More pipeline. More signal.
More deals closed.

A SaaS company's Q2 churn was 12% — 3x their target. Churn signals (declining logins, unresolved tickets, champion departures) were never monitored systematically — only discovered when renewal conversations started.

SAM built an account health scoring model: login frequency (40% weight), support ticket sentiment (25%), expansion vs. downgrade activity (20%), stakeholder engagement (15%). Scored all 340 accounts weekly. Flagged 23 accounts as high-risk with specific recommended actions per account.

SAM's churn prediction model can flag accounts before they go dark, enabling proactive outreach. Of accounts flagged, around 61% typically renew — recovering ARR that would otherwise silently churn. CS teams that run SAM's model as their first Monday screen can often prevent $800K+ in annual ARR loss.

A VP of Sales was spending 90 minutes every Friday reviewing pipeline manually — pulling deal data from Salesforce, updating a forecast spreadsheet, and preparing a summary for the CEO. Pulling data from 60+ opportunities is data-entry, not strategy.

SAM automated the weekly pipeline review: pulled all open opportunities by stage, flagged deals with no activity in 14+ days, identified deals where close dates had slipped 30+ days, and generated a one-page summary with coverage ratio and top 5 deals by probability-weighted value. Delivered 30 minutes before the CEO sync.

SAM's pipeline intelligence can reclaim 70 minutes per week for a VP of Sales and improve pipeline coverage ratios from 2.8x to 3.4x — often by surfacing stale deals that should be reclassified before they distort the forecast. The summaries are typically cleaner and more actionable than what a human RevOps person produces at the same cadence.