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.
It can be difficult to calculate Average Revenue per Paying Customer directly inside of Snowflake; that's where Causal comes in.
Causal is a modelling tool which lets you build models on top of your Snowflake data. You simply connect Causal to your Snowflake account, and then you can build formulae in Causal to calculate your Average Revenue per Paying 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 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.
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 Paying Customer.