If sales is part art, and part science, then sales forecasting is a dark art. According to recent numbers from CSO Insights, roughly 54% of all forecasted deals by sellers never make it to the finish line.

Which begs the question: what if we just had sales forecasting technology run the numbers for us?

As it turns out, the very traits that make sellers great at selling, also make them lousy at forecasting. Salespeople tend to be optimistic, which is great if you’re looking for a group of folks who can keep going in the face of severe adversity – but killer if you’re looking for an accurate assessment of what the future holds.

Salespeople are also diplomatic and politically astute, with often a high degree of “willingness to please” their constituents. Again, not a bad thing if you’re selling complex, high-end services, but a sure way to sales forecasting that is way (way) off the mark.

The reasons why the very best sellers make the very worst forecasters are simple: human bias and social dynamics. Many sellers either have “happy ears” (meaning they are likely to substantially overestimate the likelihood of a deal happening), or they do what is called “sandbagging”.

According to Investopedia, “(To) Sandbag is a tactic used to hide or limit expectations of a company’s or individual’s strength in order to produce greater than anticipated results. Sandbagging, in business, is most often seen when company managers temper the expectations of superiors or shareholders by giving guidance below what they know will be achieved. Once the better than expected results are presented, the firm looks all the better.”

In other words, when sandbagging, reps deliberately underestimate the likelihood of a deal happening in an effort to look better to their peers/boss when it does.

And it’s not just about the estimate itself – a recent study by Implisit found that opportunities closed a full 54 days after sales reps estimated it would.

Whether over- or underestimating, the end result is the same: sales forecasts that are way off the mark, which can have a dramatic impact on corporate earnings, share price and investor confidence. Not to mention the future career prospects of the individual seller and his/her manager.

But what’s the alternative? Well, sales forecasting technology.

As it turns out, by using predictive analytics, pattern-matching and machine learning, sales forecasting technology is far better at sales forecasting than any human ever could be.

According to data from Entrepreneur Magazine, “these (technology) processes achieve an average 82 percent forecast accuracy on a deal-by-deal basis (versus the 46 percent CSO Insights reported) and over 95 percent accuracy in the aggregate (versus the industry average of 76 percent).

I’ll take those odds any day of the week.

So, with that in mind, let’s take a look at 4 reasons why you should invest in sales forecasting technology Click To Tweet

Better forecasts = better decisions, budgeting and resource allocation.

Unless you’re a highly analytical person, you probably don’t forecast for the sheer joy of it. Much like forecasting the weather, we forecast because we want to do something with that information.

Maybe we’re looking to reinvest revenue generated from product sales. Decide to launch a new product or service line based on the success of an existing one. Budget hires, skills development and training for the sales force.

Whatever the case, better sales forecasting means better decisions.

It’s better for the salesforce, too.

Take your average sales rep. How much do you think they enjoy the forecasting process?

Not only is it fraught with the potential for mistakes and career-damaging errors (as we’ve seen before), but it’s a horrible use of time for your highly-paid outside sales force. If you’re anything like most sellers (at least the good ones), you’d rather gouge your eyeballs out with an ice pick than typing forecast data into Excel.

As it turns out, it’s a bit of a time sink as well. The average seller reports spending around two and a half hours on forecasting per week. Think about it: every single seller, sales manager and leader is spending roughly 5% of their time on nothing but forecasting.

Every. Single. Week.

Technology forecasts actually get better with time.

Through machine learning, technology actually “learns” how to forecast better with time. Once you set your model, over time it will become more and more accurate (instead of staying the same, or potentially getting worse).

And the best news of all? It will do this automatically, without you having to lift a finger, or invest time and money in training.

You can forecast further out, and run simulations.

In some industries, sales cycles can either be extremely long or extremely short. Neither is great for improving the accuracy of sales forecasts. If you’re trying to build a quarterly forecast with sales cycles averaging 7-10 days, good luck getting it right.

Technology can use the same predictive analytics it uses to forecast on a quarterly basis to simulate what would happen two, three, four, or sixteen quarters out. With the click of a button.

Plus, you can run simulations (much) easier. What would happen if sales for a specific product line increased by 5% ? If we added three more inside sales reps? If sales temporarily dropped due to seasonal changes?

As it turns out, if sales forecasting is a dark art, then using technology to forecast sales may just be your magic wand.