Forecasting leadership can actually trust.

Context

CS forecasts were directional at best. Renewals slipped late in the quarter and 'surprise churn' kept showing up in board decks. Leadership couldn't plan headcount or revenue against the numbers we were giving them.

Challenge

We had data — usage, NPS, support, payments — but it lived in silos and the team was guessing at which signals actually predicted churn vs. which were noise. The job was to turn fragmented signal into a defensible operating layer.

Approach

1. 01

   AI-driven health scoring

   Built a multi-factor health score (product usage, sentiment, executive sponsorship, support volume, payment behavior) calibrated against historical churn. Real-time, inspectable, with the math visible to CSMs — not a black box.

2. 02

   Renewal forecasting cadence

   Unified CS metrics + health scoring into a single renewal cadence. Every renewal had a forecast confidence and a named risk. Weekly variance review with leadership made the number defensible.

3. 03

   Proactive save-and-expand motions

   When the score moved, the playbook moved. Risk → executive sponsor outreach + value review. Healthy + expansion signal → coordinated upsell motion with AE. CS stopped being reactive.