What Is Variance Analysis?
Variance analysis compares planned results to actual results. Find out why it matters and how to use it to improve forecasts.
Key Takeaways
- Variance = actual minus budget
- Favourable variances improve profit; adverse variances reduce it
- Price variance and volume variance explain most revenue gaps
- Analysing variances improves the quality of future forecasts
What is a variance?
A variance is the difference between what you planned and what actually happened. If you budgeted £50,000 in sales and achieved £47,000, the variance is minus £3,000. Variance analysis is the discipline of investigating why that gap exists and what it means for the future.
Favourable vs adverse
A favourable variance moves profit in the right direction — selling more units than expected or spending less on materials than budgeted. An adverse variance does the opposite. Note that a favourable variance is not always good news: selling far more than planned can strain operations, and underspending on marketing might mean missed growth.
Price vs volume variance
Most revenue variances have two root causes. A price variance means you sold at a different price than planned. A volume variance means you sold a different number of units. Separating the two is critical: if revenue is down £20,000 and that is entirely a price variance, the fix is pricing strategy. If it is entirely volume, the fix is sales or marketing.
Cost variances
On the cost side, split between rate variance (you paid a different price per unit of input) and efficiency variance (you used a different quantity of input than planned). A supplier price increase is a rate variance. A production process using more material than the standard formula is an efficiency variance.
Using variances to improve forecasts
The most valuable use of variance analysis is improving the future, not explaining the past. If you consistently under-forecast revenue in Q4, that pattern tells you to revise your seasonal uplift assumption. Teams that do honest variance analysis every month build forecasting accuracy over time.