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Mastering B2B Sales Forecasting: Importance, Methods, Pitfalls

April, 25 2024
Mastering B2B Sales Forecasting: Importance, Methods, Pitfalls

In this guest blog from Alex Zlotko, CEO at Forecastio, he provides a masterclass in sales forecasting optimisation and strategy.

Sales forecasting has become a ubiquitous term, popping up in countless articles, LinkedIn posts, and influencer blogs. Despite its extensive discussion among experts and the abundance of dedicated resources available, the challenge of achieving accurate sales forecasting persists. According to the Gartner State of Sales Operation Survey, over 50% of sales leaders lack confidence in the accuracy of their sales forecasting.

Let's delve deeper into why accurate sales forecasting is crucial, the various forecasting methods available to B2B sales organizations, and the common pitfalls faced by sales leaders and RevOps teams in their quest for accuracy.

The Importance of Sales Forecasting

Have you ever heard phrases like, 'It's impossible to predict sales accurately because the market changes so quickly,' or 'We're too small to focus on forecasting; as a startup, our priority is closing new accounts'?

If so, you're not alone. Many people underestimate the importance of sales forecasting for business survival.

Highlighting the negative consequences of inaccurate forecasting is the most effective way to underscore its importance. Let's delve into these consequences.

Resource Allocation

Inaccurate sales forecasting often results in increased spending. Companies may allocate excessive resources to areas like marketing, recruiting, tools, and innovations, leading to rising costs. Consequently, less attention and resources are directed towards improving efficiency, resulting in lower profitability and higher cash burn rates.

Investor Confidence and Fundraising

When a company fails to accurately forecast its sales, it often indicates a lack of understanding or control over its market dynamics and revenue streams. This uncertainty can erode investor confidence, as investors rely on sales forecasts to gauge the company's growth potential and assess the risk associated with their investment.

When seeking funding from investors, companies typically need to provide financial projections, including sales forecasts, to demonstrate their growth prospects and repayment ability. If these forecasts are unreliable or overly optimistic, it can undermine the company's credibility.

Investments in Development

This scenario presents a contrast to the situation discussed regarding inefficient resource allocation. However, it occurs in some companies, as leadership within each organization behaves differently.

If your sales organization consistently falls short of revenue forecasts by a significant margin, it diminishes predictability.

Uncertainty about future revenue, coupled with management's lack of confidence in sales forecasts, leads to a focus on mitigating cash risks.

As a result, less funding is allocated to hiring top talent, pursuing innovations, and conducting experiments.

Revenue Leaders Credibility

It doesn’t matter who is responsible for preparing sales forecasts. Whether it’s a Sales Leader or a dedicated RevOps team, inaccurate forecasts damage their credibility.

Inaccurate forecasts indicate issues with processes, data, teams, and tools.

Low credibility raises questions about overall leadership performance and decreases morale, leading to even more efficiency gaps.

Pursuing high accuracy in sales forecasting is crucial for building a healthy and growing business.

Now, let’s discuss some common forecasting methods and models that B2B sales organizations can utilize, taking into account their specific circumstances.

Sales Forecasting Methods

There are many approaches to sales forecasting. However, it must be acknowledged that the 'one size fits all' rule cannot be applied when it comes to forecasting.

Each company is unique, considering factors such as:

  • The amount of historical data available
  • The maturity of the sales process
  • The sales model employed
  • The market in which it operates
  • And more.

Therefore, it's essential to consider these factors when discussing forecasting methods.

Let’s discuss some of the most common methods of forecasting.

Forecasting Based On Current Pipeline

This method is based on analyzing opportunities in a pipeline to make predictions.

It operates with metrics such as opportunity amount ($), win rate, close dates, and opportunity probability.

For instance, to create a forecast for the next quarter, this method will do the following:

  1. Select all opportunities with close dates in the next quarter.
  2. Multiply each opportunity amount by the probability of closing.
  3. Sum up the resulting values.


You have two opportunities that are supposed to close next quarter.

The value of Opportunity #1 is $5,000, and the value of Opportunity #2 is $7,000.

The probability of Opportunity #1 is 90%, and the probability of Opportunity #2 is 50%.

The projected revenue will be: $5,000 x 90% + $7,000 x 50% = $8,000.


An apparent advantage of this method is its simplicity, making it accessible to any business, even those with a lack of historical data, such as startups just beginning their sales efforts.

However, the Achilles' heel of this method is its reliance on opportunity probability as a key parameter. Opportunity probability is inherently subjective, leading to a significant role for human factors that may introduce mistakes.

