Academic journal article Management Accounting Quarterly

Can Variance Analysis Make Media Marketing Managers More Accountable?

Academic journal article Management Accounting Quarterly

Can Variance Analysis Make Media Marketing Managers More Accountable?

Article excerpt


Arguments and assumptions made more than 50 years ago essentially established how we calculate cost variances today. It is time to review the accuracy and relevance of our traditional calculations, especially as variance analysis moves into new fields, such as marketing, and new applications, such as using nonfinancial performance measures. Indeed, models appropriate for the paper-and-pencil world of a hands-on analyst in 1950 may be ready for improvements, especially given the widespread use of computers and database control systems today.

We will demonstrate the errors in the traditional cost variance formulas and propose a new set of equations for calculating variances using the Minimum Potential Performance Budget (MPPB) model. After showing how this new model correctly calculates cost variances in all four economic situations, we will apply it to an advertising campaign using the nonfinancial performance measures of reach and frequency. First, though, we will provide background information on the assumptions and explain why they have been generally accepted.


More than 75 years ago, Henry Maynard wrote about variance analysis, "Its essential value lies in the fact that it is a control system."1 Fifty years ago, detailed discussions arose concerning the algebra, formulas, and calculations to use in practice when evaluating financial performance.2 In 1997, Josef Kloock and Ulf Schiller revisited some of the criticisms regarding variance analysis when companies used it to help improve decision making and in assigning responsibility for performance evaluations.3

Assumptions in Variance Analysis

The basic premise of variance analysis is that larger variances are symptoms of larger control problems. The accuracy of variance calculations, however, hinges on two basic assumptions.4 First, small errors due to the allocation of small joint variances should be of little concern, and, second, the conventional two-variance model (a price and quantity variance) provides the correct calculations in most practical cases.

Considering the first assumption, marketing settings are plagued with large joint variances and thus large potential calculation errors not often expected in traditional manufacturing cost applications. As for the second assumption, we will demonstrate that the conventional two-variance analysis (price and quantity) inflates variances in three of the four possible economic situations. We will also show that the normative threevariance solution (price, quantity, and joint variances) is equally flawed. The traditional debate about the efficacy of the three-variance solution over the practical simplicity of the two-variance solution is made moot when we realize both are inaccurate.

To provide accurate, unbiased measures of the primary variances (price and quantity), we need a new method. The solution lies in the economic geometry behind variance analysis and is found in the Minimum Potential Performance Budget.

Reasons for the General Acceptance of the Two-Variance Solution

Apparently, two related causes led to the general acceptance of the traditional two-variance algebraic model taught in current management and cost accounting texts as well as in practice. One was the first Industrial Revolution and the Scientific Management strategy that organized work in the new capital-intensive factories. The other was the emphasis on external financial reporting in the United States.

To support the development of large, capitalintensive factories during the first Industrial Revolution, companies needed significant investment capital, so top management desired information about investment efficiency. Because these investments were directed toward converting materials and labor into manufactured products, cost accounting systems evolved to provide detailed information about the manufacturing costs of products. …

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