How To Forecast SaaS Revenue: A Basic Guide For Saas Businesses
The SaaS business model is one of the most attractive business models today. While most traditional businesses only receive a one-time payment for each transaction or sale they close, the subscription model allows companies to generate a recurring revenue stream.
Not only does this ensure a steady flow of income to your SaaS company, but it also opens up the opportunity to predict future revenue and growth.
This enables you to make long-term decisions with confidence. More specifically, knowing how to forecast SaaS revenue can help you in budgeting, cash flow planning, financial analysis, and more.
But how do you actually predict how much revenue you’re going to earn in the future?
In this article, we will discuss some key SaaS metrics to use and basic methods to help you forecast how much your SaaS business will bring in.
What Is SaaS Revenue Forecasting?
SaaS revenue forecasting, or SaaS sales forecasting, is the process of predicting how much revenue your SaaS business will generate in the future.
This helps you plan how to allocate resources (developers, customer service reps, etc.), how to allocate funds for marketing, and how to budget for other expenses.
It also helps inform decisions like how many new customers you need to acquire in order to reach a certain goal or how much extra capital you should raise in order to expand.
By taking into account key metrics and forecasting models like the ones we will discuss below, you can begin building a more accurate prediction of your SaaS company’s future performance.
Let’s explore these topics in more detail.
Key Metrics You Need In SaaS Revenue Forecasting
When forecasting the future revenue of your business, there are a few key SaaS metrics you need to take into account. Let’s talk about them in detail:
The word “churn” refers to the act of customers canceling their subscriptions from your SaaS product. And your churn rate is a key metric to consider when forecasting how much revenue you can expect in the future.
Now, there are two types of churn rate you need to consider: the customer churn rate and the revenue churn rate.
Customer Churn Rate: This metric measures how many customers cancel their subscriptions within a certain time period (usually a month or a year).
For example, if you have 100 customers in a given month and 10 of them cancel their subscriptions by the end of that month, then your customer churn rate is 10%.
Revenue Churn Rate: This metric measures how much revenue you lose due to customer cancellations.
For example, let’s say that your initial monthly recurring revenue (MRR) at the start of the month is $10,000. Then the total amount of money those 10 churned customers were paying each month was $500 and they all canceled within the same month.
That would give you a revenue churn rate of 5% (from the calculation $500 / $10,000 = 5%).
Your annual recurring revenue (ARR) and monthly recurring revenue (MRR) are two of the most important metrics to consider when forecasting how much you will make in the future.
After all, they measure—you guessed it—the amount of recurring revenue you earn at different time periods.
Your ARR is the total amount of money your SaaS company makes in a year, while your MRR is the total amount of money your customers pay you each month.
If you have multi-year plans or contracts, you should also keep track of how much money will come in from those contracts each month or each year.
Now, there are several things or actions that may add or deduct from your recurring revenue. Here are some that you need to track as well:
New MRR: How much money you are making from new customers each month. While some SaaS businesses track new subscription revenue from returning customers (those who have churned before but have returned) as a separate metric, it’s usually included in the “New MRR”.
Expansion MRR: This is how much extra recurring revenue you are making from existing customers due to upselling or increased usage of your SaaS product.
Contraction MRR: This is how much money you have lost due to downgrades or decreased usage of your product by existing customers.
Churn MRR: This is how much subscription revenue you are losing each month because of customer churn.
Set Up Fees & Service Revenue
Your recurring revenue only includes how much money you are making from your customers’ subscription plans.
But if you have setup fees, onboarding fees, or any other service-based revenue, then you should also include them when forecasting your SaaS company’s overall future income.
What’s more, you should also take note of the average percentage of customers who opt-in for these additional services and how much they are paying for them.
This will give you a better insight into how much extra money you can expect each month or year from these services.
Average Revenue Per User (ARPU)
Your Average Revenue per User (ARPU) is how much a single customer pays for your SaaS product over a month or a year.
This metric can be calculated by taking the total amount of revenue you make in a month and dividing it by the total number of customers you have.
For example, if your total revenue in a given year was $1 million and you had 1,000 customers, then your ARPU would be:
$1 million / 1,000 customers = $1,000 per customer
You may also track your ARPU for each customer segment you have.
It’s important to track this metric as it gives you an insight into how much money each customer is bringing in on average. It also helps you identify how well your pricing model is performing and if there are any opportunities to increase your ARPU through new features or upselling.
What’s more, ARPU is crucial to calculating another metric you need in SaaS sales forecasting.
That brings us to our next SaaS metric…
Customer Lifetime Value (CLV)
Your Customer Lifetime Value (CLV) is how much money you can expect to make from each customer throughout the entire duration of their subscription.
To calculate this, you need to take into account how long customers tend to stick around with your SaaS product. You can do this by finding your average customer lifespan.
As its name suggests, the average customer lifespan is the average length of time a customer stays with your SaaS product.
For a very simple example, let’s say you have three customers who have been with your SaaS product for 3 years, 5 years, and 7 years, respectively. That means that the average customer lifespan is 5 years (3 + 5 + 7 = 15 / 3 = 5).
But of course, in the real world, you will have so many more customers (hopefully). So if you want to track your average customer lifespan, you will need at least a spreadsheet to help you crunch those numbers.
Now, to calculate your CLV, multiply your average customer lifespan by your ARPU.
