Calculate CAC in Google BigQuery

Making the most of your
Google BigQuery
data

What is CAC?

Customer acquisition cost (CAC) is the cost associated with acquiring a new customer.

CAC is calculated by taking the total amount of money spent on acquiring new customers and dividing it by the number of new customers acquired.

CAC is a very important metric because it helps you determine how much money you're spending to acquire new customers. If you're spending too much money on acquiring new customers, you may need to adjust your strategy.

CAC is also important because it helps you determine how much money you're making from your customers. If you're making more money from your customers than you're spending to acquire them, you're on the right track.

How do you calculate CAC in Google BigQuery?

It can be difficult to calculate 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 CAC.

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 CAC. 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.

Causal lets you add visuals in a single click, letting you plot out graphs and distributions for metrics like CAC.

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Start building your own CAC models, and connect them to your Google BigQuery data.

How to Calculate CAC in Google BigQuery

Making the most of your Google BigQuery data

What is CAC?

Customer acquisition cost (CAC) is the cost associated with acquiring a new customer.

CAC is calculated by taking the total amount of money spent on acquiring new customers and dividing it by the number of new customers acquired.

CAC is a very important metric because it helps you determine how much money you're spending to acquire new customers. If you're spending too much money on acquiring new customers, you may need to adjust your strategy.

CAC is also important because it helps you determine how much money you're making from your customers. If you're making more money from your customers than you're spending to acquire them, you're on the right track.

How do you calculate CAC in Google BigQuery?

It can be difficult to calculate 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 CAC.

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 CAC. 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.

Causal lets you add visuals in a single click, letting you plot out graphs and distributions for metrics like CAC.

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