Loyalty with structural equation modelling
How structural equation modelling shows which guest experiences actually drive loyalty, with a hotel path model, total effects and an investment decision.
Data Science & Analytics
Using data to drive decisions and uncover insights
Loyalty: the three Rs, the spectrum, and designing earn and burn
How to tell whether a loyalty programme changes behaviour or just pays for it: the three Rs, earn and burn design, breakage and the real liability.
Media mix modelling: adstock, saturation and where the next pound goes
How media mix modelling uses adstock and saturation curves to find the marginal ROI per channel and where the next pound should go, with a hamper shop example.
Server Side Tracking: The Technical Guide to Fixing Your Broken Analytics
A technical deep dive into server side tracking for marketers and developers who are tired of watching their conversion data degrade. Starts with an accessible explanation of why browser based tracking is failing, then gets properly technical with architecture diagrams, GTM Server Side Container configuration, Meta Conversions API implementation, Google Enhanced Conversions setup, deduplication logic to avoid double counting, consent mode integration, first party data enrichment strategies, infrastructure hosting options, debugging techniques, and honest cost analysis. Written for people who actually need to implement this, not just read about it.
Dynamic Pricing and Algorithmic Marketing: The Complete Technical Playbook
A deeply technical guide to building dynamic pricing systems that integrate competitor intelligence, margin-aware advertising budget allocation, and profit maximisation algorithms. Covers the economics of price discrimination, why static pricing leaves money everywhere, how to crawl competitors and respond in real time, how to automatically reallocate ad spend based on product margins and inventory levels, and complete Ruby on Rails Solidus implementation code with working examples. Includes mathematical formulas for price elasticity, optimal markup, and budget allocation, plus system architecture diagrams. Written for developers and growth engineers who actually build these systems.
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 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 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.
Modelling the value of marketing communications
How to put an honest number on one more email, webinar or retargeting ad: response curves, marginal value, holdout groups and the frequency that pays.
Tools of segmentation: k means, latent class analysis and going beyond RFM
k means, latent class analysis and RFM compared on a real hamper shop: how to choose k, profile and name segments, check stability and activate them.
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.
Segmentation: strategy before algorithms
What a segment really is, the five tests it must pass, a priori versus post hoc splits, and why one churn model per segment beats one model for everyone.
Principal components and factor analysis for marketers
How PCA and factor analysis turn 24 survey items or 18 behavioural variables into a few dimensions you can name, score and act on, with hotel and hamper examples.
Simultaneous equations: when marketing causes sales and sales cause marketing
Budget rules make sales drive ad spend while ad spend drives sales. Why one regression overstates the ad effect, and how two stage least squares corrects it.
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.
Forecasting demand: autocorrelation, seasonality and honest error bands
How to forecast weekly demand with seasonal dummies, handle autocorrelation, judge models on a holdout and plan against an 80 percent error band.
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.
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.
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.
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.
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.
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.
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.
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.
Panel regression and same store sales
How panel regression separates a good store from a good quarter, why pooled data lies about marketing, and how fixed effects power like for like sales.
Survival analysis, churn and customer lifetime value
How survival analysis tells you when customers leave, not just whether, and how to turn Kaplan Meier curves and a Cox model into a lifetime value you can defend.
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.
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.
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.
Logistic regression, lift charts and market basket analysis
How logistic regression ranks customers, where the marginal brochure stops paying for itself, and how the same model turns baskets into cross sell recommendations.
Poisson regression: modelling how many times customers do things
Orders, tickets, visits: when the outcome is a count, ordinary regression misbehaves. Poisson regression, rate ratios and overdispersion, shown on a hamper shop.
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.
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.
Polynomial distributed lags: how long does marketing keep working?
Why an email keeps producing trials for weeks, how the Almon polynomial lag model measures that curve, and how Werkbank used it to pick a three weekly send cadence.
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.
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.
Modelling demand and price elasticity with regression
A year of hamper sales, one regression and an elasticity of minus 1.6: how to model demand with OLS, read the output and find the price that pays.
What is an insight, and why most reports do not contain one
A finding says what happened. An insight names the decision it changes, prices it and states the risk. How to tell them apart, score them and write one.
The Complete Guide to Forecasting: How to Predict What Your Business Actually Needs
A practical guide to forecasting for business. Learn different forecasting methods, from simple moving averages to machine learning models like SARIMA, Prophet, and XGBoost, with Python examples.
AI vs Machine Learning: Why They're Not the Same Thing (And Why It Actually Matters)
AI and machine learning get thrown around like synonyms, but they're not. Understand the real difference, the technical details, Python libraries, and why it matters for your projects.
When Your AI Starts Fighting Itself: The Curious Problem of Competing Ecommerce Optimisation
What happens when multiple shops use the same clever algorithm to beat each other? Explore the fascinating multi agent conflict problem in ecommerce AI, from Nash equilibrium to algorithmic collusion risks.
Lookalike Audiences Explained: The Complete Guide to Finding Your Best Customers on Google and Meta
Lookalike audiences help you find new customers who share characteristics with your best buyers. This guide covers how to set them up on Google and Meta, their benefits and limitations, and why they work the way they do.
Consumer behaviour is the basis of marketing strategy, not the afterthought
Why strategy has to start from how people actually decide, how stated and revealed preference differ, and how to turn a founder belief into a data question.
The True Cost of Outsourcing Business Critical Development
A cautionary tale about outsourcing Ruby on Rails and Solidus development to low-wage countries. Learn why saving money often ends up costing you everything.
AI in Business: The Complete Guide to Opportunities, Dangers, and Getting It Right
A comprehensive guide to implementing AI in your business. Learn about cloud vs self hosted solutions, privacy risks, GDPR compliance, and hybrid architectures that give you the best of both worlds.
