LTV:CAC is a ratio that compares the lifetime value of a customer to the cost of acquiring that customer. It's a great way to determine whether your company is getting a good return on its marketing investment.
LTV:CAC is calculated by dividing the lifetime value of a customer by the cost of acquiring that customer.
For example, if your company spends $500 to acquire a customer and that customer is worth $1,000 in revenue over the course of a year, your LTV:CAC would be 2.5 ($1,000 / $500).
If you're getting a good return on your marketing investment, your LTV:CAC should be greater than 1. If it's less than 1, you're losing money on each customer you acquire.
It can be difficult to calculate LTV:CAC 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 LTV:CAC.
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 LTV:CAC. 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 LTV:CAC.