Issue #77: This AI System Predicts (And Stops) Churn 90 Days Out [Part 1]

Good morning.

A good friend, Marcus had 487 customers. Every month, 39 of them disappeared.

8% monthly churn.

It doesn’t sound like a lot but it’s death by a thousand cuts.

He'd tried everything. Exit surveys (12% response rate). Win-back emails (2.3% success). Desperate discount offers in the final week (saved maybe 3 customers).

Then, with my help, he built a churn prediction system that could see 90 days into the future.

Now he saves 67% of at-risk customers.

Monthly churn dropped to 2.9%.

Revenue retention went from 76% to 94%.

The system cost him ~$300 to build. It “saves” him about $37,000 per month.

Marcus didn't hire a data science team. Didn't write a line of code. Didn't even know what a "random forest model" was.

He used ChatGPT and Claude, Google Sheets, and Gumloop. Total setup time: 4 days.

Most businesses are sitting on a goldmine of behavioral signals that predict churn with 85% accuracy. They just don't know how to read them.

Today, I'm showing you how to build your own Customer Success Oracle, which is a system that identifies who's about to churn, why they're leaving, and exactly how to save them.

These are the systems my clients use to predict and prevent churn ~90 days before it happens.

Even if you don’t have a subscription offer, the strategies and methods can help you glean insights about your customers that can help you sell more, and more often.

This is PART 1 out of 2.

The next issue will follow in a few days.

Let's build your oracle.

— Sam

IN TODAY’S ISSUE 🤖 

• The 90-Day Warning System (What Actually Predicts Churn)

• Building Your Behavioral Radar (The Signals Everyone Misses)

• The Save Playbook (Interventions That Actually Work)

• Tech Stack for Mortals (No Data Scientists Required)

• The ROI Reality (What This Actually Delivers)

• Your Implementation (Start Monday, See Results Friday)

Let’s get into it.

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Let’s get into this Part 1 (out of 2) on how to leverage AI to predict, prevent, and recover churned customers.

The 90-Day Warning System

Sarah's SaaS had a churn problem. Not a dramatic one. Just the slow bleed that kills companies.

"Look at this," she said, sharing her screen. "Customer just canceled. Paid us $2,400 per year. Never complained. Never asked for support. Just... gone."

I pulled up their usage data.

The warning signs had been screaming for 73 days:

• Daily logins became weekly (Day -73)

• Stopped using their core workflow feature (Day -61)

• Team seats reduced from 5 to 2 (Day -45)

• Last meaningful action 31 days ago

"You had 73 days to save them," I said. "You just couldn't see it."

Here's the thing about churn:

It doesn't happen suddenly. Most customers don't wake up and decide to cancel.

They drift away slowly, leaving breadcrumbs of behavioral signals that most businesses completely miss.

A leading multinational bank discovered they could predict customer churn with up to 90-days of warning, distinguishing between normal fluctuations and actual churn signals.

The pattern is always the same:

1. Engagement drops (but slowly, so you don't notice)

2. Usage narrows (they stop exploring, stick to basics)

3. Team shrinks (key champion leaves or loses interest)

4. Ghost mode (they're technically active but functionally gone)

5. Cancellation (the formality of what already happened)

By the time they hit "cancel," they've been emotionally gone for 60-90 days.

The Real Churn Timeline:

• Day -90: First behavioral changes appear

• Day -60: Pattern becomes statistically significant

• Day -30: Traditional metrics finally notice

• Day -7: Desperation discounts offered

• Day 0: Customer churns

• Day +30: Win-back campaign (3% success rate)

Most companies operate in the last 7 days. The winners operate in the first 60.

Building Your Behavioral Radar

One tool, the Churn Assassin tracks 300+ behavioral signals that reveal real churn risk, not logins or surface-level activity, predicting at-risk customers 3 to 8 months in advance.

But you don't need 300 signals. 

You need the right 12.

