Lifetime Value (LTV) is a metric that shows how much money a customer will bring in over the course of their entire relationship with your company.
Lifetime Value is calculated by multiplying the average revenue per user (ARPU) by the customer's average lifetime (in months or years).
For example, if your ARPU is $10 and your average customer lifetime is 6 months, your LTV would be $60.
The lifetime value of a customer is a critical metric that can be used to determine the profitability of your company's products and services.
Lifetime value can be used to determine the profitability of a single customer, or the profitability of a certain product or service.
It can be difficult to calculate Lifetime Value 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.
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. 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.