The Essence of SaaS Revenue Forecasting
The most basic way to make a SaaS revenue forecast is to multiply an average monthly revenue by the number of months. From this, you will get a pretty rough prediction…but it won’t bring much value either to you or to your investors.
A smarter forecast calculation formula should rest on multiple variables including historical data, seasonality trends, and many other essential factors. This will let you increase the accuracy of your SaaS sales forecasting to get a real picture of what you can expect in the future. So, the rule of thumb is that a good forecast is a data-driven forecast. In this article, we’ll talk more about how to make efficient forecasts, what data you should include, what models you can use, and other best practices to facilitate SaaS forecasting.
What is SaaS forecasting?
There are different metrics associated with a SaaS business that you can forecast, for example, the number of users, conversion rate, expenses, etc.
However, as a rule, SaaS forecasting means the prediction of SaaS revenue, i.e. how much you will earn in the future.
Is there any difference between SaaS sales forecast and SaaS revenue forecast?
No, there isn’t. You can judge for yourself from their definitions:
- SaaS revenue forecasting is the process of estimating the total revenue that a product will generate from sales during a particular period.
- SaaS sales forecasting is the process of calculating the revenue from the expected product sales.
So, these terms are interchangeable, and we’ll use both of them in this article.
What is a SaaS recurring revenue forecast?
Recurring revenue is a common metric in the SaaS industry. It’s the total revenue the business generates either monthly, known as MRR (monthly recurring revenue), or yearly, known as ARR (annual recurring revenue).
So, a recurring revenue forecast SaaS is mostly a prediction of the MRR/ARR progress or growth over a certain period. We’ll talk more about the value of these metrics below.
Why is SaaS revenue forecast that important?
Proper SaaS revenue forecasting can solve many tasks and achieve different goals. One of the main goals is to grow your SaaS product. However, how is this possible? you may be asking, let’s dive in and see.
The first question that a SaaS revenue forecast answers is how much cash you expect to generate from product sales over time. However, your expectations and the actual forecast results can differ. For example, you assumed that the revenue in a year would be $100K, but based on the sales forecasting SaaS, you would reach a maximum of $50K.
Then, the second question comes out: what can you do to level up the SaaS revenue forecast? This is more about defining your company strategy from what you have now and where you want to go. Since forecasting SaaS is basically a calculation of multiple variables, to get better results, these variables need to be better as well. A simple example is that:
- To increase sales, you need to increase sign-ups.
- To increase sign-ups, you need to increase traffic/number of leads.
- To increase traffic, you need to increase the number of sales and marketing channels or their productivity.
- …
As a result, SaaS sales forecasting turns into a decision-making incentive that drives your SaaS business in the direction of growth. The search for an answer to ‘how much will we earn’ guides you to the answer to ‘what you should do to earn more’.
At the same time, it’s quite possible that your SaaS sales forecast results and your expectations are on the same line. For example, you assumed to earn $100K in a year, and the forecast confirmed your expectations. Even in this case, will you be interested to know how you can increase your sales? I bet you will, and so will most SaaS businesses.
At first glance, the value of forecasting SaaS revenue for business growth is not that obvious. As a result, many businesses do not set high priorities for financial projections and prefer to focus on more ‘valuable’ things for making their product better. To avoid this trap, you should keep in mind that SaaS revenue forecasting is not a gut-feeling-driven prediction. It’s a sophisticated analysis of historical data and multiple affecting factors that is meant to provide you with a close-to-real picture of your sales progress. At least, the forecast has to be so if done in a proper way.
How to forecast SaaS revenue
Now the main course – how can you do SaaS revenue forecasting and what is required for this? Let’s take a look at a checklist that you’ll have to go through.
SaaS forecast checklist
- Define a forecast model. A SaaS forecasting model or technique is a method you rely on to process the data and build a forecast based on it. There are different traditional forecast models, such as moving average or linear regression. For SaaS sales forecasting, there are often used historical, lead-driven, opportunity stage, and other models. However, it’s more efficient to create your custom model using any of the traditional ones as a basis.
- Collect and centralize the sales historical data. You most likely use different apps and tools for your sales activities. It’s good to have all the data accumulated by those sources in one repository. And it’s even better…no, it’s mandatory to have this centralized data updated automatically. So, you’ll need to set up a few data flows to a single database. This will let you have access to the historical data and build not only sales forecasts but actionable SaaS sales dashboards.
- Analyze the sales pipeline. Speaking of sales dashboards – these will be very helpful in analyzing your sales pipeline including how many deals are won/lost over a period of time, sales performance by channels or regions, etc. Here is an example of what a sales funnel overview dashboard may look like:
In terms of SaaS revenue forecasting, you’ll need this information to optimize your projections by tracking some seasonal trends or other behaviors. Taking these factors into account will let you increase the accuracy of your sales forecast.
