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Churn risk alerts

Scores every company on how likely they are to stop ordering and creates a ticket in the Action Hub for the biggest risk, so a sales rep can call before the customer leaves. It runs automatically twice a week.

Runs: Outside Propeller · Trigger: Schedule · Difficulty: Medium

What you need​

How it works​

Every Monday and Thursday at 06:00 the workflow fetches your companies and compares each company's last 30 days with the 60 days before, in batches. Three signals make up a score from 0 to 100:

  • Ordering less often (40 percent of the score)
  • Spending less per order (30 percent)
  • Quiet for longer than their usual ordering gap (30 percent)

Companies that score 30 or higher count as at risk. The highest scoring company gets a ticket in the Action Hub with a short explanation of what changed. Companies that already have an open churn ticket are skipped, so you never get duplicates.

Install it​

  1. In n8n, import the downloaded JSON as a new workflow.
  2. Create a header auth credential with your Propeller API key and select it on every HTTP Request node.
  3. In the Build Ticket Payload node, change DEFAULT_ADMIN_USER_ID to the backoffice user who should receive the tickets.
  4. Run the workflow once manually and check that a ticket appears in the Action Hub.
  5. Activate the workflow.

Make it yours​

  • Schedule: change the cron expression in the Schedule node. Default is Monday and Thursday at 06:00.
  • How many tickets per run: change TOP_N in the Rank & Select Top N node.
  • Sensitivity: change SCORE_THRESHOLD in the Score Batch node or adjust the three signal weights.
  • Ticket text and language: edit the texts in the Build Ticket Payload node. The default description is in Dutch.

Use it outside n8n​

Copy the JSON into an AI assistant and follow Adjust a workflow with an AI assistant.

Good to know

The workflows and agents in the Agent Hub are free to use and adapt. They are examples, not supported product features, so test them in a staging environment and check the results before using them in production.