Clarifying the difference between commonly misunderstood financial terms

What pages did we generate?

We used OpenAI's GPT3 to generate a list of 141 different pairs of financial metrics that are commonly confused (think ARPU vs ARRPU).
Our pages focused on the differences between these metrics, to help users improve financial literacy. The metrics we chose were largely designed to be the sorts of metrics that business owners/financial specialists would be interested in, given that these are good use cases for Causal.
This was an example of a 1-dimensional structured campaign. It might look like a 2-dimensional campaign, given that each page title has 2 metrics, but we treated the pair of metrics as one whole variable. This was because we didn't want to generate pages for all possible combinations of financial metrics (nobody is confused about the difference between profit and debt, for example!), rather only metrics that were commonly mistaken for one another.
You can see an example page here.

Why did we do this?

Finance can be confusing; it's various metrics even more so. We wanted to target users who were confused about the difference between various commonly confused financial metrics for a variety of reasons:
  • These users could make good Causal customers. If they're confused about finance, then they're unlikely to already have a financial modelling product like Causal, and may be in-market for one.
  • The sheer number of combinations of different financial metrics made this a great candidate for AI generation. No brand is going to pay a human to write all of these articles, after all.


Pages Generated
Total Sessions
Sessions per Month
Position #1 Keywords


  • A powerful addition to this format would've been to link to all other pages that contain one of the constituent metrics. For example, the page EBITDA vs Operating Income could've linked through to EBITDA vs Net Income. This would've helped with internal linking, and reducing bounce rate.
  • This format is quite easily applicable to any technical domain, where searchers are potentially confused about the difference between specific terms or concepts.
Last modified 11mo ago