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How to Calculate Average Revenue per Customer in SAP S/4Hana

Making the most of your SAP S/4Hana data

What is Average Revenue per Customer?

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.

How do you calculate Average Revenue per Customer in SAP S/4Hana?

It can be difficult to calculate Average Revenue per Customer directly inside of SAP S/4Hana; that's where Causal comes in.

Causal is a modelling tool which lets you build models on top of your SAP S/4Hana data. You simply connect Causal to your SAP S/4Hana account, and then you can build formulae in Causal to calculate your Average Revenue per 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 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 Customer.

A gif showing how you can build visuals in Causal

Start building models with your 

SAP S/4Hana

 data

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