Analytical Explainability
Variance Decomposition for Business Users
Variance decomposition explains changes using business‑friendly components.
Rendering diagram...
Total change splits into price, volume, and mix.
TL;DR
- • Explain “what changed” in components users understand.
- • Price‑volume‑mix is a common pattern.
The problem (layman)
- • Business users see changes but not the underlying reasons.
- • AI explanations often lack structure.
Why it matters
- • Structured explanations accelerate decision‑making.
- • It reduces debate about root causes.
Symptoms
- • Teams disagree on why a KPI moved.
- • AI produces inconsistent explanations.
Root causes
- • No decomposition measures in the model.
- • Drivers are not mapped to business levers.
What good looks like
- • Standard decomposition measures (price, volume, mix).
- • Explanations align with business levers.
How to fix (steps)
- • Define decomposition logic for key KPIs.
- • Store driver measures in the model.
- • Use consistent explanation templates.
Pitfalls
- • Over‑simplifying decomposition for complex metrics.
- • Mixing multiple decompositions without clarity.
Checklist
- • Decomposition measures defined.
- • Drivers linked to business levers.
- • Explanations validated by users.