Average Revenue per Active User (ARPA) is the average revenue that you generate from each of your active users.
ARPA is calculated by dividing the total revenue generated by your company by the number of active users that you have.
For example, if you have 100 active users and your total revenue is $10,000, your ARPA is $100.
ARPA is a great metric to use when comparing your company's performance to that of your competitors. If you're seeing a higher ARPA than your competitors, it means that you're doing something right.
If you're seeing a lower ARPA than your competitors, it means that you're doing something wrong.
It can be difficult to calculate Average Revenue per Active User directly inside of Google BigQuery; that's where Causal comes in.
Causal is a modelling tool which lets you build models on top of your Google BigQuery data. You simply connect Causal to your Google BigQuery account, and then you can build formulae in Causal to calculate your Average Revenue per Active User.
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 Active 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 Active User.