Flag customers likely to churn and alert your team automatically

Every day, AI scores your active customers for churn risk and sends your team a list of who needs attention.

How the work actually flows

It branches. Exactly one path is taken; runs once per each active customer.

Pattern: Exclusive Choice (4) · Multiple Instances without Synchronization (12)

flowchart TD trig(["daily scheduled scan"]):::trigtime s0[["pull customer usage and support data"]]:::mi s1["calculate engagement signals"]:::task s2["AI scores churn risk"]:::task trig --> s0 s0 --> s1 s1 --> s2 gx{"× is customer high risk"}:::gate s2 --> gx p00["alert team on Slack or email"]:::task gx -->|"high risk"| p00 p10["no alert sent"]:::task gx -->|"medium or low risk"| p10 p00 --> out p10 --> out out[/"daily list of at-risk customers"/]:::out pay{{"catch churn before losing customers"}}:::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 stepRuns once per itemOne path onlyResultPayoff
Build size
Advanced

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

Business functions
Email AutomationMessaging & NotificationsSpreadsheet & Database OpsEducation & Training
Connects
SlackPostgreSQLOpenAI

The problem it solves

You only find out a customer is about to leave after they've already cancelled, and by then it's too late to save the account. Your team is too busy with daily work to comb through usage and support data looking for warning signs, so early red flags go unnoticed until it's a lost customer instead of a saved one.

Who it fits

A subscription business or customer success team responsible for retention.

How it works

  1. Each day, the system pulls recent activity, spend, and support data for active customers
  2. It calculates engagement signals like login frequency and days since last activity
  3. An AI model scores each customer's likelihood of churning
  4. Customers are sorted into high, medium, and low risk
  5. High-risk customers trigger an alert to your team by email or Slack
What you get

At-risk customers you spot before they leave

You get a daily list of customers scored for churn risk so your team knows who needs attention.

What you get

A daily list of at-risk customers with risk scores and recommended next steps, delivered to your team.

What you need

A customer database, a Slack workspace or email account, and access to an AI model for scoring.

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

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