## Growth metrics for single and multi period business growth rates gas examples matter

The highest level objective for profit making companies is, in principle, "increasing owner value." In practical terms, however, firms approach this objective by earning profits. After a profitable period, owner value increases when the Board of Directors turns the period’s profits into shareholder dividends and Balance Sheet retained earnings. The analyst can rightly say that the firm’s future depends, above all, on its ability to earn and grow profits.

Not surprisingly, Annual Report growth metrics usually focus first on recent earnings performance and prospects for future earnings. For many, this means highlighting Income Statement "bottom-line" profits—known more formally as Net Income, Net Earnings, or Net Profit after taxes.

Other companies, however, choose instead to highlight selective profit metrics such as Operating Income or Earnings Before Interest and Taxes EBIT. These firms believe that selective metrics such as these paint a truer picture of earning performance in the firm’s core line of business, than do bottom line Net Profits.

Business firms must enough maintain liquidity to meet short immediate financial obligations. Business firms define liquidity in terms of metrics that measure the firm’s access to working capital or, in some cases, available cash. For that reason, officers and senior managers have a strong interest in tracking the firm’s working capital and cash balances. Negative growth rates in these metrics are warnings that normal operations are at risk.

The firm may receive alerts that liquidity growth is negative, when it discovers it cannot pay for infrastructure maintenance or equipment upgrades. Negative liquidity growth can also mean the firm will soon be unable to fund marketing programs or develop new products. In extreme cases, the firm may even be unable to meet payroll. Understandably, management looks to liquidity metrics *growth rates* for advance warning that such problems are imminent.

• Current assets are assets of several kind that could—in principle—transform into cash soon. Cash is a Current asset, of course, but also assets such as Inventories, Accounts Receivable, Short Term Investments, Marketable Securities, and Prepaid Expenses.

One cash metric for this purpose is the Net Cash Flow result the firm reports each period. Net CF for the reporting period is taken as the difference between two totals on the firm’s Statement of Changes in Financial Position (or Cash Flow Statement): Sources of Cash, and Uses of Cash.

Dividing the firm’s annual sales revenues by the average size of the employee population provides a simple but informative measure of the firm’s ability to generate revenues from its workforce. Dividing the firm’s Net Profits by the same headcount figure also gives a rough but useful measure of the firm’s ability to use employees productively.

The interpretation of __employee population__ growth rates can be a complex subject, however. The optimal intercorrelations among *growth rates* for employee headcounts, *sales revenues*, and profits, for instance, change as firms move through phases of the business life cycle. And, preferred growth rates for these factors also depend on the firm’s industry, business model, cost structure, and competitive situation.

As a result, some financial officers and other senior managers spend quite a few years with a firm and its industry, before they are confident they know the optimal target size for the *employee population*. This understanding develops from long experience tracking growth rates for employee headcount, as well as other business performance metrics in this section.

One conclusion stands out in Exhibit 6: The years 2004 – 2012 saw very high revenue growth rates (annual growth exceeding 10% per year). The years 2013 – 2017, however, showed generally lower growth rates and less predictability. Exhibit 7, below, will further show that revenue growth from 2004 to 2013 was highly predictable because revenues in that period grew in close conformance with an exponential growth model. Predicting Revenue Growth With Exponential Modeling

Exhibit 7, below, plots *Apple sales* revenues themselves. Note that the graph line in black, with data markers, represents sales the revenue figures from Exhibit 6. Some will notice immediately that a portion of the revenue curve looks very much like the exponential growth curves in Exhibit 2 above.

Consider, for instance, **Apple sales** revenues for 2003-2013, from the Exhibit 5 table above. Revenues clearly show strong growth for this ten-year period: 2013 revenues of $170.9 billion are more than 27 times the 2003 revenues of $6.2 billion. Simply quoting the 27-fold figure, however, does not communicate a "feel" for year-to-year growth rates that Apple sustains across these years. For this, analysts will calculate the cumulative average (annual) growth rate, CAGR.

Exhibit 7.Net sales figures for Apple for 2002-2017 (solid black line) plotted alongside mathematical exponential curve (solid green line). For the period 2003 – 2013, both curves grow at an average annual rate of 39.3% for ten years. For this period, the actual sales curve conforms closely to the exponential curve. For this period, therefore, CAGR fairly represents year-to-year sales growth. Modeling and predicting sales growth for the years after 2013 present different challenges. Calculating Cumulative Average Growth Rate to Model Revenue Growth

Remember that the CAGR metric is really an average (the geometric mean) of a number of individual growth rate figures. CAGR—like all averages—is meant to stand as the typical, or representative, value for the data set. Like all averages, CAGR says nothing about differences among individual values within the data set. Put another way, CAGR—like all financial metrics—reveals data set characteristics that might not easy to see in a simple review of the numbers, but at the same time is "blind" to other aspects of the data.

Because Apple’s sales figures approximate a true exponential curve, it is appropriate to use the CAGR (39.3%) to represent all year-to-year growth rates for this ten-year period. For this period, in other words, the exponential model made apple revenue growth highly predictable.