Why I Ditched Google Analytics

Over the years, I've watched analytics go from simple visitor counters to the mess we have today.

And don't get me started on every marketing platform now pushing "first-party signals"—the data collection got even worse after everyone panicked about cookie deprecation.

Google Analytics became the default not because it was the best tool, but because it was free and good enough. That free lunch came with a bill we're only now understanding.

The Analytics Arms Race Nobody Asked For

I remember when Google Analytics felt revolutionary. Suddenly, we could see everything—every click, scroll, microsecond on a page. The data was addictive. We started tracking metrics because we could, not because they helped us make better decisions.

Then came GA4 in 2024.

If you've worked in marketing recently, you know exactly what I'm talking about. Google forced everyone to migrate from Universal Analytics to GA4, and it was a disaster. The interface was confusing. Reports took forever to load. And simple tasks that took two clicks suddenly required a PhD in data science.

I can't count how many times I've heard, "Remember when I could just do this in old google analytics?" in team meetings. Some marketing teams I know are still struggling with basic reporting two years later. Others have given up and are running campaigns blind.

But here's the thing—most of the advanced features in Google Analytics are just "analytics theater". Your executive team doesn't care about user flow visualizations. Your conversion optimization doesn't need to know that someone from Des Moines spent 2.3 minutes reading your about page before bouncing.

What you should care about is whether your marketing is working. For that, you need maybe five metrics. Everything else is procrastination disguised as analysis.

• traffic volume

• traffic sources

• popular content

• conversion events

• basic demographic data

The Real Cost of "Free" Analytics

Google Analytics isn't free—it's subsidized by your users' privacy. Every visitor becomes a data point in Google's advertising machine. As someone who's spent millions on Google Ads, I can tell you exactly how this works: the more Google knows about your visitors, the more you'll pay for ads to reach similar audiences.

You're paying Google twice—once with your users' data, then again when you buy ads. It's brilliant from Google's perspective, less so from yours.

The GA4 migration made this worse. Google killed Universal Analytics and forced everyone onto a platform most people hate using. I've talked to dozens of marketing leaders who've basically stopped looking at their analytics because GA4 is so frustrating. They're making marketing decisions based on gut instinct because they can't figure out how to generate a simple traffic report.

But the bigger issue isn't just usability. It's practical. GDPR compliance with Google Analytics is a nightmare. I've sat through countless legal reviews, implemented cookie banners that tank conversion rates, and watched traffic data become unreliable as users opt out. The "free" tool suddenly requires legal fees, development time, and acceptance of incomplete data.

What Actually Works (And What I Use Now)

After testing Plausible, Matomo, Fathom, and PostHog across different use cases, we went with PostHog. All of these alternatives share one advantage: they're cookieless by design. No more consent banners. No more compliance headaches. No more data gaps from users who opt out.

Plausible: Clean and Simple

We tested Plausible first because of its reputation for simplicity. It delivers exactly what it promises—basic web analytics without the complexity or privacy concerns.

The dashboard shows weekly traffic trends, top referral sources, and popular pages. That's it. No session recordings, no user journey maps, no demographic breakdowns. Just the numbers that matter for making basic decisions about content and campaign performance.

The lightweight script loads fast, which matters more than most marketers realize. A 100ms delay in page load time can cost you 1% in conversions. Google Analytics can add 200-300ms. Plausible adds maybe 50ms.

Matomo: Comprehensive but Complex

We tested Matomo because we needed more detailed analysis than Plausible offered. It's the most feature-complete alternative to Google Analytics, with everything from funnel analysis to heat maps.

The self-hosted version gives you complete control over your data, which matters if you're in a data protection-conscious industry. The learning curve is steeper than other options, and honestly, it felt like we were just trading one complex system for another. The interface still feels dated compared to modern product analytics tools.

Fathom: The Middle Ground

We tested Fathom as a compromise between Plausible's simplicity and Matomo's complexity. It includes goal tracking and email reports, plus some nice touches like uptime monitoring. The interface is clean, the data is reliable, but we found ourselves wanting more depth for product analytics.

PostHog: Our Final Choice

PostHog won because it gave us everything we needed for both marketing and product analytics. Unlike traditional web analytics tools, it's built to understand user behavior throughout the entire customer journey.

We can track feature usage, run A/B tests, analyze funnels, and understand cohort retention—all in one platform. The session replay feature has been invaluable for understanding why users drop off at specific points.

What sealed the deal was the integrated approach. Instead of juggling separate tools for web analytics, A/B testing, and user research, we have everything in one place. It's not just a Google Analytics replacement—it replaces 2-3 other tools in our stack.

The Uncomfortable Truth About Analytics

Most marketing teams suffer from analysis paralysis. We collect data because we can, not because we should. We create dashboards because they look impressive. Not because they actually inform decisions.

The companies I've seen succeed with marketing analytics focus on a small number of meaningful metrics and act on them consistently. They spend less time generating reports and more time running experiments.

Privacy-first analytics platforms force this discipline. When you can't track every possible user interaction, you focus on the interactions that matter. When you can't segment users into micro-cohorts, you optimize for broader patterns that actually impact revenue.

Why I Made the Switch (And Why You Should Too)

After years of dealing with Google Analytics' complexity, compliance issues, and performance impact, switching to privacy-first analytics was liberating. My team spends less time generating reports and more time acting on insights. Our site loads faster. It's easier to pass our annual SOC II certification.

Most importantly, our data is reliable. No more traffic drops because of iOS updates or cookie deprecation. No more privacy headaches. Just clean, consistent data that helps us make better marketing decisions.

The transition isn't just about privacy or compliance—it's about getting back to what analytics should do: provide clear, actionable insights that help you grow your business. Everything else is noise.