Most analytics setups fail at the very first move, which is reaching for a tool. You install Google Analytics, drop a pixel, wire up a tag manager, and three months later you have a dashboard full of numbers that nobody acts on, because nobody decided what the numbers were for. The tool was never the problem. The missing decision was.
So start one step further back. Write down the handful of moments in your business that actually mean something: a qualified demo booked, a trial started, a paid plan activated, a deal closed. Those are your real events. Everything else is context that helps you explain why those numbers move, not a thing you optimise for in its own right.
The test I use is simple. For every metric you are about to track, ask what decision it would change. If a number going up or down would not make you do anything differently next week, you do not need it on the dashboard, and tracking it just adds noise you will later have to ignore. A small set of decision-driving metrics beats a wall of vanity counters every time.
This is also where you separate the two questions analytics answers, because people constantly blur them. One question is "how many", which is volume and trend. The other is "why", which is behaviour and path. You want both, but you size the setup around the "how many" first, because that is what tells you whether the business is working at all.
Do this and the rest of the setup becomes easy, because every tool choice and every tag now has an obvious job. Skip it and you will instrument everything, trust nothing, and quietly stop opening the dashboard within a quarter.
