How to Forecast Revenue Growth

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Forecast revenues using several methods, reconciling the results of each method for a final estimate.
Forecast revenues using several methods, reconciling the results of each method for a final estimate. (Image: isak55/iStock/Getty Images)

Forecasting revenues is the first step in a variety of financial analyses, including widely used valuation models, such as the discounted cash flow method and comparable security analysis. There are a number of methods you can use to forecast revenues, and regardless of which method you rely on, you must be able to support your assumptions. In this respect, the more quantitative the analysis is, the easier it should be for other investors to understand.

Historical Basis

There are few better indicators of future performance than the past. This is particularly true for businesses situated in cyclical industries. Even for start-up companies with no operating histories, you can use comparable company histories as a guide for expected growth. In analyzing historical results, look for trends and cycles. If a company's historical revenue growth is highly stable, this allows you to use historical growth figures with a higher level of confidence. If historical growth is erratic, you may want to use a historical average.

Turnover Ratios

Using turnover, or utilization ratios, is a way of combining historical results with the quantitative approach. It requires calculating turnover ratios such as total asset turnover, and then applying these ratios to the company's balance sheet figures to generate projected revenues. Total asset turnover is equal to sales divided by total assets. For example, if a company's total asset turnover ratio is historically stable and has an average of 0.2, and next year's total assets are forecast to be $10 million, you can back in to forecast sales by multiplying 0.2 by $10 million, implying revenues of $2 million next year.

Market Share

If you are able to estimate your subject company's market share -- its share of total industry sales -- you can project its sales growth by using overall industry growth as a baseline. In doing so, you should track the rate at which the company's market share is rising or falling. Stability makes forecasting more reliable. If total industry sales are projected to be $1 billion next year, and your subject company has a market share of 3 percent, then next year's forecast sales for your company will be $30 million.

Linear Regression

Thanks to the increased power available via computers, anyone can use spreadsheets to perform linear regression calculations for forecasting. Linear regressions show the relationship between a dependent variable and an explanatory variable. Multi-linear regressions perform the same calculation but use more than one explanatory variable. In this case, revenues is the dependent variable. Select a variable that you believe has the greatest impact on changes in sales. For example, if the subject company was a real estate firm, you might select new construction as the explanatory variable. Plot historical values for sales and explanatory variables in separate columns in a spreadsheet. In a separate cell, enter "=forecast," and then highlight the numbers in the column representing sales, followed by a comma, immediately followed by highlighting the numbers in the column representing explanatory variables.

Inflation

In the long run, a company's revenue growth will always be equal to, or lower than, the economy's rate of inflation. A company that doesn't increase the number of units sold should still record revenue increases based on rising prices over the long term. As the company's own costs increase, it must raise prices in order to maintain the same level of profitability. You can use the survey method to compile professional economists' inflationary expectations, and use this is as a baseline to which you can make slight upward or downward adjustments.

In linear regression, the closer a value is to 1, the more the dependdnt variable is related to the explanatory variable.
In linear regression, the closer a value is to 1, the more the dependdnt variable is related to the explanatory variable. (Image: Andrey Malinkin/Hemera/Getty Images)

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