“Sales forecasting is done for very different reasons,” emphasizes Jerry Colletti, managing partner at Colletti-Fiss. Some firms need to know how much inventory they must stock. Others want to know when the sales will actually close so that services implementation will begin. Still companies want to know whether more sales recourses, either people or marketing, must be committed to meet essential goals. Companies forecast for all these purposes and more.
Colletti notes that Aemmer, Angus, and Allen appear to use mostly subjective forecasting processes, rather than formal and institutionalized methods. That may be appropriate, given the nature and size of their individual forecasting challenges. But Colletti’s experience suggests that both companies and sales leaders should continuously work to improve sales forecasts, however these are made. “no one will tell you that they ever get them 100 percent right. But sales leaders can often improve forecast accuracy by following a few simple steps.”
First, Colletti urges managers to be very explicit about exactly what they are forecasting and why accuracy is important. This means that the exact volume measures, total units, unit by product line, or total dollar revenue should be specified. Managers should also be explicit about why they are doing the forecasting in the first place. What other departments are going to use the forecast, and to make what decisions? Colletti says sales forecast that will be used support manufacturing or purchasing decisions must usually be much more accurate than forecasts that are used only to plan selling capacity.
Second, managers should not get tangled up online in spread sheets or in subjective discussions. Use a combination of both qualitative and quantitative forecasting methods.
There are a number of qualitative bases for forecasts. A manager can estimate total usage of a product for both customers and prospects. Or he can tap his own sales force for these estimates, especially the most experienced salespeople and ones who have worked with particular accounts for three more years. “These people tend to be fairly accurate in their estimates.”
Quantitative forecasting methods are also available. Mangers can use historic data on actual purchases by each account or customer, well analyzed to reveal any trends or special factors that will influence the future. There are also statistical analyses of total product demand. These are typically used when managers care most about total demand, not demand for each customer, and when several factors will influence sales. Managers must look for the relationship between sales and the factors that affect it most heavily. For example, a lumber company should predict futures sales based on housing starts, interest rates, and the seasonal shifts in demand during summer months.
Third, Colletti urges managers to do both a well-defended sales forecast and a “what-if” analysis. Sales forecasting is not pure science. “Once a forecast is prepared, a series of what-if questions should be asked to identify a range of critical uncertainties that could influence the forecast, by plus or minus 5, 10, or 15 percent.”
