Predictive Churn: Knowing Which Customers Will Leave (Before They Do)

Your customer just cancelled.

You’re shocked. They seemed happy. Last month they were praising your product in an email. What happened?

You ask them: “Why are you leaving?”

They say: “I’m just not using it anymore.”

The truth: They stopped using it three weeks ago. But nobody was watching.

If you’d noticed that they didn’t log in for 14 days, you could have intervened. You could have sent an email. You could have offered a discount. You could have scheduled a check-in call.

Instead, you found out after they already made their decision.

This is the problem with reactive customer success. You only notice problems after they become cancellations.

Predictive Churn flips this. We watch for early warning signals and alert you before the customer leaves.

The Signals of Churn

Customers don’t cancel randomly. They show warning signs weeks or months before.

Here are the behavioral signals we monitor:

1. Product Usage Drop (Strongest Signal)

If a customer normally logs in 5 times per week and suddenly logs in once per week, that’s a red flag.

If they go from 0 logins in a week to their average of 5, they’re leaving.

We track this per customer:

Expected usage (based on their historical pattern)
Actual usage (this week)
Deviation (if actual is 50% below expected, we flag it)

A usage drop of 30%+ predicts churn within 30 days.

2. Feature Adoption (Secondary Signal)

Some customers adopt new features quickly. Some never use them.

If a customer stops adopting new features you release (features that would help them), they’re losing interest.

Example: You release a “Team Collaboration” feature. Your customer never even looks at it. That’s a signal they’re thinking about downsizing or switching.

3. Support Ticket Volume (Tertiary Signal)

This one is counterintuitive: More support tickets can mean higher churn risk, not lower.

If a customer suddenly starts filing lots of tickets about basic issues they used to handle themselves, they’re getting frustrated or confused. That’s a churn signal.

Conversely, if a customer files zero support tickets ever, they’re not engaged enough to care.

We look for the inflection point: A significant increase in tickets beyond their historical average.

4. Payment Issues (Critical Signal)

A customer’s credit card declines. They don’t update it. They ignore reminder emails.

This is 95% predictive of churn within 30 days. They’ve already mentally left.

5. Engagement with Communication (Signal)

They used to open your emails. Now they don’t.

You send them a product newsletter; they don’t read it.

You send them a feature announcement; they don’t click.

This is a late-stage signal (they’re already mentally gone), but it’s still useful for targeting retention campaigns.

6. Cohort Performance (Contextual Signal)

Sometimes individual signals are noise. But when you look at their cohort, patterns emerge.

Example: All customers who signed up in January 2024 have higher churn than average. Why?

Was there a product bug in January that we fixed later?
Did we change pricing in February?
Are they a specific customer segment that has lower product-market fit?

Understanding cohort-level churn helps you prevent future cohorts from having the same problem.

Building a Churn Score

We combine all these signals into a single Churn Score (0-100, where 100 is “they’re leaving tomorrow”).

Here’s how we calculate it:

Usage Decay (30% weight)

Customer’s 30-day average usage: 4 logins/day
This week’s usage: 1 login/day
Deviation: -75%
Churn score contribution: 75 × 0.30 = 22.5 points

Feature Adoption (20% weight)

Customers in their segment average 6 features used
This customer uses 2 features
Below peer average: Yes
Churn score contribution: 10 points

Support Tickets (15% weight)

Historical average: 1 ticket/month
Last 30 days: 5 tickets
Increase: 400%
Churn score contribution: 12 points

Payment Health (20% weight)

Payment status: Active (no failures)
Churn score contribution: 0 points

Email Engagement (15% weight)

Historically opens 60% of emails
Last 10 emails: opened 0
Decline: 100%
Churn score contribution: 15 points

Total Churn Score: 59.5/100

This customer is in the “High Risk” zone. We alert your customer success team immediately.

The Intervention

Now, knowing they’re at risk, you can do something about it.

Our system automatically triggers:

1. Immediate Alert to CSM (Customer Success Manager) “Customer X has a churn risk score of 59/100. They haven’t logged in for 7 days (below their 4/day average). Last 3 support tickets were about basic features. Recommend: Outreach call within 24 hours.”

2. Automated Email Sequence (if no manual intervention) If your CSM doesn’t call within 24 hours, we send a personalized email: “We noticed you haven’t used [feature X] in a while. We just released [new feature] that solves the problem you emailed about. Want a walkthrough?”

