I Used AI to Track Every Customer Touchpoint.Churn Dropped 18%.
I was staring at customer data scattered across email, Slack, and invoices. No pattern. No way to see who was slipping away until they were already gone. So I built a simple AI workflow that ingests every interaction—support tickets, purchase history, engagement metrics—and flags accounts showing early warning signs of disengagement.
The insight wasn't complicated: customers who stop asking questions are customers about to leave. AI can spot these patterns faster than any human reviewing spreadsheets. I set it up to surface accounts where engagement dropped 40% month-over-month, then paired that with automated outreach—not sales pushes, just genuine check-ins asking if something was broken.
That's when retention tightened. Not because AI did anything magical, but because I could act before the relationship deteriorated. Our AI automation approach focuses on this: use the machine to see what's happening, then use humans to fix it.
Export your last 90 days of customer interactions into a single spreadsheet (email opens, support tickets, login frequency, last purchase date). Feed it into ChatGPT with the prompt: 'Flag accounts where engagement dropped more than 30% in the last 30 days.' Review the list and call three of them yourself this week.
