Why Machine Learning is a Game-Changer for Customer Health
Most customer health scores fall short. They’re built on rules like “low logins = high risk,” but they’re slow to update, based on assumptions, and miss what really drives churn. They don’t scale with your business and they don’t earn your team’s trust.
A well-built machine-learned (ML) health score changes that. It learns directly from your historical data to surface early signs of churn, adapts as your product and customers evolve, and flags risk up to 180 days in advance giving your team time to act.
Whether you’re a CS leader, data-savvy founder, or RevOps partner trying to answer “Who’s at risk, and what do we do about it?”
This guide walks you through how to build a model your whole team can trust and act on.
What you'll learn
How to define a churn prediction goal that’s actually useful
What data you need (and how to structure it)
Which product, engagement, and support metrics actually predict churn
How to train, validate, and calibrate your model (even without a data science background)
How to turn predictions into clear, usable risk tiers
How to make your model explainable and actionable for CS
How to monitor, retrain, and maintain trust over time
Download the full guide or chat with our team
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