How to calculate and track key SaaS performance metrics for usage-based pricing models

In SaaS, metrics are critical for strategic decision-making, providing key insights that drive growth, retention, and profitability. However, for companies with a usage-based or hybrid pricing model, traditional (and very important) SaaS metrics – such as annual recurring revenue (ARR), customer lifetime value (CLV), and net revenue retention (NRR) – is much more complex. 

Whether you’re new to usage-based models or refining your current approach, understanding how to adapt these key metrics will help you better evaluate performance and steer your SaaS business toward more sustainable growth.

This article will make that easier, showing you (with examples) how to apply key SaaS metrics to a usage-based pricing model to measure and track your company’s performance.

How is usage-based revenue calculated? 

Within the highly complex world of SaaS pricing, while pricing schemes can vary significantly, most SaaS companies use one of three basic pricing models: 

• Subscription-based pricing, the traditional pricing model where customers pay a recurring fee on a monthly or annual basis to access products and services. 
• Usage-based pricing, where customers pay for a product or service based on how much they use it.
• Hybrid pricing, which combines two or more pricing models within a single plan, such as a subscription fee with one/more usage-based pricing components or a plan with two or more usage-based components. 

Hybrid pricing is the most complex of the three models. For simplicity, in this article, we’ll focus on usage-based pricing as the approaches described in this article apply to any pricing model that includes a consumption-based component.  

Given their inherent variability, usage-based pricing models do not map cleanly to the monthly recurring revenue model that most subscription pricing businesses in the SaaS industry use. Nonetheless, many consumption-driven businesses do report their usage-based revenue as annual recurring revenue (or some version of it). 

‍Annual recurring revenue (ARR) is a SaaS metric that tracks the recurring revenue gained from subscriptions, normalized for a single calendar year. However, with usage-based pricing, calculating ARR can become very complicated. 

Ideally, all recurring revenue needs to be considered when calculating ARR. Recurring revenue is revenue that is generated from subscriptions that are charged at regular intervals, which may be every week, month, or year. This type of revenue is steady, predictable, and can be scaled up over time. 

In contrast, the revenue generated from usage-based pricing models isn’t really recurring revenue. While many companies with usage-based pricing models treat this revenue as ARR, we draw a distinction between ARR and usage-based revenue because with usage-based pricing, customers have the ability to vary their consumption. Therefore, the usage-based revenue you’re generating may or may not recur. 

To gain a better understanding of this distinction, let’s first look at the formula for the more traditional, subscription-based ARR where customers pay a monthly flat fee:  

Now, let’s look at a formula for calculating revenue for a hybrid pricing model, which includes both recurring, subscription-based revenue (ARR) and variable, usage-based revenue. 

Let’s understand the terms:

• Beginning ARR: ARR at the start of a specific period, e.g., fiscal year, quarter, etc.
• Ending ARR: ARR at the end of the above-mentioned specific period (which becomes the beginning ARR for the following time period
• New ARR: New subscriptions
• Expansion ARR: Subscription upgrades
• Variable revenue: Additional revenue generated from the usage-based components of pricing, which includes any overages or shrinkage in usage.
• Contraction ARR: Downgraded subscriptions
• Churn ARR: Canceled subscriptions

Keep in mind that minimum commitment is the revenue that a SaaS company will receive from a customer (contractually guaranteed), even when their usage is below the agreed minimum threshold. This ensures a baseline of recurring revenue. Hence, in the above formula, New ARR, Expansion ARR, Contraction ARR, and Churn ARR pertain only to the minimum commitment revenue as that's the only predictable component (the only ARR).

Once you understand how to calculate your ending ARR for usage-based pricing each month, you can model it going forward with a basic corkscrew model, wherein the ending ARR for the current month will become the starting ARR for the following month. 

How usage-based pricing affects other SaaS metrics

Most SaaS metrics are built upon the assumption that a customer, or cohort of customers, produces a known (predictable) ARR. In usage-based pricing, there is higher variability in predicting revenue due to the fluctuations around customer usage. Therefore, it becomes all the more complicated to measure and track revenue, especially when in a hybrid model that can have a mix of fixed and usage-based components. 

