Acme Corp entered 2024 with a churn problem. Their monthly churn rate had crept up to 4.2% — not catastrophic, but compounding into a meaningful drag on growth. The CS team was reactive, spending most of their time on customers who were already in escalation rather than preventing problems upstream. The leadership team knew they needed to change the model; they didn't have a clear picture of how.
Over the following 12 months, they deployed a churn prediction model, redesigned their CS workflows around proactive intervention, and automated the first layer of retention outreach. By month 12, monthly churn was at 2.5% — a 40% reduction.
Phase 1: Getting the Data Right (Months 1–3)
Before building any model, the team audited their data infrastructure. Product usage data was in Mixpanel, support history in Zendesk, billing in Stripe, and CRM data in HubSpot — none connected. Building a unified customer profile was the prerequisite for any predictive work. They used a lightweight ETL pipeline to write daily snapshots of all four systems into a single Postgres schema, normalized around account ID.
This phase took longer than expected (it usually does) but produced a secondary benefit: the CS team, for the first time, had a single dashboard showing complete account context. This alone improved the quality of renewal conversations.
Phase 2: The Prediction Model (Months 4–6)
With unified data available, they trained a gradient boosting model on 18 months of historical account behavior, using churn (cancellation within 90 days) as the target variable. The strongest predictors were login frequency decline, support ticket volume increase, and failure to adopt a second core feature within 45 days of signup.
The model produced a daily risk score per account (0–100). They validated it against a holdout set before deploying: precision at a threshold of 70 was 71%, meaning 7 out of 10 accounts flagged as high-risk did churn within 90 days if no intervention occurred.
Phase 3: Automated Retention Workflows (Months 7–12)
The risk score fed directly into Customer2.AI's workflow automation. Accounts crossing the 70 threshold triggered an automated outreach sequence: a personalized email from their assigned CSM (drafted by AI, reviewed and sent by the CSM), followed by an automated in-app message 3 days later if no response, followed by a task creation in HubSpot for a phone call if still no response at day 7. The result: 68% of flagged accounts received meaningful outreach within 5 days of crossing the threshold, compared to roughly 20% before the workflow existed.