---
title: "New customer onboarding: AI Use Case"
description: "Guide each new customer through the first weeks with timely, relevant steps based on what they bought and what they have done so far, and alert a person when someone gets stuck."
url: https://lidvizion.ai/use-cases/new-customer-onboarding/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

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

# New customer onboarding

Guide each new customer through the first weeks with timely, relevant steps based on what they bought and what they have done so far, and alert a person when someone gets stuck.

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

Where it starts

## The trigger.

A new customer signs up, pays or signs a contract.

How it runs today

- Every customer gets the same generic welcome email, or none at all.
- The team finds out a customer is stuck when they cancel or complain.
- Onboarding depends on which team member happens to own the account.

The workflow

## Four bounded steps.

Each step has an owner and a stop rule.

1. 01 / Plan
   Define the first-weeks journey for each product or service: steps, timing and success signs.
2. 02 / Send
   Send the next useful step at the right time, based on what the customer has and has not done.
3. 03 / Watch
   Notice when a customer stalls or asks for help, and summarize where they are.
4. 04 / Hand off
   Alert the account owner with the summary so a person can reach out.

Where people stay in control

## Approval points.

- Messages come from approved templates the team has reviewed.
- Stuck or unhappy customers always reach a person, not another automated email.

How we would measure it

## Against a baseline.

Measured before and after, on the same work.

### Share of new customers who reach the first success milestone.

### Time to first success, before and after.

### Support requests in the first month.

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.
- [Onboard new customers so they reach first success](https://lidvizion.ai/guides/onboard-new-customers-so-they-reach-first-success/): A four-step playbook to define what early success looks like for a new customer, send the right next step at the right time and bring in a person as soon as someone stalls.
- [How to Reduce Early Customer Churn](https://lidvizion.ai/answers/how-to-reduce-early-customer-churn/): How can a company reduce customers leaving in their first few weeks?

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/)