One of any sales forecasting systems critical tasks is estimating just how much business the sales organization will actually close. That is, you're trying to boil that (hopefully) giant pile of suspects and prospects down to a single, critical number. By far the most widely-used method for doing this is to assign a close probability to each deal, and the total forecast is then the sum of each deals size, times its close probability. But how should you assign a probability to each deal? You have two basic choices:
Many argue that Method 1 (management judgment) is the more precise, since we all know sales is a complex, subjective process. Some bluebirds that arrive over the transom at the last minute do close, while some in the final stages of a months-long campaign don't. That observation is especially true since the object of all this computation is to estimate how much business will close in a given period.
Even so, my strong recommendation is to use Method 2 (the system assigns the probabilities), for the following reasons:
I suggest that Method 1 is the better approach only if (a) your company typically has a relatively small number of relatively large deals in your pipeline, (b) sales managers typically get personally involved in the close process, and (c) the ways deals get closed in your company are so diverse that a standardized view of deal stages just doesn't make much sense anyway.
In my last post, I observed that trying to improve systems and processes sometimes causes us to forget that better is the enemy of good. Inputting close probabilities into a sales forecasting system is not only an example of that dynamic, but also shows that moderate precision that serves a real purpose is better than extreme precision thats pointless.