How to Improve Your Sales and Revenue Forecast Accuracy - New Research

Dec 28, 2022
5 min read
Revenue forecasting has never been more important. From navigating economic uncertainty to sustaining profitable and predictable revenue, accurate forecasting provides both confidence and context for key decision makers.

What Is Revenue Forecasting and Why Is It Important?

Revenue forecasting is the process of predicting your company’s revenue over a period of time, ranging from annual forecasts to weekly revenue predictions. The process uses historical data and insights from multiple departments to set expectations for future revenue.

Forecasting revenue is important because it allows leaders to plan how their business will grow over time. Rather than making strategic decisions based on intuition alone, revenue forecasts enable leaders to act based on data. This leads to more accurate predictions, greater profitability, and more sustainable long-term growth.

The Challenge: Creating Reliable Revenue Forecasts

Gartner has found that just 45% of sales leaders and sellers have confidence in their forecasting accuracy, while Forrester reported that 93.6% of B2B organizations missed their first-day quarterly forecast by 10% or more in 2021. These problems are compounded by economic volatility: The Harvard Business Review suggests a forecast which might normally be inflated by 8% could end up being off by as much as 50% during a period of instability.

Fixing these problems is a priority. But what can your organization do to improve its forecasting process? And what is likely to get in the way?

Our recent research answers these questions. Based on a survey of 261 companies across the globe, we provide a benchmark for revenue forecasting efforts. This not only reveals the common problems organizations face—it allows us to carve out a productive path forward.

Key Findings

  • Revenue forecasting is a critical cross-functional process...
  • …But revenue forecast accuracy is still a major challenge.
  • Poor quality data is to blame for inaccurate revenue forecasts…
  • …While manual processes and outdated technology limit data accessibility and create silos. 


  1. Organizations should automate manual processes and other forecasting activities to improve data quality, consolidate fragmented data sources, reduce errors, and create more efficient ways of working cross-functionally.
  2. Cross-functional collaboration between aligned teams is crucial to accurately predict existing revenue growth, which is 1.5-3x more cost-effective than acquiring new customers.
  3. Organizations should strive to increase the frequency of their revenue forecasts, with the goal of achieving a weekly forecast cadence.

How effective are companies at forecasting revenue?

Revenue forecasting is (almost) universal

Regular revenue forecasts are vital for organizations, enabling them to make informed decisions about everything from how much to spend on marketing to how to manage their sales teams and inventory. Our survey suggests that the vast majority of organizations understand this, with just 2% stating that they don’t practice revenue forecasting.

However, New Customer Revenue is the most common factor companies forecast, suggesting that many organizations are missing out on opportunities for easier growth.

Just 76% of our respondents forecast Existing Customer Expansion Revenue, even though such revenue is often 1.5-3x more efficient to acquire.

Simply increasing their forecast efforts in this area could therefore be an easy win for companies looking to increase revenue during these economic times.

…But few companies forecast accurately

Forecasting revenue only creates value when it’s accurate. Otherwise, it can lead to false confidence, missed sales opportunities and wasted resources. 

This is particularly problematic as companies scale and increase the number of stakeholders who rely on forecasts to make decisions. A company might make use of revenue forecasts when preparing for a financing event, and will take a reputational hit if investors discover that they have based their decisions on inaccurate data.

And yet, despite their efforts, our survey finds that the majority of companies are currently producing highly inaccurate revenue  forecasts. Just 9% of companies provide a forecast within 5% of their actual revenue results,  meaning 91% of companies are missing the mark:

Revenue Forecasting Accuracy: Just 9% of companies achieve a forecast within 5% of their actual revenue results; meaning 91% are missing the mark.

This problem finds its root in the poor quality and inaccessibility of data. The top three challenges our respondents cited when creating forecasts were “data quality,” “manual processes,” and “fragmented data sources”.