The statistics every marketer actually needs
Mean, median, standard deviation, confidence intervals and correlation, explained on a hamper shop's orders so you can read marketing numbers honestly.
A Unified Campaign Automation Stack
From APIs to low code tools to custom scripts, I explain how I combine Zapier, Segment, Python, Google Ads API, Postmark and more into a cohesive marketing engine that adapts in real time to customer and business signals.
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.
Triggering Ads from Email Activity and Vice Versa
Combining email and PPC logic creates more intelligent journeys. In this article I explain how I sync ad creative and email flows through user signals, UTMs, and intent paths, with real time cross channel triggers.
The Shift to Black Box AI Campaigns: How I Engineer Results in the Age of Performance Max and Automation
Performance Max and black box ad platforms have changed the rules. In this in depth guide, I explain how I work with automated systems to influence bidding, placement and creative outcomes without manual control.
The Importance of Statistical Significance and Error Margins in Performance and Growth Marketing
Understanding statistical significance, error margins, and sample sizes is crucial for data driven decision making in marketing. In this post, we explain these concepts and how they impact your marketing campaigns, especially in ecommerce.
Strategic Interaction Models Every Growth Hacker Should Know
Learn the key game theory and economic models I use to forecast competitor behaviour, launch smarter and structure pricing strategy, including Stackelberg, Cournot, Bertrand and more.
Optimisation Under Uncertainty: Stochastic Programming for Budget Allocation and Pricing Strategy
Learn how I use stochastic programming, constraint relaxation and duality theory to optimise pricing and budget allocation when behaviour and returns are uncertain, with real examples from ad spend and dynamic pricing.
Setting Up Google BigQuery for Ecommerce Analytics with Rails Solidus Integration
Learn how to set up Google BigQuery for your Rails Solidus store and connect it to various data sources. We explore how to analyse marketing campaigns and calculate key metrics such as lifetime value and campaign driven sales.
Webhooks and Serverless for Fully Reactive Campaign Logic
By combining webhooks with serverless functions like AWS Lambda and Cloudflare Workers, I create real time marketing logic that updates ads and email based on product, pricing and user events, with zero delay and full control.
Programmatic SEO and Content Automation: Scaling with Strategy and Structure
Programmatic SEO enables the creation of large scale content assets through automation, but real success comes from precision and value. In this article, I explore how I build automated content systems for long tail traffic and local visibility without sacrificing quality.
Predictive Automation Using Behavioural Segments
Learn how I use real time behavioural data to anticipate intent and trigger automated campaigns before conversion happens, with practical examples using session signals, referral context and cohort scoring.
The Pirate Metrics Framework (AARRR): How I Use It to Diagnose Growth Bottlenecks
Learn how I use the AARRR framework as a live diagnostic tool to find and fix growth bottlenecks across acquisition, activation, retention, referral and revenue.
Merging First Party Data with SEO: Preparing for the Cookieless Future
As third party tracking fades, businesses must shift towards first party data strategies. In this article, I explore how SEO and data capture can work together to drive performance, retention and deeper insights.
Mastering UTM Tracking for Your Online Campaigns
Learn how to track your online campaigns with UTM URLs, use Google's UTM Builder, and ensure consistency in your tracking parameters. We cover best practices, common pitfalls, and how to track even offline campaigns effectively.
Game Theory in Growth Strategy: Pricing, Positioning and Strategic Interactions
Learn how I use Nash equilibrium, dominant strategies and payoff matrices to shape real world growth decisions in pricing, positioning and competitive response.
Event Tracking and Consent Mode: Engineering Data Collection Without Losing Performance
Learn how to implement Google Consent Mode and server side tracking that keeps you compliant with GDPR while preserving the marketing data you need.
Decision Theory for Founders: Choosing Under Uncertainty with Maximum Expected Value
Learn how I use decision theory, utility functions and expected value models to support rational, risk aware growth strategy when choices must be made under uncertainty.
Data Warehousing for Smart Marketing Automation
A modern growth stack starts with centralised data. In this article I explain how I use BigQuery and Snowflake to join ecommerce, CRM and campaign signals, then trigger smarter emails, ads and segment logic in near real time.
What Data Science Actually Delivers for Growth
Learn how I use data science to drive segmentation, trigger campaigns, predict revenue and make growth systems smarter, with practical examples from SaaS and eCommerce.
Cross Language Attribution Modelling: How I Track Growth Across German, Swiss and Austrian Channels
Attribution becomes more complex when you are dealing with the same language in different cultural and legal contexts. In this article, I explain how I track growth across DACH markets using BigQuery, GA4 and CRM integrations, with clean attribution across dialects, subdomains and consent regimes.
Competing in Markets with Homogeneous vs Differentiated Products: A Data Driven Approach
Learn how data science gives businesses the edge in both homogeneous and differentiated product markets, from real time competitive pricing to customer segmentation.
Causal Inference in Marketing: Moving Beyond Correlation with Propensity Scores and Instrumental Variables
Learn how I use propensity scores, instrumental variables and causal models to estimate true marketing effect when controlled experiments are not possible, and avoid wasting budget on false positives.
Beating Price Wars and Big Players in the Homogeneous Product Market
Learn how to break through price wars and beat the competition in homogeneous product markets by leveraging creative strategies, data science and innovative marketing.
Auto Bidding Based on First Party and External Data Sets
Learn how I build custom bidding logic that pulls in CRM signals, profit margins, inventory levels and LTV metrics to drive more intelligent paid media spend, going beyond Smart Bidding with your own intelligence layer.