Segmentation Analysis
Produces segments that are actionable and stable, since statistically clean but unusable is the standard failure.
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Running it, start to finish
- Name what would be done differently per segment before analysing.
- Check every variable is observable at the point of action.
- Validate that segments differ on the outcome, not just on the inputs.
What you get back
The output this produces, every time.
- Requires a differing treatment per segment before any analysis is run.
- Checks segments are identifiable at the moment of acting, which kills most segmentations.
- Tests stability over time, which is usually discovered only after deployment.
Getting better results
Where this usually goes wrong, and how to avoid it.
- Match segment count to operational capacity. Twelve segments where three treatments exist produces nine descriptions and no additional action.
- Prefer behavioural variables. What people did predicts better than who they are, and demographic data is merely easier to obtain.
- Validate against the outcome. A clustering that separates cleanly on inputs but not on what you care about has found structure and no value.
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Written for The AI University. Every prompt in this library is original work — authored, tested and revised here, not collected from elsewhere. 365 of them, free with an account.