The Go-To Guide to Sales Forecasting [Top 7 Approaches Explained]

Partners 02 May 2024

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 45% of sales leaders lack confidence in the accuracy of their sales forecasting.

Let’s explore why accurate sales forecasting is crucial, the various forecasting methods available to B2B sales organizations, and the common pitfalls sales leaders and RevOps teams face in their quest for accuracy.

What is sales forecasting, and who is accountable?

Let’s start with the definition of sales forecasting to understand what this article is about clearly.

Sales forecasting is the process of estimating future sales. It helps businesses predict how much they might sell in a given period, based on past sales data, market trends, and economic conditions.

Accountability for sales forecasting can lie with various roles, usually depending on the company’s organizational structure. Additionally, a company’s organizational structure is always tied to its business maturity and annual revenue.

While the company is small, a VP/Head of Sales is responsible for forecasting sales. As the business grows, it establishes a sales/revenue operations team that takes responsibility for predicting future sales.

Why Is sales forecasting important?

My name is Alex Zlotko, and I’m CEO at Forecastio. Why should you believe my words? I’ve spent over 15 years in B2B sales, serving as both a seasoned sales leader and a consultant. My work involves helping companies establish their sales departments from scratch or enhance existing ones.

Have you ever encountered statements like, “Predicting sales accurately is impossible due to the rapidly changing market,” or “As a startup, we’re too small to worry about forecasting; our main focus is on acquiring new accounts“?

You’re not the only one. Many overlook the critical role that sales forecasting plays in ensuring business survival.

There’s no better way to demonstrate the significance of precise forecasting than by detailing the dire outcomes that can result from poor forecasting.

Emphasizing the negative impacts of inaccurate forecasts is an effective method to highlight their importance. Let’s explore these impacts.

Resource allocation

Inaccurate sales forecasting frequently leads to increased expenditures. Companies might allocate too many resources to sectors such as marketing, recruiting, tools, and innovations, driving up costs.

As a result, less attention and fewer resources are devoted to enhancing efficiency, leading to reduced profitability and elevated cash burn rates.

Investor confidence and fundraising

When a company cannot accurately forecast its sales, it usually reflects a poor grasp of its market dynamics and revenue streams. This lack of certainty can diminish investor confidence since investors depend on sales forecasts to evaluate the company’s growth prospects and the risks of their investment.

In pursuit of investment, companies generally must present financial projections, such as sales forecasts, to illustrate their potential for growth and ability to repay. If these forecasts prove to be unreliable or excessively optimistic, it can weaken the company’s credibility.

Investments in development

This scenario stands in contrast to the previously discussed issue of inefficient resource allocation. Yet, it happens in some companies, as leadership varies from one organization to another.

When your sales organization regularly misses revenue forecasts by a considerable margin, it reduces predictability. Uncertainty regarding future revenue, combined with management’s diminished confidence in sales forecasts, shifts the focus toward mitigating cash risks.

Consequently, less funding is directed towards recruiting top talent, pursuing innovations, and conducting experiments.

Leadership credibility

It’s irrelevant who prepares sales forecasts, be it a sales leader or a dedicated RevOps team; inaccurate forecasts harm their credibility.

Inaccurate forecasts signal problems with processes, data, teams, and tools.

Diminished credibility prompts concerns about leadership performance and dampens morale, exacerbating efficiency gaps.

Understanding the importance of achieving high accuracy in sales forecasting is vital for fostering a healthy, growing business.

Now, considering their unique circumstances, let’s explore various forecasting methods and models commonly used by B2B sales organizations.

What are 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
The maturity of the sales process
The sales model
The market
The industry

Therefore, it’s essential to consider these factors when discussing sales forecasting methods. Let’s discuss some of the most common methods of forecasting.

Forecasting based on the current pipeline

This approach relies on analyzing pipeline opportunities to make predictions.

It utilizes metrics like opportunity amount ($), win rate, close dates, and opportunity probability.

For example, to generate a forecast for the upcoming quarter, this method will:

Identify all opportunities with close dates in the next quarter.
Calculate the product of each opportunity amount and its probability of closing.
Total the resulting values.

Example:

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

Opportunity #1 has a value of $10,000, and Opportunity #2 has a value of $20,000.

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

The projected revenue will be: $10,000 x 90% + $20,000 x 50% = $19,000.

In summary:

One clear benefit of this method is its simplicity, enabling accessibility for any business, including those with limited historical data, such as startups initiating their sales endeavors.

However, this method’s weakness lies in its dependence on opportunity probability as a crucial parameter. Opportunity probability is inherently subjective, which increases the influence of human factors and may result in errors.

Forecasting based on opportunity stage

This approach functions akin to the preceding one, except it doesn’t assign a closing probability to each opportunity; instead, it employs a parameter called opportunity stage probability.

As an opportunity progresses through the sales pipeline, its likelihood of closing increases.

In simpler terms, when an opportunity advances to a specific pipeline stage, it is assigned the closing probability linked to that stage.

Example:

Your pipeline consists of three stages:

Demo
Proposal
Negotiations.

Each stage has an associated probability of closing: 40% for Demo, 70% for Proposal, and 90% 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 40% + $10,000 x 70% + $12,000 x 90% = $19,800

In summary:

It’s a simple yet potent forecasting method, particularly effective when stage probabilities are derived from historical data rather than intuition.

