Most sales managers look forward to producing a sales forecast with the same level of enthusiasm reserved for a trip to the dentist.
Sales forecasting is typically a torturous process that involves interrogating sales reps about the status of their deals and analyzing self-reported sales activities that managers rightly suspect of bias.
Not only is the process of creating a sales forecast usually unpleasant for all involved, it also tends to produce inaccurate results based on wishful thinking instead of hard data. Moreover, a Forrester Research study found that only 6% of sales leaders say they are "very confident" in their company's sales activity data, and a whopping 66% don't even use automated data to develop their forecasts.
That's a huge problem. Sales managers and company executives make critical business decisions based on sales forecasts. Those decisions regarding how to allocate resources, what new products to develop, and whether to expand marketing campaigns often hinge on sales forecasts that are more akin to "hopecasts" than fact-based estimates.
A Better Way
One of the major drawbacks of the traditional forecasting method is that it provides almost no insight into what buyers are thinking.
Sales managers query individual sales reps on how close the buyer is to making a purchase, but unlike some sales cycles—such as B2C purchasing patterns captured on e-commerce sites—the traditional sales forecast doesn't factor in buyer behavior. It substitutes it with a second-hand and self-serving account of it.
Because of modern data collection and analytic capabilities, today there is a better way to build a sales forecast. Companies that use a digital business interaction platform can capture buyer behavior data and factor in more detailed reporting collected at the interaction point to develop a forecast that is less wishful thinking and more driven by hard data.