Churn Prediction for B2B SaaS

The problem

SaaSify was losing 8% of customers monthly. By the time the customer success team knew an account was at risk, it was too late. They needed early warning to intervene before customers decided to leave. The challenge was identifying the signals that predicted churn.

What we built

We analyzed 2 years of customer data to find patterns that preceded cancellations. The resulting model scores every account daily based on product usage, support tickets, billing events, and engagement metrics. At-risk accounts are flagged 60 days before predicted churn, giving the team time to act.

Tech stack

Results

Monthly churn dropped from 8% to 4% within 9 months. The customer success team now has proactive conversations with at-risk accounts, offering help before problems escalate. Customer lifetime value increased 40% as a result of improved retention.