Master sales forecasting: Essential tips & practices

Sales forecasting is one of the most important things a company does as part of its business plan. It fuels sales planning and is used throughout an enterprise for staffing and budgeting. Despite its importance, many organizations use outmoded practices that produce bad forecasts.

A comparison could be drawn with times past, when farmers depended on signals like cats washing behind their ears or the ache in an old-timer’s knee to forecast the weather. With the advent of  supercomputers, weather prediction has vastly improved. But in large enterprises, the tools used to foresee sales remain only somewhat more reliable than an arthritic knee.

Just how dubious are sales forecasts? A full 55% of sales leaders, and 57% of quota-carrying sellers lack confidence in forecast accuracy, according to Gartner.

While you might think this state of affairs will improve over time, Gartner estimates that even by 2025, “90% of B2B enterprise sales organizations will continue to rely on intuition instead of advanced data analytics or their B2B CRM, resulting in inaccurate forecasts, sales pipelines and quota attainment.”

What is sales forecasting?

Sales forecasting is the process of estimating a company’s sales revenue for a specific time period – commonly a month, quarter, or year. A sales forecast is prediction of how much a company will sell in the future often utilizing advanced forecasting software.

Producing an accurate sales forecast is vital to business success in meeting customer demand. Hiring, payroll, compensation, inventory management, and marketing all depend on it. Public companies can quickly lose credibility if they miss a forecast.

Forecasting goes hand-in-hand with opportunity stage forecasting and sales pipeline management. Getting an accurate picture of qualification, engagement, and velocity for each deal helps sales reps and managers provide data for a reliable sales forecast.

A forecast is different than sales targets, which are the sales an enterprise hopes to achieve. A sales forecast uses a variety of data points, including historical sales data, to provide an accurate prediction of future sales performance.

Why is sales forecasting important for business?

Sales forecasting isn’t just about predicting numbers; it’s foundational to any business strategy. Here’s why:

• Strategic decision making: Sales forecasts provide a clear picture of where a business is headed, which factors into making decisions about product launches, market expansions, or even potential mergers and acquisitions. Understanding these forward looking projections can help businesses make informed decisions that align with their long-term goals.
• Resource allocation: A close-to-accurate sales forecast ensures that resources – whether it’s labor, capital, or technology – are allocated efficiently. Proper allocation prevents over-spending in areas that might not yield returns, and ensures that high-potential areas receive attention and investment.
• Budgeting and goal setting: Accurate and reliable sales forecast data is foundational to estimating future revenue and costs, as well as setting realistic yet challenging goals for revenue teams. Such data-driven insights help businesses allocate resources efficiently, ensuring that teams are equipped to meet their targets while also safeguarding a company’s financial health.
• Proactive problem solving: One of the most significant roles for sales forecasting is the ability to spot potential issues before they become major problems. For example, if a sales team is trending below its quota, sales managers can take timely action, preventing minor setbacks from escalating into significant ones.

Essentially, sales forecasting is like a compass that guides a business through unpredictable markets. It offers foresight and paves the way for sustained growth and strategic planning. It may be a critical differentiator between businesses that stay ahead of the curve and those that fall behind.

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Three main sales forecasting methods and techniques 

Although different organizations can have vastly different sales structures and processes, the majority tend to use one or a combination of the following three primary approaches to sales forecasting:

1. Use of historical data to forecast future results. Looking at historical data is perhaps the most common as well as most straightforward approach. The data is readily available, and it makes sense that variations based on factors like seasonality and new product introductions would provide directional insight. The limitation, of course, is that external, macro trends that impact sales aren’t necessarily considered – at least not in a systematic fashion.
2. Funnel-based forecasting. For many companies, the current state of the sales funnel is viewed as the most accurate predictor of likely sales outcomes. As long as sellers are providing accurate and frequently updated information about the state of given pursuits, use of the funnel can be a reasonably reliable means upon which to make forecasts.
3. Forecasting based on multiple variables. Given that both of the above approaches have inherent limitations, some organizations are looking to build more complex forecasting models that incorporate techniques such as intelligent lead scoring alongside macro factors that are likely to impact the closing of deals. The trick is to put in place an approach that’s sophisticated enough to be meaningful without being too complex to manage and maintain.

Beyond these three foundational methods, there are other techniques often used for sales forecasting, including:

• Regression-based analysis: Statistical analysis — specifically a regression-based method — can help analyze the relationships between different micro and macro variables to predict sales outcomes. For instance, it might analyze how a change in advertising spend correlates with sales figures, offering businesses a clearer understanding of market dynamics.
• Quantifying lead potential for revenue projection: This method analyzes various value attributes such as past interactions, purchase history, and engagement metrics, to assign a value to each lead. This approach can help prioritize efforts by helping businesses focus on the most promising leads.
• Forecasting based on the length of the sales cycle: With this technique, an enterprise takes into account the typical duration of a sales cycle in predicting future sales. By understanding how long it generally takes to convert a lead into a sale, businesses can better anticipate their revenue streams, making for more accurate forecasting.
• Combining data with seasoned intuition: This approach combines hard data with the seasoned intuition of veteran sales professionals. By leveraging the experience and insights of a sales team, businesses can make predictions that account for subtleties that might not be immediately evident in the numbers.

Many businesses combine forecasting techniques to navigate market fluctuations, while other businesses might rely on a single forecasting method that they’ve found works reliably well for their individual environment. The choice often hinges on the unique challenges and nuances of each enterprise.