In numerous CRMs (and other free sales tools), users must manually assign probabilities to each stage, introducing subjectivity and potentially compromising forecasting precision.

I suggest leveraging RevOps platforms or forecasting software like Forecastio that computes pipeline stage probabilities using historical performance data.

Forecasting based on length of sales cycle

This approach employs the average duration of the sales cycle to determine the likelihood of closing a deal. Essentially, as an opportunity advances through the sales cycle, its chances of closure increase.

Example:

If your typical sales cycle lasts 8 months and the current opportunity has been in progress for 4 months, you can approximate a 50% probability of closing this opportunity.

In summary:

This forecasting approach is simple and depends on fundamental mathematical principles. However, it requires a well-defined sales process with a clearly outlined sales cycle.

Moreover, accurate calculation of the average sales cycle length necessitates adequate historical data. Without these prerequisites, the precision of sales forecasts may be compromised.

Historical forecasting

Historical forecasting employs past sales data to analyze prior performance and forecast future outcomes. This method assumes that all conditions remain consistent.

For example, when forecasting for the upcoming quarter, you would review performance data from the corresponding quarter in previous years. From there, you would estimate year-over-year growth and make projections based on this analysis.

Example:

To forecast for Q4, consider your historical year-over-year growth rate. If your growth rate was 60% in the past and you closed $200,000 in Q4 last year, you can expect to close $320,000 this year using the historical forecasting method.

In summary:

Although the method is straightforward and doesn’t require automation, its accuracy is uncertain.

Moreover, this approach is not suitable for companies with a limited operating history. A minimum of 3 years’ worth of sales data is necessary to implement this method. Furthermore, internal and external conditions are subject to constant change.

Time series forecasting

Time-series analysis assists in forecasting future sales by utilizing past sales data stored by the business to identify the prevailing trend. Additionally, time-series analysis identifies the factors influencing the observations within the time series, primarily various fluctuations, to discern changes in sales levels during specific time periods.

Once the variations are separated from the trend, both the time series and the trend are projected into the future to anticipate upcoming sales levels in the short term and the long term.

This forecasting method relies on advanced mathematical models.

Three common methods of time series analysis in sales forecasting include:

Trend extrapolation.
Fluctuations.
Moving averages.

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

In summary:

In contrast to previous methods, time series forecasting has the potential to produce highly precise long-term forecasts. However, it demands a significant volume of historical data.

This approach is not typically found in mainstream CRM solutions and can be implemented using dedicated 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:

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

In summary:

This approach has the potential to generate the most precise sales forecasts. However, its effectiveness relies on access to substantial volumes of precise real-time data sourced from various channels.

Moreover, this method is not supported by standard CRM tools. You must utilize specialized tools equipped to conduct predictive analytics.

Qualitative forecasting

Qualitative forecasting relies on subjective opinions, whether from a panel of experts or your frontline sales representatives.

This method entails examining your current pipeline and collecting sales insights or opinions from your team regarding future performance.

These viewpoints and insights are typically gathered during pipeline reviews.

Moreover, experts may contribute by offering their projections on future performance based on their understanding of market conditions.

In summary:

This represents the most basic forecasting method and is employed by businesses worldwide. However, it leans heavily on intuition and gut feeling rather than on data, resulting in low forecasting accuracy.

What are the most common mistakes and pitfalls?

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

Data limitations

This is a common challenge faced by many startups and newly established businesses.

Forecasting becomes difficult without a sales history. Therefore, it’s crucial to prioritize defining what data to collect and how to collect it from the outset.

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

Data inaccuracy

Arguably, the primary 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 beginning.

Human error significantly impacts data accuracy, especially when sales data is manually inputted by sales representatives. To minimize errors in data entry, consider the following strategies:

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

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Irrelevant 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 impede accurate forecasting and projections.

This challenge often affects companies that rely on spreadsheets for sales forecasting.

Frequently, these spreadsheets are not linked to CRM tools or other data sources. Consequently, essential parameters necessary for forecasting are manually entered with significant delays.

The solution is straightforward: implement forecasting software that operates on real-time CRM data and information from B2B data provides.

Lack of stakeholder buy-in

The desire for accurate sales forecasting should be ingrained in a company’s culture.

A sales leader shouldn’t have to battle alone on this front.

I’ve observed instances where a company’s senior management doesn’t prioritize sales forecasting, deeming 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 specific case.

Undefined Process

Sales forecasting isn’t a one-time event; it’s an ongoing process that requires 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.

Inappropriate forecasting methods

One size does not fit all. The most significant issue arises when companies try to implement forecasting models that are not suitable for their unique circumstances.

For example, you will not achieve 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.

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

Select the method that best fits your current situation to attain accurate forecasts.

Failure to employ forecasting software

This is a crucial aspect.

Many companies still invest a considerable amount of time preparing sales forecasts using spreadsheets.

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

Furthermore, maintaining spreadsheets requires significant time and manual effort for data updates.

On the contrary, most popular CRM tools only offer 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 utilizing specialized software that can be seamlessly integrated with your CRM or other data sources, without requiring extensive and complex implementation processes.

Final words

I’ve been engaged in sales forecasting ever since I entered the B2B sales arena. I’m an advocate for sales forecasting, emphasizing its importance for companies to achieve predictable and efficient growth.

I believe that every company, regardless of its size and sales model, should invest time in sales forecasting and explore ways to enhance forecast accuracy.