Blog
Guides, case studies, and research from the CausoAI team.
Discounted deals close more often. That is not evidence discounting works — it may be evidence your reps discount the deals that were already going to close. Here is how to tell the difference.
A churn model that is 90% accurate can still be useless for retention. Prediction and intervention are different problems, and the features that predict churn are usually the wrong things to act on.
CausoAI drafts the diagram, but you have to check it — and that check is about your business, not about statistics. Here is what to look for, in order.
You need less than you think, but it has to be shaped a particular way. A practical checklist for getting your data ready — and the three mistakes that quietly ruin an analysis.
Correlation tells you what happened together. Causation tells you why — and what you can do to change it. Here is why the distinction decides whether your budget is well spent.
A score of 82 means something specific. Understanding the five checks behind it tells you how far to trust a result — and exactly what to fix when it is low.
Last-touch, first-touch, linear — every attribution model is wrong in the same way. They measure correlation, not causation. Here's why this matters and what to do about it.
Holding the wrong thing constant. Pointing an arrow backwards. Leaving out the factor behind both sides. These are the errors that quietly produce confident wrong answers.
Upload a marketing file, check the diagram CausoAI drafts, see how far the result can be trusted, and run a what-if — start to finish, with the numbers you would actually see.
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