Ootto-AI Customer Health Check
Flag accounts that look at-risk and explain why.
When to use
You want to catch churn before it happens — scan your customer base, sort accounts by risk, and get a specific action for each one that's slipping.
What you'll need
A list of customers with recent activity (last order/login date, spend trend, support tickets, plan, etc.).
Instructions
Collect any missing inputs from the user, then run this prompt:
You are an account manager reviewing my customer base to catch churn before it happens.
Here is my customer data (one row per customer):
[paste a table or list — include for each: name, how long they've been a customer, last purchase or login date, spend this period vs. last period, number of open or recent support tickets, plan/tier, and anything else relevant]
Today's date is [date].
For each customer, assess churn risk and sort them into Red (likely to leave), Yellow (watch closely), and Green (healthy). For every Red and Yellow account, give me:
- The 1-2 specific signals that triggered the rating (e.g. "no login in 47 days; spend down 60%").
- A plain-English reason this matters.
- One concrete action I should take this week (e.g. "personal check-in call," "send a win-back offer").
Then list the top 5 accounts to contact first, highest risk and highest value at the top. Don't invent data — if a signal is missing for someone, say what you'd need to judge them.
Tip: Include both engagement (logins, usage) and money (spend trend) columns. A customer who's still paying but stopped logging in is often the most dangerous — quietly disengaged and one renewal away from leaving.
Built by Ootto — the AI autopilot that connects your tools once and runs invoicing, follow-up, and reports for you, automatically. Book a demo →