---
title: "At-risk customer alerts: AI Use Case"
description: "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."
url: https://lidvizion.ai/use-cases/at-risk-customer-alerts/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

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

# At-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.

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

Where it starts

## The trigger.

A customer’s activity, tickets or tone change in a way that often comes before cancellation.

How it runs today

- Account owners learn a customer is unhappy when they cancel.
- Signals are spread across support, billing and usage tools.
- Nobody has time to review every account each week.

The workflow

## Four bounded steps.

Each step has an owner and a stop rule.

1. 01 / Gather
   Pull recent tickets, usage changes and account notes for each customer.
2. 02 / Flag
   Mark accounts that match agreed warning signs, with the evidence attached.
3. 03 / Summarize
   Draft a short brief for the account owner: what changed and why it matters.
4. 04 / Act
   The owner decides whether and how to reach out, and logs the outcome.

Where people stay in control

## Approval points.

- Account owners decide every outreach.
- Warning-sign rules are reviewed against what actually happened.

How we would measure it

## Against a baseline.

Measured before and after, on the same work.

### At-risk accounts reviewed each week.

### Outreach before cancellation, against the baseline.

### Accuracy of flags against actual outcomes.

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.
- [Spot at-risk customers early](https://lidvizion.ai/guides/spot-at-risk-customers-early/): A four-step playbook to agree the warning signs that come before cancellation, flag matching accounts with evidence, and give account owners a short brief to act on each week.
- [How to spot at-risk customers with AI](https://lidvizion.ai/answers/how-to-spot-at-risk-customers-with-ai/): How can a subscription or services business use AI to notice customers who may be about to leave while there is still time to help?

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.
- [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.
- [New customer onboarding](https://lidvizion.ai/use-cases/new-customer-onboarding/): Customer support 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.

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/)