I’ve Been Scared of Google Analytics for Years. Here’s What Changed.
by Meghan Brenner
Last week I was telling a friend about the GA4 setup I'd been working on. The containers, the custom channel groups, the UTMs. I wasn't showing off. I was just recapping what I'd done.
She stopped me and said she never would have guessed I'd been scared to touch any of it.
That stopped me for a second. Because I had been.
The Thing I'd Been Avoiding
For years, I'd heard about Google Analytics the way you hear about something that's clearly not for you. Developers used it. People who understood what "firing a tag" meant used it. Not me. I was certain that if I got anywhere near the code running our website, I'd break something in a way that couldn't be undone.
So I avoided it entirely. I had GA4 running on our site the same way I "have" a gym membership. It was there. I wasn't really using it.
What finally got me into it wasn't a tutorial or a course. It was a specific business problem. I'd been doing AEO work, rewriting content so AI tools could parse and cite it, and I had no real way to measure whether any of it was doing anything. That meant I needed to understand where our traffic was actually coming from. Which meant I needed GA4.
What I Did Differently This Time
I didn't start by trying to learn the tool. I started with the thing I was trying to find out.
I needed to see whether traffic from Claude, ChatGPT or Perplexity was showing up in our analytics, and whether it was behaving differently than traffic from Google or LinkedIn. That was the question. Everything else was in service of answering it.
So I walked through the setup with Claude, one question at a time. Not "explain GA4 to me." More specific than that: What is a container and why does it need to be there? What does a UTM actually do to the data, and what happens if I don't use one? Why would AI traffic show up under "referral" instead of having its own label? What do I actually click to create a custom channel group?
Each answer gave me enough to take the next step. I wasn't learning GA4 as a system. I was learning what I needed to know to accomplish a specific thing. Those aren't the same thing, and that difference turned out to matter a lot.
The Phrase That Explains It
My friend's reaction made me think about why that distinction matters. She assumed I'd gone and learned the tool. I hadn't. I'd learned what I was trying to accomplish well enough to ask useful questions about it, and then asked them in the right order.
The result looked like expertise from the outside. From the inside it felt like a very productive conversation.
I keep coming back to a phrase that I think describes what I've been doing for the past few months across all of this work. Dangerous enough to ask the right questions. Not expert enough to build it from scratch. Not confused enough to not know where to start. Just enough conceptual understanding of what I'm trying to do that I can get real, specific, useful answers instead of generic ones.
That zone is available to most non-technical operators. But it requires one thing first: being honest with yourself about what outcome you actually need, before you worry about how to get there technically.
How to Use AI This Way (Without Getting Generic Answers)
The difference between a useful AI conversation and a useless one usually comes down to how specific your first question is. Here's what worked for me:
Start with the outcome, not the tool. "I need to see whether AI traffic is behaving differently than search traffic" is a better starting point than "explain GA4 to me." The first question gives the AI something specific to work with. The second one produces a textbook.
Ask why, not just how. When I asked what a UTM does, I also asked what happens to the data if I skip it. That second question was the one that made the setup actually make sense. Understanding the consequence of not doing something is often more useful than understanding how to do it.
Follow the answer with the next specific question. The goal isn't one comprehensive answer. It's a sequence of small answers that each unlock the next step. Treat it like a conversation, not a search engine.
Name what you don't understand. "I'm not sure what a container is or why it matters" is a better prompt than nothing. The AI can meet you where you are if you tell it where that is.
Stop when you have enough to act. You don't need to understand the whole system. You need to understand enough to complete the specific task in front of you. That's a much lower bar, and it's the one that actually moves things forward.
What This Means for the Way You Learn
The friend who said she never would have guessed I'd been scared wasn't wrong, exactly. I had learned something. I just hadn't learned it the way I expected to.
I didn't take a course. I didn't read documentation. I didn't become a GA4 expert. I became someone who understood what she was trying to find out, and who knew how to ask useful questions until she found it.
You don't have to understand the tool. You have to understand what you're trying to find out. Once you know that clearly, the questions write themselves.
I write about exactly this kind of thing, in real time, in The Muddy Middle, a newsletter for non-technical operators figuring out AI as they go. Subscribe here and I'll see you in your inbox.
Meghan Brenner is COO at JB Sales and founder of The Operator's Notebook.
