Predict customer churn and trigger retention offers automatically

Watches customer usage and billing data to flag who might cancel, then sends a retention offer before they do.

How the work actually flows

A straight line. Runs once per each at-risk customer.

Pattern: Sequence (1) ยท Multiple Instances without Synchronization (12)

flowchart TD trig(["regular usage and billing check"]):::trigtime s0["flag at-risk accounts"]:::task s1[["build personalized offer"]]:::mi s2["send retention email"]:::svc trig --> s0 s0 -->|"one per each at-risk customer"| s1 s1 --> s2 out[/"retention emails sent to at-risk customers"/]:::out pay{{"fewer customers cancel unnoticed"}}:::pay s2 --> out 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 stepAn outside serviceRuns once per itemResultPayoff
Build size
Advanced

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

Business functions
Email AutomationSpreadsheet & Database OpsReporting & Analytics
Connects
StripePostgreSQLGmail

The problem it solves

You often find out a customer is unhappy only after they've already canceled, and by then it's too late to do anything about it. Manually watching usage patterns and billing data across every account to catch warning signs isn't something you have time for.

Who it fits

A subscription or SaaS business trying to reduce customer cancellations.

How it works

  1. The system regularly checks customer usage and engagement data
  2. It flags accounts showing signs they might cancel
  3. For at-risk accounts, it puts together a personalized offer based on their usage and profile
  4. A retention email is sent automatically to try to keep the customer
What you get

Customers reached before they cancel

You get at-risk customers flagged and sent a personalized offer before they churn.

What you get

A list of at-risk customers and an automatically sent retention email offering them a personalized deal.

What you need

A product analytics tool, a billing system like Stripe, a PostgreSQL database, and a Gmail account.

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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