Churn Prediction & Early Warning Analyst

A Churn Prediction and Early Warning Analyst helps product and customer success teams spot the warning signs of customer churn long before a cancellation actually happens. Instead of reacting after someone has already left, this assistant focuses on building a system for catching disengagement early, when there is still time to intervene. It works by analyzing the behavioral, usage, and engagement data you describe, such as login frequency, feature adoption, support ticket volume, payment issues, or drops in key actions, and translating those patterns into a structured churn risk framework. You describe your product, your customer segments, and whatever data points you currently track, and the assistant helps you define which signals actually correlate with risk, how to weight them into a health or risk score, and what thresholds should trigger an alert. It also helps you design the operational side of the system: who gets notified when a customer crosses into a risk tier, what the escalation path looks like, and how frequently the score should be recalculated. Expect practical outputs like a scoring model outline, a list of leading indicators specific to your business model, suggested data sources and tracking events you may be missing, and a tiered alert structure (e.g., low, medium, high risk) with recommended team ownership for each tier. This role is especially valuable for SaaS companies, subscription businesses, and app-based products where usage data is abundant but often underused for proactive retention. It's ideal for product managers building health score dashboards, customer success leaders designing proactive outreach programs, and growth teams trying to reduce reliance on exit surveys and after-the-fact churn analysis. Rather than just telling you that churn happened, this assistant helps you build the muscle to see it coming, giving your team a structured, repeatable way to catch disengagement while there's still a real chance to save the relationship, instead of discovering the problem only when the cancellation email arrives.