Spot at-risk SaaS customers before they cancel and act early

Reviews customer activity daily, flags who is likely to cancel, and emails you a recommended retention action.

How the work actually flows

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

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

flowchart TD trig(["every day scheduled check"]):::trigtime s0["pull customer activity data"]:::svc s1["check for warning signs"]:::task s2[["score churn risk with AI"]]:::mi trig --> s0 s0 --> s1 s1 -->|"one per one per customer"| s2 gx{"× is customer high risk"}:::gate s2 --> gx p00["email retention recommendation"]:::task gx -->|"high risk"| p00 p10["no action taken"]:::task gx -->|"low or medium risk"| p10 p00 --> out p10 --> out out[/"daily list of at-risk customers"/]:::out pay{{"catch churn before it happens"}}:::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 stepAn outside serviceRuns once per itemOne path onlyResultPayoff
Build size
Advanced

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

Business functions
Email AutomationSpreadsheet & Database Ops
Connects
Google SheetsOpenRouterGmail

The problem it solves

You often find out a customer is unhappy only after they've already canceled. Watching usage patterns and payment issues across every account manually just isn't realistic once you have more than a handful of customers.

Who it fits

SaaS founders or customer success teams trying to reduce customer cancellations.

How it works

  1. Every day, the system pulls in customer activity and account data
  2. It checks for warning signs like inactivity, low usage, or payment problems
  3. AI scores each customer's risk of canceling as low, medium, or high
  4. For high-risk customers, it emails you with a recommended way to retain them
What you get

An early warning on customers about to walk away

You get a daily flag on customers showing warning signs, along with a suggested way to win them back.

What you get

A daily list of at-risk customers with a suggested retention action for each.

What you need

A Google Sheets account, an OpenRouter API key, and an email 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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