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A consulting team advises a retail client to test a new in-store display. They roll out the new display in their 10 best-performing urban stores and compare sales to the 10 worst-performing rural stores. They run the "test" for one week. The result? A beautiful PowerPoint deck showing a 50% lift, leading to a multi-million dollar national rollout. This is analytical malpractice. The two groups of stores were never comparable (selection bias), and a single week of data is mean

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

This is the most classic form of "Kindergarten Data": you ask for a tool without defining the problem. The result? A "dashboard graveyard." Your team spends weeks building a beautiful, complex dashboard with 50 filters. You look at it, get overwhelmed by charts and numbers that don't lead to a clear decision, and after two weeks, you stop opening it. The data team is frustrated. You’re disappointed. And a massive amount of time and money has been wasted creating a useless

we have to think like physicists. We must start with the Potential Outcomes Framework. It’s a simple but profound idea: for any person, city, or store, there are multiple potential realities. There’s the reality where they got your new marketing campaign, and an alternate, counterfactual reality where they didn't. The "causal effect" is the difference between these two states. The fundamental problem is that we can only ever observe one of these realities at a time.

Here are the types of questions I live to answer: “Our dashboard shows that when sales reps make more calls, customer churn increases. Are the calls driving them away?” (A classic confounding question!) “We launched a new wellness program, and employee productivity went up. How do I prove the program caused the improvement, and it wasn't just a good quarter?” "What is the single most dangerous, 'looks-right-but-is-wrong' metric you've ever seen a C-level executive

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.

I’ve seen it countless times, from agile startups to corporate giants. A business leader asks for a simple, weekly report—say, on project profitability. The tech team, brilliant and eager to use the latest tools, returns three months later with a real-time, Kafka-powered, microservice-architected, auto-scaling data pipeline. The cost? Astronomical. The complexity? A nightmare to maintain. The business value? The exact same as the simple, static report the leader originally ne