Everything You Need to Know About SaaS Sales Forecasting Methods

Everything You Need to Know About SaaS Sales Forecasting Methods

Forecasting is an essential practice for growing businesses, especially considering the lightning-fast speed at which technology and the internet continue to change. Skillful forecasting can keep your company nimble, helping you maneuver around sudden market changes, seasonal shifts, competitor challenges, and other obstacles.

As your company grows, forecasting will also guide major decisions when seeking investment and deciding how to use capital. Forecasting is particularly essential—and challenging—for B2B SaaS companies. That’s why we’re sharing this guide to SaaS revenue forecasting.

What is Forecasting?

Sales forecasting or revenue forecasting is a business process used to estimate future revenue by analyzing historical data, current trends, and other factors. This analysis can include everything from reviewing prior years’ sales figures and seasonal comparisons to performing current market research and social listening.

Collecting and analyzing the data required for accurate forecasting is labor-intensive and can become quite expensive. It’s important to approach it with a clear knowledge of what forecasting can and cannot do, as well as which type of forecasting is best suited to your SaaS business.

Reasons to Create a Revenue Forecasting Model

SaaS revenue modeling is an important part of your company’s strategy, helping you manage business growth and appeal to investors. Past conventional wisdom held that companies didn’t need a revenue model in place until they were ready to pursue Series A funding. However, today’s faster-paced market has shifted that expectation, and it’s much more common to find companies building revenue models at earlier stages of business development.

Common Forecasting Challenges

Unfortunately, forecasting is fallible. It’s not uncommon for businesses of all types and sizes to miss the mark on revenue forecasts, sometimes with a wide margin of error. No matter how much data you have or how skilled your analysts are, there’s no way to predict the future. For example, no one could have predicted the COVID-19 pandemic, which threw plenty of business forecasts into chaos with massive labor shortages and supply chain disruptions, as well as surprise booms in demand for products as varied as yeast, lumber, craft supplies, and hand sanitizer. 

Pandemics aside, revenue forecasting has always presented challenges. Some of the main “wild cards” include: 

  • Demand fluctuations
  • Competitor behavior
  • Regulatory changes
  • Strikes and labor shortages
  • Seasonal factors
  • Climate-related disruptions
  • Global economic trends

There are also some elements of SaaS businesses that make revenue forecasting particularly difficult, namely the recurring nature of SaaS revenue and the variance in customer billing structures. Building a SaaS revenue model that accurately reflects your business requires several data inputs: 

  • Annual Recurring Revenue (ARR)
  • GAAP Revenue
  • Cost of sales
  • Operating expenses
  • Working capital
  • Deferred revenue
  • Fixed assets
  • Debt/equity financing 

Despite the challenges, there are concrete ways to improve the accuracy of revenue forecasts. The most crucial is deciding which revenue forecasting method to use. 

Three Types of Forecasting

When determining which forecasting model is right for your business, there are three basic models to choose from.

Qualitative Forecasting

Qualitative forecasting uses qualitative data, or data that can’t easily be parsed into numbers. For example, an expert who analyzes a trend or demographic shift and then uses their experience, knowledge, and intuition to make a prediction is performing qualitative forecasting. This forecasting method makes use of industry knowledge, trends, market research, and surveys.

Time Series Analysis

Time series analysis is a quantitative forecasting method. It uses year-over-year data (or month-over-month, week-over-week, etc.) to compare one period of time to another. This type of forecasting is most effective when you have data from several years to compare, and when trends for that product are stable. This apples-to-apples style of analysis allows you to identify trends over time, like seasonal fluctuation or multi-year cycles that repeat regularly. (Think of the influx of demand retailers experience in the holiday season.)

In order for time series analysis to be effective, you need a large amount of historical data, which is why it’s a method favored by large, well-established businesses. When enough data is available and there are no monkey wrenches thrown into the mix, this method can provide fairly accurate forecasts. 

Causal Forecasting

Causal forecasting uses a variety of methods and data sets to predict a causal relationship. This type of forecasting is based on an understanding that the variable being predicted has a cause-effect relationship to other identified variables. The causal approach to revenue forecasting attempts a holistic approach, taking many possible factors into account. 

Which Revenue Forecasting Method is Best for Your SaaS Business?

Each type of revenue forecasting has its own strengths and limitations, so rather than choosing just one method to trust, you should think about how they fit together and show you different pieces of the puzzle. This is an area in which a RevOps team can be extremely helpful.


Ultimately, the revenue forecasting method you choose for your SaaS business will rely on accurate and robust data. This may seem like a given, but the key to building your revenue forecasting model is to have a strong foundation of accurate data. Unfortunately, many SaaS companies are still relying on spreadsheets to record sales data, resulting in errors and miscalculations that can severely hinder the accuracy of a revenue model. 

Download our Complete Guide to SaaS Revenue Modeling to learn how to improve your revenue forecasting.