Traditional analytics tools like Google Analytics and Mixpanel fall short when understanding onchain user behaviour. They cannot see wallet interactions, smart contract events, or the full journey from a website visit to an onchain transaction. These tools leave major blind spots that Web3 teams cannot afford—blind spots that can lead to missed growth, poor decision-making, and wasted marketing spend.

If you are building a DeFi protocol, NFT marketplace, Web3 game, or any onchain application, this guide will show you how to fix that. You will learn how to combine onchain and offchain data, track all critical events, connect marketing wins to user actions, and unlock growth with data-driven product and marketing decisions.

Leverage these best practices to transform fragmented analytics into a single source of truth: one that drives better products and smarter campaigns.

What is Web3 Event Analytics?

Web3 event analytics is the process of systematically collecting, processing, and interpreting both onchain and offchain data to track user behaviour across decentralised applications (dApps) and DeFi protocols.

Unlike traditional web analytics, which rely heavily on page views and click data, Web3 analytics uncover interactions that take place directly on blockchain networks—capturing wallet connections, smart contract calls, and token transfers alongside classic web engagement metrics.

Key differences from traditional analytics:

• Data sources: Web3 analytics tap into public blockchains, using wallet addresses as unique (but pseudonymous) user identifiers rather than cookies or login accounts.

• Privacy: There’s a much stronger user privacy baseline—no invasive tracking or fingerprinting.

• Insight depth: You see all the steps between marketing touchpoints, in-app actions, and final onchain results (like deposits, swaps, staking events).

Key components of Web3 event analytics include:

• Onchain events: Track smart contract interactions, token transfers, and transaction volumes directly from public blockchain data.

• Offchain events: Capture website visits, button clicks, and form submissions via SDK or API integration.

• Cross-chain tracking: Monitor user journeys that span multiple EVM-compatible networks or chains like Solana or Polygon.

• Wallet clustering: Group multiple wallet addresses under a single user entity for a clearer understanding of user behaviour.

• Instant insights: Access real-time dashboards and alerts so your team can respond quickly to changing user needs.

Understanding Web3 Analytics: Data Sources, Challenges, and Solutions

Data Sources in Web3 Analytics

A robust web3 event analytics stack combines onchain and offchain insights to give you a complete pictures of the user journey.

Onchain Data Sources:

• Blockchain transaction logs: Every onchain action is logged with immutable detail—sender, receiver, method, parameters, gas cost, and more.

• Smart contract events: Events emitted from contracts (e.g., Transfer, Stake, Unstake) provide granular user interaction details. Understand exactly what happened with full business context.

• Token balance changes: Monitor how user balances fluctuate post-interaction; ideal for measuring impact of swaps, mints, or LP events.

Offchain Data Sources:

• Website and in-app interactions: Button clicks, navigation paths, session durations, and conversion rates.

• Marketing attribution codes: Use UTM parameters to connect paid ads, influencer content, and organic marketing to downstream events.

• Mobile application events: App opens, in-app purchases, and screen flows for dApps with mobile presence.

• Social media engagement: Pull in Discord, X (Twitter), Telegram analytics to map community and campaign effectiveness.

• Customer support and feedback: Tag support cases to wallet owners or user segments to close the loop on feature requests or pain points.

Unique Data Challenges in Web3 Analytics

1. Pseudonymous Users
The lack of traditional signups is both a feature and a challenge. With wallets as the primary user ID, one user might have many wallets, and wallets might be shared or ephemeral. This increases complexity in tracking active users, power users, or churn risk.

2. Cross-Chain Fragmentation
User journeys don’t always happen on a single chain. For example, a user might start on Ethereum, bridge to Arbitrum for low fees, and then interact on both. Without multi-chain analytics, you’re only seeing half the story.

3. Complex, Low-Level Data
Raw blockchain data is optimised for machine efficiency, not human readability. Raw transaction payloads must be decoded, and events must be mapped to meaningful business context.

4. Real-Time Expectations
The DeFi and onchain landscape move at lightspeed. Product and marketing teams need real-time monitoring to react to exploits, trend spikes, or campaign wins the moment they happen.

