Data Science & Analytics

Using data to drive decisions and uncover insights

Cookieless Marketing: The Complete First-Party Data Strategy for 2026

A comprehensive guide to marketing in the post-cookie era. Covers why third-party cookies are effectively dead even without Google killing them, the four pillars of cookieless marketing (first-party data, zero-party data, contextual advertising, privacy-preserving measurement), technical implementation including Customer Data Platforms, server-side tracking, and consent management, practical tactics for collecting first-party data through value exchange, how to activate first-party data for targeting and personalisation, privacy-preserving measurement approaches including Marketing Mix Modelling and incrementality testing, and a detailed implementation roadmap for SaaS and eCommerce businesses.

Data ScienceeCommerceGDPRGrowth HackingMarketing AutomationSaaS

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

Data Engineer, Data Scientist, Data Analyst: Who Does What, and Why Your Growth Depends on Them

A comprehensive guide to understanding the differences between data engineers, data scientists, and data analysts, using a single running example of a coffee subscription business to illustrate exactly what each role does and why. Covers the data engineer as the plumber who builds reliable data infrastructure, the data analyst as the translator who explains what is happening in plain English, and the data scientist as the predictor who forecasts what will happen next and designs experiments to test interventions. Explores why these roles matter for growth hacking, software development, and digital marketing, and what happens to businesses that skip them. Includes practical advice on hiring order and avoiding expensive mistakes.

Data ScienceeCommerceGrowth HackingMarketing AutomationSaaS

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

Onboarding and Habit Formation: The Science of Making Products Stick

A comprehensive deep dive into the psychology and mechanics of user onboarding and habit formation. From finding your product's Aha moment through cohort analysis to Nir Eyal's Hook model, James Clear's habit stacking, the Zeigarnik effect, endowed progress effect, and goal gradient effect. Includes Chamath Palihapitiya's Facebook growth team methodology, research data, Python code for measuring activation, and practical implementation strategies.

Consumer PsychologyData ScienceGrowth HackingMarketing AutomationSaaS

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

Behavioural Economics Frameworks: The Science of How Customers Actually Decide

A comprehensive deep dive into the behavioural economics frameworks that shape consumer decisions. From Kahneman's dual process theory to Thaler's choice architecture, cognitive biases, hyperbolic discounting, mental accounting, choice overload, and status quo bias. Includes when each framework applies, how to design for real human behaviour, and Python code for measuring effectiveness.

Consumer PsychologyData ScienceeCommerceGrowth HackingSaaS

Referral and Viral Mechanics: Engineering Exponential Growth

A comprehensive deep dive into the mathematics and psychology of referral programmes and viral growth. From K-factor calculations to viral cycle time, two-sided incentives, network effects versus viral effects, referral psychology, Jonah Berger's STEPPS framework, and NPS as a growth predictor. Includes Python code for modelling viral growth and measuring programme effectiveness.

Consumer PsychologyData ScienceeCommerceGrowth HackingGrowth HacksSaaS

Social Proof and Trust: The Psychology of Why We Follow the Crowd

A comprehensive deep dive into the psychology of social proof and trust in marketing and product design. From the six types of social proof to review psychology, authority signals, trust stacking, reciprocity, and herd behaviour. Includes when each strategy works (and when it backfires), research from Spiegel and Baymard Institute, and Python code for measuring effectiveness.

Consumer PsychologyData ScienceeCommerceGrowth HackingGrowth Hacks

Scarcity, Urgency, and Loss Aversion: The Psychology of Now or Never

A comprehensive deep dive into the psychology of scarcity, urgency, and loss aversion in marketing and product design. From Cialdini's scarcity principle to the endowment effect and IKEA effect. Includes when each strategy works (and when it destroys trust), how to measure effectiveness with data science, and Python code for experimentation.

Consumer PsychologyData ScienceeCommerceGrowth HackingGrowth Hacks

Less Well Known but Battle Tested: The Hidden Psychology That Actually Converts

A comprehensive deep dive into 18 lesser-known but highly effective psychology principles for conversion and retention. From goal-gradient acceleration and temporal landmarks to identity-based marketing, implementation intentions, foot-in-the-door, labour illusion, operational transparency, defaults as nudges, status games, sunk cost retention, social comparison, reactance, and risk reversal. Includes research data, Python code for measuring impact, and practical implementation strategies backed by academic literature.

