We don’t mean to imply that companies implementing SFA/CRM systems have failed to realize any benefit, especially in the realm of improvements in forecasting accuracy. Indeed, many have experienced some success in that area. But in many cases, part of the difficulty with these implementations has to do with poorly managed expectations about how much forecasting accuracy will increase, and when.
A few years ago, while working with a CRM vendor, we asked the vice president (VP) of Sales if the company “ate its own dog food” (i.e., used its own software to forecast). This question elicited an enthusiastic yes! from this executive. He then went on to say that he usually came within 5 percent of his quarterly forecast—a level of accuracy most sales executives can only dream about. We asked for details, and within seconds, he had his laptop fired up so that he could share his forecasting secrets with us.
He showed us that the company had defined seven milestones in its sales pipeline. Beginning with the first month that a salesperson started reporting on his or her pipeline, the software heuristically captured close rates at each of the milestones. When it came time to forecast, the software took each salesperson’s gross pipeline and applied that salesperson’s unique factors to the dollar volume represented by each of the seven milestones. In this way, he achieved his enviable forecasting accuracy.
We then asked: “Are your salespeople telling their prospects that if they use your software package, they will achieve a similar degree of forecasting accuracy?” He acknowledged that, as we hoped and expected, they were.
Next, we began to dissect how his miraculous results were being achieved, and the degree to which other companies would find them replicable. The hard fact was that it would take months or years for other companies to gain the historical close rates by salesperson that were the key component in our client’s ability to predict revenue. Ironically, the only reason he could be so accurate with his forecast is that the software tracked historically how inaccurate (i.e., overoptimistic) his salespeople tended to be, in that they overstated their pipelines at each of their seven pipeline milestones. Any new users of this CRM system—in other words, all the new purchasers of the software—could only assign estimates of close rates at various milestones. Most likely, these would be across the board for all salespeople, and would become homed in at the individual salesperson level only over time.
Even with the software in place and defined milestones, moreover, forecasting accuracy could continue to be adversely affected by a range of internal and external factors:
When salespeople leave, their historical data are no longer relevant.
When new hires join, there are no historical data.
Salespeople who are below quota are liable to overstate their pipeline.
New offerings don’t have the benefit of historical data.
New vertical industries present new challenges.
A changing economic climate can undercut the relevance of historical data.
The changing fortunes of clients within the product segment can similarly undercut historical data.
Offerings by competitors may raise the bar.