Sales forecast accuracy

This article will explore the importance of sales forecasting, the best way to measure forecast accuracy and improve the process — which includes understanding the internal and external factors affecting sales forecasting — and the benefits of forecast accuracy delivered by modern, AI-driven tools.

Read more below.

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Sales forecast accuracy

A commonly held notion in the business world is that accuracy should be the ultimate goal of a sales forecast. Afterall, the purpose of sales forecasting is to estimate future sales so as to inform key business decisions and hold sales teams accountable. Yet several studies lament how inaccurate sales forecasting has become. In 2020, Gartner reported that fewer than 50% of sales leaders and teams have high confidence in their forecasting accuracy. McKinsey, too, reported that about 40% of CFOs said their forecasts were “not particularly” accurate and the process was too time-consuming.

Rather than focusing solely on accuracy — a target that many companies have been unable to hit — perhaps companies should be asking, “What is accuracy in forecasting providing for our team, and how can we improve it?

The answer: With the right tools enabled by artificial intelligence (AI), sales forecasts can become a powerful benchmark, providing teams with projections that help to dictate the next best actions for generating growth. That certainly sounds better than opinion and augmented forecasting, both of which depend too much on sheer instinct or the past — when we know neither of these techniques can clearly see the future.

This article will explore the importance of sales forecasting, the best way to measure forecast accuracy and improve the process — which includes understanding the internal and external factors affecting sales forecasting — and the benefits of forecast accuracy delivered by modern, AI-driven tools.

Why is sales forecasting important?

First, let’s review the importance of sales forecasting. Although the word “sales” is in the name, sales forecasting is used by almost every department in a company to plan and manage for the future. This means:

  • Sales teams use forecasting to make estimates about prospects and deals, assess how likely they are to close a deal, and learn the average time to close a deal. Sales forecasting is often also used by sales leaders to create performance benchmarks for their teams.
  • Marketing teams use forecasting to plan and execute marketing campaigns designed to generate more prospects and deals for sales teams. They also use sales forecasting to budget for things such as ad buys.
  • Finance teams use forecasting to track finances, develop budgets, and monitor potential fiscal risks.
  • Leadership teams lean on sales forecasts to track the ins and outs of the company and how well it’s performing. They also use them to make business decisions, such as hiring.

Types of sales forecasting

For about as long as businesses have used forecasting, they’ve relied on one of two approaches or types of sales forecasting: opinion and augmented forecasting.

Opinion forecasting

Opinion forecasting is the oldest form of forecasting. In this type of forecasting, the opinions of business and industry leaders and sales team members are gathered and analyzed to make projections about how sales will grow — or fall — in the upcoming weeks, months, or year. While qualitative information gathered from experts who are in the know can be useful, opinion forecasting is prone to inaccuracy due to bias, overconfidence, or lack of knowledge about various external factors.

Augmented forecasting

Augmented forecasting uses AI-lite technology to focus on interpreting and analyzing historical data to build predictive models about projected revenue for the future. Applying statistical methods to past performance data to predict future performance gives sales teams a more objective look at where they’ve been and where they might be headed. Augmented forecasting is often more accurate than opinion forecasting — as it relies more heavily on data — but it lacks the benefits provided by true AI that can make regular intelligent predictions.

While opinion and augmented forecasting can certainly inform a solid, well-balanced forecast, they’re missing one key element: a modern approach to forecasting that makes full use of the advanced technology that’s available to teams. That’s where prescriptive forecasting comes in.

Prescriptive forecasting

Prescriptive forecasting is the newest method of forecasting, which uses a neural network — a set of machine learning algorithms that mimic the human brain to identify underlying relationships in data sets — combined with deep learning to analyze data, taking forecasting up a notch. Deep learning uses a vast amount of internal and external data to provide a broader picture of the market that updates regularly, so sellers aren’t focused on reaching a static sales goal.

Why is forecast accuracy important?

Sales forecast accuracy is a crucial part of the day-to-day workings of a business and is essential to long-term planning and strategizing. The benefits of forecast accuracy include:

  • More accurate budgets: It goes without saying that accurate sales forecasting leads to more accurate budgeting and less guesswork, so all departments can work with better budget numbers.
  • Better organizational alignment: Forecast accuracy can improve leadership teams’ ability to make informed staffing decisions. They’ll have a better idea of revenue growth and what kinds of positions they may have a need for. Forecasting also drives better inventory predictions, giving manufacturers or vendors more time to get companies the products they need.
  • Enhanced cash flow management: Accurate budgeting is one thing, but an understanding of cash inflow and outflow can improve a company’s day-to-day operations by providing finance teams with a picture of when deals will close — and, by extension, when cash will be coming in.
  • Increased confidence in sales: The better the forecasting, the more likely salespeople are to hit their benchmarks. Accurate forecasting gives teams a solid, attainable goal to shoot for. This may in turn increase the trust a company’s leadership team has in its sales teams, who are undoubtedly doing what they need to do to hit their benchmarks: reaching out to prospects, closing deals, and everything in between.