3. Retention Offer (if still no engagement) If they ignore the email, we trigger a targeted offer: “We’d love to keep you. Here’s a 30% discount on your next three months if you stick with us. But more importantly, let’s talk about what’s not working.”

4. Exit Interview (if cancellation is imminent) When a customer requests to cancel, we have a checklist:

Record the reason
Understand what competitor they’re switching to (if any)
Capture feedback on missing features or pricing
Attempt a discount (if their lifetime value justifies it)

Real-World Example

Let me walk you through a real scenario.

The Customer: A mid-market software company using your product for team communication. Contract value: $5,000/month. Customer for 18 months.

The Signal: On September 1st, their churn score jumps from 12 to 64.

Usage dropped 60% (from 8 logins/day to 3 logins/day)
They opened 0 of the last 5 emails
Support tickets increased 300% (mostly about the same feature)

September 1st Afternoon: Alert sent to their CSM: “High churn risk. Recommend immediate outreach.”

September 1st 6 PM: CSM calls them.

Conversation: “Hi Sarah, I noticed you haven’t been using the platform as much. Is everything okay?”

Sarah: “Yeah… we’ve been testing Slack’s new built-in communication tools. We might just move everything there.”

CSM: “I understand. Can I ask what features you’re using most? Maybe we can show you something you didn’t know existed.”

Sarah: “We mostly use the message threads and notification settings.”

CSM: “Interesting. Have you seen the new automation feature we launched last month? It connects your notifications to Slack. Might actually reduce the switching cost.”

Sarah: “I didn’t know you had that.”

CSM: “Can I set up a 15-minute demo this week?”

Sarah: “Sure.”

Result: Sarah gets a 15-minute demo. She sees the automation feature. It solves her integration concern. She renews for another year.

Without the churn prediction, Sarah would have silently churned. By the time you noticed (when she didn’t renew in October), it would be too late.

Churn Score Benchmarks

What’s a “healthy” churn score for your business?

For SaaS with annual contracts:

Score 0-20: Very Low Risk (renewal confidence: 95%)
Score 20-40: Low Risk (renewal confidence: 85%)
Score 40-60: Medium Risk (renewal confidence: 60%)
Score 60-80: High Risk (renewal confidence: 30%)
Score 80-100: Critical (renewal confidence: 5%)

We alert your CS team whenever a customer hits 50+.

The Churn Cohort Analysis

Beyond individual customer churn scores, we track cohort-level churn.

We ask: “Which cohorts of customers have the highest churn?”

Example findings:

Customers acquired in January 2024 have 40% annual churn (vs. 15% average)
Customers in the “Healthcare” vertical have 25% churn (vs. 15% average)
Customers on the “Starter” plan have 35% churn (vs. 10% for “Pro” plan)

When you see these patterns, you can address them proactively:

January cohort: Was there a product issue we fixed in February? Can we backport the fix?
Healthcare vertical: Do we need healthcare-specific compliance features?
Starter plan: Is the plan price-sensitive? Should we create a better entry-level option?

The Limitation: Churn Score Isn’t Perfect

Important caveat: Churn scores are predictions, not certainties.

Some customers will have high churn scores and not leave (they’re just busy that week).

Some customers will have low churn scores and suddenly cancel (their company ran out of money; has nothing to do with your product).

We’re not trying to predict with 100% accuracy. We’re trying to surface which customers to pay attention to.

A high churn score means: “This customer probably needs attention. Check in with them.”

It’s a signal to investigate, not a crystal ball.

Automating the Intervention

We build your system to automate the mechanical parts:

Week 1: Set up churn scoring model based on your historical data Week 2: Calculate churn scores for all 500+ customers Week 3: Integrate with your CS tool (HubSpot, Salesforce, Gainsight) Week 4: Set up alerts (Slack notification when score > 50) Ongoing: Update churn scores weekly; flag new at-risk customers

Your CS team sees an alert every Monday morning: “3 new high-risk customers this week. Check in with them.”

They do the human work: calls, empathy, problem-solving.

You handle the pattern: discovering why certain cohorts churn, fixing systemic issues, improving product.

The Takeaway

You can’t prevent all churn. Some customers will leave no matter what.

But you can prevent the churn you don’t see coming.

By watching for early behavioral signals, you catch 30-40% of at-risk customers before they leave.

At $5,000 per customer, retaining even 5 extra customers per year pays for this entire system.

We automate the prediction. You do the retention.