Average Revenue per Customer (ARPC) is the average amount of revenue you receive from each of your customers.
ARPC can be calculated by dividing the total revenue you received from your customers during a certain time period by the number of customers you had during that time period.
For example, if you had 100 customers during a quarter and you received $1,000,000 in revenue from them, your ARPC would be $10,000.
ARPC is a very important metric for any company, because it helps you determine whether or not you're charging enough for your product or service. If your ARPC is too low, you're probably not charging enough. If your ARPC is too high, you're probably charging too much.
If your ARPC is too low, you should consider lowering your prices. If your ARPC is too high, you should consider raising your prices.
It can be difficult to calculate Average Revenue per Customer directly inside of VISMA e-conomic; that's where Causal comes in.
Causal is a modelling tool which lets you build models on top of your VISMA e-conomic data. You simply connect Causal to your VISMA e-conomic account, and then you can build formulae in Causal to calculate your Average 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 Average 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 Average Revenue per Customer.