ARR forecasting made easy with the corkscrew financial model

For SaaS businesses, what is that one metric—the prime indicator—of their ability to grow? The answer lies not in vanity metrics, but in a single, powerful number: Annual Recurring Revenue (ARR).

ARR describes the consistent inflow of revenue that you generate from subscriptions over a period of one year. Calculating ARR can help you make decisions about pricing, marketing strategies, customer acquisition, and resource allocation. And, it’s the first thing potential investors and lenders look at when evaluating your company’s prospects.

Given this, knowing how to project your ARR is critical. The corkscrew model is a great tool for this. Like any model, it begins with assumptions ideally based on solid data. It considers the revenue inflow and outflow during a given period to provide you with a projection of how much ARR you will end up with at the end of that period. By rolling forward the ending ARR from one period to next, the corkscrew method helps you see how ARR changes over time and facilitates accurate forecasting. 

The significance of the corkscrew financial model in SaaS

The corkscrew financial model helps with financial planning for your SaaS business as it goes beyond tracking beginning and ending ARR for the period to show you everything that impacts your ARR during the period. 

The ARR formula below illustrates well the many factors that can impact your ARR in any given period. They include new subscriptions, upgrades, renewals, cancellations, and downgrades.  

ARR forecasting and corkscrew financial modeling

The corkscrew financial model is pretty simple in concept and is very versatile as models go. For example, in addition to ARR forecasting, you can use it to create a debt schedule corkscrew to track your business debt, to calculate depreciation, and capital expenditures.

In this article, we’re applying it to do ARR forecasting, specifically. Here is the basic ARR corkscrew formula:

The basic steps in a corkscrew model are the same. Here’s how you use it for ARR forecasting: 

• Step 1. Choose a starting point. Begin with the ARR for a specific month as your starting point. This will become the beginning balance in your model. 
• Step 2. Add any new revenue: This can come from new ARR from new customers, Expansion ARR from existing customers that upgrade their subscriptions, renewal ARR, and resurrected ARR from customers who canceled their subscriptions but came back.  
• Step 3. Subtract any revenue lost: Losses can occur as a result of churn and subscription downgrades. These are referred to as churned ARR and contraction ARR, respectively. 
• Step 4. Calculate the ending balance for the period: Calculate the month's ending balance by adding together all the new revenue gains and subtracting all the revenue losses. 
• Step 5. Roll the ending balance forward: The ending balance for the month becomes the next month’s starting balance. 
• Step 6. Repeat Steps 1-5: You can continue your corkscrew for as far out as you want to forecast your ARR. The key thing to remember here is that as you move forward to the next period, you roll forward the ending ARR from the previous period, which then becomes the beginning ARR for the new period.  

Defining SaaS metrics for ARR corkscrew calculation

To accurately calculate and analyze ARR using the corkscrew model, it's essential to understand all the different types of ARR that factor into the calculation. 

Having a clear understanding of SaaS metrics related to ARR is also important for strategic planning as well as investor management and communication. 

So, let’s start with a bit of a refresher: 

• New ARR — new subscriptions
• Expansion ARR — subscription upgrades
• Renewal ARR — existing subscription renewals
• Churned ARR — canceled subscriptions
• Contraction ARR — downgraded subscriptions

Note that you may also have some resurrected ARR (sometimes called reactivation ARR) from customers who canceled their subscriptions but signed back up during the time period. For the purposes of our calculation, we’ll treat that as new ARR. 

Applying these metrics to the ARR corkscrew calculation, and rolling the ending balance up to become the beginning balance for the next period gives you the classic corkscrew model:

A note about your assumptions…

We all know that the key to accurate forecasting is feeding your model with good data. Despite the relative simplicity of the ARR corkscrew model, it’s important to bring to the exercise both historical data and a good understanding of what’s happening in your business. 

This is why it’s always a good idea to consult with different teams in your business when forecasting ARR. For example, your sales team can probably give you a pretty good sense of how much new ARR you can expect over the next quarter at least from the results of its sales forecasting and pipeline coverage ratio calculations.

Similarly, your customer success team can probably give you insights on expected churn and contraction. These insights can help you calibrate the assumptions you use in the model for more reliable results.  

Benefits of using the corkscrew method to calculate ARR

The corkscrew financial model ensures you have a clear and continuous view of your company’s revenue growth by helping you:

• Track performance trends over time
• Identify and address issues like high churn rates
• Forecast future revenue more accurately
• Make data-driven decisions 

Challenges of calculating ARR using the corkscrew model and how to beat them

While the ARR corkscrew model is a powerful framework for ARR forecasting, its real-world applications present a few challenges. 

Data quality issues

Accurate and consistent data from multiple sources are needed for the corkscrew method.  Bad data can inflate your ARR numbers, which can lead to unwarranted assumptions.

The problem here is that trusting the numbers you’re feeding into your model can be difficult if most or all of them were derived from spreadsheet calculations with data manually downloaded from multiple systems.

This is where using a financial modeling tool – one that can integrate with multiple sources to automatically aggregate your data for you – can be invaluable.  

Predicting churn

Accurately predicting customer churn is an important factor in the ARR forecast. This can be challenging because it depends on things like giving services for free or customers behaving in a manner that are difficult if not impossible to predict. 

This is where both consulting with your customer success team and having historical data can help. 

Teasing out the difference between new and expansion ARR

The difference between new ARR requires careful tracking and attribution, which can get quite complicated when customers overlap or there are a lot of products in the mix. 

This said, since both types of ARR are additive for the purposes of forecasting, distinguishing between them isn’t absolutely necessary. It just gives you a finer-grained understanding of your ARR. 

Finding the right balance between accuracy and model complexity can be a challenge, but one that is well worth thinking through before you begin building your financial model.  

Metric definitions

ARR metrics are pretty hard to define consistently and accurately across different  companies. They can vary considerably based on the differences in business models and customer behaviors. 

For example, if a customer paused their subscription for a month, one company might  consider that churned, while another one might not. This is why it’s critical to make sure everyone in your organization is on the same page with how metrics are defined. 

There needs to be a clear, universally accepted definition across all teams of what new ARR, expansion ARR, churned ARR and contraction ARR mean to ensure accurate forecasting.

Leverage the simplicity of the corkscrew with the power of technology for next-level financial modeling

The versatility of the corkscrew financial model makes it an important and effective tool for SaaS companies to track and forecast their ARR.

For any financial model to be accurate, data consistency is key. Using financial planning and analysis (FP&A) software also helps automate the application of rule-based logic and enables you to track and recognize the different types of ARR. 

Consider using a strategic FP&A software like Drivetrain that not only consolidates all your data from multiple sources into a single platform, but also allows you to slice and dice it any way you want to arrive at more actionable insights in real time. 

Learn more about Drivetrain and how it can help you understand the different ways in which you earn recurring revenue each year.