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How to Calculate Lifetime Value per Paying Customer in Snowflake

Making the most of your Snowflake data

What is Lifetime Value per Paying Customer?

Lifetime Value per Paying Customer (LTV/P) is the average revenue that a customer will generate for your company over the course of their entire relationship with you.

LTV/P is an extremely useful metric for determining how much money you can expect to make from each customer. It's also a great way to determine how much you can afford to spend on acquiring a new customer.

LTV/P is calculated by taking the total amount of revenue generated by a customer over the course of their lifetime and dividing it by the number of customers you have at the end of that time period.

For example, if you have 100 customers at the end of a quarter, and those customers have generated $1,000,000 in revenue, your LTV/P is $10,000.

How do you calculate Lifetime Value per Paying Customer in Snowflake?

It can be difficult to calculate Lifetime Value 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 Lifetime Value 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 Lifetime Value 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 Lifetime Value per Paying Customer.

A gif showing how you can build visuals in Causal

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