Here are the signals that matter the most: 

Usage Signals

1. Login Frequency Decline: 40% drop = red alert

2. Feature Abandonment: Stop using 2+ core features

3. Session Duration Collapse: Time in app drops 50%

4. Breadth Narrowing: From 10 features to 3

Team Signals

5. Champion Ghosting: Your power user goes dark

6. Seat Reduction: Team shrinking = budget pressure

7. Invite Velocity Zero: No new users added in 30 days

8. Admin Login Decline: Decision maker disengaged

Financial Signals

9. Payment Failure Patterns: 2+ failures = 73% churn rate

10. Discount Requests: Seeking lower price = value doubt

11. Billing Contact Changes: New person = new evaluation

12. Usage-to-Cost Ratio: Below 0.3x = poor ROI perception

Research shows that when customers stop using key features or only use basic functions, it suggests they're not finding the full value in your product (and this is a critical early warning sign).

The trick is to turn those signals into a "score", almost like lead scoring (of good leads versus bad leads).

You do this with a Behavioral Scoring Formula:

Churn Risk Score =

(Login Decline × 0.3) +
(Feature Abandonment × 0.25) +
(Team Shrinkage × 0.2) +
(Support Sentiment × 0.15) +
(Payment Issues × 0.1)

A good rule of thumb is:

• Score > 70 = Immediate intervention required

• Score 50-70 = Proactive engagement needed

• Score < 50 = Monitor weekly

But here's a key insight:

It's partially about the signals but more so about the velocity of change.

A customer who goes from 100 logins to 50 in a week is in crisis.

A customer who goes from 20 logins to 10 over three months is drifting.

Different problems. Different solutions.

The Signal Combination Matrix

Certain signal combinations are exponentially more predictive than individual signals:

• Login decline + support ticket spike = 89% churn probability

• Champion ghosting + seat reduction = 76% churn probability

• Feature abandonment + discount request = 82% churn probability

• Payment failure + admin absence = 91% churn probability

Advanced Pattern Detection with AI

Here's a powerful prompt to uncover hidden churn patterns in your customer base:

Analyze these customer cohorts for hidden churn patterns:
- Industry: [X]
- Company size: [Y]
- Contract value: [Z]
- Historical behavior: [paste 90 days of usage data]

Identify:
1. Non-obvious correlations between features
2. Seasonal patterns unique to this segment
3. "Champion behavior" vs "at-risk behavior" 
4. The exact tipping points where engagement becomes irreversible
5. Which combinations of signals are most predictive for THIS specific segment

Output a ranked list of early warning indicators specific to this cohort.

The Save Playbook

This is where AI can help.

One company, Hydrant, saw a 260% higher conversion rate and a 310% increase in revenue per customer by using predictive AI to identify likely churners and implement targeted campaigns.

Here's what actually works when you spot an at-risk customer:

The Intervention Hierarchy

Level 1: Automated Nudge (Days 1-7)

When score hits 50, trigger:

• Personalized "miss you" email with specific feature reminder

• In-app notification highlighting unused value

• Success tip based on their industry/use case

Success rate: 23% stop churning

Level 2: Human Touch (Days 8-14)

When automation fails:

• CSM sends personal video (under 90 seconds)

• Offer 15-minute "success audit" call

• Share relevant case study from similar customer

Success rate: 41% stop churning

Level 3: Executive Intervention (Days 15-21)

For high-value accounts:

• Executive reaches out directly

• Business review with ROI analysis

• Custom success plan with clear milestones

Success rate: 67% stop churning

Level 4: The Hail Mary (Days 22-30)

Last resort before they're gone:

• Pause account instead of cancel

• Significant discount (30-50%) for 3 months

• Switch to lower tier with upgrade path

Success rate: 19% stop churning.

Not bad numbers.

And it's primarily through email.

The Value Realization Audit

Instead of just checking in, have AI analyze their actual usage versus their stated goals from onboarding. 

Create a personalized "you're only using 23% of what you're paying for" report.

Value Gap Analysis Prompt:

Customer signed up for: [paste original goals from onboarding]
Current usage shows: [paste actual behavior data]
Contract value: [$X/month]

Task 1: Calculate their "value realization percentage"
Task 2: Identify the top 3 gaps between intention and execution
Task 3: Write a 2-minute personalized video script showing exactly how to bridge gap #1
Task 4: Create 3 quick wins they can implement today to get 20% more value

Make it specific, actionable, and reference their exact use case.

The Email Sequences That Work Pretty Well

Another company, Outreach, uses activity-based sequences where they track engagement patterns and trigger automated educational sequences when usage drops below 80%.

