Many temporary staffing professionals, on both the buyer and supplier side of the market, are responsible for estimating temporary staffing demand for their organization. Staffing providers need to forecast revenue and resource requirements and buyers need to establish and refine budgets. While company-specific factors (such as changes to sales strategies, investment and divestment plans, or recent financial performance) play a leading role in forecasting, many organizations rely on external measures of the national economy to build or support their forecasts. The problem is that those predictions are often, well, unpredictable.
Over the past several years, gauging the direction of the U.S. economy has humbled even the most qualified and experienced economists. Although today’s economic climate is particularly mercurial, the challenge isn’t new. The science of U.S. economic forecasts reflects the complexities of the economy itself. For any given measure (e.g., unemployment, GDP, inflation) there are dozens of variables to consider. Predictions must also reliably estimate the relative importance of each factor, and how one factor affects each of the others.
Finally, many forecasts are based on other forecasts within the same model (the saying “an enigma wrapped in a riddle, surrounded by a mystery” comes to mind). For example, inflation forecasts are highly reliant on equally complex predictions about oil prices. One wrong assumption about a single variable can send a calculation off course. It’s no wonder that distinguished economist John Kenneth Galbraith once said, “The only function of economic forecasting is to make astrology look respectable.”
Former Federal Reserve Chairman Alan Greenspan also acknowledged the flaws of national economic projections but retained a degree of optimism (and humor) when he said, “The fact that our economical models at the Fed, the best in the world, have been wrong for 14 straight quarters does not mean they will not be right in the fifteenth quarter.”
As Greenspan inferred, even an imprecise but thorough estimate of a national economic indicator can be useful. If you rely on external forecasts about the national economy to develop your own company-specific temporary staffing projections, here are a few suggestions to consider.
- Confirm the relevance of the measurement. While using multiple variables can certainly help the accuracy of forecasts, “more is better” isn’t universally true. One irrelevant factor can dilute the value of other highly relevant factors. For example, while consumer confidence is a great leading indicator for certain purposes, it’s not particularly useful for predicting corporate staffing demand. Make sure the variables you use are helping, not hurting.
- When available, use more than one source for the same data. The good news is that there’s no shortage of government agencies, financial institutions, industry associations and think tanks making predictions out there. When available, identify multiple sources of the same measurement and consider using the average of each source to smooth out differences (or if you want to get really fancy, use a weighted average based on prior accuracy of each source).
- Validate the track record of data sources. Although even the best sources are often off the mark, the relative success of a source is important. The Congressional Budget Office publically reviews its own forecasts, and compares its accuracy to that of other forecasters making the same predictions. Assess how close your sources were when “actuals” are available.
- Internal variables trump macroeconomic indicators. I mentioned this earlier but it’s worth repeating. Generally, macroeconomic indicators don’t swing wildly from one quarter (or even year) to the next. However, company-specific variables can be much more dynamic and have a much more significant effect on the usage of temporary labor. Did your company have a bad 10-Q or 10-K filing? That’s likely to have a much bigger and faster impact on your forecast, and more than offset a .05 percent decrease in total U.S. unemployment.
Whenever possible for your own model, use data sources that are tailored to the staffing industry or your company’s industry. For example, Staffing Industry Analysts focuses specifically on the variables and measurements that are directly relevant in this space. Your particular industry may also have specialized associations and analysts that publish their own reports and forecasts about human capital and temporary staffing.