- Consider the side factors influencing the sale progress. Different internal and external factors can influence your forecasting. Internal ones include the shortage of resources, billing strategy change, and others. External factors are those associated with the market, economic trends, and other things that are mostly unpredictable. Considering these factors means that your forecast should be flexible to these blind spots and adaptive to different scenarios.
We can’t elaborate on each of these steps here since this would make the article boring…even more than it is now.😄 What we can do is demonstrate a simple revenue forecast example using one of the traditional methods.
Plain SaaS forecast model example
Take a look at the sales funnel SaaS dashboard below. It already contains historical information about the number of leads and how many of them were converted to paid ones. The monthly progress including the MRR is also here.
You can make a simple SaaS revenue forecast based on a linear regression model using the built-in Google Sheets functionality. Let’s go to the tab with the historical raw data, and apply the FORECAST Google Sheets function as follows:
The more historical data you can embrace, the more accurate the linear regression forecast will be. But let’s be honest – the sales SaaS forecast is unlikely to be accurate if it’s simple.
To make an accurate and advanced forecast, you need more than a simple linear regression model.
How to create an advanced SaaS revenue forecast model
Smart SaaS revenue forecasting is not an easy task since it requires data analytical expertise such as knowledge of SQL or machine learning. For example, BigQuery allows you to build complex forecasts with their machine learning models.
The best you can do is to let highly skilled data analysts build and manage a forecast model that will meet your custom requirements. You can find such data experts at the data analytics consultancy service by Coupler.io.
They are proficient in versatile data management-related activities such as building dashboards, making forecasts, automating data flows, and so on. By the way, all the dashboards you’ve seen above and this one below, have been created by our data analytics experts.
If you feel that you’d like to delegate all sorts of forecasting SaaS to experienced data experts, just click the button to hire us.
The role of historical data in SaaS forecasting
We’ve already mentioned that the deeper the historical data you have, the more accurate the forecast will be. So, historical data plays the biggest role in your revenue projection. But what lies behind it?
Historical data means past experience. To make your sales forecasts accurate and reliable, the forecast calculation model should integrate this experience to derive the average trend. And if you use machine learning for making predictions, the presence of past experiences in your forecast model is a must. ML algorithms are able to learn from historical data and the more information you can input, the better their ‘education’ will be.
The gap between the revenue forecast and the actual revenue will be less with an algorithm that rests on a thousand records rather than hundreds of them. So, the historical depth and quality of the data are essential to increase the quality of your SaaS sales forecasts.
The optimum history of your experience is 6-12 months. A larger period could be harmful since it can ‘overteach’ your machine learning model. It’s also important to exclude past periods from the model if you’ve implemented significant changes such as modifying the billing model or anything of this kind. You need to only feed your algorithms with relevant data. This will let the forecast model cover the maximum number of influencing factors, both internal and external.
Types of data crucial for SaaS revenue forecasting
We already mentioned that there are two types of factors that can affect SaaS revenue forecasting: internal and external. The influence of these factors can be expressed via the data or variables that you can later use in your forecasting model. So, let’s explore what data you may need to get.
Internal factors influencing SaaS sales forecasting
Internal factors are mostly associated with the resources and processes within your SaaS business. So, getting this type of data won’t be an issue for you. Here are the most common internal data to use in SaaS forecasts:
| Pricing | Marketing or funnel information | Development | Human resources |
|---|---|---|---|
| – Subscription plans – Discounts – Affiliate programs – Special offers – Etc. | – Marketing channels – Regions – Social networks – Etc. | – New features – Releases – UI changes – Etc. | – New hires – Planned vacations – Expected relocations – Etc. |
External factors influencing SaaS forecast
External factors are the variables that are beyond the borders of your SaaS product. They are specific to the niche or environment of your SaaS business and hence can affect sales. This data should be taken into consideration when building your SaaS forecast model as potential risks although it can have a positive impact on sales.
| Seasonality | Competitors’ activity | Regulatory | Customer behavior |
|---|---|---|---|
| – Vacations and holidays – Sales ups and downs at a specific time of year – Etc. | – Special offers – Billing update – Rebranding – New competitors – Etc. | – Tax policy changes – Regulatory context variations – Etc. | – New trends – Change of buying habits – Etc. |
Both internal and external factors are the way to make your SaaS revenue forecast more accurate and flexible to potential risks. However, certain data may be deemed as ‘noise’ without any substantial effect on the forecast. So, pay particular attention to the factors that may have a strong impact on your sales progress.
The best SaaS forecast is a data-driven forecast
Let’s wrap up our article now with a shallow yet undisputable statement:
Make your forecasts powered by data, not intuition.
This does not mean that you should abandon your gut feeling completely. In some cases, it can be an additional input to the decision you make. However, the foundation of all the projections about revenue, growth, and other SaaS progress should only be driven by past experiences rendered in historical data.
