4 Reasons Your Deal Forecasts Probably Aren’t Accurate

Sales and deal forecasting are vital parts of any business’s planning, but it is also hard to argue that there are major issues with how we prepare for the future.

Based on a number of sources, the level of inaccuracy with current tools is astounding. A study found that only 28.1% of sales teams were within a 5% deviation of their forecast, and 47% of 90-day predictions were off by a margin of more than half — and sales reps overestimated by an average $91,000 and underestimated by only $47,000.

CSO Insights cites that 60% of forecast deals do not close, and even organizations that formally track and review their processes still lose 40% of predicted closures. A SiriusDecisions’ analysis pegged that “79 percent of sales organizations miss their sales forecast by more than 10 percent”, and in another analysis, an asset manager says he just cuts 20% off the top of a forecast since he doesn’t think they’re reliable.

The good news is that recent years have seen the rise of a new market category dedicated to addressing these issues head-on. Revenue Intelligence solutions aggregate, automate, and make actionable the data from disparate systems to improve the efficiency of customer acquisition initiatives. But to properly deploy such solutions, we must first understand exactly why so many sales organizations still struggle with inaccurate forecasting.

Why are forecasts so inaccurate?

Incorrectly tracked data

Sales reps are only human, and under the threat of particularly aggressive forecasts, it’s common for people to update data strategically — it’s not that they’re “cooking the books,” it’s that they’re being selective about when to input information, which leads to inaccurate data tracking.

The best fix for this is data accountability and ensuring that sales representatives are being timely and accurate in how they track information, primarily through auditing past months’ pipeline. The current generation of revenue intelligence tools can automatically track this information, and prevent data errors — intentional or not.

Lack of specificity

People who have been in the industry for a long time have a sense of how things are going, and there is a need to translate that raw feeling into a more pure, data-driven methodology. The Harvard Business Review cites an example of a tech firm sales team that evaluated deal health based in part on their personal view of “strategic alignment.”

But in today’s world, gut instinct will only get you so far.

According to the research with the McKinsey Global Institute, 40% of tasks within the traditional sales function can now be automated. With projected advancements in technology, especially in natural language processing, the research suggests this could top 50%. For strategic, centrally focused users of sales analytics (CSOs, C-suite peers and EVPs of major divisions, for example), AI will enhance decision making by flagging patterns and forecasting broad outcomes better than humans can do.

This isn’t to say that computers are coming to take anyone’s job – quite the opposite. As the CEO of a revenue intelligence company that aims to help sales professionals make informed decisions by delivering machine-analyzed signals and calculated impact predictions, I’ve seen how offloading the “busy-work” to machines allows teams to unleash their human – not artificial – intelligence.

Insufficient data

Which data is recorded and which is used for forecasting is a science unto itself. Data can be duplicated, out of date, or just plain wrong. According to Gartner, around 30% of data within CRM systems is outdated within 12 months, and another study showed “40% of B2B generated leads are either invalid, incomplete or have duplicate entries.” This can lead to sales reps spending almost 30% of their time cleaning up and recovering bad data which wastes time and leads to inaccurate results.

Maintaining a clean, up-to-date database makes sure that bad information isn’t muddying the feed. Revenue intelligence can also help remove the pressure from individual employees to enter the data themselves, by automatically collecting and syncing data between your CRM, email, and other channels.

Incorrect analytical tools hamper deal forecasts

This is possibly the hardest factor to quantify, but also one of the most important.

How do you know that the analyses you run are the correct ones for accurate deal forecasting?

Even if you had perfect information, perfect data entry, and perfect sales rep compliance, would your system be able to accurately predict the next quarter?

The good news is that the most advanced and rapid growth in the industry is happening in these areas under the umbrella of revenue intelligence.

Revenue intelligence helps analyze sales data and turn it into actionable insights. The progression of machine learning and AI has seen monumental leaps and bounds in the way tools are able to extract data, and what they are able to extrapolate from it. These analyses are more accurate, and allow you to know which deals are on track and which ones aren’t, how to spot concerns before they become problems.

Deal forecasting has been plagued by poor accuracy since the concept was first devised — as data and analytical tools have evolved, results have become more and more accurate, but there is room to improve. By addressing pipeline problems and embracing revenue intelligence, we can reach a point where deal forecasting is accurate enough to be reliable in our day-to-day business.

[To share your insights with us, please write to sghosh@martechseries.com]