Logo for the integration

How to Calculate Average Revenue per Paying Customer in Google BigQuery

Making the most of your Google BigQuery data

What is Average Revenue per Paying Customer?

Average Revenue per Paying Customer is the average amount of revenue that each of your paying customers generates.

This metric is calculated by dividing the total revenue generated by your customers in a set time period by the number of paying customers you had at the beginning of that time period.

For example, if you have 100 customers and they generate $1,000,000 in revenue in a quarter, your ARPC is $10,000.

This is a very important metric to track because it gives you a good idea of how much revenue you can expect to generate from your existing customers in the future.

If you have a high ARPC, you're in good shape. If your ARPC is low, you need to find ways to increase it.

How do you calculate Average Revenue per Paying Customer in Google BigQuery?

It can be difficult to calculate Average Revenue per Paying 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 Average Revenue per Paying Customer.

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 Customer. This makes your models easy to understand and quick to build, so you can spend minutes, not days, on your models.

A comparison of formulae in Excel and Causal

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.

A gif showing how users can adjust model inputs, and how they're reflected in dashboards

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

A gif showing how you can build visuals in Causal

Start building models with your 

Google BigQuery

 data

Thanks! We'll be in touch soon.
Oops! Something went wrong while submitting the form.