Statistical Methods

Statistical thinking and analysis

Data Roles in Practice: How Data Engineers, Scientists, and Analysts Actually Grow SaaS and eCommerce Businesses

A deeply practical guide to what data engineers, data scientists, and data analysts actually do to grow SaaS and eCommerce businesses. Each use case includes why it matters, exactly how it is done, the measurable impact with real revenue and profit examples, and an importance rating from 1 to 10. Covers everything from building unified customer views and calculating true customer lifetime value to predicting churn, optimising pricing, running incrementality tests, and building recommendation engines. No theory, just tactics with numbers.

Data ScienceeCommerceGrowth HackingMarketing AutomationSaaSStatistics

The Complete Guide to Digital Marketing Testing Methods

A comprehensive deep dive into every major testing methodology used in digital marketing. Covers running without tests, A/B testing with two variants, multi-split testing with three or more variants, multivariate testing for element combinations, holdout control groups for measuring incremental lift, geographic split testing for regional campaigns, audience split testing for segment-specific optimisation, and sequential time-based testing for low-traffic scenarios. Each method includes detailed explanations of when to use it, statistical requirements, implementation considerations, advantages and disadvantages, and real-world examples across e-commerce, SaaS, fintech, travel, and retail industries.

Data ScienceeCommerceGrowth HackingMarketing AutomationSaaSStatistics

Conversion and UX Psychology: The Science of Why Users Click or Bounce

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.

Consumer PsychologyData ScienceeCommerceGrowth HackingSaaSStatistics

The Math Behind It All: Growth Marketing Mathematics Explained

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.

Data ScienceeCommerceGrowth HackingMarketing AutomationSaaSStatistics

AI for Reducing Cart Abandonment and Returns

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.

AIData ScienceeCommerceMachine LearningStatistics