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Common sales forecasting mistakes

The pressure’s on for sales teams to deliver, putting the spotlight on forecasting. Facing stiff competition and an uncertain market, expectations for salespeople keep rising – and forecasts are the means by which sales activity, including expanding into new markets, and by extension the health of the business, is most readily monitored.

Unfortunately, enterprises continue to make the same mistakes in their forecasting processes. Here are some of the common pitfalls:

1. Sales data fails to provide insight into deal status. A limitation of existing forecast approaches is they are heavily reliant on sellers to provide accurate information about the status of specific opportunities. Given the pressure on sellers, it’s not surprising that the information they provide is often rosier than the reality.
2. Time-consuming manual processes cut into valuable selling time. It’s estimated that sales reps spend 2.5 hours per week on forecasting, while their managers spend an average of 1.5 hours. Every hour that’s devoted to these time-consuming – and manual – activities would be better spent on actual sales.
3. In the push to commit revenue, accuracy is often sacrificed. Under pressure to provide positive numbers, sellers typically overestimate the number of deals that will close. Perhaps not surprisingly, 79% of sales organizations report typically missing their forecasts by more than 10%. Meanwhile, 54% of the deals forecast by reps never close.

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How to forecast sales: Important steps to forecast sales accurately

Fortunately, there are ways sales organizations can build a forecast process that helps achieve greater accuracy – and, ultimately, better sales results.

At the most fundamental level, improving sales forecasting means using data to more accurately  predict performance and manage planning to ensure sales success. This includes steps like:

1. Establish a common agreement about the sales process. Seems like a no-brainer, right? Your sales teams operate from a common lexicon about the sales funnel and the stages within it that your organization employs. In reality, there’s frequently a genuine disconnect. To avoid this, codify a common sales process with clear stages and a consistent vocabulary so that every team member structures their work the same way, and the stages of the pipeline mean the same thing to everyone.
2. Set realistic sales goals or quotas and communicate them. Again, this may seem obvious. But many companies either set unrealistic sales quotas, or fail to effectively communicate individual goals and how they ladder up to the broader plan. Set realistic and measurable sales goals based on past performance and market conditions, so every team member knows their targets and how they contribute to a larger plan.
3. Benchmark your basic sales metrics. Forecasting involves using historical data to effectively estimate future results. Benchmarking ensures there’s a sound basis for comparison with historic data, and is more effective when combined with a robust CRM system to track leads, sales stages, and customer relationships with real-time data and metrics for forecasting revenue.
4. Understand your current sales pipeline. If you want to achieve better forecasting, accuracy starts now. New technologies provide sales teams with intelligence that enables them to scrub leads that aren’t actually viable, realistically assess those that are, rescue ones at risk, and commit to a higher degree of precision going forward.
5. Choose your forecasting techniques and test often. Determine a predictive method that’s best suited for your business model and market complexity. Continually test these methods to improve accuracy, and change the approach based on market conditions.
6. Integrate data from marketing, product, and finance. Leverage data from across departments to get a more realistic view of business performance. By integrating data, you’ll not only get a more accurate representation of the market potential, but also spot internal constraints. This can provide more realistic predictions that guide business decisions.
7. Glean insights by reviewing previous forecasts against outcomes. Implement a process of continuous improvement by conducting periodic assessments of past forecasts versus realized results. This retrospective analysis can help identify patterns in forecasting performance and areas where new methods might be required.
8. Formalize and iterate the process. Document your forecasting process and make it an integral part of the sales strategy. With every iteration, revisit the process and improve with new data and insights so that sales forecasting remains relevant and adaptable to changing conditions internally and externally.

One commonality across these points is that they illustrate the need for cultural change in the sales organization. In other words, you can only drive accuracy in forecasting if salespeople don’t feel pressure to inflate the forecast.

And, by extension, they need to feel comfortable sharing information about deals even when it’s not favorable.

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Integrating data is essential to improving the accuracy of sales forecasts

Given all the benefits of accurate sales forecasting, what’s keeping companies from pursuing more modern approaches?

For one thing, regardless of approach, the quality of forecasts is inextricably linked to the quality of the data on which they are based. And it’s not enough to merely have all the data available; it needs to be integrated in such a way that it can be readily analyzed in real time.

Unfortunately, this type of data integration is anything but common. According to APQC’s Planning and Management Accounting Benchmark, only 14% of organizations currently house operational and finance data in a single integrated system. This means that for most companies, forecasting requires the gathering of data across organizational silos and disparate systems, which becomes time consuming and costly.

The good news, however, is that data integration enables organizations to take better advantage of technologies such as AI and machine learning, which are ideally suited for spotting the types of trends that data can reveal.

For example, integration with the marketing department ensures that promotional strategies are in sync with sales cycles and expected sales volumes. This assists in planning and optimizing marketing campaigns to generate demand when and where needed.

Integration with the finance department ensures that budgeting and financial planning are directly tied to the forecasted sales figures. This aids in efficiently allocating capital to resources and activities that are most likely to generate the best financial returns for the business.

Similarly, integration with business operations and supply chain can help anticipate increases in demand, thereby optimizing inventory levels and logistics, including the planning of staffing needs to meet market demand.

By incorporating state-of-the-art tools into an integrated approach for data analysis, organizations can transform sales forecasting into a strategic advantage.