The "We Noticed" Sequence:

Email 1 (Day 0): The Observation

Subject: Quick question about [specific feature they stopped using]

Hey [Name],

Noticed you haven't used [specific feature] in the last two weeks.

This usually means one of two things:

- You found a better workflow (tell me about it!)

- Something's not working as expected

Either way, I'd love a quick reply to make sure you're getting maximum value.

Three customers similar to you saved 5 hours/week once they figured out [feature].

Worth a 12-minute call to explore?

This is followed by the next email a few days later.

Email 2 (Day 3): The Success Story

Subject: How [Similar Company] solved exactly your challenge

Share specific, relevant case study with metrics.

And then a third email.

Email 3 (Day 7): The Direct Question

Subject: Should I stop emailing you?

I've sent two emails about helping you get more from [Product].

Haven't heard back, which tells me either:

- You're all good (great!)

- Wrong timing (I'll check back later)

- We're not the right fit (it happens)

Quick reply would help me help you better.

P.S. If you're thinking about canceling, let's talk first. I might have options you haven't considered.

Your attempts at preventing and stopping churn doesn't have to be more complicated than that.

Some will churn no matter what you tell them. Others, though, could be swayed.

The Pre-Churn Honeypot

Before customers fully churn, create a "pause" option that's easier than canceling. This gives you 30-90 more days to win them back and provides cleaner data on why they're leaving.

The Pause Playbook:

• Offer 3-month account freeze at 20% of regular cost

• Keep their data and settings intact

• Send monthly "what's new" updates

• One-click reactivation with bonus month

• 43% of paused accounts reactivate within 90 days

Tech Stack for Mortals

A basic version of this can be slapped together pretty easily.

You don't need a data science degree. You don't need $100K in software.

You need these four things:

The Minimum Viable Stack (~$300/month)

Data Collection: Segment or Google Analytics (Free)

• Tracks every user action

• 15-minute setup

• Connects to everything else

Analysis: ChatGPT Plus ($20/month)

Run this churn risk analysis prompt with your data:

Analyze this customer behavior data for churn risk.

Last 30 days:
- Logins: [X] (down from [Y] previous period)
- Features used: [list]
- Team size: [current] (was [previous])
- Support tickets: [number] with sentiment: [positive/negative]
- Last payment: [date] status: [successful/failed]
- Score churn risk 1-100 and explain the primary risk factors.
- Recommend specific intervention.

To automate this, you've got options. Anything from Zapier, Lindy, to Gumloop and n8n (or dedicated software you can sign up for) will work.

It's never about the tool. It's always about the intelligence of what's happening. And that intelligence is AI.

Example Automation: Gumloop ($97/month)

Gumloop is built for teams who need more than an app-to-app automation, offering a visual canvas where you build workflows like a flowchart, adding AI steps without touching code.

Build this workflow:

1. Daily data pull from analytics

2. AI analysis of all customers

3. Flag anyone over 50 risk score

4. Trigger appropriate intervention

5. Log everything to tracking sheet

The Decision Tree Automation

Here's the specific workflow logic to implement in your automation tool:

IF churn_score 50-60 AND contract_value < $500:
  → Trigger educational drip campaign
  → Log to "monitor" sheet
  
IF churn_score 60-70 AND contract_value > $500:
  → Alert CSM immediately
  → Create Slack notification
  → Generate personalized intervention plan
  
IF churn_score 70+ AND recent_support_complaint:
  → Executive escalation within 2 hours
  → Prepare account rescue package
  → Schedule emergency save call
  
IF payment_failed 2x AND engagement < 30%:
  → Send pause account offer
  → Alert finance team
  → Prepare downgrade options

Tracking: Google Sheets (Free). Simple dashboard with:

• Customer risk scores

• Intervention history

• Save success rates

• Revenue impact

Super simple. Super easy to set up and operate.

The Feedback Loop System

Track which interventions actually work and feed that data back into your AI system.

