CSO Insights has been conducting research on sales for almost 15 years. In one of their surveys to over 1,000 sales leaders, they ask about the percentage of forecasted deals that actually close. On first blush, you would think this would be a fairly high number.
In many companies, committing a deal to a forecast is serious business – some sales managers almost want it ‘written in blood’. After all, for many sales executives and managers, accurate forecasts speak to their ability to manage the business of sales.
In their 10+ years of doing their sales performance study, they have found that approximately 50% of deals do NOT close as originally forecasted. That’s one out of two. They have also found that about 50% of firms state that they need improvement in accurately forecasting their business.
Most sales organizations seem (if you ask them) to understand the adverse impacts of less than stellar forecast accuracy. For manufacturing businesses, it can mean more inventory carrying costs, increased stock-outs, and greater levels of working capital to run the business. For professional services firms, it can mean lower resource utilization, disengaged talent, and poor project delivery. There are real financial impacts to poor sales forecasts, but the biggest one for sales managers may be more qualitative:
Why don’t you understand your business well enough to predict it?
The ability to accurately forecast is difficult. Sales is both art and science, and on any one deal, there are numerous unforeseen trajectories it can take – from the company being acquired, to spending ‘freezes’, to your internal champion being fired. I’ve seen all of them derail a deal. So what do you do?
One thing that CSO Insights has found is that a better needs analysis up-front in the sales process improves forecast accuracy on the back-end. The cause-and-effect here may not be ‘airtight’, but it makes sense – the better you can identify and qualify the needs of your prospect early in a sales (buying) cycle, the more insight you’ll have around whether you have a credible value proposition to address those needs and whether you can bring the deal to a close.
There’s been a lot of ‘ink spilled’ on conducting a needs analysis for sellers, so I won’t go in depth on it … but there is one quote by Rudyard Kipling, the late English novelist, that sums it up: “I keep six honest serving-men (They taught me all I knew); Their names are What and Why and When And How and Where and Who.”
Getting back to the title of this post, there is no Silver Bullet to improving forecast accuracy.
Not surprisingly, there are some Big Data explorations that look at all the various attributes around a deal and then try to determine the predictive validity of each in terms of forecasting. This work will likely help to improve forecast accuracy in the aggregate, but the art of sales tells me that it will be hard to identify and control all of the intangibles involved in closing a complex, multiple decision maker, B2B opportunity. The competitive dynamics alone – assuming the company doesn’t have a monopolistic position – is hard to gauge in-and-of-itself.
From a sales effectiveness and behavioral perspective, accurate sales forecasts are an outcome of how well you execute other, often upstream, sales activities. For example, if you don’t follow a buyer-aligned sales process or rely on customer evidence to guide where you are in the sales cycle, it is very hard to determine how to forecast a deal, which is all about whether it will actually close (probability of winning) and when (timing).
If you have no demonstrable markers as to how you are doing on a deal, how can you “call the ball” in terms of a forecast? You can’t. You are probably guessing.
Although sales forecasting is not the exact same as economic forecasting, they do “rhyme” in terms of similarities. In the late 1980’s there was a ‘forecasting tournament’ launched by a professor from the University of Pennsylvania. He collected 28,000 predictions around various events. Per the magazine The Economist:
“The results were startling. The average expert did only slightly better than random guessing.” The authors further add that we “… can’t even be sure that the forecasts guiding our decisions are more insightful than what we would hear from oracles examining goat guts. Worse still, we often don’t know that we don’t know.”
In a nutshell, to create better-than-average sales forecasts, focus your sales teams on comprehensive and thorough needs analyses (as part of their buyer-aligned sales process) so that they have more credible insight and data to forecast their deals … and they can identify what they don’t know, which should help determine their go forward sales tactics. In the aggregate, a big data algorithm might work well for sales forecasts, but on a deal-by-deal basis, it’s better to master the intangibles and the process.