It’s hard to overstate the importance of accurate revenue forecasting for a growing company. Forecasts give your team the knowledge and power to predict your growth rate, adapt to market changes, decide where to invest capital, and secure funding for the future. Done right, these predictions can also help your entire organization consistently generate more revenue.
However, achieving forecast accuracy is notoriously difficult. It’ll take a combination of the right revenue forecasting methods, sales processes, and data tools to understand where your business is headed. In this article, we’ll compare and contrast different forecasting techniques for B2B SaaS companies and give you remedies to a common forecasting mistake.
Difficulties of Accurate Forecasting
In a landmark survey of over 400 B2B companies, only 15% of revenue leaders said they were “very satisfied” with their forecast process. Regardless of their revenue forecasting method, companies large and small frequently miss their revenue forecasts by a wide margin.
The fact of the matter is that predictability is tough to deliver.
Sales leaders have to account for a number of highly variable factors, from sudden economic shifts like the COVID-19 pandemic to missing data in their sales forecasting tools. Even if your team has solid sales figures and clean, organized data sets, business age is one of the biggest determinants of forecast accuracy. This is because revenue forecasting methods fall into two main categories:
- Quantitative revenue forecasting methods: Quantitative forecasting is numbers-centric, relying on trends and historical data gathered throughout your company’s history. The goal is to reduce the amount of guesswork and subjectivity within your forecast and instead build on concrete sales numbers. This type of forecasting typically requires years of data to be accurate, so quantitative models are mostly used by experienced businesses.
- Qualitative revenue forecasting methods: Qualitative forecasting is largely based on estimations, business expertise, industry knowledge, and feedback like surveys. It’s a go-to technique for early businesses that don’t have enough sales data points to use quantitative techniques. It may also be the better model if your SaaS business experiences seasonality or frequent sales fluctuations.
For most businesses in early growth stages (generally under three years old) forecasting is an educated guess. Businesses in later growth stages (over three years old) will have historical data and signals such as conversion rates and value retention figures to determine whether they’re moving toward growth or attrition. In both cases, it’s crucial to choose the appropriate sales forecast method and support it with as many hard numbers and observations as possible.
Overview of Revenue Forecasting Methods for B2B and SaaS Companies
Forecasting techniques range from super simple to highly sophisticated. We’ve seen sales teams run regular forecasts for their full operation, however, the most common approach is to create forecasts by business segment. For example, you can predict future revenue for each customer tier, geographic location, rep tier, or separate your forecasts by your growth business and client success segments. 1.
Quantitative Revenue Forecasting Methods
There are three main types of quantitative models you can use:
- Straight-Line Method
- Moving Averages
- Linear Regression (Simple or Multiple)
Here’s how they work:
The straight-line forecasting method is one of the easiest ways to accurately calculate future sales. In the simplest version of the formula, you take your past sales revenue and multiply it by your past sales growth rate to find your future revenue. The minimal math and simple metrics makes it an accessible choice for business owners and sales professionals who are scaling.
A moving average forecast uses your average revenue figures to predict future sales. Over time, the model replaces old data with new data to calculate a fresh average, create a stable trendline, and maintain forecast accuracy. It’s a favorite for short-term predictions, but it doesn’t account for seasonality. In addition, if your historical data has inaccuracies, a moving average forecast quickly reflects it.
Linear regressions are widely used because of their detailed approach to revenue forecasting. They establish a cause and effect relationship between two variables — such as a sales action and revenue during the same period — to estimate growth.
A simple linear regression compares one set of independent and dependent variables (like an ad campaign and sales), while a multiple linear regression tracks several different sets. It’s more complex and requires a higher level of statistical knowledge, but yields a more complete picture of sales and optimizes your budgeting for different sales activities.
Top-Down Sales Forecasting
With top-down forecasting, you use high-level market data (the “top”) to set a revenue target for a given period and build goals for your sales reps. You’ll determine a realistic target based on your company’s total addressable market (TAM) and how much of the TAM you plan to capture during that period. Then, multiply your predicted market share by your TAM to create your top-down forecast.
Although you can leverage secondary market data from sources like Forrester and Gartner, hiring a third-party research firm delivers more tailored data and can increase your forecast accuracy.
Bottom-Up Sales Forecasting
The bottom-up forecasting process is the reverse of the top-down method’s high-level approach. You predict growth based on your sales reps’ opportunities in a given period (the “bottom” of your revenue operation) and the likelihood they’ll close them. For the most success, your sales managers need to partner with reps and perform thorough deal inspections to validate the resulting projections.
Tooling: The Real Obstacle to Accurate Forecasts
After working with B2B SaaS across a variety of industries, we’ve found that the real trouble with accurate sales forecasting isn’t the method, it’s how it’s tooled. From sales reps updating deals by hand in Excel spreadsheets, to disorganized data in pipeline or CRM software, to a lack of deal inspection or automation, there’s a widespread issue of tooling and processes not supporting the forecasting that businesses rely on for decision-making.
This problem crops up most in a couple different scenarios. First, small sales teams and teams that don’t yet have the budget for a robust, dedicated sales platform, gravitate toward familiar, low-cost tools like spreadsheets. Manual input is time-consuming, but the greater risk is human error. A single error or omission can render your data inaccurate and compromise your forecast accuracy.
Second, businesses of all sizes have made the mistake of using a CRM or a combination of sales tools to measure their sales performance without the resources to inspect, validate, and reconcile the different data sources. While a CRM is essential to managing customer information, it wasn’t built to support the revenue process for modern B2B SaaS companies. And according to Membrain, organizations use an average of 4.9 sales tools, increasing the chance of duplicating data, losing information, and impeding forecast accuracy.
One major tip to improve your revenue forecast tooling? Consider hiring a revenue operations (RevOps) professional or team. Demand for RevOps professionals is expanding quickly among B2B companies. It’s in large part because RevOps is a company’s forecasting eye in the sky, managing data, optimizing tech stacks, and collaborating across departments to create high-quality forecasts and recommend sales enablement tools.
Learn more in our complete guide to revenue operations, and find tips to navigate this emerging field with our monthly RevOps Roundup.
The revenue forecasting methods we’ve covered in this article give you a line of sight into the actions your company needs to take now to grow later. Each model comes with pros and cons, but they could all have a time and place in your forecasting strategy.
To get the most accurate glimpse of future growth — and make it a reality — remember to support your forecasting with the right tools and data hygiene processes.