Use specialized onchain analytics—combining wallet intelligence, real-time monitoring, and privacy-first tools—to track meaningful metrics, attribute user journeys, and optimize Web3 product retention and growth.
Web3 product teams face unique challenges when making data-driven decisions because blockchain apps produce transparent, immutable data that requires specialized interpretation. Onchain analytics platforms read blockchain data directly to provide real-time insights into wallet behavior, transaction patterns, and user engagement across decentralized applications. With the right tools, teams can measure meaningful metrics, understand user journeys, and optimize applications for better retention and growth. This guide reviews top onchain analytics platforms and best practices for implementing data-driven product strategies in Web3.
Define Key Metrics for Onchain Product Analytics
Choosing the right metrics is essential for informed product decisions. Onchain metrics capture user and asset activity recorded on a blockchain—wallet interactions, smart contract calls, and token movements—offering visibility unavailable to traditional web analytics.
Essential metrics:
Active addresses: unique wallets interacting over time (DAU/WAU).
Transaction volume: economic activity and usage intensity.
Wallet retention: return rate after first interaction; reveals product-market fit.
Smart contract interactions: feature popularity and adoption.
Token flows: asset movement and economic patterns.
Gas usage (network fees): user cost and UX friction.
NVT ratio: protocol efficiency by comparing network value to transaction volume.
Metric | Business Question | Use Case |
|---|---|---|
Active Addresses | How many unique users are engaging daily/weekly? | Track DAU/WAU growth |
Transaction Volume | What's the economic activity level? | Measure product usage intensity |
Wallet Retention | Are users coming back? | Optimize onboarding and features |
Smart Contract Interactions | Which features are most popular? | Prioritize development resources |
Token Flows | How do assets move through the ecosystem? | Understand economic patterns |
Gas Usage | What's the user experience cost? | Optimize transaction efficiency |
Prioritize actionable metrics that correlate with user satisfaction and business objectives rather than vanity metrics like raw TVL. Focus on measures that guide product decisions: retention, adoption, and feature usage.
Select the Best Onchain Analytics Tools for Your Product
Onchain analytics platforms aggregate and interpret blockchain data to reveal wallet behaviors and transaction trends, helping teams make data-driven product decisions. The landscape includes general-purpose dashboards, wallet-intelligence suites, and compliance-focused solutions.
Platform | Strengths | Supported Blockchains | Ideal Use Case |
|---|---|---|---|
Formo | Privacy-first, unified analytics | Ethereum, Polygon, BSC, Arbitrum | Product teams needing comprehensive user insights |
Nansen | Wallet labeling, AI insights | 15+ major chains | Research and institutional analysis |
Dune Analytics | Custom queries, community dashboards | Ethereum, Polygon, Solana | Data analysts and researchers |
Chainalysis | Risk detection, compliance | 100+ blockchains | Enterprise compliance and security |
Elliptic | AML/KYC, investigation tools | Bitcoin, Ethereum, others | Financial institutions and compliance |
When evaluating platforms, consider: privacy-first approaches, wallet intelligence, AI-powered insights, risk detection/compliance, and behavioral segmentation. The right choice depends on technical resources, privacy requirements, and the specific analyses you need.
Formo's Privacy-First Analytics Solution
Formo is positioned for privacy-conscious Web3 teams that want unified insights across web and blockchain without invasive tracking or complex integrations. It combines web, product, and onchain analytics into a single dashboard and emphasizes privacy by design.
Formo’s core capabilities:
Real-time attribution: follow user actions from first touch to conversion across web and blockchain.
No third-party cookies: maintain privacy while collecting actionable signals.
Wallet intelligence: segment users by onchain behavior and holdings.
Unified dashboard: view web analytics, product KPIs, and blockchain data together.
Privacy-first SDK: simple integration that respects user privacy.
Advanced segmentation: create cohorts based on wallet activity and engagement.
Formo connects wallet addresses to user journeys, enabling attribution that spans marketing touchpoints through onchain transactions, eliminating data silos and speeding product optimization.
Track and Analyze User Behavior Onchain and Offchain
Behavioral analytics for Web3 combines onchain wallet intelligence (signups, swaps, governance votes, transactions) with offchain product and web events (pageviews, clicks, time on site) to reveal retention patterns and churn signals. The combined data enables comprehensive behavioral segmentation and accurate attribution.