Ultimately, many of these issues are at root problems of technology adoption. Companies primarily rely on Excel (66%) and/or Google Sheets (32%) to generate forecasts, which helps explain why accuracy is so poor. Few companies utilize forecast management software (12%) or more holistic, cross-functional tools like business or revenue intelligence platforms (13% and 5% respectively). This helps to explain the challenge of poor data quality reported by 52% of revenue leaders in our survey.

Revenue Forecasting Challenges and Solutions: chart begins with manual spreadsheets and the corresponding challenge of time-consuming/error-prone manual processes, answered by in-house system; second challenge lack of accountability from Sales Reps is met with CRM software; next fragmented data sources is met by the solution of a Business Intelligence platform; quality of data from tools and teams, solved by Forecast Management software; and forecast frequency solved by Revenue Intelligence software.

Manual processes limit revenue forecasting frequency

Specialized Forecast Management Software and Business Intelligence (BI) platforms are used by a small number of businesses. But these solutions tend to provide visibility into the revenue forecasts themselves, rather than enabling companies to accept input signals across processes or platforms. As a result, these companies still cannot automate the modeling or planning process.

These issues not only make accurate revenue forecasts far more difficult, they also inhibit the frequency of forecasts. Because they are undertaken using manual processes, the majority of companies have to settle for either monthly or quarterly revenue forecasts, with just 10% of organizations able to achieve a weekly forecast cadence:

Revenue Forecast Cadence: 10% forecast weekly; 52% forecast monthly; 33% forecast quarterly.

In times of uncertainty, this may not be frequent enough to provide real value. Monthly or quarterly revenue forecasts fail to account for rapid market changes or short-term trends, meaning revenue teams are unable to gain up-to-date insights that could help them make better strategic maneuvers.

Cross-functional collaboration is crucial to accurate revenue forecasting

Given the number of factors that contribute to an organization’s performance, revenue forecasting is an inherently cross-functional process. Without input from aligned teams, it is nearly impossible to gain a clear picture of the current landscape upon which to base your revenue predictions. 

Our research found that across New Customer Acquisition, Existing Customer Expansion, and Existing Customer Retention, at least two departments were responsible for Forecasting revenue. But in reality, Sales, Finance, and Customer Success all play key roles in producing reliable and accurate revenue forecasts.

Of total respondents, the following departments were cited as responsible for New Customer Revenue Forecasting: 51% Finance; 74% Sales; 25% Marketing; 7% Customer Success; 28% Revenue Operations; 1% Do Not Forecast; 4% Other.

These graphs reflect organizations’ struggle to create truly cross-functional forecasting processes. It is understandable that Sales and Finance play key roles in New Customer Revenue Forecasting. But the relative lack of involvement of both Marketing and Customer Success suggest organizations may be missing key pieces of the puzzle—especially given how inaccurate most organizations’ forecasting is.

We see a similar pattern in Existing Customer Expansion Revenue Forecasting: Account Management and Marketing both underrepresented in the process. These findings may help explain why revenue forecasting efforts struggle so much with data silos, inaccuracy and collaborative difficulties.

Out of total respondents, percentage who answered to each department's responsibility for existing customer revenue forecasts: 49% Finance; 50% Sales; 20% Account-Management; 11% Marketing; 41% Customer Success; 24% Revenue Operations; 5% Do not forecast; 4% Other.

Improving Your Revenue Forecasts

These findings provide a clear path forward. Organizations must prioritize data quality and accessibility to create a sales to revenue pipeline that involves teams across the entire customer cycle. This will facilitate greater cross-functional collaboration and ensure revenue forecasts take into account all relevant information.

The first step is to fix fragmented data sources and remove error-prone manual processes. Leaders should apply automation and data integration to ensure data is accurate and available in real-time. But this will require a cohesive approach to revenue intelligence.

To see further survey results and discover how to improve data and cross-functional collaboration for more reliable revenue forecasting, read our full report on the State of Revenue Intelligence.

  • Forecasting