User behavior is the backbone of marketing; it helps you to understand how your users engage with your product or brand so you can make informed decisions.
In Web2, behavioral data is typically collected through cookies and tracking pixels on centralized platforms. However, in Web3 the users’ journey span across two different worlds: offchain and onchain. Web3 Behavioral Analytics combines onchain data (transactions, wallet activity, smart contract calls) with offchain behavior (page visits, clicks) so you get a complete view of the full user journey.
In this guide, you’ll learn about:
What Web2 and Web3 behavioral data is
Tools you can use to capture and analyze behavioral data
Behavioral Analytics use cases and examples
Key Takeaways:
Web3 behavioral data is captured across your dApp, protocol, and blockchains to help Web3 product and marketing teams understand user intent, conversions, and drop-off points.
With behavioral data, teams can conduct behavioral analysis through transaction patterns, funnel, or path analysis.
Behavioral analytics can be helpful for product, marketing, and data teams as it helps identify power users, segment users based on behaviors, and make data-driven decisions.
What is Web3 Behavioral Data?
Behavioral data captures how users interact with your product. In Web2, these interactions include page views, sign-ups, clicks, purchases, log-ins, etc.
In Web3, behavioral data is more complex. User activity is spread across multiple dApps, protocols, and blockchains. A web3 user's journey spans across both on-chain and off-chain channels. Instead of email or IRL identifiers, users log in through crypto wallets. Key conversion events such as swapping on a DEX happen onchain, beyond the reach of existing tools. Traditional analytics tools like Mixpanel and Google Analytics cannot see onchain data, so you miss out on crucial information.
Capturing onchain behavior helps you understand:
Which channels, campaigns, and referrals bring users to your product
User intent based on past onchain activity
Activation rates (A-ha moments) and drop-off points in user journeys
Feature adoption and overall user flows
Conversion rates across your core funnels
Pain points that cause friction or abandonment
By analyzing these interactions, you gain a clearer picture of how users discover, engage with, and adopt your product—allowing you to optimize both growth and retention.
Web3 behavioral analytics combines both onchain signals and offchain signals to give product teams a complete view of the user journey to drive growth without guesswork.Key Differences Between Web2 and Web3 Marketing
Challenges of Collecting Behavioral Data in Web3
Web3 behavioral data can help teams make informed decisions, but it also introduces its own set of problems:
Traditional Tools Aren’t Made for Web3
Common analytics platforms like GA4 or Mixpanel are designed for Web2, using cookies, pixels, and session tracking for analysis. However, these tools cannot track onchain interactions like wallets or smart contract transactions, making it hard to understand users’ onchain actions and calculate conversion rates.
Today's top engagement and retention tools ignore the wealth of data available onchain. For example, marketing solutions like Hubspot and Klaviyo do not support on-chain actions and events, while product analytics systems like Mixpanel and Amplitude do not natively handle blockchain data. Data about user acquisition, drop-off, and retention from a large chunk of the web3 user's journey is missing.
On the other hand, building analytics in-house is time consuming and technical complex, requiring teams to have dedicated data engineers or developers. Scrappy teams are unable to invest in proper analytics because they are often busy with building their product.
Fragmented Data
User data in Web3 fragmented into many data silos, from web activity data to onchain user data. Web3 user interactions happen across platforms, wallets, chains, and dApps, which raises questions:
How can I link multiple wallets into a single user?
Who are my top users by volume and revenue?
Where do my top users come from?
How do I track or segment users who interact anonymously?
How can I track conversion rates when users switch between platforms and chains?
To do it right, you need extensive SQL and data engineering expertise to collect, process, and analyze data. You'd need a dedicated team of data engineers and analysts to create data pipelines and dashboards for your project. As a result, product teams spend valuable resources on infrastructure and integrations instead of shipping user-facing features.
Building in-house data pipelines and analytics infrastructure is beyond the means of many web3 teams. Not every team can allocate resources away from building their core product. As a result, analytics often falls by the wayside. They remain in the backlog forever.
Even with analytics tools, blockchain data is raw and often without context, making it hard to interpret data. For example, when users abandon transactions, how do you know if it’s due to high gas fees or confusing UI?
It is challenging to extract actionable insights from data that is fragmented and without context, you need crypto-native analytics tools that can capture, unify, and transform data into valuable insights for your team.
How to Collect Behavioral Data
Offchain behavioral data is collected through event tracking, which captures user interactions on Web2 sites, web applications, and mobile applications. Through Software Development Kits (SDKs), you can set up triggers depending on specific actions you wish to track.
Onchain behavioral data is public and reveals all user interactions across the blockchain. Blockchain data is globally open to anyone with an internet connection so you don’t need approval from any platforms (unlike Web2 data). You also don’t need an API key or a business deal since there is no gatekeepers either.
