How to Get Started with Web3 Analytics

Successfully implementing web3 analytics requires a systematic approach that balances technical requirements with business objectives. This section provides actionable steps for teams beginning their analytics journey.

Phase 1: Foundations

Begin with essential infrastructure that provides immediate value whilst building towards more sophisticated capabilities:

• Basic Event Tracking: Implement fundamental page view and interaction tracking using privacy-friendly methods

• Wallet Connection Monitoring: Track when users connect wallets and which wallet types they prefer

• Transaction Attribution: Link wallet addresses to website sessions to begin connecting onchain and offchain behaviour

• Simple Dashboard Creation: Build basic dashboards showing user counts, popular pages, and wallet connection rates

Start with tools that offer quick implementation whilst providing upgrade paths for advanced features. Focus on data collection consistency rather than complex analysis in this initial phase.

Phase 2: Enhanced Data Collection

Once basic tracking is stable, expand data collection to include more sophisticated metrics:

• Custom Event Definition: Define specific events that matter for your protocol (token swaps, liquidity additions, governance votes)

• User Journey Mapping: Track complete user flows from first visit through onchain transactions

• Cross-Chain Integration: Add tracking for user activities across multiple blockchains

• Advanced Attribution: Implement UTM tracking, referral source analysis, and campaign attribution

This phase requires more technical sophistication but provides significantly deeper insights into user behaviour patterns.

Phase 3: Intelligence and Automation

The final phase involves building predictive capabilities and automated insights:

• Wallet Scoring Systems: Develop algorithms that rank users based on value and engagement potential

• Predictive Analytics: Build AI models / agents that forecast user churn, lifetime value, or feature adoption

• Automated Alerting: Create systems that notify teams about important user behaviour changes

• Integrations: Connect data with marketing tools, customer support systems, and product development workflows through Slack, Telegram, and others

Key Web3 Analytics Metrics and KPIs

Core Engagement Metrics

Track fundamental metrics that indicate platform health and user satisfaction:

• Daily/Weekly/Monthly Active Wallets: Similar to traditional DAU/WAU/MAU but based on wallet addresses

• Transaction Volume and Frequency: Monitor both the number and value of transactions per user

• Session Duration and Depth: Track how long users spend on your platform and how many pages they visit

• Feature Adoption Rates: Measure what percentage of users engage with different platform features

Growth and Acquisition Metrics

Monitor how effectively you're attracting and converting new users:

• Wallet Acquisition Rate: Track how many new wallets connect to your platform over time

• Conversion Funnel Analysis: Measure conversion rates from first visit to wallet connection to first transaction

• Channel Attribution: Understand which marketing channels drive the most valuable users

• Cost Per Acquired Wallet (CPAW): Cfalculate acquisition costs for different user segments and channels

Retention and Value Metrics

Measure long-term user value and platform stickiness:

• Wallet Retention Curves: Track what percentage of users return after 1 day, 7 days, 30 days, etc.

• Transaction Recency, Frequency, Monetary (RFM) Analysis: Segment users based on transaction patterns

• Lifetime Value (LTV): Calculate the total value users provide over their relationship with your platform

• Churn Prediction: Identify users at risk of leaving based on declining activity patterns

Best Practices for Web3 Data Collection

Effective web3 audience insights require sophisticated data collection and integration approaches that bridge the gap between onchain and offchain user behaviour. The key lies in creating unified user profiles whilst respecting privacy preferences.

Build with Onchain Data Sources and APIs

Blockchain Node and Data Integration

Direct blockchain integration provides the most comprehensive and real-time onchain data:

• Full Node Access: Running your own nodes ensures data completeness and reduces dependency on third-party services

• Archive Node Requirements: Historical analysis requires archive nodes that store complete blockchain state history

• Multi-Chain Infrastructure: Supporting multiple blockchains requires coordinated node infrastructure across different networks

• Data Indexing Strategies: Efficient indexing systems enable fast queries across large datasets

Third-Party Data Providers

Specialised blockchain data providers offer processed, clean datasets that accelerate implementation:

• GraphQL APIs: Providers like The Graph offer structured, queryable blockchain data through GraphQL interfaces

• RPC API Services: RPC APIs from services like Alchemy or Infura provide convenient access to blockchain data

• Webhook Integration: Real-time event notifications enable immediate response to relevant onchain activities

• Historical Data Archives: Pre-processed historical data helps teams analyse long-term trends without extensive infrastructure

Data Quality and Reliability

Ensuring data accuracy and completeness requires systematic validation approaches:

• Cross-Source Validation: Comparing data from multiple sources helps identify inconsistencies or gaps

• Real-Time Monitoring: Automated alerts for unusual patterns or data anomalies prevent decision-making based on faulty information

• Data Freshness Tracking: Understanding lag times between onchain events and data availability ensures timely insights

• Error Handling Procedures: Robust error handling prevents incomplete data from corrupting analysis

Use Privacy-Compliant Tracking Methods

Cookieless Analytics Implementation

Modern web3 analytics requires approaches that don't rely on traditional tracking cookies:

• First-Party Data Focus: Collecting data directly through user interactions with your platform ensures compliance and accuracy

• Session-Based Tracking: Using ephemeral session identifiers instead of persistent cookies respects user privacy whilst enabling measurement and analysis

• Wallet-Based Identity: Using wallet connections as primary identity markers aligns with web3 user privacy expectations

• Consent Management: Clear, granular consent mechanisms allow users to control their data sharing preferences

GDPR and Regional Compliance

Web3 platforms must navigate complex international privacy regulations:

• Data Minimisation: Collecting only necessary data reduces compliance burden and respects user privacy

• Purpose Limitation: Clearly defining and communicating data usage purposes builds trust and ensures compliance

