ARPA quantifies the average revenue each customer contributes to the business over a specific period, typically monthly. It’s a foundational metric for gauging customer value on a granular level and is often used as an input for estimating unit economics metrics like LTR, LTV, and Payback Period.
Because it’s a simple blended metric, ARPA can be used as a shorthand to assess a company’s market positioning relative to competitors. A higher ARPA would suggest a premium product and go-to-market motion targeting upmarket customers with a higher willingness to pay, while a lower ARPA might suggest a focus on appealing to a broader base of customers at the low end of the market.
Remember that ARPA is a snapshot in time that can mask underlying dynamics in your business because it’s measured on a small time scale. For example, ARPA can change over time due to pricing changes or an evolving competitive landscape. In these cases, it’s best to use a cohort analysis that captures over-time dynamics in customer spending rather than rely solely on a blended ARPA metric.
The formula to calculate ARPA is straightforward:
ARPA is sometimes used interchangeably with Average Revenue Per User (ARPU), which quantifies the average revenue contributed by an individual user versus a company account purchasing multiple licenses for its employees:
However, ARPA and ARPU can diverge significantly based on how your business monetizes its customer relationships. For example, a company with per-seat pricing might use ARPU rather than ARPA. Companies with multiple pricing models (e.g., Figma, which caters to individual licensees and company accounts) might track ARPA in addition to ARPU to tease apart dynamics across these different businesses. Finally, a company with freemium and paid tiers might want to break out Average Revenue Per Paying User (ARPPU) separately:
ARPA can also be broken out across the components of ARR to get a more granular understanding of revenue dynamics within a customer base:
This allows companies to better understand customer behaviors and make adjustments accordingly. For example, when making a pricing change, we may want to monitor New ARPA relative to Expansion ARPA to assess whether the change improves our ability to monetize incoming customer cohorts.
Higher is not always better when optimizing ARPA, as a business’s “ideal” ARPA varies based on its market positioning and target customer segments. Instead, it’s about balancing maximizing revenue per account and maintaining a healthy customer acquisition and retention rate.
One of the primary levers that influence ARPA is pricing. While increasing price points may grow ARPA, it could decrease ARPA if the new pricing limits adoption or causes existing customers to churn and contract. Additionally, changes to packaging (i.e., how you break up your product into distinct things customers can buy) can also impact ARPA. For example, in the early days of Intercom, as the product evolved and became more complex, customers were confused about what they needed to buy. This resulted in people buying more than they needed to start, artificially increasing initial ARPA, which eventually showed up in increased contraction and churn.
To strike the right balance, consider a comprehensive set of inputs. For example, customer feedback and competitive analyses can provide insights into whether your pricing and packaging align with market expectations and customers’ perceived value.
There’s also no substitute for pricing experimentation. Try different pricing models early and often, then observe their impact on customer acquisition and retention to ensure you’re maximizing your ability to monetize customers without hurting retention.