I Used AI to Track Every Customer Touchpoint.Churn Dropped 18%.
I had 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 to surface accounts where engagement dropped 40% month over month, then paired that with 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 work focuses on exactly this: use the machine to see what's happening, then use a human to fix it. The tool's job is the early warning.
Yours is the conversation that saves the account, and that split is where the 18% churn drop actually came from.
Export your last 90 days of customer interactions into one spreadsheet: email opens, support tickets, login frequency, last purchase. Feed it to an AI tool and ask it to flag accounts where engagement dropped more than 30% in 30 days. Then call three of them yourself this week.