Solutions for Web3 Analytics Challenges

Wallet Clustering Techniques

• Identity resolution: Link multiple wallets to a single user.

• Transaction pattern analysis: Detect clusters based on fund flow, time-of-interaction, and function call similarity.

• ENS domain resolution: Link .eth domains to wallet addresses for additional context based on each user's public ENS profiles.

• Behavioral and social graph analysis: Map interactions across Discord/Telegram metadata, onchain DAO governance, and even offchain communities.

Multi-Chain Data Unification

• Standardise event schemas: Keep event naming and payloads consistent across EVM chains for simpler aggregation.

• DeFi positions and token holdings: Track DeFi positions and token balances to know when users move assets—and intent—between chains.

• Unified user profiles: Build composite profiles across Ethereum, Polygon, Arbitrum, and beyond using clustering and temporal analysis. Understanding which chains users are active on helps inform BD and partnerships.

• Real-time indexing: Choose web3 analytics providers that natively index multiple major chains for real-time, cross-chain analysis.

Benefits of Web3 Event Analytics

Accelerate User Growth

• Track and optimise the complete funnel: Know which campaigns don’t just drive clicks, but actual onchain actions (mint, stake, swap).

• Increase conversion rates: Identify drop-off points and friction in onboarding or transaction flows, then remove blockers to boost active user counts.

• Accelerate product-led growth: Tie product feature launches or improvements to measurable lifts in user actions, TVL, or protocol engagement.

• Data-backed user acquisition: Target top sources (influencers, communities, ads) driving high-value, high-retention users.

Example:
A DeFi app uses funnel analytics to find that 65% of users connect their wallets, but only 35% complete a first transaction. By improving their onboarding flow, transaction completion rates rises by 20%.

Track and Improve Retention

• Pinpoint high-churn cohorts: Identify if new users from paid campaigns are less sticky than community referrers—and adjust targeting or onboarding accordingly.

• Drive feature stickiness: See which product or game features most correlate to long-term retention, letting you double down on what works.

• Personalise lifecycle messaging: Trigger in-app or email nudges when users show signs of churn (e.g., wallet inactive for 30 days).

Example:
A Web3 game uses cohort analysis to uncover that players who mint their first NFT within 24 hours retain twice as well. This insight fuels a new mission focused on NFT collection.

Measure and Optimize ROI

• Measure the impact of incentives: Understand the stickiness of your incentivized TVL. Are you attracting mercenary users or loyal contributors?

• Maximise budget allocation: Invest in acquisition and retention levers with proven LTV/CAC, not gut feel.

• A/B test marketing creatives: Deploy and measure multiple campaign variants, tying onchain events all the way back to the original touchpoint.

• Accelerate feedback on product bets: Test, learn, and iterate faster with instant, full-funnel analytics.

Why Event Analytics Matters

Key Use Cases for Product Teams

Web3 product teams miss critical insights with only basic wallet or transaction counts. Event analytics unlock a data-driven approach to building better dApps.

• Optimise onboarding: Map the user journey from landing to wallet connect, approval, and first transaction. If you spot a drop-off at the wallet connect stage, test different connectors (like MetaMask or WalletConnect) and update CTAs for clarity. Measure which change moves more users to connect.

• Feature rollouts: Segment users who adopt new features. If whales jump on a new staking tool while newcomers hesitate, add in-app tips or targeted tooltips for education.

• Reduce friction: Track failed transaction events. If failures spike post-upgrade, quickly surface contract issues or UX bugs before users churn.

• Experiment velocity: A/B test onboarding screens, button copy, or in-app surveys. Attribute each change directly to onchain actions and retention.

Example: A DeFi dApp launches an improved onboarding flow. The team tracks each stage: site visit, wallet connect, tutorial completion, and successful transaction. Analysis shows that 40% of users were dropping off after the wallet connect step. By reviewing the funnel, they discover users were confused by the wallet selection dialogue. The team adds contextual tooltips and quick links to wallet setup guides. After deploying these changes, event analytics reveal a 27% increase in wallet connects and a 19% boost in users completing their first transaction.