Example conversion attribution workflow:
Track initial touchpoint (campaign landing page).
Monitor engagement (time on page, pages viewed).
Capture wallet connection to the app.
Analyze onchain actions (first transaction, token swaps, feature usage).
Measure retention (return visits and ongoing onchain activity).
Attribute conversion to the original marketing source.
Native unification of onchain and offchain data yields attribution and user insights neither source can provide alone; purely onchain or offchain views miss critical parts of the customer journey.
Implement Real-Time Monitoring for Agile Product Decisions
Real-time monitoring tracks blockchain activity and user behaviors—wallet transactions, smart contract interactions, feature usage—to enable rapid product responses in a fast-moving Web3 environment. Immediate alerts help teams act on emerging issues or opportunities before they escalate.
Monitor and alert on events such as:
Transaction volume spikes (viral adoption or scaling pressure).
Increased failed transactions (contract bugs or network congestion).
New-user onboarding patterns (successful campaigns or friction).
Whale wallet movements (market shifts or strategic interest).
Smart contract upgrade adoption (feature rollout response).
NFT mint participation (community engagement).
Set up actionable alerts that require a response rather than passive notifications. Nansen provides institutional alerting; Formo offers real-time event tracking geared to product teams. Focus alerts on metrics tied to SLA, UX, or growth objectives.
Combine Quantitative Analytics with Qualitative User Feedback
Quantitative data (onchain activity, retention, transaction volumes) shows what users do; qualitative feedback (surveys, AMAs, community posts, interviews) explains why. Combining both gives context to metrics and guides targeted product changes.
Data Type | Information Provided | Collection Methods | Example Insights |
|---|---|---|---|
Quantitative | Usage patterns, retention rates, transaction volumes | Analytics dashboards, blockchain data | 40% of users drop off after first transaction |
Qualitative | User motivations, pain points, feature requests | Surveys, AMAs, Discord feedback | Users find gas fees confusing and unpredictable |
Use analytics to detect anomalies (e.g., post-upgrade drop-offs) and qualitative research to identify root causes (UI confusion, technical issues, or perceived value). Validate hypotheses about low adoption by interviewing users to determine whether discoverability, complexity, or value perception is the problem.
Successful teams build integrated feedback loops: identify patterns with onchain analytics, then deploy qualitative research to uncover user needs and design fixes informed by both data types.
Iterate and Optimize Product Features Based on Insights
Optimize through a systematic loop: monitor metrics, analyze patterns, hypothesize improvements, test on targeted segments, and iterate based on measured results. This ensures changes are validated by user behavior rather than assumptions.
Segmentation and retention:
Segment users by transaction frequency, token holdings, recency, and feature engagement to tailor experiments.
Use retention windows (1-day, 7-day, 30-day) to evaluate onboarding and long-term engagement; users active after 7 days often behave differently than early churners.
Practical approach:
If a key feature has low usage, diagnose whether it’s discoverability, UX complexity, onboarding gaps, or low perceived value.
Run targeted experiments with control groups and measure impact on retention, adoption, and satisfaction.
Iterate quickly, tracking effect size via updated dashboards and refining interventions until metrics improve.
Measure every change and keep control cohorts to ensure observed effects are causal and reproducible.
Frequently Asked Questions About Onchain Analytics for Product Teams
What Metrics Should I Focus on for Web3 Product Growth?
Focus on active addresses, transaction volumes, wallet retention rates, and smart contract interactions; these track engagement, economic activity, stickiness, and feature adoption and provide actionable signals for product changes.
How Do Onchain Analytics Tools Help Improve User Retention?
They reveal wallet activity over time, expose churn patterns and feature engagement, and identify at-risk segments so teams can target interventions before users disengage.
What Are Best Practices for Using Wallet-Level Segmentation?
Group wallets by transaction frequency, token balances, feature engagement, and recency; align segments with business goals and tailor onboarding, messaging, and experiments to each cohort.
How Can I Ensure Privacy Compliance While Analyzing Onchain Data?
Use privacy-first platforms that aggregate pseudonymous wallet data without PII or third-party cookies, apply anonymization best practices, and focus on behavioral and aggregate insights, not individual identity.
How Do I Create Custom Dashboards to Monitor Key Product KPIs?
Select your core KPIs, combine onchain and offchain metrics, apply cohort filters, build visualizations, and configure real-time alerts; iterate dashboards as priorities evolve to keep them actionable.
