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How to Calculate Revenue Growth Rate (year over year) in Google BigQuery

Making the most of your Google BigQuery data

What is Revenue Growth Rate (year over year)?

Revenue growth rate is the percentage increase in revenue from the previous year. For example, if your company's revenue was $100,000 in the previous year, and $120,000 in the current year, your revenue growth rate would be 20% (120,000/100,000).

This metric is a great way to see how your company is performing over time. If your revenue growth rate is consistently increasing, it's a good sign that your company is doing well. If your revenue growth rate is decreasing, it's a sign that your company is struggling.

If you're a startup, you should be aiming for a revenue growth rate of at least 20% per year. If you're a more established company, you should be aiming for a revenue growth rate of at least 10% per year.

How do you calculate Revenue Growth Rate (year over year) in Google BigQuery?

It can be difficult to calculate Revenue Growth Rate (year over year) 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 Revenue Growth Rate (year over year).

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 Revenue Growth Rate (year over year). 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 Revenue Growth Rate (year over year).

A gif showing how you can build visuals in Causal

Start building models with your 

Google BigQuery

 data

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