Sun Elevated: $2.1M ARR Retained Through AI Churn Prevention

The Situation

Sun Elevated is a roofing and solar installation company serving residential customers across Arizona, California, and Nevada. They sell long-term service plans—renewable annual contracts for roof maintenance and solar panel cleaning.

When we first met, Sun Elevated had a $6.2M annual recurring revenue base. But they were losing roughly 28% of customers at renewal—about $1.7M/year in churn. The problem wasn't that their service was bad. The problem was they had no system to predict *which* customers were quietly becoming unhappy.

By the time a customer didn't renew, it was too late. No one saw it coming. No one reached out to save the relationship.

The Approach: Building Predictive Signals

Data Integration

We pulled data from four systems:

• HubSpot CRM — customer info, renewal dates, past interactions
• Jobber (field service platform) — actual service visit history, completion rates, technician ratings
• Stripe (billing) — payment failures, payment delays, overdue invoices
• Google Analytics — web activity (portal logins, help-desk ticket views)

Signal Engineering

We identified which behavioral patterns correlated with churn:

• Service gaps: 2+ months without a scheduled or completed service visit
• Payment friction: 1+ failed payment attempts in the last 30 days
• Support sentiment: 3+ help-desk tickets in 60 days (often a sign of frustration)
• Engagement drop: No portal login in 45+ days (customers who go dark tend to leave)
• Rating trends: Service completion ratings declining month-over-month

We fed these signals into a machine learning model and scored every customer on a 0–100 churn risk scale. A score of 75+ meant "intervention needed within 14 days."

Intervention Engine

A high churn score triggers an automated workflow:

• Day 1: Account manager receives notification + recommended action (call, email, or service credit offer)
• Day 3: If no action taken, automated email to customer with special renewal offer
• Day 7: If still no response, SMS reminder 7 days before renewal date
• Day 14: Final offer window closes; renewal needed or account cancels

Results

Churn reduction: 28% → 18.5% (34% reduction in absolute churn rate)

Customers retained: 412 customers / $1.9M ARR saved in Year 1

Cost per retention: $1,240 (automation + time). Average customer LTV: $8,100 per year. Payback: 56 days.

Model accuracy: The churn predictor had 82% precision on the 75+ risk bucket. Of the customers flagged, 8 in 10 were actually at risk.

Why This Mattered for the Business

In a subscription business, churn is the biggest lever for growth. You can spend enormous sums acquiring new customers, but if you're losing 28%, you're on a treadmill.

Sun Elevated went from a "leaky bucket" model (acquisition-driven) to a "retention-first" model. By retaining 412 customers, they:

• Reduced customer acquisition cost targets (didn't need to buy as many new customers)
• Improved unit economics (higher LTV → lower CAC ratio)
• Became acquisition-ready for investors (predictable, retained revenue base)
• Freed up cash flow that was going to acquisition to invest in service quality instead

The ChurnBlock Origin

This project became the foundation for ChurnBlock. After Sun Elevated's success, other service companies (HVAC, pest control, solar installers) approached us asking if we could build the same system for them.

Rather than run this as a custom services engagement for each client, we packaged it into a product: ChurnBlock. It now serves companies across home services, membership platforms, and SaaS with recurring revenue models.

The same predictive signals work across industries because churn *always* has early warning signs. Customers don't leave silently—they leave data breadcrumbs. ChurnBlock's job is to read those breadcrumbs and act before the customer is gone.