Sales forecasting, or the ability to predict future revenue growth is a key capability for businesses across all industries. From empowering data-based decision-making to improving cash flow management, there’s no shortage of benefits to be gained when you are able to optimize your company’s forecasting process. Today, there are numerous sales forecasting methods and revenue forecasting models that SaaS companies use to predict future revenue. In this article, we’ll take a deep dive into nine of these models in addition to covering how revenue forecasting works and why it is so important.

Revenue forecasting is the process of predicting the amount of future revenue that your business is likely to receive over a specific time span. This data is then used to power informed decisions and provide transparency into a company’s financial posture.

The fundamentals of revenue forecasting are based on several internal and external factors, including factors such as market conditions, seasonality, competition, global events, and many more. With so many factors to take into account, forecasting processes are rarely an exact science. However, the baseline data provided by an effective revenue forecasting process can still be highly valuable when it comes to making predictions about your company’s future revenue.

Data and metrics are the fuel that keeps companies running at maximum efficiency. By perfecting the process of forecasting future revenue, you can provide decision-makers within your company with another insightful source of valuable data.

Revenue forecasting vs revenue projection

Revenue forecasting and revenue projection are two related yet ultimately different processes. With revenue forecasting, companies attempt to predict future revenue by analyzing historical data and the company’s past performance. In this way, revenue forecasting provides companies with a realistic estimate of their future revenue growth.

Revenue projection, on the other hand, is the process of projecting revenue based on a variety of assumptions or scenarios. For example, a company might use revenue projection to predict the impact of a new marketing campaign or a change in product pricing. Revenue projection, therefore, allows companies to wargame various scenarios to see their impact on revenue. And while revenue projection isn’t our focus in this article, it is often a vital process for SaaS companies as well.

Accurate revenue forecasts can provide a litany of benefits for any SaaS company.

The first key benefit that revenue forecasting provides is improved budgeting and the ability to create a more reliable financial plan. When you are able to keep a finger on the pulse of your company’s profitability and accurately project its revenue growth, you are able to set budgets, manage cash flow, and make other financial decisions with much more confidence.

Revenue forecasting also helps SaaS companies anticipate their future staffing needs. By enabling you to predict your company’s growth, revenue forecasting makes it easier to predict how many employees your company is likely to need in order to sustain its momentum.

Revenue forecasting is likewise vital anytime a company is considering investment opportunities. Outside investors, for instance, will insist on reviewing a company’s revenue projections before opening up their checkbooks. Similarly, it would be prudent for a CFO to do the same before allowing their organization to take on any new investments of its own.

In a wide variety of ways, revenue forecasting empowers informed decision-making in a SaaS company. By using a combination of revenue forecasting and revenue projections, you can ensure that your SaaS company is making the decisions that will best position it for future success.

When it comes to financial modeling, there’s more than one way to go about achieving results. Depending on factors such as the company’s industry and business model, there are a variety of models that can be used to project revenue and sales growth.

To help you choose which approach is best suited for your company, let’s take a detailed look at 9 of the most commonly-used revenue forecasting models.

Top-down forecasting

Top-down forecasting uses factors such as your company’s historical data and market conditions to predict future growth rates. With top-down forecasting, companies start by estimating the total size of their market. They then calculate their potential share of that market to estimate future revenue.

Top-down forecasting requires components such as market size analysis, industry growth rates, and market share calculations. This revenue forecasting model is best suited for large enterprises with a significant market presence, well-defined market data, and a broad customer base.

Bottom-up forecasting

Bottom-up forecasting entails a granular approach to revenue projections. With bottom-up forecasting, a company will analyze the historical performance of individual products or services in order to forecast revenue on a product-by-product and service-by-service basis. Usually, this means working closely with your sales teams and performing a detailed analysis of your sales pipeline in order to gather the necessary data.

Once you have calculated revenue projections for each individual project or service that your company offers, these sums can then be added together to calculate a total revenue projection for your company.

Bottom-up forecasting entails processes such as unit sales projections, average price per unit calculations, and product-level forecasts. It is best suited for companies with diverse product or service offerings, startups launching new products, and businesses with accurate sales data.

Backlog forecasting

Backlog forecasting focuses on orders or commitments from existing customers that have yet to be fulfilled. This revenue forecasting model is only useful for companies with long sales cycles or companies with a backlog of orders. However, if you know how many customers have placed orders or commitments with your company over a specific time period, estimating your company’s future revenue over that same period is a relatively straightforward process.

Backlog forecasting requires companies to analyze open orders, contract values, and anticipated delivery schedules. It is a revenue forecasting model that is well-suited for manufacturing or service companies with a backlog of open orders as well as any other company with a long sales cycle.

Pipeline forecasting

Pipeline forecasting is a revenue forecasting method that allows companies to estimate future revenue based on a detailed analysis of their sales pipelines. By tracking active opportunities and estimating the revenue potential of deals at various stages of the sales process, companies can predict how many sales they are likely to close over a period of time and thus predict their revenue over that period.

Forecasting accuracy is one key benefit of pipeline forecasting; when you are able to develop an in-depth understanding of your company’s sales pipeline, making accurate revenue projections becomes much easier.

Of course, performing this type of detailed analysis isn’t always a quick and simple process itself, and you will need to work closely with your sales team to gather data on sales opportunities, sales cycle stages, and the probability of closing deals.