Forecasting Based On Opportunity Stage

This method works similarly to the previous one, but instead of setting a probability of closing for each opportunity, it utilizes a parameter known as opportunity stage probability.

The further along an opportunity is in the sales pipeline, the more likely it is to close.

In simpler terms, once an opportunity reaches a certain pipeline stage, it is assigned the probability of closing associated with that stage.


Your pipeline consists of three stages: Demo, Proposal, and Negotiations. Each stage has an associated probability of closing: 30% for Demo, 60% for Proposal, and 80% for Negotiations.

Currently, there are three opportunities in the pipeline with amounts of $5,000, $10,000, and $12,000, respectively. Opportunity #1 is at the Demo stage, Opportunity #2 is at the Proposal stage, and Opportunity #3 is at the Negotiations stage.

The projected revenue will be: $5,000 x 30% + $10,000 x 60% + $12,000 x 80% = $17,100


It’s a straightforward yet powerful method for forecasting, especially when probabilities for stages are calculated based on historical data rather than relying on gut feeling.

In many CRMs, users are required to manually set probabilities for each stage, which introduces subjectivity and can significantly impact forecasting accuracy.

It’s highly recommended to utilize RevOps platforms or forecasting software that calculates pipeline stage probabilities using historical performance data.

Forecasting Based On Length of Sales Cycle

This method utilizes the average length of the sales cycle to establish the probability of closing a deal. In essence, the further an opportunity progresses along the sales cycle, the greater the likelihood of its closure.

For instance, if your sales cycle typically spans 8 months and the current opportunity has been active for 4 months, you can estimate a 50% probability of closing this opportunity.


This approach to forecasting is straightforward and relies on basic mathematical principles. However, it necessitates a well-established sales process with a clearly defined sales cycle.

Additionally, sufficient historical data is required to accurately calculate the average length of a sales cycle. Without these prerequisites, the accuracy of sales forecasts may be compromised.

Historical Forecasting

Historical forecasting utilizes past sales data to analyze previous performance and make projections for the future. This method operates under the assumption that all conditions remain constant.

For instance, when forecasting for the upcoming quarter, you would examine performance data from the corresponding quarter in previous years. You would then estimate year-over-year growth and make projections based on this analysis.


To forecast for Q3, consider your historical year-over-year growth rate. If your growth rate was 50% in the past and you closed $100,000 in Q3 last year, you can expect to close $150,000 this year using the historical forecasting method.


While the method is simple and doesn't require automation, its accuracy is questionable.

Additionally, this method is unsuitable for companies with a short operating history. To utilize this method, you need at least three years of sales data.

Furthermore, internal and external conditions constantly change.

Time Series Forecasting

Time-Series Analysis aids in predicting future sales by leveraging past sales data stored by the business to uncover the prevailing trend. Additionally, Time-Series Analysis pinpoints the elements impacting the observations within the time series, primarily diverse fluctuations, to discern the fluctuations in sales levels during specific timeframes.

Once the variances are distinguished from the trend, both the time series and the trend will be projected into the future to anticipate forthcoming sales levels in both the short-term and long-term.

This method of forecasting utilizes sophisticated mathematical models.

There are three common methods of time series analysis in sales forecasting:

  1. Trend Extrapolation.
  2. Fluctuations.
  3. Moving Averages.

For instance, in the Forecastio Platform, we utilize an autoregressive integrated moving average method to generate accurate long-term forecasts.


Unlike previous methods, time series forecasting can yield highly accurate long-term forecasts. However, it requires a substantial amount of historical data. This method isn’t available in popular CRM solutions and can be employed by using specialized RevOps platforms or forecasting software.

Multivariable Forecasting

This method involves analyzing many factors and huge amounts of real-time data from various sources to make projections.

Among the factors and parameters that are taken into consideration:

  1. Win Rates, average length of a sales cycle, pipeline growth rate, and more;
  2. Individual performance of each sales rep on the team over a period of time;
  3. Pipeline stages duration and conversions;
  4. Type of leads and lead sources;
  5. Seasonality;
  6. Market changes and fluctuations;


This method can indeed produce the most accurate sales forecasts. However, it is effective when applied to large amounts of accurate real-time data from different sources.

Also, this method isn’t available in CRM tools. You need to employ specialized tools capable of performing predictive analytics.

Qualitative Forecasting

Qualitative forecasting is based on opinions, whether from a group of experts or your sales representatives who work on the front line.

This method involves analyzing your current pipeline and gathering insights or opinions from your team on future performance.

