Why Organized Data Makes or Breaks Revenue Forecasts

Blog
Mar 16, 2021
3 min read
The days before revenue forecasts are due are often long and frantic because the data needed is either lost in communication silos or contained in manual systems. Learn how to stop the information scramble in its tracks by adopting AI-powered tools and technology to leverage your data effectively.

Creating an accurate revenue forecast doesn’t require black magic, a fortune teller, voodoo, or a blood sacrifice—you just need accurate and reliable data. While Artificial Intelligence (AI) can perform feats that seem like sorcery, it’s the “magic” of your historical and predictive data that can pull the curtain back and give your sales organization the competitive advantage it needs. 

One of the best places to dip your toes into the evolving world of predictive analysis would be with your annual sales forecast, which is arguably the most important number in your company. If you nail it, your organization can invest and grow with confidence. If you miss it, well, let’s just say “don’t!”

Importance of Having an Accurate Sales Forecast

Your sales forecast drives everything in business forward, so that means that accuracy is key. Your projections determine how well you can invest and grow as a company. If you get it right, you can essentially ensure smooth sales operations and be agile when it comes to reacting to a volatile market.

As every sales manager knows all too well, producing an accurate sales number is inherently difficult. But the value that comes from an accurate projection is invaluable when it comes to having a more informed, introspective, and strategic decision-making process.

Why Manual Forecasting Is Outdated

When a revenue forecast isn’t accurate, it can lead to mistrust, indecisiveness, and an inability to confidently invest in the future of your business. 

Using spreadsheets for forecasting, companies put themselves at risk of handcuffing their sales planning processes to unreliable and stale data. In fact, according to Miller Heiman, fewer than 20 percent of sales organizations have forecast accuracy of 75 percent or greater. 

Historically, using spreadsheets is easy, yes, but this isn’t the early 2000s anymore. Technology is evolving and maturing, and so should revenue-driving processes. If you’re still relying on Bob three cubicles down to provide a document with accurate numbers, that’s just not a realistic approach in today’s time. Data is not a stagnant thing. Those numbers become obsolete the moment they are manually entered. Without an AI-powered tool to automatically add data in real-time, the forecasting approach is never truly accurate.

Organizations face many of the same issues when trying to forecast sales. For example:

  • Inefficient processes
  • Lack of alignment among departments
  • Data and people operating within silos
  • Misaligned forecasting and sales planning
  • Poor data hygiene
  • Lack of visibility into data and past performance
  • Little insight into historical data
  • Time wasted on mundane administrative tasks versus strategic initiatives

If your organization suffers from any of the issues above, you can expect these issues to resurface in a multitude of ways during the forecasting process. This in effect can affect you negatively in the long run if not addressed immediately.

When you can nail your revenue forecast, excuse the pun, it pays off! Sales organizations that leverage a formal and structured review process increase their win rates of forecasted deals by 25 percent versus those that take a less formal approach.

Having a thumb on the pulse of your forecasting efforts can make a positive impact on your organization. The numbers are in, and this is what recent research has to say on the topic:

  • 79 percent of sales organizations miss their forecasts by more than 10 percent.
  • 54 percent of the deals forecast by reps never close.
  • Sales reps are only 20 percent accurate when forecasting deals at the beginning of a quarter.
  • 67 percent of businesses lack a formalized set of rules for their sales processes.
  • Companies that embrace digital technology innovation experience nearly 20 percent higher gross margin than the laggards.
  • 80 percent of CFOs recognize that investing in data can help them replace spreadsheets; however, they remain deterred by the perceived cost and complexity of new systems. 

The math is different for every company, but the potential revenue and opportunities that would be left on the table is a risk no company can afford to take.

AI can’t be AI without Accurate Data

While many companies are looking into and adopting Artificial Intelligence (AI) and Machine Learning (ML) technologies, success relies on more than just the algorithms within the tools. Organizations need the right data in order for AI/ML to "learn" and be truly effective. To put it simply, machines can't learn and predict without the data to teach them. Machines need a basis of information to build up from, continue to add additional data to, and eventually derive learnings from it.

Advancements in cloud technologies and online systems have brought about the democratization of data processing, opening up a bevy of new opportunities for AI/ML learning and insight. 

Accurate Data is the First Step Towards Accurate Forecasting

Whatever the case may be for your sales organization, you need the right processes to accurately forecast company growth and make strategic decisions. The key to successful sales forecasting is data.

What you put in (accurate data) is what you get out (accurate forecasts). By harnessing the power of advanced sales analytics and data, organizations can now benefit from the support of sophisticated, predictive forecasting solutions that allows leaders to identify setbacks, plan accurate revenue projections, and navigate continued uncertainty. 

To learn more about sales forecasting using AI and how to leverage the right tool to assist in your planning processes, check out our recent guide, “6 Strategies to Building an Accurate Sales & Revenue Forecast.

  • Analytics and Technology
  • Forecasting
Author
Emily-Jahn
Emily Jahn
,
Content Marketing Manager

Emily Jahn is a Content Marketing Manager at Xactly. She earned a degree in advertising from The University of Colorado - Boulder and has experience in copywriting, social media, and digital marketing.