Analytical Explainability
Contribution Analysis: Turning Totals Into Reasons
Contribution analysis breaks totals into components that explain change.
TL;DR
- • Totals are not explanations.
- • Contribution analysis adds reasons.
The problem (layman)
- • Users see total changes without knowing what drove them.
- • AI answers lack supporting breakdowns.
Why it matters
- • Contribution analysis makes explanations credible.
- • It helps prioritize actions.
Symptoms
- • “Revenue up 8%” without explanation.
- • AI highlights a single driver without context.
Root causes
- • No measures for contribution or share.
- • Segment breakdowns are not standardized.
What good looks like
- • Standard contribution measures for key dimensions.
- • AI explanations cite top contributors.
How to fix (steps)
- • Define contribution measures (share of total).
- • Add top‑N contributor logic.
- • Include contribution tables in AI responses.
Pitfalls
- • Ignoring negative contributors.
- • Presenting contribution without base totals.
Checklist
- • Contribution measures defined.
- • Top contributors identified.
- • Explanations include context.