Small decisions shape a sales leader's day.

Whether to approach a customer in a new vertical, follow up with a prospective client now or give them an extra day, offer additional services as part of a package — these decisions shape what the next quarter, or year, will look like.

Today's business leaders don't rely on gut instinct to choose what's next. Instead, they look at what the data suggests is the ideal outcome. Once each scenario has played out, the data informs sales forecasts and shapes broader management strategies. 

As the majority of sales leaders struggle to get sufficiently accurate forecasts, the data powering those insights might be the culprit of missed estimates.

Less than half (45%) of sales leaders and sellers have high confidence in their organization's forecasting accuracy, according to research from Gartner.

To improve, experts suggest expanding the scope and quality of the data, gaining a better understanding of a customers' business, and integrating data from different sources.

Process mismatch

When businesses fail to realize their customers don't follow a linear sales process, forecasts are doomed.

"There are a number of twists, turns and revisits in their decision-making process," said Steve Rietberg, senior director analyst at Gartner, in an interview with CIO Dive.

When asked to rank the effectiveness of sales operations' forecast in specific areas of a deal, the two traits where leaders ranked themselves as least efficient were opportunity velocity and overall pipeline conversion.

Trying to predict whether a lead is moving forward or when it's going to convert is "a constant struggle" for leaders, Craig Riley, senior principal analyst at Gartner, told CIO Dive.

Listening to the customer is paramount, and sales leaders already know that. 

But failing to take into account the nature of buyers' cycle can further stray leaders from accurate forecasting, according to Anand Shah, co-founder and CEO of Databook, in an interview with CIO Dive. 

"One of the challenges is getting under the skin of what's going on their sales cycle," said Shah. As the buyer becomes more relevant, the accuracy of forecasting will improve. 

Data quality

Similar to the success of artificial intelligence implementation, forecasting outcomes depend on the quality of data available. 

The proliferation of business apps — with the average business using 88 across their organization — leads to challenges in data integration and literacy, which in turn impact the accuracy of forecasting. Almost half (47%) of sales leaders say they have very high data quality, while 13% say their data is of low quality.

"The good news is that a lot of the reasons organizations traditionally struggle ... are within the client's span of control," said Riley.

There are three specific actions businesses can take to maximize the accuracy of their forecasts, according to Riley:

• Do a data quality inspection: A deep look into data quality can help business understand which data sets influence forecasts, how accurate they are, and which other parts of the business should contribute data.
• Produce forecasting accuracy reports: A straightforward review of forecasts for the past quarters will help understand where a forecast might have gone wrong, and what can be done to remedy it.
• Internal data literacy programs: These programs will become increasingly popular as sellers themselves will need to become more data literate. "Starting to work on that organizational or institutional muscle today is going to be incredibly important," Riley said.

Moving forward, there's an expectation that sales teams will be able to rely more heavily on artificial intelligence and machine learning to supplement their forecasting efforts, said Rietberg. Some of these measures will help boost analytics once AI comes into the picture.