# How to Calculate Average Revenue per Paying User in Braintree

## What is Average Revenue per Paying User?

Average Revenue per Paying User (ARPPU) is a very important metric for SaaS companies. It is calculated by dividing the total revenue generated by the total number of paying customers.

For example, if you have 100 paying customers and you generated \$100,000 in revenue, your ARPPU is \$1.00.

ARPPU is a very useful metric because it helps you determine how much money you are making per customer. It also helps you determine how much you can spend on acquiring new customers.

For example, if you have an ARPPU of \$1.00 and you spend \$0.50 on acquiring a new customer, you are spending \$0.50 to make \$1.00. This is a very good deal.

On the other hand, if you have an ARPPU of \$1.00 and you spend \$1.00 on acquiring a new customer, you are spending \$1.00 to make \$1.00. This is a very bad deal.

ARPPU is a very important metric because it helps you determine how much money you are making per customer. It also helps you determine how much you can spend on acquiring new customers.

## How do you calculate Average Revenue per Paying User in Braintree?

It can be difficult to calculate Average Revenue per Paying User directly inside of Braintree; that's where Causal comes in.

Causal is a modelling tool which lets you build models on top of your Braintree data. You simply connect Causal to your Braintree account, and then you can build formulae in Causal to calculate your Average Revenue per Paying User.

## What is Causal?

Causal lets you build models effortlessly and share them with interactive, visual dashboards that everyone will understand.

In Causal, you build your models out of variables, which you can then link together in simple plain-English formulae to calculate metrics like Average Revenue per Paying User. This makes your models easy to understand and quick to build, so you can spend minutes, not days, on your models.

When you're done, you can share the link to your model with stakeholders. They'll be able to view your model's outputs in a visual dashboard, rather than a jumble of tabs and complex formulae. The dashboards are interactive, letting viewers tweak your assumptions to see how they affect the model's outputs.

Causal lets you add visuals in a single click, letting you plot out graphs and distributions for metrics like Average Revenue per Paying User.

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