Annual Recurring Revenue per Customer (ARR) is the amount of revenue that a customer brings in over the course of a year.
ARR is calculated by taking the total revenue generated from a customer over the course of a year and dividing it by the number of months in a year.
ARR is a great metric to use to determine the value of your customers. It's also a great way to determine how much you should be spending on customer acquisition.
For example, if you have a customer that generates $1,000 in revenue over the course of a year, you should be spending $333.33 on customer acquisition.
For a more detailed explanation of ARR, check out this blog post .
It can be difficult to calculate Annual Recurring Revenue per Customer 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 Annual Recurring Revenue per Customer.
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 Annual Recurring Revenue per Customer. 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 Annual Recurring Revenue per Customer.