Pipeline forecasting is best suited for sales-driven organizations, businesses with complex sales cycles, and those aiming to optimize sales strategies.

Historical performance forecasting

As its name suggests, historical performance forecasting is a model that uses a company’s historical data and past performance data to estimate future revenue. By analyzing data points and metrics such as past growth rates, recurring revenue, and customer retention rates, companies can accurately predict what their future revenue will be.

Along with analyzing past sales data, historical performance forecasting also requires companies to consider internal and external factors that may influence future revenue. This includes factors such as seasonality, market conditions, and competition.

Historical performance forecasting entails considerations such as historical revenue data, growth rate analysis, and seasonal adjustments. This revenue forecasting model is best suited for companies with consistent revenue growth patterns and industries with predictable market cycles.

Moving average forecasting

Historical data is a great place to turn to if your company is looking to estimate revenue growth. However, these data points often aren’t granular or focused enough to provide the kind of accurate projections that companies need.

Moving average forecasting is a revenue model that seeks to smooth out fluctuations in historical data by breaking time periods up into smaller chunks or time series and calculating the average revenue for each one. For example, a company might choose to analyze its historical sales data on a month-by-month basis, looking closely at each one before moving on to the next month in order to gain a more granular understanding of its past performance. This allows for companies to better account for seasonal changes and the ebbs and flows of their sales cycles.

Moving average forecasting is best suited for businesses with volatile revenue patterns and irregular fluctuations in demand.

Straight-line forecasting

Straight-line forecasting assumes a linear growth or decline in revenue over a set time series. It provides a simplified approach to projecting revenue by assuming constant rates of change.

Straight-line forecasting involves analyzing data points from historical data to determine the company’s revenue growth trends. However, rather than also accounting for factors such as changes to market conditions and seasonality (like historical performance forecasting requires), straight-line forecasting assumes that past trends will continue at a linear rate.

This method is not as accurate at predicting future revenue as many other revenue forecasting models, but it is beneficial for companies that want to perform a quick and simple forecast. Straight-line forecasting is also best suited for companies with steady, predictable growth patterns and industries that are not rapidly evolving.

Simple linear regression forecasting

Simple linear regression forecasting is more of an analysis technique than it is a revenue forecasting model. However, it can still be used to unearth valuable insights regarding your company’s revenue trends.

Simple linear regression forecasting involves looking at your historical data to analyze the relationship between two data points. Sales and profitability, for example, are two variables that companies often compare using simple linear regression forecasting.

This analysis is used to shed light on key revenue trends and provide the data your company needs to enable more accurate predictions from its other revenue forecasting models. Simple linear regression forecasting is a great method for any company that wants to better understand the relationship between various variables that impact revenue and situations where historical patterns may not be linear.

Multiple linear regression forecasting

Multiple linear regression forecasting takes the simple linear regression model a step further by incorporating multiple independent variables. It considers the impact of several factors on revenue, allowing for a more comprehensive analysis.

Like its two-variable counterpart, multiple linear regression forecasting is able to provide companies with the insights they need to create better revenue forecasting models.

Revenue forecasting and the revenue predictions it allows you to make can be highly beneficial for SaaS companies. That said, there are several pitfalls you will need to avoid if you want to get the most value out of your revenue forecasting efforts and prevent costly mistakes.

Here are some of the top revenue forecasting mistakes to avoid as you go about creating revenue forecasting models for your company:

• Underestimating external factors: It can be easy to fall in love with your company’s historical data and assume that past revenue trends will continue at the same rate. While historical data is certainly the backbone of most revenue projection models, it’s essential not to underestimate the impact of external factors such as market conditions, seasonality, and global events.
• Lack of quality data: Quality data is an absolute necessity for accurate revenue forecasting. Make sure that your company prioritizes collecting quality data on both its past performance and its current sales pipeline.
• Confusing revenue with cash flow: Many companies emphasize cash flow and ignore revenue forecasting. However, revenue forecasting can provide insights that analyzing cash flow alone won’t and is something every SaaS company should invest in.
• Neglecting scenario analysis: Future revenue can be impacted by a wide range of potential scenarios, which is why revenue projecting is a key part of revenue forecasting. By analyzing the impact of numerous potential scenarios, you can account for variability and ensure that nothing catches your company by surprise.
• Lack of continuous monitoring: Revenue forecasting models should be continually updated based on new data and changing market conditions. If you fail to continuously monitor and optimize your revenue forecasting models, the data they produce will become increasingly less reliable.

By avoiding these five common revenue forecasting mistakes, you can ensure your company is able to produce accurate and reliable revenue predictions.

Revenue forecasting is a vital process for SaaS companies, but it’s also a process with a lot of complexities and time-consuming requirements. If your company is still using Excel spreadsheets to perform market share calculations and predict future sales, revenue forecasting and projection is likely to feel like a nightmare for whoever is assigned the responsibility.

One of the best ways to streamline and simplify your company’s revenue forecasting process is to use the right tools. With revenue forecasting software such as Maxio, you’ll be able to view your company’s sales and revenue data in real-time, organize and access this data via customizable, user-friendly dashboards, and automatically perform a wide range of forecasts, projections, and calculations.

With Maxio, revenue forecasting is easier and more reliable than ever. See for yourself how we can help you forecast future revenue with ease and confidence with a Maxio software demo today!