These benefits provide a compelling argument in favor of sales forecasting, but there’s one problem. The traditional sales forecasting methods — opinion and augmented — do little to ensure the kind of accuracy sales teams and leaders want, leaving these benefits up to sheer chance. What’s more, focusing solely on accuracy may put the wrong kind of pressure on sellers to do whatever it takes to meet the target, even if that means pushing aside long-term potential deals for a quick sure bet. And opinion and augmented forecasting certainly don’t provide key action items to move from point A to point B to close deals like prescriptive forecasting does.

What is the best measure of forecast accuracy, then? In short, better sales outcomes. To elaborate, sales forecasts should be a measurement that guides teams toward a target — not a bull’s-eye that must be met. By focusing on better results, teams will be incentivized to focus on the right actions: letting go of dead deals, staying flexible, and focusing on the right deals at the right time for the buyers — not just hitting the forecast at any cost.

With prescriptive forecasting tools such as Collective[i], teams gain access to networked intelligence that updates forecasts in real time. Revenue estimates can be automatically updated — not only with information on prospects and leads, but also with real insight into buyers and how they’re interacting in the marketplace in general. These capabilities help teams achieve those better outcomes.

How do you make a sales forecast more accurate?

In short, let a machine do it. AI, automation, and machine learning have given sales teams the ability to drive better, regular forecasts with more accurate information. Collective[i]’s Predictive PipelinesTM provides pipeline health assessments and odds and deal optimizations to increase transparency on the status of deals.

Collective[i]’s Intelligent ForecastTM uses AI to remove time, bias, and human error from the forecasting process. It augments traditional opinion and historical sales forecasting data with a broad network of market data. All of this results in a daily, dynamic sales forecast — in addition to updates, recommendations, and risk alerts.

But besides modern tools, let’s take a closer look at the kinds of best practices involved in making a forecast more accurate.

Get better data

Improving the types of data available — and the sheer quantity of data — can increase forecast accuracy. However, while it’s fairly straightforward to enter historical sales information into a CRM, it’s extremely tedious. Before your sales team drops everything to enter more information into a CRM, let’s explore the types of factors that can influence forecasting accuracy.

There are two primary types of factors that can affect the sales forecasting process: internal and external. Internal factors are those controlled by a company or business. External factors occur outside of a company’s control.

What are the internal factors considered in forecasting?

  • Past performance of sales or marketing initiatives
  • Internal policies or changes that might have driven fluctuations
  • Marketing efforts in the past and those planned for the future
  • Product offerings and pricing increases or decreases

What are the external factors considered in forecasting?

  • The state of the economy, including the stock market and other outside influences
  • The state of the industry, including any new innovations or trends
  • Changes in demographic market analysis
  • Seasonal demand for products or services
  • Competitors’ products or services
  • Socioeconomic or political conditions, including elections

While companies don’t need to cover all the above factors in their sales forecasting, having an understanding of the factors of forecasting, market fluctuations, and other related trends can improve accuracy. Platforms such as Collective[i] can provide teams with that up-to-date trustworthy data — and give sales teams greater context around the work they do.

Be flexible — and make a flexible forecast

Forecasts aren’t static. The people creating and executing them shouldn’t be either. No one can predict when the next economic downturn or industry innovation will happen. And when events such as these do happen, pivoting fast is important. Rather than carrying on as usual, sales teams can use their forecast as a guide to recover and realign.

Use time-saving tools

Sales forecasting is a long process, especially if your team is still practicing Forecast Fridays. But if your sellers are still spending hours of their week entering data manually into a CRM, there’s a better way. Upgrade to an AI-enabled tool that automatically refreshes the data in your CRM. Collective[i]'s Intelligent WriteBackTM automatically records every interaction between buyers and your selling team and updates contacts and data as deals progress. Sales teams will have better data on top of more time to do what they do best: sell. Virtual DealRoomsTM provides a dedicated space for buyers and sellers alike to move through the entire buying process, helping sellers make good on their sales projections quicker.

Keep it simple

Perfectly predicting what will happen next season is impossible. Trying to predict what will happen in two or four years is a fool’s errand. Focus on the short-term as much as possible, and rely on tools that give salespeople the leg up they need to succeed. Forecasts change and evolve. The easier they are to update, the better equipped teams will be to make those changes and keep moving.

If you’re ready to boost the accuracy of your sales forecasting with Collective[i], contact us today.

Work together, win together

Request an invitation to join IntelligenceTM, the world’s first global network of sales professionals.

On no, we ran into an issue submitting this form. Please ensure each field was filled out correctly and resubmit.

If this problem persists, please reach out to us and we will be more than happy to follow-up from there.

We’ll be in touch soon

In the meantime, explore Collective[i] and find answers to frequently asked questions.