a reply for an agent to check.
- [At-risk customer alerts](https://lidvizion.ai/use-cases/at-risk-customer-alerts/): Customer support Spot customers who may be about to leave from support tickets, usage and account notes, and give the account owner a short summary to act on.
- [Return and refund requests](https://lidvizion.ai/use-cases/return-and-refund-requests/): Customer support Check each return or refund request against your policy and order records, prepare the response and the system updates, and send anything outside policy to a person.

An illustrative workflow pattern, not a result from a specific customer.

Bring the messy version

## One workflow. A better way to run it.

Show us the tools, the handoffs, and the part that keeps slowing your team down.

[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)