lapsewatch: find customers who stopped buying

Customer retention and churn analysis from an orders CSV: see who's at risk, who you've lost, and how much yearly revenue is in danger. For e-commerce stores, service businesses and small B2B companies.

Keeping a customer usually costs less than finding one, but nobody notices when a regular quietly stops ordering. lapsewatch compares each customer with their own buying pattern, so someone who orders monthly is flagged sooner than someone who orders twice a year. It's an MCP server and a single Go binary, with no account, no API key and no network access.

What you can ask your agent

• "Which customers have stopped buying?"
• "Who should I contact first to win back revenue?"
• "How much yearly revenue is at risk?"
• "What's my repeat customer rate, and how much do I depend on my top customers?"

Install

As a Claude Code plugin, which adds the MCP server and a skill that tells Claude when to use it. You need Node, which runs the server through npx:

claude plugin marketplace add GillMacedo/lapsewatch
claude plugin install lapsewatch@lapsewatch-marketplace

For other MCP clients:

{
  "mcpServers": {
    "lapsewatch": {
      "command": "npx",
      "args": ["-y", "lapsewatch", "serve"]
    }
  }
}

Or build the binary with Go 1.27 or newer:

go install github.com/GillMacedo/lapsewatch@latest

Usage

Without installing anything, run npx lapsewatch check orders.csv. This example uses the made-up file in examples/:

$ go run . check examples/orders.csv
As of 2026-09-27: 8 customers, 75.0% bought more than once, 14598.00 revenue, top 10% bring in 49.3%
AT RISK: contact these first (2)
  demo-bakery@example.com  197 days since last order, usually every 60  worth 5475.00 a year
  cafe@example.net  92 days since last order, usually every 30  worth 3771.67 a year
LOST (1)
  erin@example.org  297 days since last order, usually every 20  worth 1368.75 a year
ONE-TIME (1)
  finn@example.net  397 days since their only order  spent 60.00
3 active, 1 new.
SKIPPED row 68: no valid date, customer or amount
9246.67 a year at risk from 2 customers.

Add --as-of 2026-12-31 to measure from a specific date, --top 50 to list more customers, and --json for machine-readable output.

How it decides

The usual gap is the median number of days between a customer's orders. Yearly value is their average order times 365 divided by that gap, so it's an estimate. Rows on the same day count as one order, and refunds reduce totals without counting as orders.

MCP tools

Both are read-only, and neither can contact anyone. customers_check_lapsed returns customer names or emails to the MCP client, so they reach whichever AI model you use. The CLI keeps everything on your machine.

FAQ

How do I find inactive customers? Export your orders as CSV

... View full README on GitHub