Analytical Explainability
Cohorts and Segmentation: Explainability at the Right Level
Segmentation and cohort analysis provide context for why metrics move.
TL;DR
- • Aggregate explanations often hide important differences.
- • Segments and cohorts reveal true drivers.
The problem (layman)
- • Aggregate metrics mask changes in sub‑groups.
- • AI explanations lack segmentation context.
Why it matters
- • Segment‑level insights are more actionable.
- • Cohorts help explain time‑based behavior.
Symptoms
- • Overall KPI stable but key segments move dramatically.
- • AI narratives don’t mention cohort shifts.
Root causes
- • No cohort definitions in the model.
- • Segmentation dimensions not linked to KPIs.
What good looks like
- • Standard cohort and segment definitions.
- • Explanations include top segment contributions.
How to fix (steps)
- • Define cohorts (e.g., first purchase month).
- • Create segment measures and filters.
- • Include segment breakdowns in AI responses.
Pitfalls
- • Too many segments without prioritization.
- • Cohorts defined inconsistently.
Checklist
- • Cohort definitions stored in model.
- • Segment metrics linked to KPIs.
- • Explanations include segment context.