The durable guides.
Each guide anchors a topic and links down to every comparison, explainer and decision piece beneath it. Start here, then go deep.
- Hub A
The honest guide to analytics you can trust
Most analytics problems are not tooling problems. They are honesty problems: nobody wrote down what the numbers mean, nobody verified what actually fires, and everybody quietly stopped trusting the dashboard. Trustworthy data is a practice, not a product.
- Hub B
Choosing an analytics tool: GA4, its alternatives, and when to switch
There is no best analytics tool, only trade-offs: capability, data ownership, and maintenance burden. The right pick depends on your questions, your ad spend, and your appetite for running infrastructure. Comparisons here are argued one pair at a time, honestly.
- Hub C
Tracking implementation and the dataLayer, done properly
Most broken analytics is broken implementation, not a bad tool. A tracking setup is only real when every event has an owner, a definition, and a test against actual network traffic. The dataLayer is a contract between your site and your data, and most companies never wrote the contract down.
- Hub D
Server-side tracking, without the hype
Server-side tracking is plumbing, not magic. It recovers real data and improves ad-platform match rates, at real recurring cost, and it is not a consent bypass. I sell the add-on, and I still tell people when the math does not work for them.
- Hub E
GDPR-compliant analytics, without the theater
Compliance lives in what actually fires, not in what the banner claims. Most GDPR analytics problems are engineering problems wearing a legal hat: verify at the network level, fix the leaks, then let a lawyer map the obligations. I map the technical truth; I am not a lawyer.
- Hub F
Attribution and marketing measurement, honestly
Your platforms will never agree, because they measure different things. Attribution work is not making the numbers match; it is knowing exactly why they differ and picking one source of truth per decision. Anyone promising perfect attribution is selling the word, not the thing.
- Hub G
Product and app analytics, beyond the pageview
Most SaaS and app companies run marketing analytics and call it product data. Web analytics tells you how people arrived; product analytics tells you what they did, whether they stayed, and why they paid. Different questions, different instrumentation, and you rarely need two paid tools on day one.
- Hub H
Build vs buy: custom data tooling without the regret
The honest default is buy. Custom software earns its keep only where your process is genuinely unusual and someone will own the code after launch. I build custom tools for a living and still talk most prospects out of it; the ones I don't talk out of get tools that fit like nothing off a shelf can.