Intervention Performance Learning Prompt:

Based on these 50 intervention attempts from the last 30 days:
- Email sequence A: 12% success rate with [customer profile 1]
- Personal call B: 34% success rate with [customer profile 2]  
- Discount offer C: 8% success rate with [customer profile 3]
- Pause option D: 28% success rate with [customer profile 4]

For this specific customer:
- Profile: [industry, size, contract value, usage pattern]
- Churn score: [X]
- Primary risk factors: [list]

Recommend:
1. The optimal intervention sequence
2. Best timing for each touchpoint
3. Probability of success for each approach
4. Custom message angles most likely to resonate

The ROI Reality

Let me show you the actual math from three implementations:

Case Study 1: B2B SaaS (500 customers)

• Before: 8% monthly churn (40 customers)

• After: 2.9% monthly churn (14.5 customers)

• Saved: 25.5 customers/month × $500 MRR = $12,750/month

• Cost: $297/month in tools + 10 hours/week labor

• ROI: 2,150% in first 90 days

Case Study 2: E-commerce Subscription (2,000 customers)

One company achieved more than 20% reduction in churn with 10x return on investment, saving over $39k every month during pilot.

Case Study 3: Professional Services (50 clients)

• Before: 20% annual churn (10 clients)

• After: 8% annual churn (4 clients)

• Saved: 6 clients/year × $15,000 = $90,000/year

• Cost: $897/month in tools

• ROI: 737% annually

The Compound Effect:

Aside from keeping more customers, you also get:

• Referrals they generate (saved customers refer 2.3x more)

• Upsells they accept (60% higher acceptance rate)

• Case studies they become (success stories from near-churns convert)

• Product insights they provide (understanding why they almost left)

It's about 5x cheaper to retain existing customers rather than finding and acquiring new ones.

Your Implementation

Monday (2 hours): Set Up Your Tracking

1. Install Segment or set up Google Analytics events (30 min)

2. Create Google Sheets tracking template (30 min)

3. List your 12 key behavioral signals (30 min)

4. Set up daily data export (30 min)

Tuesday (3 hours): Build Your Analysis System

1. Create ChatGPT analysis prompt (30 min)

2. Test with 10 customer examples (1 hour)

3. Refine scoring based on results (30 min)

4. Set up Gumloop account (30 min)

5. Build basic analysis workflow (30 min)

Wednesday (2 hours): Create Your Interventions

1. Write 3-email sequence for each risk level (1 hour)

2. Create quick CS playbook (30 min)

3. Set up automation triggers (30 min)

Thursday (2 hours): Test and Refine

1. Run analysis on all customers (1 hour)

2. Identify top 10 at-risk accounts (30 min)

3. Launch interventions for 5 test accounts (30 min)

Friday (1 hour): Monitor and Adjust

1. Check intervention responses (30 min)

2. Refine based on early feedback (30 min)

Total time investment: 10 hours

That's not a lot of time to set up something that could save you thousands of dollars in revenue.

Results you can expect:

• 10-20 at-risk customers identified

• 3-5 immediate saves

• 1-2 "wow, perfect timing" responses

• Clear patterns emerging in your data

And of course, you continue to maximize and optimize.

Your 30-Day Evolution Plan

Week 2: Segment and Specialize

• Segment customers by risk patterns (2 hours)

• Create industry-specific intervention templates (2 hours)

• Build executive dashboard in Google Sheets (1 hour)

Week 3: Test and Optimize

• A/B test intervention messages (ongoing)

• Analyze which save tactics work best per segment (1 hour)

• Refine your scoring algorithm based on results (2 hours)

Week 4: Scale and Systematize

• Document your playbook for team training (2 hours)

• Set up monthly reporting automation (1 hour)

• Create customer health score cards (1 hour)

Month 2: Flip the Script

• Implement predictive upsell identification (same signals, opposite direction)

• Build "expansion ready" scoring system

• Launch proactive growth conversations with high-potential accounts

Cool?

Get started and in PART 2, you’ll get more tactics to pull this off.

What's your biggest business challenge you’re trying to solve with AI right now?

Hit reply and tell me. I read every email, and your challenge might become the next issue’s deep dive.

Until then, start listening to your customers' behavior.

They're telling you everything you need to know.

Also, this was PART 1 out of 2.

In PART 2, you’ll get the actual automation architecture that powers these churn prediction systems in the wild (down to the workflows, trigger logic, and AI prompt design).

That issue goes out in a few days.

If you like what you’re getting, consider joining Cortex: 

JOIN CORTEX at $50/month HERE

Until next time,
Sam Woods
The Editor

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