What is variance analysis?

At a glance

• Compares actual figures systematically against plan, budget, or prior-year data, and quantifies differences by cause, direction, and amount.
• Aims to uncover the underlying causes, such as volume, price, mix, or structural effects.
• Analyzed variances enable targeted corrective action.

Variance analysis: definition and classification

Variance analysis examines the difference between a reference value – such as a plan, budget, prior-year figure, or forecast – and the value actually achieved (actual). It answers a fundamental question in controlling: Why did the result turn out differently than expected?

Companies plan on the basis of assumptions: expected sales volumes, prices, costs, exchange rates, or market developments. In practice, actual figures routinely deviate from those assumptions. Variance analysis makes these differences visible, quantifies them, and clarifies their causes – transforming a simple numerical comparison into genuine management information.

Variance analysis is closely related to the plan-actual comparison, but goes significantly further. While a plan-actual comparison merely shows that a difference exists, variance analysis explains how that difference came about and which factors contributed to it – and to what degree.

What are the different types of variances?

In practice, variances can be classified along several dimensions:

By direction

• Favorable variance: The actual value exceeds the planned value – for example, higher revenue or lower costs than budgeted.
• Adverse variance: The actual value falls short of the planned value – for example, lower margins or higher overhead than planned.

By cause

• Price variance: Actual prices (for purchasing or sales) deviated from the plan.
• Volume variance: More or fewer units were produced, sold, or consumed than planned.
• Mix variance: The composition of the sales or product portfolio shifted relative to the plan, affecting contribution margins or overall profitability.
• Structural variance: Changes in organizational structure, production methods, or distribution channels affected the outcome.
• Exchange rate variance: For internationally active companies, currency fluctuations affect reported results.
• Efficiency variance: Deviations in productivity or resource utilization compared to planned assumptions.

By controllability

• Controllable variances that can be influenced by management decisions.
• Non-controllable variances attributable to external factors such as economic conditions, commodity prices, or regulatory changes.

This distinction is essential for meaningful performance assessment and for deriving appropriate responses.

What are the different methods of variance analysis?

The most important methods of variance analysis are as follows:

• Simple plan-actual comparison: The most straightforward approach: planned value minus actual value yields the absolute variance. Adding the percentage deviation provides an initial picture. This is often sufficient for operational steering purposes, but insufficient for deeper analytical work.
• Variance decomposition: Complex variances are broken down into their component parts. A revenue shortfall, for example, may consist of a price decline, a volume decline, and a mix shift – each with a different sign and magnitude. Decomposition makes these effects individually visible and quantifiable.
• Bridge analysis (waterfall analysis): Like variance decomposition, bridge analysis breaks a total variance into smaller segments. It shows step by step how one moves from the planned value to the actual value, with each influencing factor represented as a separate bar that accounts for part of the overall effect.
• Commentary and narrative: A variance analysis is only complete when deviations are accompanied by causes and proposed actions. Good controlling does not end with “revenue was below plan.” It ends with: “The revenue shortfall of €2.3 million is attributable 60% to price erosion in Segment B and 40% to the loss of a key account. Countermeasures X and Y have been initiated.”

How to conduct a variance analysis – A step-by-step example

The following example illustrates the process using a revenue analysis in monthly reporting.

Step 1: define the reference base

Before any analysis begins, the question must be answered: What are we comparing against? Possible reference values include plan/budget, prior-year figures, or the current forecast. The choice determines the analytical direction: a budget comparison assesses goal attainment; a prior-year comparison reveals business development; a forecast comparison measures forecast accuracy. In practice, several reference values are often used in parallel.

Example: A company generates revenue of €9.2 million in October. The planned value was €10.0 million; the prior-year figure was €8.8 million.

Step 2: calculate the variance

Compared to the prior year, the result is positive: the company is growing. Yet it has missed its own target. Both perspectives tell a different story – and both matter.

Step 3: analyze the causes

This is where the actual analysis begins. The total variance of –€0.8 million is broken down into its drivers:

• Price effect: Selling prices were 3% below plan due to increased competitive pressure → –€0.5 million
• Volume effect: Slight shortfall caused by a delayed large order → –€0.2 million
• Mix effect: Disproportionately high sales of higher-margin products partially offset the decline → +€0.1 million

The conclusion is clear: the problem lies in pricing, not in volume. Countermeasures must be directed accordingly.

Step 4: assess materiality

Not every variance requires immediate action. Controlling assesses whether a deviation is material in absolute terms, affects a core business area or critical cost position, and represents a one-time effect or a sustained trend. In this example, the price effect is relevant on all three dimensions – it requires substantive commentary and a clear management response.

Step 5: comment and define actions

The analysis does not end with a number. It ends with a statement:

“October revenue of €9.2 million was €0.8 million (–8%) below plan. The primary driver is a price decline in Segment B resulting from intensified competition; the volume shortfall reflects a large order deferred to November. Pricing stabilization measures have been initiated; the deferred order is confirmed for calendar week 46.”

This commentary names the variance, explains its causes, weights the effects, and closes with a concrete outlook. That is precisely what distinguishes professional controlling from mere number reporting.

What are the common mistakes in variance analysis?

Despite its central importance, variance analysis is frequently not applied with sufficient rigor in practice. Typical weaknesses include:

Application examples: where is variance analysis used?

Variance analysis accompanies nearly every management process in a company. The following examples show typical fields of application in various areas and industries.

Summary

Variance analysis is one of the most important methods in controlling: it transforms the gap between plan and actual into actionable management information. By systematically decomposing deviations into their causes – price, volume, mix, structure – it enables targeted and well-founded decisions. With DeltaMaster, Bissantz makes this process faster, more transparent, and AI-assisted: variances are visualized directly in graphical tables, their causes are traceable, and corrective actions can be derived immediately.