You're closing deals to replace the ones walking out.

At $10M ARR with 2% monthly churn → 30 new customers before $1 of net new revenue

Your CS team finds out customers are leaving when the Stripe webhook fires. The decision was made 30 days ago. The signals were there 60 days ago. Nobody was watching.

First at-risk list in 2 weeks · $4,997 · Money-back guarantee
Stop the Leak → Watch the breakdown ↓

For B2B SaaS leaders at $3M–$80M ARR

Watch: Why acquisition can't outrun churn

A $150K ARR customer lost = $1M+ in lifetime profit gone.

That's not churn — it's a million-dollar leak. Reducing gross revenue retention from 66% to 83% adds $8.9M ARR over 3 years to a $10M company. Churn reduction isn't a CS initiative — it's the single highest-impact growth lever.

Support tickets were predicting churn 60 days early at one company.

Accounts with 3+ tickets in a 14-day window churned at 3.2x the baseline rate. The signal sat in the data for 18 months. Nobody was watching.

The industry treats churn as a reporting problem.

Dashboards with red and green dots that tell you someone left after they left. Churn is a prediction problem. You need to know who's at risk before they decide to leave.

THE TREADMILL

Five signs you're running to stand still.

Your CS team works from a renewal-date spreadsheet.

No risk scores. No behavioral triggers. Pure calendar-based outreach. High-value accounts churn with no prior indication.

You add 30 new customers and net revenue barely moves.

At 2% monthly churn, replacement math dominates. You're hiring SDRs to outrun the leak instead of fixing the pipe.

QBRs are already too late.

Quarterly business reviews happen after the damage is done. Customers expect real-time response. By the time you review, the decision was made weeks ago.

You can describe your churn rate. You can't explain it.

Calculating churn is easy. Knowing which behavioral signals predict it — login patterns, feature adoption, support ticket velocity — requires a model, not a formula.

Your best intervention is a discount.

Because you can't intervene early, the only lever left is price. You're buying time with margin instead of addressing the root cause.

What the leak actually costs.

$8.9M

ARR added over 3 years

by improving GRR from 66% to 83%. At a $10M company. That's the single highest-impact lever — higher than pricing, higher than acquisition.

$1M+

lifetime profit from one customer

from one $150K ARR customer. Every one you lose is a million-dollar leak.

18 months

the signal was sitting in your data

on average, before someone builds the model to see it.

THE TRANSFORMATION

Before and after. Two weeks apart.

TODAYAFTER 2 WEEKS
Churn detectionStripe webhook (after they cancel)30–60 days early warning
At-risk identificationGut feel + renewal calendarBehavioral risk score, updated weekly
CS workflowRenewal spreadsheet, reactive callsMonday morning at-risk list, top 3 churn drivers per account
Signal analysisNobody's watching85+ behavioral signals feeding the model
InterventionDiscount when they threaten to leaveProactive outreach triggered by usage decay, not calendar date
ImpactUnknown — can't measure what you preventMonthly save rate tracked, ARR impact quantified

THE PROCESS

Two weeks. From reactive to predictive.

DAYS 1–5

Signal Mapping

We connect to your analytics, billing, and support data. Map behavioral signals across every category: login patterns, feature adoption trends, support ticket velocity, billing changes. Identify which signals predict churn in your specific business.

DAYS 6–10

Model Training

The churn prediction model is trained on your historical data — not industry benchmarks. Backtested against actual churn events. Calibrated for your customer segments and contract types.

DAYS 11–14

Deployment + First List

First at-risk customer list delivered to your CS team. Each account shows: risk score, top 3 behavioral drivers, recommended intervention. Monday morning delivery via Slack or email — this is a weekly operating tool, not a quarterly report.

WHAT HAPPENS NEXT

The model rescores weekly. Updated monthly with new data. Your CS team gets a fresh at-risk list every Monday. The sprint deliverable works independently. If you want ongoing optimization, we can discuss that after you see results.

WHY OUR MODEL WORKS

Trained on your data. Not industry benchmarks.

Most 'churn prediction' tools use generic models trained on aggregate SaaS data. They produce risk scores that look sophisticated and mean nothing for your specific business. Our model is trained on your behavioral data — the signals that predict churn for your customers, in your product, with your contract types.

The output isn't a dashboard. It's a weekly operating tool: a ranked list of at-risk accounts with the specific behavioral drivers for each. Your CS team doesn't need to interpret a score — they get a list, a reason, and a recommended action.

One healthcare SaaS company went from a renewal spreadsheet to a Monday morning at-risk list. In one quarter, their CS team saved 4 accounts worth $180K ARR using the playbook we built from the model's signals.

85+
behavioral signals
30–60 days
early warning
Weekly
at-risk list delivery

THE WORK

Specific results from specific engagements.

HEALTHCARE SAAS
4 accounts

saved in one quarter

$180K

ARR recovered via weekly at-risk list

E-COMMERCE SAAS
3.2x

churn rate for accounts with 3+ support tickets in 14 days

If the model can't identify at-risk accounts better than random, you pay nothing.

We train the model on your data, backtest it against real churn events, and show you the accuracy before you deploy. If it doesn't outperform random assignment, full refund.

Stop the Leak — $4,997 →

Questions.

Or book a call →
What data do you need?+
Analytics platform access (Amplitude, Mixpanel, PostHog), billing data (Stripe), and support data (Intercom, Zendesk) if available. The model improves with more signal sources, but we can start with analytics + billing alone.
How accurate is the model?+
It depends on your data depth. Typical results: top 10% of risk scores capture 35–45% of actual churns. That means your CS team can focus on the accounts most likely to leave.
We only have 500 customers. Is that enough?+
Usually, yes. The model needs enough churn events to train on (typically 50+). If you're pre-scale, we'll tell you in the scoping call — before you pay anything.
Can't we build this ourselves?+
You can, if your data team has capacity. Most don't. The sprint gives your team a working model in 2 weeks instead of 3–6 months of internal development.
What's the investment?+
$4,997 for the Churn Prediction Sprint. 2-week delivery. Money-back guarantee.

Stop losing customers you could have saved.

2 weeks. $4,997. Money-back guarantee.

Stop the Leak →