• User Rights Implementation: Providing easy access, correction, and deletion capabilities for user data

• Cross-Border Data Considerations: Understanding data residency requirements for international users

Adopt Real-Time Analytics

Event Stream Processing

Modern web3 analytics require real-time data processing capabilities:

• Message Queues: Using message streaming platforms like Kafka to handle high-volume onchain event data

• Stream Processing Frameworks: Tools like Apache Flink or Apache Storm enable real-time data transformation and analysis

• Event Schema Design: Standardised event schemas ensure consistency across different data sources and processing systems

• Backpressure Management: Handling varying data volumes without losing critical events or overwhelming processing systems

Dashboard and Alerting Systems

Real-time insights require responsive user interfaces and notification systems:

• Live Dashboard Updates: WebSocket-based dashboards that update immediately as new data arrives

• Threshold-Based Alerts: Automated notifications when key metrics exceed predetermined thresholds

• Custom Alert Logic: Flexible alerting systems that support complex conditions based on multiple metrics

• Mobile-Friendly Interfaces: Responsive design ensures insights accessibility across different devices and contexts

Performance Optimisation

High-performance analytics systems require careful optimisation:

• Database Indexing: Strategic database indices enable fast queries across large datasets

• Caching Strategies: Redis or Memcached integration reduces database load for frequently accessed data

• Query Optimisation: Efficient SQL queries and database schema design minimise response times

• Horizontal Scaling: Distributed architecture enables growth as data volumes and user bases expand

How to Choose the Right Web3 Analytics Tool

Evaluation Criteria

Choose analytics platforms based on your specific web3 requirements:

Formo: Specialised web3 analytics platform offering unified onchain and offchain tracking, wallet intelligence, and privacy-friendly data collection. Ideal for teams needing sophisticated audience insights without extensive technical setup.

Custom Solutions: For teams with specific requirements or existing data infrastructure, building custom analytics solutions provides maximum flexibility but requires significant technical investment.

Hybrid Approaches: Many teams combine specialised web3 analytics tools with traditional platforms like Google Analytics for comprehensive coverage.

Integration and Ease of Use

Successful analytics implementation requires careful integration planning:

• API Documentation: Ensure chosen platforms provide comprehensive API documentation for custom integrations

• Data Export Capabilities: Verify that you can export data for external analysis or reporting

• Webhook Support: Real-time integrations require reliable webhook systems for immediate data synchronisation

• SDK Quality: Evaluate SDKs for ease of implementation and maintenance requirements. Require SDKs to be open source for optimal security.

Team Training and Adoption

Analytics tools only provide value when teams know how to use them effectively:

• Dashboard Training: Ensure team members understand how to read and interpret analytics dashboards

• Query Building: Train technical team members to create custom queries and reports

• Alert Configuration: Set up meaningful alerts that notify relevant team members about important changes

• Regular Review Processes: Establish regular meeting schedules to review analytics insights and plan actions

Build vs Buy: Scale Faster with Formo

Building an in-house data pipeline for web3 analytics can drain resources fast. Expect months of engineering time, ongoing maintenance, and expensive data infrastructure — not to mention hiring 1-2 specialist data engineers. That’s budget and bandwidth you could invest elsewhere.

• Get started instantly with Formo — the all-in-one data platform for onchain teams

• Skip dev cycles and maintenance

• Stay agile with new features and integrations as the ecosystem evolves

Spend less time on analytics and more time shipping products. Sign up.

FAQs

What are onchain analytics and why are they important for Web3 projects?

Onchain analytics involve analyzing data from public blockchains to understand user behavior. This data includes transactions, token holdings, and smart contract interactions. They are crucial for:

• Understanding Users: See what your users do onchain, both inside and outside your app.

• Improving Products: Make data-driven decisions to enhance user experience and retention.

• Measuring Growth: Track key performance indicators (KPIs) like active users, transaction volume, and user retention.

How does wallet intelligence help in understanding the Web3 audience?

Wallet intelligence turns anonymous wallet addresses into detailed user profiles. By analyzing a wallet's transaction history, token holdings, and DeFi positions, you can identify patterns and segment your audience. This helps you understand user personas, such as DeFi power users, NFT collectors, or airdrop hunters, allowing for more targeted product development and marketing.

What are the main privacy concerns with Web3 analytics?

While blockchain data is public, user privacy remains a key concern. The challenge is to gather actionable insights without compromising user anonymity. Privacy-first analytics platforms avoid invasive tracking methods like cookies or device fingerprinting. They focus on aggregated and anonymized data to provide insights while respecting user privacy.

How can I track user activity across different blockchains?

Tracking cross-chain activity is essential for a complete view of your users. Many users interact with dApps across multiple chains like Ethereum, Polygon, and Base. To do this effectively, you need an analytics tool that can:

• Integrate data from multiple EVM-compatible blockchains.

• Unify a user's activity across these chains into a single profile.

• Analyze cross-chain behaviors like bridging, DeFi positions, and token holdings.

What's the difference between building in-house analytics and using a tool like Formo?

Building an in-house analytics solution requires significant resources. You would need to hire data engineers, build and maintain complex data pipelines, and manage infrastructure. This can be costly and time-consuming.

Using a dedicated platform like Formo allows you to get started immediately. It provides unified web2 and web3 analytics, wallet intelligence, and cross-chain tracking without the overhead of building it yourself. This frees up your team to focus on building your core product.

How can I analyze both onchain and offchain user data together?

To get a full picture of the user journey, you need to connect offchain data (like website visits or social media clicks) with onchain actions (like connecting a wallet or making a transaction). This requires a unified analytics platform that can:

• Track user behavior from initial visit to onchain conversion.

• Combine web2 metrics with web3 data for a complete view.

• Help you understand your acquisition channels and optimize your marketing funnels.