The specific questions clients ask.
Tool comparisons, vertical playbooks, definitions — written once, referenced often. No clickbait, no executive summary you could’ve generated yourself.
Tool comparison
Matomo vs PostHog: two privacy-friendly tools, two different jobs
Both are open source and GDPR-friendlier than GA4, which is where the similarity ends. One replaces web analytics, the other explains product behavior.
Matomo vs Plausible vs Fathom: the cookieless analytics roundup
Three real ways to measure a website without tracking cookies, from near-GA4 depth to radical simplicity. What each gives up, and who should pick which.
Firebase Analytics vs GA4 for apps: mostly the same thing
The comparison everyone searches for is mostly a misunderstanding: Firebase is how app data gets in, GA4 is where it lands. The real decisions live elsewhere.
Friendly Analytics vs Google Analytics: sovereignty vs free
Managed Matomo hosted in Switzerland versus the free default from Google. What you gain, what you give up, and who should actually make the switch.
GA4 alternatives for EU e-commerce: six real options, compared
Six real alternatives, compared on what EU stores actually need: e-commerce depth, consent survival, and where the data lives. No affiliate links, no invented scores.
GA4 vs Matomo for EU e-commerce: the GDPR-honest comparison
GA4 is free and legally unsettled; Matomo costs money or server time and is yours. Which trade-off fits an EU store, argued honestly, migration costs included.
GA4 vs Plausible: depth you rarely use vs simplicity you will
One gives you every report you might ever need and a consent headache. The other gives you one page of numbers you will actually read. Which trade is right depends on how you make money.
GA4 vs PostHog: web analytics vs product analytics
One tracks how people find you, the other tracks what they do once inside. The honest comparison, plus the case where running both is the right answer.
Plausible vs Fathom: near-twins, and how to actually pick one
Two cookieless, privacy-first analytics tools that solve the same problem the same way. The differences are real but small, which is itself the most useful thing to know before you spend a week comparing them.
Glossary
First-party vs third-party data, explained without the fog
Who collected it, on whose property, is the whole distinction. What each kind is, what is happening to third-party data, and what to build on instead.
What is a dataLayer? The plain answer, and why yours is a mess
The dataLayer explained without vendor language: what it is, what it looks like, why most of them decay into chaos, and what a clean one looks like.