Modeling the Top Line: ARR Snowball for SaaS Revenue Forecasting Explained (Plus Template)
There are so many different ways to plan your top line. Part 1 of our series covered the bottom-up sales capacity model approach. And here, we cover the top-down approach—an ARR snowball model for SaaS revenue forecasting. Learn how to build the model yourself (or start with a plug-and-play template).
Finance Manager in Customer Success
The flexibility of your top-line plan can be both a blessing and a curse. It works in your favor that you can build a top-line plan that perfectly reflects the ins and outs of your SaaS business. But all that flexibility can make it difficult to know which approach is best for your SaaS revenue forecasting processes.
In the first part of this top-line modeling series, we walked through the sales capacity planning approach to SaaS revenue forecasting. That bottom-up approach to forecasting revenue growth is great for companies that have sales-led go-to-market motions and strong historical data around sales performance metrics. But it’s not the only way to plan your top-line.
Another approach to revenue forecasting is the top-down ARR snowball model. There are a few key steps to make sure you build one effectively.
But if you already know you want to take the ARR snowball approach to revenue planning, you don’t need to build the model from scratch. Download our plug-and-play ARR snowball template to give your top-line plan a head start.
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What Is an ARR Snowball Model for SaaS Revenue Forecasting?
The ARR snowball is a type of SaaS financial model that uses trends in historical annual recurring revenue (ARR) and monthly recurring revenue (MRR) data to project future growth. Done well, an ARR snowball will give you a strong understanding of what’s going on in your business so you can effectively manage growth.
There are four high-level outputs a subscription business get from this kind of financial model:
- New ARR: Increases in revenue from new customers signed during the given period.
- Upgrade ARR: Any revenue from expansion of existing customers during the given period. Depending on your revenue model, this might include an upgrade in subscription tier or increases in user count.
- Downgrade ARR: Losses in revenue due to the contraction of existing customer accounts, either from downgraded subscription tiers or lower usage rates.
- Churned ARR: Decreases in revenue from customers canceling or not renewing their contracts with your business.
It’s important to break ARR out into these different buckets so you can model them separately. Getting a high-level view of what’s driving your growth rate—whether it’s a land-and-expand strategy, a focus on new customer acquisition, or a balance of both—sets the foundation for your top-line plan.
The advantage of an ARR snowball model is that you don’t need such granular historical sales data compared to a quota capacity model. That can be an advantage for an earlier-stage SaaS startup that is kicking off a new funding round and needs to show clear revenue projections to investors.
But this approach to SaaS revenue forecasting can also be valuable for later-stage SaaS companies. These companies will have enough historical data to maximize the accuracy of ARR snowball outputs. And this top-down approach is a good way to cross-check more granular bottom-up forecasts (especially when you’re maintaining a rolling forecast).
The four main outputs of an ARR snowball model may seem simple enough. But building a model from scratch to accurately project how revenue continuously builds on itself and maintaining it on a monthly basis isn’t necessarily easy.
4 Steps to Build an ARR Snowball Model
Our ARR snowball model is meant to give all SaaS finance leaders a strong foundation for revenue projections. It also bridges the gap to financial reporting of GAAP revenue with an additional billings and collections build.
But we know that some companies have highly complex ARR logic. If you feel like you need to start from scratch, there are 4 basic steps to build your own ARR snowball model.
1. Aggregate Data from CRM, ERP, and Billing Systems
One reason calculating and forecasting MRR and ARR can be so difficult is that the necessary financial data lives in so many different systems of record. Without clear visibility into the data, it’s difficult to quickly calculate key metrics that go into the ARR snowball model.
The best finance teams have an architecture in place to get clean data flowing from these systems of record to their models. But for most, manual data pulls are still necessary to finish calculating SaaS financial metrics. As you build your model, here’s the data you’ll need from each system:
- CRM: Bookings and pipeline data is critical to an effective ARR snowball model. You need to pull information regarding closed-won deals, contract values, and contract changes such as upgrades, downgrades, and churn.
- ERP: Previous year revenue is the baseline for an ARR snowball. You need to pull relevant revenue, billings, and collections data from your ERP to create the backbone for financial assumptions in the model.