Consumer PsychologyData ScienceeCommerceGrowth HackingGrowth HacksMarketing AutomationSaaS

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

Pricing Psychology: The Science of Making Your Prices Irresistible

A comprehensive deep dive into the psychology of pricing. From anchoring to prospect theory, learn the cognitive biases that shape how customers perceive value. Includes when each strategy works (and when it backfires), how to measure effectiveness with data science, and Python code for A/B testing your pricing experiments.

Consumer PsychologyData ScienceeCommerceGrowth HackingPricing Strategy

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

Dynamic Pricing With AI: A Growth Hacker's Guide

The dynamic pricing software market is projected to grow from $6.16 billion in 2025 to $41.43 billion by 2033, and 55% of retailers plan to implement AI pricing in 2026. Amazon changes prices 2.5 million times a day. You don't need to be Amazon. This post breaks down how ML-driven dynamic pricing actually works - price elasticity estimation, demand signals, competitor monitoring, and margin guardrails - with practical Solidus/Rails code and Python scripts you can run today.

Data ScienceeCommerceGrowth HackingMachine LearningSaaS

AI-Powered Demand Forecasting for eCommerce

IKEA's Demand Sensing tool halved their forecast error rate from 8% to 2% by using up to 200 data sources per product. That's what AI-powered demand forecasting looks like at scale. But you don't need IKEA's budget to get meaningful results. This post compares three forecasting approaches - Prophet, SARIMA, and XGBoost - with practical examples from Solidus ecommerce and custom SaaS, showing which model works best for which scenarios and how to implement them in a Rails-based product stack.

AIData ScienceeCommerceMachine LearningSaaS

GDPR and AI: The "Right to Be Forgotten" Now Means "Unlearning"

When GDPR's Article 17 was written, 'erasure' meant deleting a row from a database. In 2026, it means something far more complicated. If a user's data was used to train an AI model, deleting the database record isn't enough. The data has been absorbed into model weights, influencing predictions for every subsequent user. The EDPB has made right to erasure its coordinated enforcement priority for 2025-2026, with 30 data protection authorities investigating how organisations handle deletion requests. And the Italian DPA already fined OpenAI 15 million euros for, among other things, failing to handle training data properly under GDPR. This post explains what machine unlearning is, why it's a nightmare for developers, and what practical architectural decisions you can make right now to avoid the problem in the first place.

AIData ScienceGDPRMachine LearningSaaS

How to Actually Integrate AI Into Your Existing Workflows (Without Breaking Everything)

Most AI projects fail. Not because the technology is bad, but because the data is messy, the systems are old, and nobody knows where to start. Gartner predicts that 60 percent of AI projects will be abandoned due to poor data quality. This is the where do I even start post. Data quality, legacy systems, realistic first steps, and real examples from Rails, Solidus, and SaaS projects I have actually built.

AIData ScienceeCommerceMachine LearningSaaS

Building Custom AI Recommendations in Solidus

A technical but accessible walkthrough of adding ML-powered product recommendations to Solidus, the open-source Ruby on Rails ecommerce framework. Covers three recommendation approaches (collaborative filtering, content-based, and hybrid), complete with Python ML scripts, full Solidus/Rails integration code, event tracking, cold start handling, A/B testing, GDPR compliance, and the honest limitations and pitfalls you'll hit along the way. No black boxes - every piece is explained and every trade-off is named.

AIData ScienceeCommerceMachine LearningSaaS

TDD and BDD in Ruby on Rails: How to Ship Without Fear, and How AI Is Changing the Game

A deep dive into Test Driven Development and Behaviour Driven Development in Ruby on Rails. What they are, how they differ, why they exist, and how they prevent the costly bugs that plagued the old way of building software. Includes a detailed comparison of RSpec, Minitest, and Cucumber, practical examples from building Auto-Prammer.at on Solidus and the Regios fintech SaaS powered by GrowCentric.ai, plus a best practice guide for using AI tools like Claude to supercharge your testing without losing control.

AIData ScienceeCommerceMachine LearningSaaS

Why I Moved from Growth Hacking to Data Driven Ecommerce Growth

In this blog post, I explain why I moved away from growth hacking informational websites to focus on transactional websites, such as ecommerce stores and subscription based apps. I delve into the role of data science in overcoming growth challenges and automating marketing campaigns, ultimately leading to more efficient and measurable growth strategies.

Data ScienceeCommerceGrowth Hacking