Onchain interactions are captured as smart contract events and transaction logs, which can be processed and analyzed through blockchain indexing. Every swap, token transfer, and smart contract interaction is stored as a transparent public record, providing valuable insights into Web3 user behavior.
Advanced Web3 analytics tools extract and enrich this raw blockchain data with contextual information such as wallet labels, social profiles, DeFi positions, asset holdings, transaction frequency, and usage patterns. By segmenting wallets with similar activity, marketers can build targeted behavioral profiles while maintaining user privacy.
Here’s why this matters for Web3 marketing:
Trustworthy analytics: Behavioral insights aren’t biased by centralized platforms hiding or filtering data.
Cross-platform user journeys: Since wallet addresses are universal, you can track activity across dApps, protocols, and chains.
Campaign optimization: With transparent conversion events (e.g., swap, stake, mint), marketers know which channels drive real onchain actions.
Permissionless and verifiable onchain data provides Web3 marketers with a reliable foundation to analyze user behavior, run behavioral analytics, and optimize their marketing strategies with accurate data.
Web3 Behavioral Analytics Methodologies
Here are some methodologies that you can use to conduct Web3 behavioral analytics:
Web3 Transaction Pattern Analysis
Transaction pattern analysis examines how users interact with blockchain protocols over time, such as frequency, volume, and type of transactions. By capturing meaningful activity such as key transactions, token swaps, deposits, and staking marketers can identify high-value users, loyal community members, or early adopters. This method reveals behavioral trends critical for building targeted Web3 marketing campaigns.
Web3 Wallet Segmentation and Clustering
Wallet segmentation groups users based on shared onchain behaviors, like similar token portfolios, interaction with the same dApps, or matching transaction histories. Clustering algorithms help detect user cohorts, such as active traders or long-term holders. This approach enables Web3 marketers to tailor engagement strategies to individual user behavior, rather than relying on demographic assumptions.
Web3 Funnel Analysis
Funnel analysis tracks how users move through the funnel, from awareness to activation and retention. This helps you identify where they drop off and where they convert. This can be helpful since it simplifies the complex Web3 user journeys into an easy-to-digest funnel.
Web3 Path Analysis
Web3 path analysis tracks users’ routes through your product. This directly shows you your users’ behavior in your apps so you can discover any inefficiencies and pain points in navigation. Through this, you can optimize your UX/UI.
Web3 Social Graph and Network Analysis
Network analysis maps relationships between wallets, showing how users are connected through shared communities, referrals, or DAO memberships. By identifying influential wallet hubs and social clusters, marketers can discover key opinion leaders and community builders who can accelerate campaign reach and engagement in Web3 ecosystems.
Web3 Lifecycle and Cohort Tracking
Lifecycle analysis tracks users from their first onchain action to advanced protocol interactions, while cohort analysis compares groups who joined during the same period. This methodology helps marketers understand user retention, churn rates, and progression patterns—vital for optimizing onboarding flows and long-term growth strategies in Web3.
Behavioral Scoring Models
Behavioral scoring assigns weighted scores to user actions—such as holding governance tokens, participating in community events, or interacting with smart contracts. This enables marketers to identify high-engagement wallets and reward them with targeted incentives like airdrops, whitelist access, or loyalty programs, driving stronger user activation and retention.
Cross-Chain Behavior Mapping
Cross-chain analytics track users who operate across multiple blockchains, revealing broader behavioral patterns and platform preferences. Understanding how wallets bridge assets or migrate between chains helps marketers align campaigns with users’ multi-chain journeys and position their project within the wider Web3 landscape.
Web3 Behavioral Analytics Use Cases
Behavioral analytics is a powerful tool for Web3 teams with use cases spanning across marketing, product, and data teams to support your growth.
Marketing teams: With behavioral data, marketing teams can identify which features or dApp interactions drive the most conversions. Understanding the behaviors of your best users lets you drive retention by personalizing campaigns and refining messages.
Product teams: Analyze patterns of behaviors that often lead to churn so you can adjust in-app flows to combat churning , e.g., incentivizing users to discover helpful features so they are more likely to stay. Behavioral data also examines feature adoption, so you can understand which features are not bringing value and eliminate them to reduce operational costs.
Data teams: Behavioral data is insightful; data teams can use behavioral analytics to get a better understanding of their users. With behavioral analytics, data analysts can make data-driven decisions, develop effective strategies that are cost-effective, and make informed predictions about future shifts in user behavior.
