PREDICTING CHURN BEFORE IT HAPPENS

➤ WELCOME BACK!

This weeks newsletter is focused on churn prediction, because at least half your churn is likely predictable (look it up).

I know what you’re thinking: “any kind of prediction requires systems in place to generate those predictions, and systems cost money.”

You’re in luck good friend. I’m gonna cover some scrappy approaches so you can build that churn prediction system on the cheap. What you’ll learn today:

• How to build a churn early-warning system that spots at-risk users 2-3 weeks before they cancel (using either Airtable automation or simple Google Sheets)

• The 3-signal framework that predicts voluntary churn (engagement decay, payment friction, lifecycle drift) with manual or automated tracking

• Systematic intervention protocols that trigger save campaigns within 48 hours (from founder emails to automated sequences)

Onward!

➤ THINGS YOU SHOULD KNOW

• California's Enhanced Auto-Renewal Law (Effective July 1, 2025): This is your reminder that California is implementing stricter requirements for subscription services.

• Product-Led Growth (PLG) Is Becoming the Standard: Product-led growth strategies are designed to remove friction and let the product itself drive user adoption, engagement, and conversions.

• Churn Rate Benchmarks & Insights for B2B SaaS Leaders: Average B2B SaaS churn rate at 3.5% (2.6% voluntary, 0.8% involuntary), with clear correlation between contract size and retention.

➤ TODAYS FOCUS

😥 Three of your subscribers just downgraded, two more haven't opened your app in 12 days, and another batch missed their last payment retry.

None of these show up as "churned" yet. But they're already gone.

Most operators wait for the official churn event; the moment someone actually cancels. By then, you're playing defense against an outcome that was decided weeks ago.

The highest-performing subscription businesses spot churn signals 14-30 days before the actual cancel event.

Here's how to build that early warning system (without touching code).

🏭 The Churn Prediction Factory (or Engine)

Think of churn prediction as an assembly line. Raw behavioral data enters one end and actionable save campaigns exit the other.

Your job: design the factory/engine that connects these dots consistently.

Most teams try to solve this with expensive ML platforms or complex SQL queries. But the best early warning systems start simple and with tools you already know.

🚦 The Three-Signal Stack

Every effective churn predictor should track three signal categories:

Engagement Decay

• Days since last session

• Feature usage drop-off

• Support ticket volume spike

Payment Friction

• Failed billing attempts

• Downgrade requests

• Billing page abandons

Lifecycle Stage Drift

• Trial extension requests

• Plan comparison page visits

• Cancellation flow entry (but no completion)

You need to track all three! Weight engagement decay the heaviest, it will predict nearly 1/3 of your voluntary churn 2-3 weeks early (I’ve seen this with my own eyes).

🛠️ Airtable as Your Prediction Engine

Skip the data warehouse complexity. Use Airtable with Zapier as your churn prediction hub. (These aren’t sponsors/affiliates. These are affordable tools and you can likely start for free.)

There are plenty of tutorials out there regarding Airtable/Zapier. And you can find very affordable help on Fiverr or Upwork if you need it (my way of saying you have few excuses for not making this work 😉).

Base Structure:

• Users table (connects to your app database via Zapier/API)

• Behavioral Events table (session data, feature usage)

• Risk Scores table (calculated fields for each signal type)

• Save Campaigns table (automated outreach triggered by risk thresholds)

The Risk Score Formula: Create a formula field that weighs…

• Days inactive × 2.5

• Failed payments × 4.0

• Support contacts × 1.5

• Feature usage decline × 3.0

Scores above 15: High risk. Scores 8-14: Medium risk. Under 8: Healthy.

⏱️ The 48-Hour Rule

Here's the operational magic: Any user hitting "High Risk" should trigger an automated sequence within 48 hours.

Not next week. Not when someone remembers to check the dashboard.

Immediately.

Connect Airtable to your email platform, push notification system, or customer success workflow (where Zapier comes in). The moment someone crosses your risk threshold, three things happen:

1. Targeted re-engagement email deploys

2. CS team gets alerted with context

3. In-app intervention campaign activates

Speed matters more than perfection here.

Important: Don’t have an email platform, push notification system, or customer success workflow? Do it manually!

⚡ Implementation Scenario: Week One (Example)

Day 1-2: Set up your Airtable base with the four tables outlined above.

Day 3-4: Connect your app data via Zapier or direct API integration.

Day 5-6: Build your risk score formulas and test with historical data.

Day 7: Deploy your first automated save campaign for high-risk users.

Start with email only. Add push notifications and in-app messaging once the system proves itself.

💪 The Bootstrap Version: Manual Churn Tracking

Not ready for Airtable and Zapier? Start with Google Sheets and weekly discipline (example at the end).

The same three-signal framework works manually. You just replace automation with process.

Your Weekly Data Ritual:

Export user data from your analytics platform every Monday (ok, I lied, some of you “might” need to do a SQL query for this data). It’s thirty minutes of work that pays for itself in saved customers.

Create a master spreadsheet with these columns:

• User ID, Email, Plan Type, Signup Date

• Days Since Last Session (manual calculation)

• Failed Payment Count (from Stripe dashboard)

• Support Tickets This Month (from your help desk)

• Feature Usage Score (1-10 subjective rating)

The Manual Risk Formula:

In Google Sheets: =((Days_Inactive*2.5)+(Failed_Payments*4)+(Support_Tickets*1.5)+(Usage_Score*3))/4

Color-code results: Red for 15+, Yellow for 8-14, Green for under 8.

Your Save Protocol:

High-risk users get personal emails within 24 hours. No templates. Real messages from founders work best at this stage.

Medium-risk users get added to your next newsletter or outreach campaign with extra value content.

Track outcomes in additional columns: Email Sent Date, Response, Outcome, Notes, etc.

The Monthly Review:

Last Friday of each month, analyze your manual data. Which signals predicted actual churn? Adjust your weightings accordingly.

Most importantly: graduate to automation once you're saving 10+ customers monthly. That's when manual process becomes your bottleneck.

Why Start Manually?

You learn the signals that matter most in your specific business. You build the discipline of systematic intervention. You prove ROI before investing in tools.

The process matters more than the platform.

👉 Here’s an example of what your spreadsheet might look like after implementing this manually (also a good guide for what your Airtable implementation might look like).

🏆 Why This Beats the Alternatives

Dedicated churn prediction platforms can cost $2,000-$10,000 monthly and take quarters to implement.

Building custom ML models requires data science resources most teams don't have.

The Airtable approach launches in one week, costs under $100 monthly (to start), and requires zero engineering resources.

The manual approach launches today, costs nothing, and requires only time and discipline.

Most importantly: both work.

Build the factory. Feed it data. Watch retention climb.

➤ TILL NEXT WEEK

That’s all…

Remember: Instead of reacting to churn, you're reshaping user behavior before bad patterns solidify.

The users you save at the "Medium Risk" stage convert to higher LTV customers. They stick around longer. They refer more friends.

Build the factory. Ship the fixes. Scale the saves.

Hope you got some value from this email!

Cheers,

~ Rick

P.S. Will you do me a favor? Please reply to this email with feedback, comments, or questions; I read all of it.

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