Key Use Cases for Marketing Teams

Web3 marketing teams can’t prove ROI with vanity metrics. Event analytics connect marketing spend directly to onchain actions and revenue.

• Attribute acquisition: See which channels—from Twitter campaigns to partner referrals—drive the highest-value users based on their onchain activity. If a Galxe campaign brings in a high volume of low-activity users, shift your budget to the Farcaster campaign that attracts power users.

• Personalise campaigns: Create user segments based on onchain behaviour. Target NFT mint announcements to wallets that have previously collected similar assets, not your entire user base.

• Measure content ROI: Track which blog posts, tutorials, or social threads lead to user activation and feature adoption. If a deep-dive on staking strategy drives more deposits than a general market update, create more content like it.

• Optimise LTV/CAC: Tie acquisition costs from each channel to the lifetime value of the users they bring in. Stop guessing and invest your marketing dollars where they'll generate the highest return.

Example: A web3 gaming dApp runs marketing campaigns on both Twitter and a popular gaming guild’s Discord. By tracking user events with UTM parameters, the team sees that Twitter drives 3x more sign-ups, but users from Discord have a 50% higher D7 retention rate and spend twice as much on in-game assets. They reallocate their ad budget to deepen the partnership with the gaming guild, improving their LTV/CAC ratio by 40%.

The Building Blocks of Event Analytics

1. Objectives & Web3 Analytics KPIs

The foundation of any Web3 analytics strategy is clarity: knowing which business objectives you’re measuring against. Instead of drowning in raw event logs, focus on the Web3 KPIs that directly tie to growth, retention, and revenue.

Growth KPIs for Web3 Projects

• Daily / Weekly / Monthly Active Wallets (DAU, WAU, MAW): The Web3 equivalent of DAU/MAU in Web2. Tracks active wallet engagement across different timeframes to measure stickiness.

• User Activation Rate: Percentage of connected wallets that perform their first onchain action (swap, mint, stake) within a set timeframe (e.g., 7 days).

• Customer Acquisition Cost (CAC) & Lifetime Value (LTV): Core business metrics adapted for Web3. CAC measures the cost of acquiring a wallet through campaigns, while LTV reflects the onchain revenue generated by that wallet across its lifecycle.

Retention KPIs for User Tracking

• Wallet Retention by Cohort (D1, D7, D30): Measures how long wallets stay active after their first interaction, segmented by signup date or behavior.

• User Journey Completion Rates: Tracks funnel completion—from wallet connection → onboarding → first transaction. Drop-offs highlight friction points.

• Protocol-Level Churn & Reactivation: Identifies wallets that went inactive and those that returned, helping optimize re-engagement campaigns.

Revenue KPIs & Onchain Attribution Metrics

• Net New TVL & Transaction Volume: Measures actual capital inflow and engagement across protocols, giving a clear picture of growth.

• Average Revenue Per User (ARPU) & LTV: Calculates wallet-level revenue across time, enabling more accurate campaign ROI analysis.

• Revenue by User Segment & Source: Helps pinpoint which cohorts or acquisition channels drive the most value

By mapping KPIs directly to business goals, you create a growth framework that provide actionable insights, not vanity metrics.

2. Event Taxonomy / Catalogue

A structured event taxonomy keeps your analytics clear, scalable, and actionable.

• Adopt a standard event naming pattern: Follow the [Noun] + [Past-Tense Verb] format for all user actions—such as Swap Reviewed, Order Submitted, or Wallet Connected. This makes events instantly understandable even years after you set it up.

• Keep event names user-focused: Always write event names to reflect the user’s action. For example, Message Sent means the user sent a message.

• Maintain strict formatting: Event names are case-sensitive. Align on one format from the start. For implementation, use lowercase with underscores for easy querying (e.g., wallet_connected_metamask).

• Cover the full user journey with event categories:

• Authentication: wallet_connected, wallet_disconnected

  • Transactions: transaction_completed_swap, transaction_initiated_mint

  • Engagement: page_viewed_landing, feature_used_staking

  • Errors: error_insufficient_gas, error_signature_denied

• Add descriptive event properties: Use properties to give context to every action, including wallet_address, chain_id, and campaign data (UTM). Include key metrics like volume, revenue, and points for direct business impact insights.