Examples of How Behavioral Data Helps
Understanding why users behave the way they do is the key driver for growth. Here are some examples of how behavioral data drives onchain growth:
Identifying High-Value Users and Power Users
Onchain behavioral data helps teams pinpoint wallets that consistently drive the most value to their ecosystem. By analyzing transaction frequency, average transaction size, portfolio diversity, and history of smart contract interactions, teams can identify “power users” who are more active. These users can then be prioritized for special benefits such as early access to new features, governance roles, or exclusive rewards. Recognizing and nurturing users with high lifetime value strengthens loyalty, encourages advocacy, and creates a strong core community that fuels organic growth.
Optimizing User Onboarding Funnels
Tracking onchain user behavior from their first wallet creation through their initial actions—like connecting to a dApp, swapping tokens, or minting NFTs—reveals where newcomers drop off.
If many users abandon the process before completing their first transaction, it signals blockages and shortcomings in the onboarding experience. Teams can then redesign onboarding flows, simplify smart contract interactions, or provide personalized tutorials to reduce complexity. This data-driven optimization reduces drop-offs and shortens the time it takes for new users to become engaged community members.
Designing Targeted Marketing and Growth Campaigns
With insights into what users actually do onchain, teams can segment audiences based on behaviors rather than guesswork. For example, a campaign targeting active DeFi traders might promote advanced yield strategies, while another aimed at long-term holders could highlight staking rewards or governance participation.
Behavioral clustering enables highly personalized messaging, ensuring that marketing efforts resonate with the right audience segments. This approach increases conversion rates, improves ROI on marketing spend, and builds stronger brand presence within the Web3 space.
Measuring Community Health and Engagement
Onchain behavioral analytics allow teams to measure the overall health of their user base by monitoring engagement indicators such as wallet activity over time, participation in governance votes, and social graph interactions (like token transfers between known community wallets).
These insights help detect declining activity early, evaluate the impact of new product launches or incentives, and ensure the community stays active. A data-backed understanding of engagement trends gives teams the ability to respond quickly to potential complaints and maintain long-term stability.
Guiding Product Development and Feature Prioritization
Onchain behavioral data gives product teams concrete evidence of how users actually interact with their products, helping them prioritize the features that matter. By analyzing patterns such as which smart contracts users call most often, or which features drive repeat transactions, teams can spot which parts of the product deliver the highest value.
They can then double down on these high-engagement features while phasing out underused ones. This approach ensures that development resources are focused on features that align with real user behavior for optimal product-market fit.
Understanding User Behaviors with Formo
Web3 behavioral data is crucial for teams looking to drive growth in Web3. Formo is a web3-native analytics platform that unifies users’ onchain with offchain engagement data so you can get the full picture of users’ journeys. With Formo, you can drive retention and fuel your growth, all without any coding.
Follow us on LinkedIn and Twitter, and join our community for more onchain growth insights!
Read more:
Web3 vs Web2 Marketing: Your Guide to Maximize Growth in Web3
Path Analytics in Web3: The Complete Guide to Understanding Onchain User Flows
FAQs
1. What is onchain behavioral data in Web3 marketing?
Onchain behavioral data refers to user activity recorded directly on the blockchain, such as transactions, DeFi positions, and net worth.. This data helps Web3 marketers understand onchain user behavior based on what they do in a privacy-friendly way without relying on personal information.
2. How is onchain user behavior data collected?
Onchain user behavior is collected by capturing the full user journey from offchain visit to onchain transaction, with unified analytics and onchain attribution.
Offchain activity is collected by web and product analytics on your website and app. This helps you understand what users do (page visits, wallet connect, starting a transaction) and where they come from (referrers, UTMs.)
Onchain activity is collected by indexing public blockchain transactions and enriching them with metadata like wallet age, transaction frequency, and protocol usage. Analytics tools process this data to build behavioral profiles that reveal how your users interact across the Web3 ecosystem.
3. Why is analyzing onchain behavior important for Web3 teams?
Analyzing onchain behavior allows Web3 teams to identify power users, track retention, optimize onboarding, and measure community engagement. This data-driven approach enables more effective marketing strategies and sustainable long-term growth.
4. How can onchain behavioral analytics improve marketing campaigns?
Behavioral analytics lets teams segment users based on real in-app and onchain actions rather than guesswork. By targeting user cohorts like active traders or whales, teams can deliver personalized campaigns that boost engagement and conversion rates.
5. What tools are used to analyze onchain user behavior?
To understand onchain behavior, you’ll need tools that analyze websites, products, and onchain interactions. Combining these tools offer a comprehensive view of the user journey from offchain to onchain to help you understand what works.
Formo is Web3-native analytics platform that unify onchain and offchain behavioral and engagement data. With Formo, team turn raw blockchain data into actionable insights for growth, retention, and community-building strategies.
