Blog
Theory, frameworks, and mental models behind analytics and causal inference.
Causal Inference for Marketing Analysts: Why Prediction Models Won't Tell You If Your Campaigns Work
Marketing analytics is dominated by predictive thinking — propensity scores, churn models, LTV forecasts. Useful, but they can't tell you whether your discount caused a purchase or just correlated with one. Here's the framework you're missing.
Time Series Analysis in Web Analytics — The Theory Behind the Trend
Most marketers read traffic charts as pictures. Time series analysis treats them as data — with structure, decomposable components, and testable properties. Here's the theory.
Introduction to Causality — Why Correlation Is Not Enough
ML is exceptionally good at prediction. It is mostly useless for answering 'what if' questions. Here is the math that explains why, and the framework that fixes it.
Your Ultimate Guide to Hiring the Right Google Analytics Consultant
Most GA4 consultants know the tool. Very few know the questions. Here's how to tell the difference before you sign a contract.