• Example event payload:

  {  "type": "track",  "event": "Product Reviewed",  "properties": {    "revenue": "20.5",    "currency": "USD",    "points": "100",    "product_id": "9578257311",    "rating": 3.0,    "review_body": "Good value for the price."  }}

• Sample events to cover common journeys:

  • Session Started

  • Wallet Connected

  • Token Swapped

  • Stake Completed

  • NFT Minted

  • Referral Used

  • Purchase Completed

  • Order Submitted

  • Page Viewed

  • Message Sent

  • Auction Bid Placed

• Track revenue, volume, and points: Include these in your event properties so your analytics reveal direct business value and help optimize incentives.

• Keep an event catalog: Maintain a shared repository documenting every event name, properties, and usage. This aligns product, growth, and engineering, and speeds up onboarding new team members.

3. Onchain Attribution (UTM→Wallet)

Traditional Web2 attribution stops at the click. Web3 onchain attribution extends that journey to the wallet, connecting campaigns to actual blockchain activity like swaps, mints, and deposits. This is essential for proving ROI on marketing spend.

Steps for Implementing Onchain Attribution:

1. Use UTMs Everywhere: Tag every outbound campaign—ads, influencer posts, content links—with UTM parameters to identify traffic sources.

2. Persist UTMs Through Wallet Connect: Store UTM values during the user’s first visit, then attach them when the wallet is connected.

3. Bind UTM to Wallet ID: At the moment of the wallet’s first interaction (mint, swap, or stake), permanently link the UTM to that wallet.

4. Attribute Onchain Actions Back to Source: Every transaction—whether adding liquidity, purchasing an NFT, or depositing collateral—can now be traced to the originating campaign.

Why Onchain Attribution Matters

• Prove which campaigns actually drive TVL, volume, or revenue.

• Compare influencer ROI by measuring wallets and transactions attributed to each partner.

• Document cross-channel user journeys from the first click to the final transaction.

When set up properly, onchain attribution ensures teams aren’t just tracking traffic but are directly connecting Web3 marketing spend to onchain business outcomes.

4. Web3 Identity & Wallet Clustering

One of the biggest challenges in Web3 analytics is moving beyond raw wallet addresses and event logs to form a clear picture of real users. A single person might operate multiple wallets across chains, making it difficult to track engagement, retention, and growth accurately. Wallet clustering in Web3 solves this by unifying scattered addresses into a single multi-chain identity profile.

Key Techniques for Cross-Wallet Profiling:

• Transaction Clusters: Map flows of tokens, NFTs, or stablecoins between wallets to infer ownership. For example, when funds consistently flow from a “main wallet” into smaller wallets, clustering models can bundle them into one user.

• Temporal Analysis: Wallets that execute transactions in near-perfect synchrony—such as swapping the same token within seconds—are likely controlled by the same entity.

• Social and ENS Signals: Onchain identities like ENS domains (.eth), combined with offchain social handles (Discord, Twitter), strengthen clustering confidence and connect Web2 to Web3 identity analytics.

• Privacy & Compliance: Always anonymize and aggregate data where possible. Transparent disclosures about how wallet clustering works build trust with privacy-conscious users.

5. Sybil Detection & Wallet Scoring

Fake wallets created to farm airdrops or distort growth metrics—known as Sybil attacks—are a persistent challenge in Web3 ecosystems. Effective Sybil detection and wallet trust scoring ensures analytics reflect human activity rather than bot activity.

Detection Strategies:

• Pattern Matching: Spot bursts of wallet creation or identical transaction sequences such as hundreds of wallets claiming an airdrop in seconds.

• Economic Outliers: Genuine users typically show deposits, swaps, or NFT purchases. Wallets with no inbound value or those that always max out reward limits are high-risk.

• Wallet Reputation Scores: Assign each wallet a trust score by analyzing activity diversity and cross-protocol history. Long-term activity across DeFi, NFTs, and governance increases legitimacy, while wallets that only target reward programs appear fraudulent.

