A mid-market B2B SaaS company was bleeding $2.1M in annual recurring revenue to churn. Their customer success team of four was buried in QBR prep and reactive fire drills. Ninety days after we deployed an AI churn prediction and retention system, net revenue churn dropped 41%, expansion revenue grew 18%, and the CS team got back roughly 22 hours per person each week. Here is exactly how we built it.

The Problem: Reactive CS Doesn't Scale

The client had 480 active customers on annual contracts ranging from $12K to $180K. Their customer success motion was the usual mid-market story. Account managers split a book of 120 accounts each. Health scores were calculated quarterly in a spreadsheet. Renewal risk was usually discovered 30 days before contract end, which is far too late to save anything.

The result was predictable. Gross logo churn was running at 19% annually. Net revenue churn sat at 11%. Customers were quietly disengaging months before anyone noticed, and the team was spending 60% of their time on QBR decks rather than actual customer work.

The CEO's mandate was direct: cut churn by a third inside two quarters, without adding headcount.

What We Built

We designed a two-part system. The first part was a churn prediction model that scored every account daily. The second part was an automated retention engine that triggered the right play at the right time based on those scores.

Part 1: The Prediction Layer

We pulled signal data from six sources the client already had but was not using together. Product usage events from Mixpanel. Support tickets from Zendesk. NPS and CSAT responses from Delighted. Email engagement from HubSpot. Invoice and payment history from Stripe. Meeting notes and call transcripts from Gong.

The model was not exotic. We used gradient boosting trained on 18 months of historical churn data. The lift came from feature engineering, not algorithm choice. Some of the features that mattered most:

• Usage decay rate. Not absolute usage, but the slope of usage over rolling 14 day windows.
• Power user drift. Whether the original champion who signed the contract was still logging in.
• Support sentiment trend. A simple LLM classifier scoring ticket tone over time.
• Invoice friction signals. Late payments, billing disputes, or finance team escalations.
• Meeting cadence breakdown. Skipped or rescheduled QBRs were a stronger signal than the QBR itself.

The model output a churn probability score from 0 to 100, refreshed every night for every account. Accuracy on a 90 day forward window hit 84% by week six.

Part 2: The Retention Engine

A prediction is useless without an action. We wired the score into an automated workflow engine that did three things based on risk tier.

Low risk (0-30): Nothing changed. The model surfaced expansion opportunities instead, flagging accounts with high usage and underutilized seats.

Medium risk (31-65): The CS rep got a Slack alert with a generated context brief. The brief included the top three risk factors, recent activity summary, and a recommended outreach play pulled from a library of templates that had worked historically.

High risk (66-100): A formal save play kicked off automatically. A discovery call was booked through the rep's calendar. An executive sponsor email was drafted for review. Product, support, and finance were notified so any open issues got prioritized.

The whole loop ran on a stack the client already mostly owned. We added an AI automation layer on top of HubSpot, Slack, Calendly, and their internal data warehouse.

The 90 Day Rollout

We deliberately did not try to boil the ocean. The rollout had three phases.

Weeks 1-3: Data and Model

We spent the first three weeks doing the unglamorous work. Connecting data sources. Cleaning event names. Reconciling customer records across systems. Training the initial model. The client wanted to skip to building dashboards on day three. We pushed back hard. If the data layer is wrong, every downstream decision is wrong.

Weeks 4-8: Shadow Mode

The model ran in shadow mode for four weeks. It scored every account daily but no automated actions fired. The CS team got a daily digest of the top 20 at-risk accounts. They compared the model's calls to their gut. By the end of week 7, the model was catching risk signals the team had missed in 62% of the accounts that later showed churn intent. The team trusted it.

Weeks 9-12: Full Automation

We turned on the retention engine in week nine. We kept a human in the loop for every high risk play in the first two weeks, then let medium risk plays run fully automated. By week twelve, the team was handling roughly 2.5x more save conversations with the same headcount, and the conversations were happening 45 days earlier on average.

The Results

At the 90 day mark, the numbers told a clear story.

• Net revenue churn: 11% down to 6.5% (41% reduction)
• Gross logo churn: 19% down to 13% (32% reduction)
• Expansion revenue: Up 18% as the model surfaced accounts with seat utilization above 80%
• CS team hours saved: ~22 hours per person per week, redirected to strategic accounts
• Forecast accuracy: Renewal forecasts within 4% of actuals, down from 14%

The ROI math was straightforward. The team retained roughly $860K of ARR that the prior baseline said would have churned. They added another $310K in expansion that the model surfaced. Total impact in 90 days was $1.17M, against a fraction of that in build and run costs.

What Made This Work

Five things separated this build from the churn dashboards that gather dust in most CS orgs.

1. We solved an action problem, not a visibility problem. Most churn projects end at a dashboard. Dashboards do not save accounts. Workflows that book calls and draft outreach do.

2. We invested in feature engineering before model complexity. A simple model with great features beats a fancy model with mediocre features every time. The usage decay slope and champion drift features alone drove most of the lift.

3. We ran four weeks of shadow mode. Trust matters. The CS team would have killed this project in week two if we had automated actions before they trusted the score.

4. We kept the human in the loop where it counted. The model triggers the play and drafts the artifacts. A person still owns the conversation. CS reps are not babysitting a bot.

5. We measured the right metric. We did not optimize for prediction accuracy. We optimized for save rate on flagged accounts. Those are not the same thing, and the difference shows up in revenue.

Where Churn AI Goes Next

The interesting frontier is not better prediction. It is closing the loop between prediction and product. The client's next project is feeding churn signals back into in-app experiences. When an account hits medium risk, the product nudges the right users toward the features that correlate with retention. The CS team becomes the last line, not the first.

If you are running a CS org that feels reactive, the prediction layer is the easy part. The hard part is having the workflow discipline to act on it. That is where most companies stall, and where an outside team can usually help. If you want to see what this would look like for your customer base, get in touch and we can scope a 30 day pilot.