- Billing Systems: There are times when you’ll need to add adjustments to your model to maintain accuracy and transparency. Refunds, credits, and other billing data that may impact those assumptions is crucial.
If you can pull clean, complete datasets from each of these systems, you’ll have the foundation needed for a strong ARR snowball model. Then, you can start building out the two main components of the model—bookings data and the financial ratios for upgrades, downgrades, and churn.
2. Set Key Assumption Drivers to Model New Bookings
The first component of your revenue forecasting model revolves around SaaS bookings. The key output is new ARR per period. But to get that number, you’ll need to build a few key assumptions around your SaaS sales numbers. The two most important are:
- New Customers: As you project out your sales pipeline metrics and new bookings per month, you need to set assumptions for how many new customers you expect to sign over the course of a given period. Instead of the bottom-up approach of calculating revenue potential of sales reps, you can set this assumption based on growth expectations.
- Average Annual Contract Value: You can make an assumption about average contract value by dividing new bookings by the number of new customers you signed for the prior period. Be sure to take a weighted average for this assumption. If you only base it on prior period bookings and new customers, you may have spikes in data that cause problems in the rest of your model.
When you multiply new customers by your weighted ACV, you get a bookings output that slots into the new ARR cell of your revenue projections.
3. Use Financial Ratios to Model Expansion, Downgrade, and Churn
The second component of the ARR snowball model is the combination of upgrade, downgrade, and churned revenue. These are the assumptions that complete your picture of ARR:
- Percent Upgrade: The percent of prior period ARR that upgrades in a given time period.
- Percent Downgrade: The percent of a prior period’s ARR that downgrades in a given period.
- Percent Churn: The percent of prior period ARR that churns in a given period.
You can use historical data regarding expansion, contraction, and churn rate to drive these assumptions off of financial ratios. This may be complicated for early-stage companies that don’t have enough historical data to come up with accurate percentages. However, if you still want to use this approach to forecasting revenue, you can work with customer success to get a more accurate view of these percentages.
But when you have that visibility, you can factor out a percentage of revenue expansion from existing accounts. This aspect of the SaaS business model is what creates the snowball effect that gives this financial model its name.
4. Account for Deferred Revenue
It’s important to account for deferred revenue in your ARR snowball model so you can align with GAAP accrual accounting requirements.
You don’t necessarily need to dedicate rows in your model specifically to deferred revenue if you’re bringing billings and collections data in. There are two key assumptions that help account for deferred revenue:
- Pre-Pay Schedule Upfront: Create a toggle in the model for different payment terms, including upfront, 50% upfront/50% Month 6, quarterly, or monthly.
- Collections from New Bookings: Create a toggle between collection terms, including Net 30, Net 60, and Net 90.
Adding these assumptions to your model provides a more accurate view of the business and creates a stronger foundation for financial reporting.
Optional Step: Add Bookings Cohorts to the Model
One of the problems with ARR snowball analysis is that it can show strong revenue growth that masks potential problems in the business. Especially for large companies where existing customers are accounting for the vast majority of revenue, ARR snowball analysis can hide churn issues. The answer to this problem is adding cohort analysis to the model.
When your model offers insight into customer cohorts, you can see how renewal rates are trending and get deeper insight into the health of your business. You can also perform a cohort analysis in Excel, but this is time consuming and can be an error-prone process.
Get a Head Start on Top-Line Planning with an ARR Snowball Template
The most strategic finance functions and SaaS founders go beyond the high-level buckets of new, upgrade/downgrade, and churn ARR to build a more impactful top-line plan either for the business as a whole or an individual product line. But adding cohorts, billings, and collections data to a basic ARR forecasting model can be complicated and time-consuming.
Download our plug-and-play template if you want to get a head start on your financial planning. Use the Loom videos that are included to understand all the different inputs and get a head start so that you spend less time structuring a model and more time on strategic decision-making.
If the ARR snowball isn’t right for your top-line plan, be sure to check out our guide to sales capacity modeling for a more bottom-up approach to financial forecasting.
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