Spot early which customers may leave

Sales identifies customers at risk of leaving in order to take action in good time

Needs setupAI Act: no extra obligationsForecasting from figuresAll industries

Fits: Leaders & strategy · Sales · Marketing & communications · Customer service

You notice which customers are going quiet before they have cancelled, and can follow up in a targeted way. Sales allocates its time by risk instead of by who is loudest.

Is this for you?

  • You have recurring customers with orders, contracts or subscriptions.
  • Order, complaint and contact data is available for at least two years.
  • Today, cancellations often come as a surprise.
First step today

For all existing customers of the last two years, export orders, complaints and last contact, with a sequential test number instead of name and customer number, plus a column showing whether the customer left. Use the template below to check which warning signs appeared beforehand.

How to go about it

  1. Define what “left” means
  2. Gather customer history and warning signs
  3. Have the risk estimated for each customer
  4. Test against known departures
  5. Plan conversations by risk
  6. Readjust every six months

Templates to copy

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The complete guide

  • 6 steps explaining exactly what to do
  • 3 proven templates to copy into your own AI
  • Safe use: data, checking, deciding, informing
  • Pitfalls, effort and how to recognise success

With the training you get all 430 guides for as long as your access runs.

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Same approach, other use cases

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A-038 · Legal status 08.10.2026