Actionable Insight: Excluding Sybil clusters from LTV (lifetime value) analysis, retention metrics, and growth funnels ensures that Web3 business analytics reflect real customer behavior—not bots.

6. Wallet Cohort & Lifecycle Analytics

Treating wallets as static addresses hides important behavioral patterns. Instead, wallet cohort analysis and user lifecycle analytics in Web3 help track how real users move from first contact to long-term retention.

Cohort Analysis Approaches:

• Time-Based Cohorts: Segment wallets by signup month (e.g., “January 2025 signups”) to measure retention and revenue trends over time.

• Behavior-Based Cohorts: Create segments such as “NFT flippers,” “power swappers,” or “governance voters.” This identifies which behaviors are most tied to long-term growth.

Wallet Lifecycle Stages:

• Visitor → Connected Wallets: Users who connect but don’t transact.

• Onboarded → First Transaction: The critical first moment of value, such as a swap or mint.

• Engaged → Repeat Usage: Repeat swaps, referrals, staking, or NFT engagement.

• Churned → Inactive Wallets: No activity for 30/60+ days.

Growth Optimization: Use lifecycle insights to trigger personalized campaigns—for example, referral rewards for engaged users or win-back emails for churned wallets. This transforms retention into a strategic growth lever for Web3 apps.

7. Web3 Analytics Dashboards

A Web3 analytics dashboard consolidates fragmented data into real-time, actionable insights for every team. Unlike static reports, dashboards allow live monitoring of KPI reporting, event data, and blockchain activity on custom charts.

Dashboards by Team:

• Executives: TVL growth, DAU (daily active wallets), CAC/LTV ratios, and revenue by source.

• Product Teams: Onchain feature adoption, A/B test results, user flow drop-offs.

• Marketing: Campaign attribution, influencer ROI in Web3, viral growth loops, referral tracking.

• Engineering: RPC error logs, failed smart contract calls, app performance impact on transactions.

With the right tools, teams can track real-time blockchain events, align on growth goals, and react instantly to new data—all from one unified dashboard.

8. Real-Time Alerts & Notifications

In crypto, events unfold in seconds. Real-time Web3 event monitoring and alerts help teams respond to growth signals, product issues, and security threats without delay.

Examples of Web3 Alerts:

• Growth Signals: Spikes or drops in wallet connections, daily active wallets, or transaction volume.

• Product Performance: Error surges or failed contract calls after a new deployment.

• Security Monitoring: Suspicious wallet clusters, flash loan exploits, or abnormal asset flows.

Integrating alerts into Slack, email, or PagerDuty ensures instant notifications reach the right teams. This not only protects protocol security but also captures opportunities by spotting user growth trends in real time.

Event Analytics Best Practices for Web3

Event analytics is the backbone of understanding how users interact with your dApp, protocol, or Web3 product. Unlike Web2, where clicks and pageviews dominate, Web3 event analytics often revolves around wallet connections, onchain transactions, token movements, governance participation, and cross-chain activity. By tracking and interpreting these signals, teams can uncover growth levers, identify friction points, and design better user experiences.

1. Start with Core Events

Before building a complex tracking system, define your minimum viable event plan. Focus on the small set of events that truly matter:

• Wallet connections (first entry point into your product).

• Key onchain transactions (swaps, mints, stakes, or claims).

• Critical feature usage (DAO voting, bridging, lending, or NFT purchases).

Validate data integrity early by checking that events fire correctly and align with expected user flows. Poor initial setup leads to noisy data that undermines later insights.

Tip: Treat this as your “North Star” event set. Everything else builds on these foundational metrics.

2. Iterate Progressively

Rather than tracking everything at once, layer in advanced analytics features gradually:

• Lifecycle segmentation → Identify patterns across first-time wallets vs. power users.

• Predictive churn detection → Flag wallets likely to go inactive.

• Campaign ROI attribution → Map wallets back to acquisition channels to see which campaigns actually drive sustainable usage.

This phased approach prevents data overload and ensures your analytics pipeline remains actionable, not overwhelming.

