How FinGrow SaaS Reduced Customer Churn by 28% With Predictive Modeling
Fintech / B2B SaaS
100-300 Employees
San Francisco, CA
Background
FinGrow provides automated accounting software for small businesses. Operating on a subscription model, their growth relied heavily on Customer Lifetime Value (LTV). While they were excellent at acquiring new customers through aggressive marketing, they suffered from a “leaky bucket” problem losing a significant percentage of users after the three-month mark. Their data team had millions of rows of usage logs but lacked the infrastructure to turn that raw data into actionable retention strategies.
The challenges
The VP of Growth, Marcus Thorne, realized that their retention efforts were reactive rather than proactive. They were sending “We miss you” emails after a customer had already cancelled. By then, it was too late. The company needed a way to identify at-risk customers before they hit the cancel button. They required a machine learning solution that could analyze user behavior patterns like login frequency drops or unanswered support tickets to flag high-risk accounts in real-time.
“We were bleeding revenue and didn’t know why. The predictive model didn’t just give us a list of names; it gave us the ‘why’ behind the churn, allowing our success team to intervene with surgical precision.“
Marcus Thorne
VP of Growth
The Solution
We engineered an end-to-end churn prediction pipeline using Python, XGBoost, and AWS SageMaker.
The Result
FinGrow moved from a reactive “damage control” strategy to a proactive “success” strategy.
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