The SaaS industry has exploded into the marketplace in the past decade. For customers, SaaS solutions provide speedier and potentially more secure access to centrally-hosted software that’s available as a subscription-based service offering. And for businesses, executives are readily embracing the sale of SaaS due to its widespread demand and significant revenue potential. Yet despite this growing industry’s traction in the software marketplace, SaaS revenue models remain somewhat of a mystery, particularly through the lens of financial planning and analysis. Even for companies who have already integrated SaaS products/services, finance leaders often lack a clear approach to measuring and forecasting SaaS-generated revenue. In this blog, we’ll define the top best practices and protocols to put in place to for a strong B2B SaaS revenue forecast.
A Good SaaS Revenue Forecast Understands Monthly Recurring Revenue
One of the primary ways SaaS companies track top-line growth is through monthly recurring revenue, or MRR. Your MRR serves as the foundation upon which your SaaS revenue forecast is based. Your company’s MRR, which is contractually agreed upon upfront with customers, can be used to quantify your predictive revenue stream, therefore making it a key metric for both investors and CFOs. So when it comes to forecasting MRR, you’ll want to adhere to the following principles.
First and foremost: MRR is NOT the same as revenue, and shouldn’t be viewed as such in your forecast. (We’ll delve into that more a little later in this blog.) Second, MRR typically does not — and should not — take into account GAAP revenue recognition factors, including the following:
- Implementation fees
- Transactional fees
- Customer usage
- When customers pay their bills
- Other incremental fees that vary on a monthly basis
To demonstrate, let’s take the most basic example. A customer signs a 12-month contract for $1,200 and pays a $500 implementation fee. In this case, the MRR on this contract would simply be $100 ($1,200/12).
Build Your MRR Forecast with 3 Separate Inputs
Now, with MRR as your foundation, how can you forecast your monthly revenue using historical data and quantitative analysis? Start by breaking out your MRR as three separate inputs, each of which has rules and nuances:
1. New Sales
SaaS sales to new customers are a key component of revenue that can be leveraged to predict future sales. Forecast your SaaS revenue on new sales based on a combination of the following factors:
- Current influencers on historical trends. Start with your historical trends on new sales, then analyze all external factors that would potentially influence this number. Let’s say your company historically sells $1M in MRR. Have you increased your sales team? Added new SaaS product? Decreased your channels or marketing budget? Quantify all factors that could offset the historical pattern.
- Your pipeline is a good early indicator of how many leads your company is bringing in that could potentially convert into sales. If your pipeline (and conversion rates) are increasing or decreasing, so should your revenue forecast. Be sure to consider the time it takes for conversion to take place too, as your pipeline typically signifies short- vs. long-term revenue potential.
- Sales force. With your sales force as a critical driver of new SaaS sales, it’s critical to factor in anything that will impact it. For example, what are your sales quotas? Is there attrition among your sales team? New hires? Add in assumptions to your analysis, too: for new hires, how much lead time is needed for training, until he/she can sell at full capacity? What percentage of your team typically achieves its sales quota? Can you quantify (by resource) the extent to which your sales quotas are being met based on historicals? The more in-depth you analyze your sales force, the more accurate your forecast.
- Market share influencers. What is your company’s penetration rate based on the total addressable market (TAM)? Your TAM analysis should take into account the market size for the product that is still attainable after factoring in company market share, competitors and other reasons the market may change, such as emerging technologies, government regulations and so on.
2. Add-On Sales
Add-on sales are simply incremental sales from your existing customers who are under current agreements. Review your historical cohort data to forecast future MRR for your existing customers. This step is extremely important in the SaaS world, as customers rarely stay constant over time. Add-on growth can take on three separate forms:
- Added users – For example, a company who uses your SaaS product grows in size and therefore requires more users/licenses.
- Added products – Your “basic” SaaS customers want to upgrade their product with new features and capabilities that are available to them.
- Price increase – MRR attributed to annual price increases that are built in Master Service Agreement of that SaaS product.
Here’s a quick example: let’s say, in year two of their contract, your customers historically grow at 15 percent; in year 3, by 5 percent, etc. Assuming a large enough sample size, you would want to forecast your growth going forward using these data points. Then take it a step further: break out that analysis by additional factors such as industry, customer size, geography, and anything else that may impact your growth pattern. This type of detailed analysis gives you an idea of your average customer behavior in relation to customer lifecycle and MRR – and provides meaningful insights to guide your forecast.
3. Customer Churn
Customer churn refers to any decrease in monthly MRR that’s attributed to two factors: customer cancellation (i.e., a customer doesn’t renew) and decrease in MRR (i.e., a customer renegotiates a price, scales back on users/licenses, cancels a product that’s part of a package, etc.). Let’s take the example of a customer paying $100/month. In the event of a cancellation, MRR drops from $100 to $0; in the event of a decrease MRR would drop from $100 to $50.
When it comes to predicting churn, the most straightforward method is to look at historical churn rates. As with your sales, identify any factors that could offset those trends to impact MRR. Are there new government regulations that could come into play? Economic factors? Or do certain businesses have “at-risk” customers due to being in a risky industry or geographic area? The more details, the better.
Know the Nuances: Why MRR Is Not Synonymous With Revenue
As mentioned previously, MRR is not the same thing as your recognized revenue – and treating them synonymously is a common stumbling point. MRR can be characterized as a function of revenue, with intricacies existing between the two. Here are the most notable factors that drive those differences:
- Days in the month – Most SAAS companies recognize and calculate revenue based on calendar days. Assuming MRR is constant, March (31 days) will have more revenue than February (28 days) or June (30 days). MRR will not fluctuate since it is based on monthly distribution; whereas revenue WILL fluctuate as it is based on a daily calculation. This difference in distribution calculations means that, when comparing MRR and revenue, it’s not an “apples-to-apples” comparison.
- Time in the month that a new contract is signed – Let’s suppose your company signs a new SaaS deal for $100K in the middle of the month. From an MRR perspective, your company increased its Total MRR for that respective month by $100K. But from a revenue recognition standpoint, you would only recognize about $50K, or half of that revenue, for that respective month.
Take the Next Step in Tackling Your SaaS Revenue Forecast
As the SaaS B2B market continues to grow, the potential for predicted revenue will grow, too. And while these are just a few of the best practices to follow — and the pitfalls to avoid — there are several additional factors to consider as you attempt to define and forecast SaaS revenue.
If you’d like to know more about forecasting, then consider downloading our financial forecasting process guide. It offers an overview of financial forecasting and some best practices that can help your business improve accuracy:
If you have any questions, or would like to connect with an 8020 Consultant about SaaS, we’re happy to start a discussion or take the next step with you.
About the Author
Ido has over 15 years of experience in financial planning and analysis, M&A and project management. He has managed budgeting, forecasting, long-range planning, industry analytics, metric analysis, complex modeling, consolidations, standard reporting, due diligence support, ad hoc analysis and senior management decision support. Ido has held key finance and strategic roles in the Banking, Entertainment, Online and SAAS industries for companies such as Move, THQ, Intuit, Blackline and Velocify. He holds a Bachelor of Science degree in Business Administration. In addition, he received his Master of Business Administration with an emphasis in Finance from Cal State Northridge.
Categorized in: Financial Planning & Analysis