What is Variance Analysis?
Variance analysis compares actual results to budgeted or forecasted amounts and investigates the reasons for differences. It answers: did we hit our plan? If not, why not?
Variances can be favorable (better than expected) or unfavorable (worse than expected). Understanding the root cause matters more than the number.
Why Variance Analysis Matters
Regular variance analysis improves forecasting accuracy over time. You learn which assumptions were wrong and adjust future forecasts. It also surfaces operational issues early.
A revenue miss might be timing (deals slipping to next month) or structural (market has changed). The response differs dramatically.
Running Variance Analysis
Compare actuals to budget monthly. Flag significant variances (typically >10%). Investigate root causes. Categorize as timing, one-time, or structural. Update forecasts based on learnings. Share findings with stakeholders. Start with a pro forma income statement as your baseline projection, then measure actual performance against it each month.
Variance = Actual - Budget
Variance % = (Actual - Budget) ÷ Budget × 100
Favorable variance: actuals better than budget
Unfavorable: actuals worse than budget
Your SaaS company reviews monthly budget variance:
- Budgeted revenue: $150,000
- Actual revenue: $135,000
- Variance: ($15,000) or -10%
Root cause: Two large deals slipped to next month ($20K). One unexpected churn ($5K). Early close on three small deals (+$10K).
Net variance: -$15K, but deal slip is timing, not lost revenue.