What is Return on Sales?
Return on Sales (ROS) is a ratio that measures the profit generated by a company's sales. ROS is calculated by dividing the net profit generated by the company by the total sales of the company.
For example, if a company has $100,000 in sales and a net profit of $10,000, the ROS is 10%.
ROS is a very important metric to track because it is a direct reflection of how well your company is doing at generating profit from its sales.
If your ROS is low, that means your company is not doing a good job at converting sales into profit. If your ROS is high, that means your company is doing a good job at converting sales into profit.
How do you calculate Return on Sales in Google BigQuery?
It can be difficult to calculate Return on Sales 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 Return on Sales.
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 Return on Sales. 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 Return on Sales.
.svg)
