On-chain user behaviour is simple: it is what wallets do, not what they say they will do. If you want to grow a Web3 product, you need to see the full path from wallet connect to first transaction to repeat use, then spot where people drop off. In today’s blog, I’m breaking down six tools founders use to track that behaviour, plus a clean way to pick the right stack without turning analytics into a full-time job.
You will also see the questions people keep asking in public forums, because they point to the real pain: ‘How do I track users without cookies?’, ‘How do I stop bots from wrecking my numbers?’, and ‘How do I connect marketing clicks to on-chain actions?’ If you can answer those, you can build faster, ship calmer, and stop guessing.
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Quick answers – jump to section
- What on-chain user behaviour tracking means in plain English
- Tool 1: Dune for custom dashboards and weird questions
- Tool 2: Nansen for wallet labels and behaviour patterns
- Tool 3: Flipside for ready-made datasets and community queries
- Tool 4: Footprint for cross-chain dashboards without the heavy lift
- Tool 5: Arkham for entity clues and wallet clusters
- Tool 6: Formo for product analytics and on-chain attribution
- How founders usually mess up on-chain analytics
- A simple setup that works for most teams
- Final Thoughts
- Frequently Asked Questions
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What on-chain user behaviour tracking means in plain English
On-chain tracking is watching what a wallet does over time. Did it connect, sign a message, swap, stake, vote, bridge, or vanish forever after paying gas once. That is behaviour. It is not a survey, and it is not a vibe check.
The tricky part is that wallets are not people, and people can have many wallets. Some wallets are bots. Some are farmers. Some are your own team testing flows at 2am. So the goal is not ‘perfect truth’. The goal is a useful view that helps you make better product calls.
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Tool 1: Dune for custom dashboards and weird questions
Dune is the tool founders use when they have a question that does not fit into a neat template. Things like: ‘How many wallets did two swaps in the first week, then came back a month later?’ or ‘Which contract call is the real activation event for our app?’ Dune is great because you can query on-chain data and build dashboards that match your product.
The public questions I see a lot sound like: ‘Do I need SQL to use Dune?’ and ‘Can I track retention by wallet?’ The honest answer is yes, SQL helps, but you can start by copying existing dashboards and editing them.
If you want a quick primer on how content systems scale, you can borrow the same thinking from how content hubs drive real growth.
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Tool 2: Nansen for wallet labels and behaviour patterns
Nansen is what you use when you want wallet context fast. Instead of staring at raw transactions, you get labels, segments, and behaviour patterns that help you answer questions like: ‘Are these wallets real users or airdrop hunters?’ and ‘Are we getting traction with DeFi power users or casual dabblers?’
A common founder question is: ‘How do I spot real usage, not wash trading?’ You start with patterns. Look for repeat actions, time gaps that feel human, and behaviour that matches your product story.
If you want a simple way to think about ‘real usage’ versus noise, stablecoin payments in 2026 is a good mental model.
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Tool 3: Flipside for ready-made datasets and community queries
Flipside is useful when you want speed. You get curated datasets, and you can use community-built queries to answer common questions without building everything from scratch. It is a solid option for early teams that need answers this week, not a perfect data warehouse next quarter.
People often ask: ‘How do I build cohorts in Web3?’ The simple version is: pick one on-chain event that marks a start, then track what those wallets do after. For example, ‘first deposit’ or ‘first swap’ or ‘first vote’. Then compare cohorts by week or month to see if retention is improving.
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Tool 4: Footprint for cross-chain dashboards without the heavy lift
Footprint is a practical choice when you are dealing with more than one chain and you want dashboards quickly. Founders ask: ‘How do I track users across chains?’ because users bridge, and your metrics get split into pieces.
Cross-chain tracking is hard because you are trying to connect identities that were never meant to be linked. The best approach is to track behaviour patterns and funnel steps per chain, then compare them.
If you are also thinking about how AI search changes acquisition, entity-based SEO for Web3 teams will help you frame how people find you before they ever connect a wallet.
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Tool 5: Arkham for entity clues and wallet clusters
Arkham is the tool you reach for when you need ‘who is behind this’ signals. It can help you cluster wallets and spot links that are not obvious at first glance. That is useful for market research, competitor watching, and sometimes for security.
A question that keeps coming up on Reddit is basically: ‘Why can’t ChatGPT just analyse these wallets for me?’ Because the model cannot fetch live chain data on its own. You still need explorers and analytics tools to pull the facts, then you can use an LLM to help you reason about what you are seeing.
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Tool 6: Formo for product analytics and on-chain attribution
Formo is built around a founder problem that sounds boring until you try to solve it: ‘Which marketing touch led to an on-chain action?’ People ask this in a hundred ways, like: ‘How do I track wallet connect to first transaction?’ and ‘How do I measure CAC and LTV without cookies?’
The useful idea is simple. Treat the wallet as the anonymous ID, then connect off-chain events to on-chain actions when the wallet connects. That gives you a funnel you can improve.
If you want to push this into a proper growth plan, 7 growth channels that still work for DeFi projects in 2026 gives you channel ideas you can track end-to-end.
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How founders usually mess up on-chain analytics
First, they pick a tool before they pick a question. Then they end up with a dashboard full of numbers that do not change any decisions. Start with the decisions you need to make, then pick the minimum tracking that supports them.
Second, they treat every wallet as a user. That inflates everything. You need basic filters for bots, farmers, and internal testing. Even a simple rule like ‘must do X twice, at least Y days apart’ can clean up your view.
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A simple setup that works for most teams
If you are early stage, start with one dashboard tool and one ‘context’ tool. For example, Dune for your core funnels and Nansen for wallet context. Add a product analytics layer only when you have enough traffic to learn from it.
Then write down your funnel in plain English. ‘See landing page, connect wallet, do first action, do second action, come back next week.’ Track those steps. If a step is unclear, instrument it. If a step is useless, delete it.
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Final Thoughts
On-chain analytics is not about spying. It is about reducing guesswork. If you can see where wallets drop off, you can fix the flow, tighten the copy, and build a product that people return to.
Pick tools based on the questions you need answered, not the latest trend. Start small, keep it readable, and treat your dashboards like product features. If they do not help you make a decision, they are just expensive wallpaper.
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Frequently Asked Questions
How do I track users without cookies in Web3?
You use the wallet as the anonymous identifier. Track off-chain events until wallet connect, then link the session to that wallet and follow on-chain actions after.
Do not try to recreate Web2 tracking pixel logic. Web3 users expect privacy. Keep the plan simple and explain what you track and why.
How do I connect marketing clicks to on-chain actions?
Use UTMs and event tracking off-chain, then map the first wallet connect to the campaign source. After that, track the on-chain actions that count as activation and retention.
If you run multiple channels, keep attribution rules consistent. Otherwise you will spend your week arguing with your own spreadsheet.
How do I spot bots and airdrop farmers in my numbers?
Look for patterns that do not feel human. Very tight timing, repeated actions across many wallets, and behaviour that only hits incentive events are common signs.
Then add simple filters. For example, require a second action after the incentive window, or require a time gap between key steps.
Which metrics should I track first?
Start with Daily Active Wallets, first action completion, and repeat action rate. If you can tie those to a funnel step, you can improve something.
Once that is stable, add cohorts, retention by week, and segment behaviour by feature use.
Do I need SQL to do on-chain analytics?
Not always, but it helps. Tools like Flipside and Footprint can get you far with templates and community queries.
If you want custom funnels that match your contracts, SQL becomes the simplest way to ask precise questions.
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