The cash conversion cycle is the amount of time it takes for a company to turn its revenue into cash.
The cash conversion cycle is calculated by dividing the average collection period by the average accounts receivable period.
The average collection period is the average time it takes for a company to collect on its accounts receivable. The average accounts receivable period is the average time it takes for a company to pay its accounts payable.
The cash conversion cycle is a great metric to use when evaluating a company's liquidity.
It can be difficult to calculate Cash Conversion Cycle 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 Cash Conversion Cycle.
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 Cash Conversion Cycle. 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 Cash Conversion Cycle.