Use cases · Customer support
Pre-arrival guest messages
Send each guest a short pre-arrival message built from their booking and your approved property information, such as check-in details, directions and add-on options, so the front desk handles fewer routine questions on arrival day.
Where it starts
The trigger.
A booking reaches the agreed number of days before arrival.
How it runs today
- Guests ask the same arrival questions by phone, email and chat.
- Pre-arrival emails are generic or are not sent at all.
- Special requests in booking notes are missed until check-in.
The workflow
Four bounded steps.
Each step has an owner and a stop rule.
- 01 / Read
Read upcoming bookings and the approved property information for each stay type.
- 02 / Draft
Draft a short message with check-in details, directions and relevant options in the property’s template.
- 03 / Flag
Flag special requests, accessibility needs and unusual bookings for staff before anything is sent.
- 04 / Send
Send approved messages on schedule and route guest replies to the reservations team.
Where people stay in control
Approval points.
- Staff handle special requests, accessibility needs and any booking changes.
- Messages use only approved property information and the guest’s own booking details.
How we would measure it
Against a baseline.
Measured before and after, on the same work.
Routine arrival questions per stay.
Share of bookings that receive a pre-arrival message on time.
Special requests flagged before arrival day.
Related
Go deeper.
More use cases
Other places to start.
Support triage and reply drafts
Classify incoming tickets, find the relevant answer, and draft a reply for an agent to check.
See the workflow Customer supportAt-risk customer alerts
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.
See the workflow Customer supportReturn and refund requests
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.
See the workflowAn 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.
---
title: "Pre-arrival guest messages: AI Use Case"
description: "Send each guest a short pre-arrival message built from their booking and your approved property information, such as check-in details, directions and add-on options, so the front desk handles fewer routine questions on arrival day."
url: https://lidvizion.ai/use-cases/pre-arrival-guest-messages/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---
[Use cases](https://lidvizion.ai/use-cases/) · Customer support
# Pre-arrival guest messages
Send each guest a short pre-arrival message built from their booking and your approved property information, such as check-in details, directions and add-on options, so the front desk handles fewer routine questions on arrival day.
[Talk through this workflow](https://lidvizion.ai/contact/?need=ai-workflow&from=%2Fuse-cases%2Fpre-arrival-guest-messages%2F#write) · [Automate the repeatable](https://lidvizion.ai/services/automation/)
Where it starts
## The trigger.
A booking reaches the agreed number of days before arrival.
How it runs today
- Guests ask the same arrival questions by phone, email and chat.
- Pre-arrival emails are generic or are not sent at all.
- Special requests in booking notes are missed until check-in.
The workflow
## Four bounded steps.
Each step has an owner and a stop rule.
1. 01 / Read
Read upcoming bookings and the approved property information for each stay type.
2. 02 / Draft
Draft a short message with check-in details, directions and relevant options in the property’s template.
3. 03 / Flag
Flag special requests, accessibility needs and unusual bookings for staff before anything is sent.
4. 04 / Send
Send approved messages on schedule and route guest replies to the reservations team.
Where people stay in control
## Approval points.
- Staff handle special requests, accessibility needs and any booking changes.
- Messages use only approved property information and the guest’s own booking details.
How we would measure it
## Against a baseline.
Measured before and after, on the same work.
### Routine arrival questions per stay.
### Share of bookings that receive a pre-arrival message on time.
### Special requests flagged before arrival day.
Related
## Go deeper.
- [Automate the repeatable](https://lidvizion.ai/services/automation/): Give recurring work an owner, a trigger, and a stop rule.
- [Client vs. server: where code runs](https://lidvizion.ai/resources/basics/backend-client-vs-server/): What belongs in the browser or app, and what must stay on a server you control.
- [Send better pre-arrival messages](https://lidvizion.ai/guides/send-better-pre-arrival-messages/): A four-step playbook to write approved message templates, draft each guest’s message from their booking, flag special requests for staff and cut routine arrival questions.
- [How to send pre-arrival guest messages with AI](https://lidvizion.ai/answers/how-to-send-pre-arrival-guest-messages-with-ai/): How can a hotel or rental operator use AI to send every guest a useful message before arrival without missing special requests?
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/)