These opinions and insights are most often gathered during pipeline reviews.

Additionally, experts can be involved to provide their assumptions on future performance based on their knowledge of market conditions.


This is the simplest method of forecasting and is used by all businesses. However, it relies more on feeling and intuition than on data. Thus, the forecasting accuracy is low.

Most Common Mistakes and Pitfalls

Let’s delve into some key factors and reasons that lead to failures in forecasting.

Lack of Data

This is a common issue for many startups and newly established businesses.

Without a sales history, forecasting becomes challenging. Therefore, it's essential to prioritize defining what data to gather and how to gather it from the outset.

However, even with limited data, there are strategies you can employ. Consider using qualitative forecasting or forecasting based on the current pipeline. These methods do not rely on historical data and can provide valuable insights.

Inaccurate Data

Arguably, the most common reason for inaccurate forecasting lies in data quality issues.

Achieving 100% accuracy in data is indeed challenging, yet companies must prioritize data accuracy from the outset.

Human error significantly impacts data accuracy, particularly when sales data is manually entered by sales reps. To minimize errors in data entry, consider the following strategies:

  • Implement regular data clean-up events.
  • Utilize rules and triggers in your CRM system.
  • Automate data enrichment processes, especially for customer information.
  • Ensure seamless integration of your CRM with other data sources.
  • Foster a culture of data accuracy within your team.
  • Provide incentives to encourage your team to maintain clean data.

Non-relevant Data

While similar to the previous point, this issue carries a slightly different implication. Even if your data is accurate, outdated information gathered without real-time updates can hinder accurate forecasting and projections.

This challenge frequently affects companies relying on spreadsheets for sales forecasting.

Often, these spreadsheets are disconnected from CRM tools or other data sources. As a result, key parameters necessary for forecasting are manually entered with significant delays.

The solution is clear: implement forecasting software that operates on real-time CRM data.

Lack of buy-in from Stakeholders

The aspiration for accurate sales forecasting should be ingrained in a company's culture.

A sales leader shouldn't have to fight alone on the battlefield.

I've witnessed cases where a company's senior management doesn't prioritize sales forecasting, considering it unimportant due to the company's size or other perceived priorities.

It's never too early to invest time and resources in forecasting. Therefore, there are many different forecasting techniques that can be applied to each particular case.

Absence of a Defined Process

Forecasting isn't a one-time event; it's an ongoing process that demands time, people, data, and tools.

To ensure accuracy in forecasting, it's crucial to establish data entry requirements, engage and motivate team members, schedule forecasting meetings and reviews, deploy appropriate tools, and continuously refine forecasting models.

Incorrect Forecasting Methods

One size does not fit all. The biggest issue arises when companies attempt to implement forecasting models that are not applicable to their unique conditions.

For instance, you will not yield positive results from time series forecasting if you lack sufficient historical data.

Forecasting based on the average length of the sales cycle is ineffective if your sales cycle is too short.

Likewise, forecasting by pipeline stages is impractical if your sales process is not properly designed and lacks sufficient historical data to calculate probabilities for each stage.

Choose the method that best suits your current situation to achieve accurate forecasts.

Failure to Utilize Forecasting Software

Many companies still spend a significant amount of time preparing sales forecasts using spreadsheets.

Unfortunately, spreadsheets do not allow for the implementation of comprehensive forecasting models, or at least not easily.

Moreover, maintaining spreadsheets requires considerable time and manual effort for data updates.

On the other hand, most popular CRM tools offer only simplified forecasting methods like weighted pipeline, lacking advanced capabilities.

Fortunately, we are witnessing the emergence of robust forecasting tools suitable for companies of all sizes and financial capabilities.

Consider using specialized software that can be seamlessly integrated with your CRM or other data sources, without requiring extensive and complex implementation processes.

Final words

Forecasting can be carried out by companies of all sizes and levels of maturity. Naturally, the better a company's sales processes and the more historical data it possesses, the more precise forecasts it can generate. Accurate sales forecasts are vital for maintaining a healthy and efficient business.

Times have changed. Now, the primary focus, especially in the tech space, has shifted from 'grow by all means' to 'grow efficiently and predictably.' Accurate sales forecasting plays a crucial role in this new reality.

About the guest blog author:

Alex is the CEO at Forecastio, bringing over 15 years of experience as a seasoned B2B sales expert and leader in the tech industry. His expertise lies in streamlining sales operations, developing robust go-to-market strategies, enhancing sales planning and forecasting, and refining sales processes.

Connect with Alex at LinkedIn