Which seven numbers count in B2B, how to calculate ROI over long cycles and what A/B tests still yield on low traffic.
How to measure the actual waiting times, which five points cost the most time, and which measures work at each of them — without pressuring the customer.
Three alternatives compared — server logs, consent-free analytics services and self-hosted solutions — with what each one gains and gives up.
Why classic A/B tests say nothing below a few thousand visitors, which five approaches work instead, and how to assess changes defensibly anyway.
Why the open rate has measured little since automatic image preloading, and what actually decides whether a message gets opened.
Why monthly comparisons are systematically wrong with long cycles, how to match costs and returns in time, and which two proxies report earlier.
Why a percentage of revenue misleads, which three quantities actually determine the amount, and how to split the budget across the four items.
Six starting points from the form field to changing channel — with effort, expected effect and the order to actually work through them in.
Fixed spelling rules, a short list of values and one central register — so the analysis still tells you something two years later.
How to recognise AI traffic, how to separate it cleanly from other sources, what the server logs add — and where the measurement honestly ends.
A repeatable procedure with a fixed question list, clean testing conditions and an evaluation that turns into concrete tasks.
What each number answers, how often to collect it and when it misleads — plus the three metrics small companies can drop with a clear conscience.
