Lifetime Value per Active Customer (LTV/AC) is a calculation that shows how much money you can expect to make from a single customer over the course of their lifetime.
LTV/AC is calculated by taking the total revenue that a customer will generate for your company over the course of their lifetime, and dividing it by the number of months that they have been a customer.
For example, if a customer has been with your company for two years and has generated $10,000 in revenue, their LTV/AC is $5,000.
LTV/AC is a very important metric because it gives you a good idea of how much a customer is worth to your company. It also gives you a good idea of how much you can expect to spend on acquiring new customers, and how much you can expect to make from your existing customers.
It can be difficult to calculate Lifetime Value per Active 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 Lifetime Value per Active 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 Lifetime Value per Active 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 Lifetime Value per Active Customer.