3. Review Data Quality

Strong event analytics depends on clean, reliable data. In Web3, this means syncing both onchain and offchain data sources:

• Automated schema validation to prevent broken or inconsistent event formats.

• Error tracking to detect missing wallet or transaction events.

• Dashboard health checks so stakeholders trust what they see.

• Routine reviews to prune outdated events and align tracking with evolving product goals.

A single broken event (e.g., failed transaction logs) can distort entire conversion funnels. Prioritize validation early and often.

4. Privacy by Design

Web3 users expect privacy and transparency in how their data is handled. Build trust and reduce compliance risk by:

• Anonymizing wallet event data when possible.

• Minimizing personally identifiable information (PII) stored offchain.

• Clear opt-in consent policies for any cross-platform or marketing tracking.

• Secure storage practices that respect both GDPR and crypto-native expectations of privacy.

  
  

5. Ask the Right Questions with Event Analytics

Well-structured event analytics should answer practical growth and retention questions:

• Onboarding: What blocks wallets from completing their first transaction?

• Acquisition: Which marketing channels deliver high-engagement, high-LTV users?

• Retention: At what stage are valuable wallets likely to churn, and why?

• Expansion: Which features or tokens drive cross-product adoption?

6. Use Advanced Analytics Techniques

Going beyond raw event counts, apply deeper analytics methods to unlock insights:

• Cohort Analysis: Group wallets by signup date, first transaction type, or campaign source. Understand retention drivers and shape cross-channel growth strategies.

• Funnel Analysis: Track how wallets move through multi-step flows (connect → fund → transact). Spot friction points and boost conversion rates.

• Segmentation: Split users into cohorts (whales, power users, explorers, dormant wallets). Learn which behaviors correlate with long-term success.

• Predictive Analytics: Use historical onchain behavior to forecast churn probability, conversion likelihood, or campaign winners. This enables proactive retention strategies.

7. Create Feedback Loops

Event analytics should not sit in a dashboard. It should inform daily decision-making:

• Weekly data syncs across teams (product, growth, engineering).

• Continuous A/B testing to validate hypotheses.

• Roadmap adjustments based on experiment results.

• Adaptive marketing campaigns that respond to real wallet activity.

The most successful Web3 companies don’t just measure - they act on event data in near real-time, creating a compounding growth advantage.

Summary

By treating Web3 event analytics as a discipline (not just a reporting tool) you’ll unlock deeper visibility into user journeys, onchain conversions, and product-market fit signals.

Web3 event analytics is foundational to any data-driven onchain teams. By unifying offchain and onchain events, building advanced attribution and clustering, and deploying actionable dashboards and alerts, teams fuel growth, optimise retention, and maximise ROI.

Next steps:

• Audit your current stack for Web3 visibility gaps

• Prioritise critical event tracking and attribution

• Roll out dashboards for all key functions

• Iterate quickly with feedback loops—data drives decisions

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Try Formo for free and see how better analytics unlocks Web3 growth.

Frequently Asked Questions

What’s the difference between Web3 analytics and traditional web analytics?

Traditional web analytics relies on cookies, browser sessions, and IP tracking to measure clicks, pageviews, and user flows. In contrast, Web3 analytics captures both onchain and offchain signals:

• Onchain events such as wallet connections, token transfers, swaps, staking, NFT mints, and DAO votes.

• Offchain signals like landing page visits, referral links, or ad clicks tied to wallet activity.

This makes Web3 analytics uniquely capable of mapping the entire wallet journey—from first website visit to cross-chain transaction—something invisible to classic analytics platforms.

How do you track users in Web3 without compromising privacy?

In Web3, tracking is pseudonymous by default. Instead of identifying users with personal data:

• Wallet addresses act as primary identifiers.

• Optional clustering (ENS domains, Lens profiles, Farcaster handles, or social logins) can enrich user profiles—without breaking privacy norms.

• No invasive cookies, fingerprinting, or hidden pixels are needed.

The result: analytics that are transparent, verifiable, and privacy-preserving, aligning with the ethos of decentralization.

Can Web3 event analytics work across multiple blockchains?

