AI Customer Success Platform Predicting Churn Before It Happens
SaaS companies lose 5-10% of revenue annually to preventable churn that Customer Success teams detect too late. Velaris combines product usage data, support ticket sentiment, and engagement patterns to predict churn risk 60-90 days before cancellation, giving CS teams enough lead time to intervene with personalized retention strategies.
Problem Statement
A customer success manager responsible for 80 accounts monitors health scores based on NPS and login frequency. A $50K ARR customer stops using a key feature and starts submitting more support tickets about alternative workflows, but the health score remains 'green' because they still log in daily. The customer churns 60 days later. The CS team had the signals but no system to correlate them.
The Idea
An AI customer success platform that predicts churn risk 60-90 days before cancellation by analyzing product usage patterns, support interactions, and engagement signals, enabling proactive retention interventions.
Why Now
SaaS companies face increasing customer acquisition costs making retention critical for unit economics. Product usage data, support data, and engagement data exist in separate silos that CS teams cannot analyze together. AI pattern recognition in 2026 can correlate multi-source signals to predict churn with 70-80% accuracy when trained on historical churn data.
Target User
Customer Success managers and VP of Customer Success at B2B SaaS companies with $2M-50M ARR
Target Market
B2B SaaS companies with 100-5,000 customers and dedicated customer success teams
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