The Problem
“Creator risk was spread across usage, billing, support, and engagement signals that were hard to evaluate manually.”
Creator churn rarely starts with one obvious cancellation event. It often shows up through smaller signals: fewer uploads, declining listener engagement, unresolved support issues, failed payments, skipped subscription actions, or creators no longer using growth tools. Without a system that connected those signals, teams could only react after the creator had already disengaged.
The Implementation
“Use Go services and a TypeScript dashboard to score churn risk and trigger practical retention workflows.”
We built the core scoring and workflow services primarily in Go, with TypeScript powering the internal dashboard and retention tooling. The system combined creator activity, billing status, support history, and engagement patterns into a risk model that surfaced at-risk accounts. OpenAI helped summarize the reason for each risk flag in plain language, while automated workflows suggested next actions like outreach, billing recovery, creator education, or human review.
The Result
“Retention teams get earlier warning, clearer account context, and a stronger path to protect creator revenue.”
Retention teams could prioritize the creators most likely to lapse and understand why each account needed attention. Instead of waiting for inactivity or cancellation, the workflow created an earlier intervention window and helped protect recurring creator revenue with more targeted follow-up.
