Analyze variance effectively

By BearingPoint

You can use variance analysis on any set of financial or operational data to answer the question "what caused this result to be different from planned or expected results?" Typically, you would conduct a variance analysis after monthly closing of revenue, expense, cash, and inventory reports, and then you would use the information to see where and how your financial plans were off the mark and adjust your financial plans for the future.

You can also apply variance analysis to any set of operating or marketplace metrics, including customer orders, quality measures, customer satisfaction, market growth, or share of wallet. Variance analysis gives you a simple and effective tool for spotting trends, issues, or opportunities that affect financial planning and economic success.

Get a grip on materiality

Before you can use variance analysis effectively, you have to know what level of variance is meaningful, or material, to your business. This is called developing a materiality threshold. Missing a revenue target by $50,000 might not mean much to a company that makes billions of dollars in annual revenue, but it would be a lot more material to a smaller business — such as a mom-and-pop retailer.

You can use leading practice ranges to develop materiality thresholds for different types of analyses. You can tweak these ranges as appropriate for your business to support proper analysis and decision-making. Income statement ranges are given as percentages. For example, a 2% cost overrun might be considered immaterial, but a 15% overrun would be cause for concern. Asset accounts give materiality thresholds in dollar amounts. For example, a $500 asset for a large business might be immaterial, but a $2 million asset would be very material for any business.

After you have the concept of materiality in mind, you need to consider context. Context is the relationship of the variance to the type of measurement. For example, if a book retailer sells 10% more in August than it did in July, a simple explanation in a month-to-month context is that the business is growing. But, if you consider factors such as the back-to-school season, it would be more appropriate to measure against the previous year's August sales to see how the retailer did in the context of back-to-school seasons. If sales dropped from the previous August, your analysis would let you know to look for a more specific cause than month-to-month growth.

Recognizing the materiality threshold for your business and adjusting the context of your analysis help you recognize important variance levels and find the real causes and trends of variance. You can apply this knowledge to improve your company's financial plans.

Take the lead in analysis

Analyzing variance is not an end in itself, but the starting point for discovering the causes of variance. Determining the root causes of negative variances, or repeatable elements that lead to positive variances, helps your company develop successful financial plans.

Leading variance analysis proceeds in the following stages:

  • Analysis preparation     Determine what results or metrics will be examined and compared to create an appropriate model. Analyses can be two-dimensional — such as comparing operating expenses from month-to-month — or multidimensional — such as comparing expenses from month-to-month across separate business units.
  • Applying materiality thresholds     Determine the level at which variance is worth noting. For instance, capital expenditures (CAPEX) that show a 1% budget overage wouldn't require cause analysis if the threshold figure is 5%, but a negative variance of 10% would be of concern.
  • Cause analysis     Investigate why a variance occurred in a particular metric. When you know the cause, you can present this information as an aid to planning and decision-making.

You can use the following variance reference table, which lists some of the common types of variance analysis, data sources, and typical causes that can drive each variance type.

Variance type Investigation data sources Typical causes
Revenue analysis
  • Order logs
  • Customer invoices
  • Customer revenue reports
  • Product revenue reports
  • Shipment logs
  • Cash receipts


  • Economic factors led to stronger sales than expected.
  • Competitive pressures didn't materialize.
  • New pricing strategy worked well.
  • Strong sales team in East region far exceeded sales targets.


  • New product launch was delayed, which hurt sales.
  • Competitor X is beating us consistently in the West region due to pricing.
  • Distributor Y ramped up three weeks later than expected.
Expense analysis
  • Purchase orders
  • Purchasing reports
  • Vendor invoices
  • Payroll reports
  • Employee expense reports


  • Cost containment strategy for general and administrative expenses led to a 10% cost reduction.
  • Centralized procurement drove down raw material pricing.
  • The Q3 lawsuit was settled favorably before litigation.


  • High labor costs in the Central region due to the new labor pact.
  • Rising commodity prices led to higher raw material costs in Q1 than expected.
  • Rises in airfare prices raised employee travel expenses.
Cash analysis
  • Bank statements
  • Bank reconciliations
  • Deposit receipts
  • Transfer statements
  • Positive pay statements
  • Debt agreements
  • Receivables logs
  • Payables logs


  • Improved collections led to X% increase in cash flow from the previous week.
  • Last month's secondary offering raised $20 million more than expected.


  • Errors in accounts payable led to seven large-dollar invoices being paid before net 30.
Inventory analysis
  • Raw material receipts
  • Cost accounting reports
  • Work-in-process (WIP) reports
  • Physical inventory analysis


  • Inventory turns increased from 3.2 to 3.4 due to increased consumer demand.
  • Cost of goods sold decreased by $5 million due to lower raw material prices.


  • Quality issues on assembly line #4 led to cost overruns during the second week of the month.
  • Finished goods inventory was $3 million higher than expected, due to poor demand forecasting.
Operating analysis
  • Customer satisfaction surveys
  • Supplier scores
  • Product/service quality reports
  • Productivity reports


  • Customer satisfaction increased by 3.3%, due to better warranty terms.
  • New quality assurance procedures improved product quality from three defects per week to two defects per week.


  • Supplier X's score decreased from 89 to 77, due to two unresolved service issues leading to production delays.
  • Productivity decreased from $320,000 revenue per employee to $315,000 revenue per employee over the last quarter, due to stable sales and recent hiring activity.

Affect your success

With an understanding of materiality thresholds, context, and data sources, you can apply variance analysis to uncover the root causes of variances in financial, operating, or even market activity. When you know the causes, you can frame meaningful variance analysis reports that help your company management make clearer, more informed decisions, that open the doors to greater financial success and more accurate financial planning.

More information

About the author     BearingPoint provides business consulting, systems integration, and managed services to Global 2000 companies, medium-sized businesses, and government organizations.

Applies to:
Excel 2003