If your SaaS business employs a usage-based model, in order to calculate reliable performance metrics, you’ll need to tackle that with your first-order metrics because the difficulties that variable pricing introduces will magnify as you incorporate your results into second- and third-order metrics.  

Some metrics will be unique to your usage-based pricing. However, you may need to add a couple of additional metrics to your mix to more accurately capture performance. 

Let’s take a look now at how you would calculate three key SaaS performance metrics – NRR, gross margin, and customer LTV – with a usage-based pricing model.  

Calculating and tracking NRR with usage-based pricing

Generally speaking, NRR is higher in any consumption-based SaaS business than it would be in a purely subscription-based business. This makes sense because expansion is built into it by design, and this expansion helps drive higher and more profitable growth.  

While usage-based pricing has become very attractive to SaaS businesses, it does complicate the process of calculating and tracking NRR. 

To give you a sense of the complexity, let’s say you wish to calculate NRR for the cohort of new customers that signed up in January. Calculating NRR for the January cohort is pretty straightforward with a subscription-based model. To get the starting ARR, you simply multiply the number of customers in the cohort by the subscription price. 

However, with usage-based pricing, it’s highly likely that within the first month, one or more of those customers from the January cohort will expand their usage. In this case:

• How would you account for the ARR associated with that increased usage? 
• Is it part of the initial ARR or should you consider it expansion ARR?

When you look at the NRR formula below, it’s easy to see mathematically how your decision can impact your results. If you consider that expansion as part of the ARR at the start of the year, your NRR will be lower than it would be if you treat it as Expansion ARR. 

The variability inherent in a usage-based pricing model also makes tracking and plotting NRR month over month even more complicated. To understand why, let’s consider another example. 

In Month one, you have $100,000. In Month 2, it’s $120,000 due to increased usage, a 20% expansion. But, in Month 3, the ARR from that cohort drops to $80,000. You expanded by 20% in the second month, decreased by $40,000 in the third month, which is 33%. 

So, how do you figure out your rolling three-month NRR? 

Use a look-back window

You can use a two-year look back window, comparing revenue from each customer over the last 12 months to the revenue from the previous 12 months—for a total of 24 months’ of data. 

This creates a two-year lag in measurement, which helps even out all the variations that arise as a result of seasonality and usage-based pricing to provide a somewhat more consistent number. 

Define a ramp-up period

Another approach to measuring NRR with usage-based pricing is to define a ramp-up period based on the customer’s usage in the first month. 

For example, if you know that your customers typically expand their usage in the first 45 days from $100 to $150, you can use the revenue from the first 45 days (as opposed to the first month) as your starting ARR. Then, after that 45-day ramp-up period, all the revenue above and beyond the initial revenue will be considered expansion ARR. With this approach, it’s useful for the purposes of clarity to describe your ramp-up in terms of the “number of days from initial use.” 

Note that it’s very important to be consistent in how you define your ramp-up and use it uniformly across all cohorts even if your data for later cohorts suggests that it has changed. While this may seem counterintuitive, you must apply your ramp-up time the same way going forward from the first cohort it’s based on because changing your ramp-up period from cohort to cohort will fundamentally change the calculation, making your NRR results unreliable (as a whole).  

If you do feel compelled to change your ramp-up period, you can do that as long as you go back and apply it to your past NRR calculations and inform all stakeholders about the change you made and why. This transparency will help you avoid any perception of metrics manipulation.    

Calculating and tracking gross margin with usage-based pricing

Usage-based pricing can also impact your gross margin as a function of your company’s cost of goods sold (COGS). To understand this, consider the gross margin equation:

COGS includes variable costs that increase in proportion to business growth. This means that as your revenue grows, your COGS grows right along with it. So, there’s already a lot of variability inherent in your gross margin. Variability is the price you pay to ensure frictionless growth (in a manner of speaking). As a result, you don’t have a consistent number for the gross margin. 

Let’s look at the variability in the context of all the new customers you gained in the previous month. Your revenue from that cohort of customers is $100,000 for the first month and on average, your customer usage is growing about 2% each month. If you assume that the revenue from all those customers will continue to grow at that same rate for 24 months, you can calculate your monthly recurring revenue (MRR). 

