Spot customers at risk of canceling before they churn

AI scores every customer's health and churn risk from their activity, then flags who needs attention and why.

How the work actually flows

It branches. Exactly one path is taken.

Pattern: Exclusive Choice (4)

flowchart TD trig>"customer activity event occurs"]:::trig s0["calculates health score"]:::task s1["AI recommends next steps"]:::task trig --> s0 s0 --> s1 gx{"× is risk level critical"}:::gate s1 --> gx p00["alerts team with talking points"]:::task gx -->|"critical risk"| p00 p10["no alert needed"]:::task gx -->|"normal risk"| p10 p00 --> out p10 --> out out[/"health score and risk level"/]:::out pay{{"catches churn risk early"}}:::pay out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
Starts itA stepOne path onlyResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
Spreadsheet & Database OpsReporting & AnalyticsAPI & Webhook Integration
Connects
SupabaseOpenAI

The problem it solves

You only find out a customer is unhappy after they cancel, and by then it's too late to fix it. You don't have time to manually review usage, support tickets, and payment history for every account to spot the warning signs.

Who it fits

Customer success teams who want to catch at-risk accounts before they churn.

How it works

  1. A customer event comes in, such as a login, purchase, or support ticket
  2. The system pulls the customer's recent activity and history
  3. It calculates a health score from engagement, product usage, support activity, revenue, and satisfaction
  4. AI reviews the score and recommends specific next steps
  5. Critical-risk customers trigger an alert with talking points for the team
What you get

At-risk customers flagged before they leave

Every customer gets a health score based on their activity, so you know who needs attention and why.

What you get

A health score, churn risk level, and a prioritized list of actions for each customer, plus alerts for the ones at highest risk.

What you need

A Supabase database to store customer data and an OpenAI API key.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

Related automations

Back to the AI Playbook