Google Analytics is fine until it isn’t. In Web3, it often breaks at the exact moment you need clean answers, because wallets hide identity, users bounce between devices, and a big chunk of the journey happens inside apps, not on tidy web pages. So founders do what founders always do: they ditch the thing that slows them down and build a stack that tells them what is working.

Today’s blog gives you seven analytics tools Web3 teams use after they stop relying on Google Analytics. The short version is this: you want one tool for privacy-first website tracking, one for product events, one for funnels and retention, and one way to connect on-chain actions to off-chain behaviour. This will help you answer the only question that matters:“What made a real user take the action you wanted them to take?”

Quick Answers – Jump to Section

Why Web3 teams quit Google Analytics
What founders want from analytics now
Tool 1: PostHog for product events and funnels
Tool 2: Mixpanel for retention and cohort answers
Tool 3: Amplitude for journeys and behaviour paths
Tool 4: Matomo for privacy-first website tracking
Tool 5: Plausible for simple, fast web analytics
Tool 6: Fathom for clean reporting without creepiness
Tool 7: A wallet and on-chain attribution layer
Final Thoughts
Frequently Asked Questions

Why Web3 teams quit Google Analytics

Two seo expert analysing a website using google analytics by Artem Podrez on pexel.com

First, Web3 users do not behave like “normal” web users. They might read your docs on mobile, connect a wallet on desktop, then finish the action inside a dApp browser. Google Analytics can record pageviews, but it struggles to tell you what happened across that messy path without a lot of duct tape.

Second, founders are tired of fuzzy numbers. They want to know which page, message, or flow step caused a wallet connect, a swap, a deposit, or a stake. If you are trying to get found in AI answers too, you also need to measure what people do after they arrive from those answers. Check out this guide on staying visible without the click.

What founders want from analytics now

When Web3 founders talk about analytics, they usually mean three things, even if they don’t say it that way. They want attribution that is not a fairy tale, funnels that show where users drop, and retention that tells them if the product is sticky or just shiny.

They also want answers that match Web3 reality. That means tracking events like wallet connect, chain switch, signature request, and transaction success. Then tying those events back to the content or channel that started the journey. If you are building a measurement stack from scratch, it helps to start with a short list of metrics VCs care about mid-sentence, because it forces you to track what matters. On-chain metrics investors watch can keep you honest.

Tool 1: PostHog for product events and funnels

PostHog is popular with Web3 teams because it is built for product analytics, not just website traffic. You can track events, build funnels, and see where users drop off in a flow, which is exactly what you need when wallet steps add friction.

Founders also like that it can sit close to the product. You can instrument events like “clicked connect,” “signed message,” and “transaction confirmed,” then see how those steps change after you ship a new onboarding flow. If you are trying to reduce wallet drop-off, you will recognise the same pain points described mid-sentence in this onboarding guide: simple steps to cut drop-off map neatly to PostHog funnels.

Tool 2: Mixpanel for retention and cohort answers

Mixpanel is the tool people reach for when they want retention answers fast. It is strong at cohorts, which is a fancy way of saying: “Show me what users who did X in week one do in week four.” In Web3, that can mean “users who bridged once” or “users who staked within 24 hours.”

The big win is that it stops arguments. Instead of guessing why retention is down, you can compare cohorts by chain, by source, or by onboarding path. That makes it easier to pick one change, ship it, and measure the result without turning your team into a debating club.

Tool 3: Amplitude for journeys and behaviour paths

Amplitude is often used when teams want to map user journeys and see behaviour paths. That matters in Web3 because users do not move in a straight line. They bounce between docs, dashboards, and wallets, and they often need a few tries before they complete a transaction.

If you want to answer questions like “What do users do right before they churn?” or “Which feature makes them come back?” Amplitude is built for that. It also helps when you are trying to connect content to product actions, because you can see which paths start with education and end with money moving.

Tool 4: Matomo for privacy-first website tracking

Matomo is a common replacement for Google Analytics when teams want more control and fewer privacy headaches. It can be self-hosted, which matters if you have strict compliance needs or you just don’t want your analytics living inside someone else’s black box.

For Web3 brands with EU users, privacy and compliance are not optional. If you are already thinking about checks like AML and sanctions screening, you will want your measurement stack to be boring and defensible too, and this compliance guide mid-sentence is a useful reminder: EU checks you cannot ignore applies to data handling more than people admit.

Tool 5: Plausible for simple, fast web analytics

Plausible is for founders who want answers without a PhD in dashboards. It gives you clean web analytics, fast load times, and a simple view of what pages and sources are working.

It will not replace product analytics, and it should not try. The point is to keep website measurement simple, so you can spend your brainpower on product and distribution. Many teams pair it with a product tool like PostHog or Mixpanel, then use Plausible for content decisions.

Tool 6: Fathom for clean reporting without creepiness

Fathom sits in the same “simple web analytics” bucket, but it is often chosen by teams that want clean reporting and strong privacy defaults. If your brand is trying to look serious, this matters, because nothing says “sketchy” like a cookie banner that looks like a ransom note.

For Web3 founders, the practical win is speed. You can check what content is working, what sources bring real users, and what pages are dead weight. Then you can fix the dead weight, instead of adding more content and hoping it works.

Tool 7: A wallet and on-chain attribution layer

This is the part people ask about the most: “How do I track users if wallets are anonymous?” The honest answer is you do not “track users” the way Web2 did. You track actions, you track sessions, and you tie those actions to a wallet only when the user chooses to connect.

So you need an attribution layer that respects that reality. That can be a simple internal event pipeline that logs wallet connect and transaction events, then joins them to off-chain sources like UTM tags, referrers, and email clicks. If you are building this, it helps to read a clear breakdown of wallet security and user flows mid-sentence, because it forces you to treat wallet steps as product, not as a marketing afterthought. How AI agents protect wallets is a good prompt for what users worry about.

Final Thoughts

If you are still using Google Analytics as your main source of truth, you are probably making decisions with half the story. Web3 users move across devices, wallets, and apps, and your analytics stack has to match that mess, or you will keep guessing.

Pick one simple web analytics tool, one product analytics tool, and one way to join on-chain actions to off-chain sources. Then keep the setup boring, keep the naming consistent, and measure the same few events every week. That is how you stop arguing and start shipping.

Frequently Asked Questions

What is the best Google Analytics replacement for Web3?

For website tracking, teams often pick Matomo, Plausible, or Fathom. For product events and funnels, PostHog is a common starting point.

Can I track wallets in analytics tools?

You can track wallet-related events, and you can link them to a wallet address only after a user connects. Before that, you are mostly tracking sessions and actions.

What events should a Web3 dApp track?

Start with wallet connect, chain switch, signature request, transaction submitted, transaction confirmed, and first value moment, like a swap or deposit.

How do I measure retention in a Web3 product?

Use cohorts based on meaningful actions, like first deposit or first stake, then check what percentage returns in week one, week four, and week eight.

Do I still need web analytics if I have product analytics?

Yes, because web analytics helps you decide what content and channels bring real users. Product analytics helps you see what those users do after they arrive.

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7 Proven Web3 Analytics Picks After Google Analytics