Churn model flagged at-risk accounts six months early and lifted retention by 15% on that cohort.

A growth-stage B2B SaaS company in the $10M to $30M ARR range, with a customer success team that was reacting to churn rather than getting ahead of it. They had plenty of product usage data and support ticket history but no systematic way to spot accounts going sideways.

By the time an account showed cancellation intent, it was usually too late to save. Usage drops, support escalations, and changes in engagement were all visible in the data, but nobody was stitching them together into a coherent early-warning signal. The CS team prioritized outreach based on gut feel and recent conversations, which meant the quiet accounts (often the ones most at risk) slipped through. Renewal conversations turned into save attempts instead of expansion plays.

The pattern repeated across every quarter. Logo retention numbers in the board deck explained themselves only in hindsight. The CS team knew which accounts they had saved and which they had lost, but they couldn't articulate which signals predicted the difference, so the next quarter's prioritization kept running on instinct. Leadership wanted a way to put the CS team's energy where it actually moved retention, with a defensible signal underneath.

The Blueprint ran four weeks: defining what 'churn risk' actually meant for the business (cancellation, downgrade, contraction), auditing the historical data to see whether the signals we wanted to use were clean enough to model on, and pressure-testing feature ideas with the CS team's pattern-matching as a sense-check. We came out with a tight feature set, a defensible label for what a churn event was, and explicit decisions on the data quality issues we'd fix versus model around.

Through the eight-week Build, we paired with the CS analytics lead in weekly working sessions. Each iteration of the model went to the CS team for a gut-check before we kept tuning: did the accounts the model flagged feel right, and were the accounts the model didn't flag actually safe? That feedback loop was where the model's interpretability had to land, not just at the technical level but at the workflow level.

Knowledge transfer included Python notebooks the analytics team could rerun, model documentation explaining each feature's contribution, a decision-making framework for when to retrain, and a runbook for the Salesforce sync. The CS analytics lead owned the model after handoff and ran the first retraining cycle himself two months in.

• 15% improvement in retention for flagged cohort vs historical baseline, within 6 months
• CS team shifted from reactive to proactive outreach on at-risk accounts
• At-risk signals identified early enough for CS to actually do something about them
• Interpretable scores meant reps trusted the model and used it in their workflow
• Model expanded from initial pilot into the primary CS prioritization tool