Revenue Anomaly Alerts
Your agent watches daily revenue data from Stripe and your billing system, then alerts when metrics deviate from the 30-day rolling average. It catches churn spikes, payment failures, unexpected downgrades, and unusual upsells 48 hours before the weekly finance review — so you fix problems before they compound.
By the time you see the churn spike, it's already 5-10 days old.
Most CEOs review revenue weekly or monthly. The numbers land in a dashboard, someone builds a slide, and eventually it surfaces in a meeting. By then, the damage is done. Customers who downgraded last Tuesday are already halfway out the door by the time you hear about it on Friday.
Involuntary churn — failed payments, expired cards, billing errors — accounts for 20-40% of total churn. Most of it is recoverable if caught within 48 hours. A $5M ARR company losing 2% to involuntary churn per month is bleeding $100K/year that's entirely preventable.
Your agent pulls revenue data every morning and compares it against the last 30 days.
The agent connects to Stripe (or your billing system) and pulls daily revenue data: new MRR, expansion, contraction, churn, and failed payments. It compares each metric against a 30-day rolling average. If any metric deviates by more than your threshold — default is 15% — it fires an alert.
The alert lands in your Slack with four things: what changed, by how much, which customers are affected, and a suggested next action. No digging through dashboards. No waiting for the weekly finance sync.
Not "revenue dropped." A specific diagnosis with a suggested fix.
Generic alerts are noise. Your agent doesn't send "MRR decreased" and call it a day. It tells you exactly what happened: which customers, what they have in common, and what likely triggered the change. It cross-references support tickets, usage data, and account history to build the full picture.
The difference between "churn went up" and "4 Enterprise customers downgraded because of API rate limits they complained about last week" is the difference between knowing and knowing what to do.
After a few months, your agent starts predicting problems before the data shows them.
Your agent doesn't just flag individual anomalies. Over months, it identifies patterns: Enterprise churn correlates with Q4 budget cycles. Failed payments spike on the 1st and 15th. Expansion revenue drops during product freeze periods. These patterns become predictive — you start fixing problems before they show up in the data.
One CEO we deployed for discovered that 70% of their involuntary churn happened within 3 days of card expiration. They set up proactive card update reminders and cut involuntary churn by 60% within two months — saving $72K annually on a $6M ARR base.