Statistical thinking and analysis
A comprehensive deep dive into the psychological laws that govern user behaviour and conversion. From Hick's law and Fitts's law to the peak-end rule, serial position effect, Miller's law, cognitive load theory, friction frameworks, and attribute framing. Includes research data, Python code for measuring UX impact, and practical implementation strategies backed by Baymard Institute research.
A comprehensive deep dive into the mathematics that powers growth marketing and data-driven decision making. From LTV:CAC ratio and payback period to cohort analysis, survival analysis with Kaplan-Meier curves, Bayesian A/B testing, multi-armed bandits with Thompson sampling, marketing mix modelling vs attribution, power law distributions, price elasticity with Van Westendorp's price sensitivity meter, and the compounding math of retention. Includes LaTeX formulas, Python code, visual explanations, and practical implementation guidance for both beginners and advanced practitioners.
The average cart abandonment rate is 70%. Ecommerce returns cost US retailers $890 billion in 2024, with fashion return rates hitting 24-30%. AI reduces cart abandonment by 18% and size-related returns by 27%. This post shows how to build predictive models that spot drop-off patterns and sizing issues before they cost you money - with Python scripts for abandonment risk scoring and return prediction, plus the Solidus/Rails implementation that wires them into your checkout and product pages.