For example, if your ARPU is $1,000 and your average customer lifespan is 5 years, then your CLV would be as follows:
$1,000 per year x 5 years = $5,000
Average Sales Cycle Length
As you may guess, your average sales cycle length is the average time it takes for you to convert a lead into a paying customer. This will vary from one SaaS business to another, depending on your target audience and how complex your sales process is.
For example, if you’re mainly targeting large enterprises, then your sales cycle will likely be much longer than for a SaaS company that caters to small businesses.
This metric is important in SaaS revenue forecasting as it helps you predict how many new customers you can expect to gain during a certain time period.
By taking into account how many leads you are getting each month, how many of them will become customers, and how long it takes for them to make the purchase decision, you can more accurately forecast how much revenue your SaaS business will generate in the future.
Your conversion rate is how many leads you manage to convert into paying customers.
This will depend on how good your sales process is and how well you are targeting the right people with the right message.
You can calculate your conversion rate by taking the total number of leads that have bought your SaaS product in a certain period of time and dividing it by the total number of leads you’ve generated in that same period.
For example, if you had 100 leads in a month and 20 of them became paying customers, your conversion rate would be:
20 / 100 = 0.2 or 20%.
It is also useful to segment your conversion rate based on how far along the sales process each lead is. This will give you an idea of how effective your sales process is from start to finish.
For example, you can track various conversion rates, such as:
- Visitor-to-lead conversion rate
- Lead-to-opportunity conversion rate
- Opportunity-to-close conversion rate
- Free trial conversion rate
Common SaaS Revenue Forecasting Models
Now that you’re familiar with the main SaaS metrics you need for revenue forecasting, it’s time to actually start predicting your future income.
There are a few different models available to help you forecast how much revenue you can expect in the upcoming months and years:
The Straight-Line Method
The most commonly used model is the straight-line method. This is because it’s fairly simple and straightforward compared to other methods. It uses historical data to predict how much money you will make in the future.
To do this, simply plot your previous MRR values on a graph and draw a straight line through these points that extend to future values. This will give you an idea of how much money you can expect to make in the future as long as nothing drastically changes with your product or customer base.
For example, let’s say that your MRR 12 months ago was $10,000 and your MRR 6 months ago was $20,000. Then you can assume that if nothing changes, your MRR now will be around $30,000.
While this forecasting model is easy to use and understand, it assumes that nothing changes over time—which almost never happens. Your existing customers may churn or upgrade their subscriptions, or your prices may change too.
So you need to be careful when using this forecasting method as it doesn’t take any of these variables into account.
Length Of Sales Cycle Forecasting
The length of sales cycle forecasting model is a more advanced method to predict your revenue. This model requires you to track—well—your length of sales cycle.
Based on the average time it takes from when a prospect first expresses interest to when they become a paying customer, you can predict how many sales you will have in the upcoming months and how much revenue those sales will generate.
For example, let’s say that your average sales cycle is 2 months. You can then assume that if things stay consistent, you’ll have twice as many customers in two months than you do now—and therefore double the amount of revenue.
However, like the first forecasting model we discussed, this one also assumes that nothing will change in your current customers. And as we mentioned above, churn, upselling, and contractions may still affect your recurring revenue.
What’s more, any changes in your marketing and sales processes can change how long it takes for a prospect to become a paying customer and therefore how much revenue you can expect in the future.
So if you’re using this forecasting model, make sure to check your sales cycle data regularly and adjust accordingly.
Opportunity Stage Forecasting
While the length of sales cycle forecasting model predicts the influx of new customers based on how long it takes for you to close them, the opportunity stage forecasting model predicts how many new customers you can expect in the near future.
The opportunity stage is the part of the SaaS sales funnel where prospects are actively engaged and may even already be negotiating a deal with your sales team.
By looking at how many leads you have in the opportunity stage and your historical opportunity-to-close conversion rates, you can accurately estimate how much revenue you’ll generate in the upcoming months.
However, like the other methods we’ve discussed, this one also assumes that your conversion rates and sales process will stay consistent. So make sure to track how long it takes on average for a lead to move through each stage of your funnel and adjust when necessary.
Multivariable Analysis Forecasting
One thing that’s common among all of the previous forecasting models is that they don’t take any external variables into account.
Multivariable analysis forecasting is a more sophisticated model that does just that.
This method uses a combination of historical data and external factors like customer churn, upgrades and downgrades, seasonality, etc., to make a prediction of how much money you can expect in the future.
Multivariable analysis forecasting is the most accurate out of all methods because it takes everything into account—including your customers’ behavior, past sales performance, current market trends, and more.
Of course, this also means that multivariable forecasting requires more effort than other models as you will need to track these variables regularly and update your prediction when necessary. That’s why you will most likely need at least an analytics tool to do this.
Final Thoughts: How To Forecast SaaS Revenue
Forecasting how much revenue your SaaS business will generate can be challenging and time-consuming, but it’s a necessary process if you want to stay on top of your finances.
What’s more, forecasting can help you optimize your sales and marketing strategies so that you’re focusing on the right goals.
By understanding how different forecasting methods work, you can make sure that you choose the right one for your situation. And ultimately, it can help you ensure that your prediction is as accurate as possible.
Still, don’t expect that your forecast will be 100% the same as your actual revenue. Forecasting is an approximation, and there will always be some amount of discrepancy between the two.
So, you would best allow some room for error when forecasting how much you can expect from your SaaS business.
Looking for more guides to help take your SaaS business to the next level? Check out our blog site here.