Here, you also have to factor in a gross retention rate. So, let’s assume that you will retain 90% of your customers from the cohort. Now, with your MRR and gross retention rate figured out—and based on your assumptions—you should be able to determine the COGS and then calculate your gross margin. 

The problem is that your assumptions, particularly those regarding usage, may not hold. Remember that with usage-based pricing, variability in usage (and thus revenue) is typically very high and harder to predict. Your MRR is continually expanding when you have a usage-based pricing model, and as a result, your gross margin will be even more inconsistent from month-to-month, even quarter-to-quarter.

Calculating and tracking customer LTV with usage-based pricing 

The problems associated with calculating a reliable gross margin when you have usage-based pricing also makes it more difficult to calculate customer lifetime value (LTV). This is because gross margin is part of the LTV equation: 

While your churn rate may be somewhat consistent, your gross margin and average MRR per customer must necessarily be based on assumptions that can be extremely difficult to accurately predict with the variability built into usage-based pricing. To the extent that any of the variables in your calculation are off, your results will be less reliable. 

Keep in mind that if you see that your customer usage patterns are somewhat consistently growing with revenue over time, you can use historical cohort data to derive a revenue trend that is perhaps more reliable. However, the variability in gross margin will still impact your results.    

Leveraging technology with usage-based pricing for better performance tracking

With all the benefits that usage-based pricing offers for SaaS companies, it’s small wonder that more and more companies today are optimizing their pricing structures with the addition of usage-based components.

But in addition to those benefits, for the growing number of businesses that are shifting to a usage-based (or hybrid using a combination of subscription and usage-based) pricing model, there’s a corresponding cost in terms of the complexity it introduces into tracking standard SaaS metrics.

Usage-based pricing makes key SaaS metrics like ARR, NRR, gross margin, and LTV trickier to calculate because it doesn’t play by the traditional rules of subscription-based pricing upon which most SaaS metrics were constructed. 

Navigating these challenges can be simplified, however, with a financial planning and analysis (FP&A) solution that can serve as a single source of truth for all of this information. Additionally, these tools automate the data validation process, empowering finance professionals the power to build whatever models they need for financial forecasting.

‍Drivetrain is a purpose-built, strategic FP&A software that seamlessly integrates with different systems and apps, and consolidates all the necessary data, in real time, on a central, user-friendly platform. The platform offers a host of features that can help you navigate the complexities of measuring and tracking the revenue obtained from using a consumption/usage-based or even a hybrid pricing model—to make sure your forecasts are more accurate. Here are a few relevant ones:

• Integrations: With over 200 integrations, Drivetrain can easily collate and access all the data and variables you need from different sources (e.g., QuickBooks, NetSuite, etc.) to perform a granular analysis of usage in a very automated manner. You can even slice and dice that data to analyze the trends around usage, churn, and expansion across all customers. 

• Formula creation: Drivetrain’s formula builder is straightforward and intuitive, enabling you to write formulas across multiple dimensions for a metric. It easily takes the values from the previous quarter (or your defined time period) and encapsulates the varied definitions for the same metric to prevent any errors, thereby helping users build more robust financial models. Drivetrain also isn’t limited to standardized formulas. To understand the different usage-based components of your revenue along with any ARR, you can easily create formulas that will adapt to any changes in the reference data (something that isn’t possible with spreadsheets).

• Multi-dimensional modeling: Drivetrain’s software helps SaaS companies build complex multi-dimensional models, by tailoring inputs, calculations, and outputs as per their business requirements, all in a fraction of the time it would usually take on spreadsheets. You can get started quickly with built-in, customizable model templates. 

• Scenarios analysis: On Drivetrain’s robust platform, you can easily build and test multiple scenarios and perform what-if analysis to understand the impact of different scenarios (e.g., seasonality, market trends, etc.), on your financial forecasts and make the necessary adjustments immediately. 

• Version control and access: You don’t need to make multiple copies of your forecasting model anymore to ensure that you use the latest version to resume your calculations. On Drivetrain, you can just save it as a version, and restore it easily whenever you need to refer to it. find and restore them very easily. The role-based access feature also helps different users across teams to collaborate on the same model, in real time. This means that the version you work on will always be the latest, updated version.

‍Explore how Drivetrain empowers you to measure and track revenue from usage-based pricing models with ease.