Average Revenue per User (ARPU) is a metric that looks at the average revenue generated by each customer over a given time period.
ARPU is calculated by dividing the total revenue generated by the number of customers you have.
For example, if you have 100 customers and you generate $100,000 in revenue, your ARPU is $1,000.
ARPU is a very important metric for companies that are trying to grow their customer base. If you have a low ARPU, it means that you have to acquire more customers in order to generate the same amount of revenue.
For example, if you have 100 customers and you generate $100,000 in revenue, your ARPU is $1,000. If you have 1,000 customers and you generate $100,000 in revenue, your ARPU is $100.
It can be difficult to calculate Average Revenue per 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 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 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 User.