Yes. Modern Web3 analytics solutions are multi-chain by design. They:

• Index events across Ethereum, Polygon, Solana, Base, Arbitrum, and other L2s.

• Track bridge interactions (e.g., ETH bridged to Polygon, assets moved cross-chain).

• Unify user behavior under one identity so you can see end-to-end journeys, not siloed data per chain.

This is essential in a multi-chain world, where users frequently move assets across ecosystems.

How accurate is onchain attribution for marketing campaigns?

Attribution in Web3 can be more precise than in Web2, provided it’s done correctly:

• UTM-to-wallet mapping links campaign sources to onchain activity.

• Clean event taxonomies ensure wallet actions are consistently categorized.

• Multi-touch models (not just last-click) improve attribution accuracy.

Well-implemented systems achieve 70–90% attribution accuracy, often outperforming traditional ad-tracking methods that suffer from ad blockers and cookie deprecation.

What’s the best way to handle bot detection in Web3 analytics?

Sybil resistance is a major challenge. The best approach is multi-layered:

1. Pattern recognition – spotting abnormal wallet creation bursts or repetitive transaction patterns.

2. Transaction clustering – grouping wallets that interact in suspiciously coordinated ways.

3. Behavioral scoring – analyzing usage patterns (time-to-first-transaction, gas usage, retention curves) that differ between bots and humans.

4. Manual review – applied selectively to high-value user segments where false positives carry high cost.

This hybrid approach balances scalability with accuracy.

What are the most important KPIs to track in Web3 applications?

The right KPIs depend on product maturity, but core metrics include:

• DAU/WAU/MAU – daily, weekly, monthly active wallets.

• Activation rates – % of new wallets that complete a key transaction.

• Retention – D7/D30 return rates post-onboarding.

• Monetization – TVL (Total Value Locked), ARPU (Average Revenue per User).

• Campaign performance – wallet conversions tied to acquisition channels.

Advanced teams also measure LTV (lifetime value) by wallet clusters and protocol stickiness across chains.

How do Web3 analytics handle cross-chain user identity?

Cross-chain identity resolution is done through onchain clustering and optional offchain enrichment:

• Clustering heuristics: wallets that interact with the same bridges, contracts, or funding sources are grouped as one user.

• Identity overlays: ENS names, social handles, or federated logins can enrich pseudonymous wallets.

• Privacy-preserving attribution ensures users remain pseudonymous while still enabling full-funnel, multi-chain insights.

This approach enables marketers and product teams to see a unified view of a wallet’s lifecycle, without violating compliance or user trust.

What are the best tools for Web3 analytics?

The best Web3 analytics tools depend on your use case:

• Product & growth analytics: Tools like Formo provide dashboards and SDKs for tracking wallet behavior, conversions, and retention.

• Protocol-level insights: Dune and DefiLlama specialize in DeFi metrics like TVL, revenue, and protocol comparisons.

• Custom data pipelines: Indexing solutions like Goldsky or hosted APIs (e.g., Alchemy, Covalent) let teams build bespoke analytics stacks.

Most teams use a hybrid approach—combining SDK-based event tracking with onchain data indexing for full-funnel visibility.

What are best practices for tracking onchain user behavior?

To get reliable insights, follow these best practices:

1. Track lifecycle milestones – wallet connection, first transaction, repeat activity, advanced features.

2. Unify onchain + offchain signals – connect UTMs, referral links, or campaign data with wallet actions.

3. Use consistent schemas – define event naming conventions (e.g., transaction_completed, token_staked) to avoid fragmented data.

4. Prioritize real-time tracking – latency matters when optimizing campaigns or preventing churn.

5. Regularly audit data pipelines – onchain indexing errors or missed contract events can distort results.

Clean, structured data makes it far easier to uncover retention drivers and optimize user journeys.

How do you measure growth in Web3?

Growth in Web3 goes beyond pageviews or app installs. Key indicators include:

• Onboarding success: % of wallets that go from connection → first transaction.

• User retention: D7, D30 wallet activity rates, measured by onchain activity.

• Liquidity and revenue: TVL, volume, revenue, fees.

Together, these provide a holistic view of sustainable growth in web3.