Your platform’s revenue is strong, but your users are slowly dying inside. Imagine a large marketplace platform. Quarter after quarter,...

This is not a philosophical question. It’s a ticking time bomb of liability and broken trust that most companies are sitting on. There’s...

Let's be brutally honest. Most corporate recommendation engines are stuck in kindergarten. They operate on a laughably simple logic: "You...

[Note for Non-Portuguese Speakers]: This article is in Portuguese because it invites Brazilian readers to take part on a participatory...

Your team launched a new marketing campaign in October. In November, sales were up 15%. Success? Maybe. But you might just be celebrating...

Your 'Gold Tier' loyalty program is full of high-spending customers. Did the program make them high-spenders, or did you just give perks...

Your regression model is beautiful. But are you sure you should be "controlling for" that variable? This is one of the most subtle—and...

Does that 20% discount coupon actually make you money, or is it just an expensive gift to customers who would have bought from you anyway? Answering this question is fundamental to your marketing ROI. And most companies get it dangerously wrong. This is a classic "Doing Data Wrong" scenario I see in Marketing Departments all the time. The Wrong Way (Observational Illusion): A team analyzes historical data. They see that customers who used a promotional coupon have a 30

Your marketing ML model is brilliant at predicting who will buy. But does it actually know why? There’s a dangerous gap between prediction and causation, and many companies are falling into it, armed with the most advanced machine learning tools. They're using a Ferrari to drive straight into a wall. This is one of the most insidious forms of “Doing Data Wrong”: using powerful predictive models to make causal decisions, like where to allocate your marketing budget.