KB article
Contribution Analysis: Turning Totals Into Reasons
Contribution analysis breaks totals into components that explain change.
arf-kbanalytical-explainabilitycontribution-analysisdriverssegmentation
TL;DR
- Totals are not explanations.
- Contribution analysis adds reasons.
The problem
- 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
- 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.
Framework placement
Primary ARF layer: Analytical Explainability. Diagnostic bridge: semantic-reliability, execution-reliability, change-reliability.