Average Revenue per Account (ARPA) is a metric that helps you determine how much money your company is making from each of its customers.
ARPA is calculated by dividing the total revenue generated by your company in a certain time period by the number of customers you have at the beginning of that time period.
For example, if you have 100 customers at the beginning of a quarter and you generate $1,000,000 in revenue, your ARPA is $10,000.
For most companies, ARPA is a critical metric because it helps them determine how much money they can expect to make from each customer.
It can be difficult to calculate Average Revenue per Account 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 Account.
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 Account. 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 Account.