Spot at-risk customers and send personalized retention offers

Analyzes customer data to flag who is likely to cancel, then sends them a personalized discount to keep them.

How the work actually flows

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

Pattern: Exclusive Choice (4) · Multiple Instances with a priori Run-Time Knowledge (14)

flowchart TD trig(["daily customer data pull"]):::trigtime s0[["gather customer data"]]:::mi s1["AI scores cancellation risk"]:::task trig --> s0 s0 --> s1 gx{"× is customer flagged high risk"}:::gate s1 --> gx p00["create discount code"]:::task gx -->|"high risk"| p00 p01["send retention email"]:::task p00 --> p01 p10["no action taken"]:::task gx -->|"low risk"| p10 jn{"○ log outcome to sheet"}:::gate p01 --> jn p10 --> jn jn --> out out[/"at risk customers sent offers"/]:::out pay{{"fewer customer cancellations"}}:::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 onlyPaths rejoinResultPayoff
Build size
Advanced

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

Business functions
Email AutomationSpreadsheet & Database Ops
Connects
OpenAIStripeGmailGoogle SheetsPostgreSQLSendGrid

The problem it solves

You often find out a customer is unhappy only after they cancel, when it is too late to fix things. Digging through usage data and support tickets to spot warning signs takes hours you do not have. Even when you notice a customer slipping away, crafting a personal outreach and discount takes time.

Who it fits

Subscription businesses and customer success teams trying to reduce cancellations before they happen.

How it works

  1. Every day, the system pulls together each customer's account details, support history, and usage activity
  2. AI reviews that information and scores how likely each customer is to cancel
  3. For anyone flagged as high risk, it creates a discount code through Stripe
  4. AI drafts a personal email offering that discount and sends it through Gmail
  5. The outcome is logged in Google Sheets so you can track who was offered what and whether it worked
What you get

Customers offered a discount before they leave

You automatically spot customers who are likely to cancel and send them a personalized discount offer to encourage them to stay.

What you get

A prioritized list of at-risk customers, each with a personalized retention email and discount already sent.

What you need

A customer database, an OpenAI API key, a Stripe account, and a Google Workspace 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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