Onchain-native teams face a unique challenge: their users are anonymous wallet addresses. These strings of characters provide little context about who the user is, how they behave, or what they value. This anonymity makes it difficult to build meaningful relationships, personalize experiences, and ultimately, drive growth.
But what if you could transform those anonymous addresses into rich, actionable user personas? The blockchain’s inherent transparency offers an unprecedented opportunity. Unlike the closed data silos of Web2, onchain data is public and verifiable. This is where wallet labeling and onchain user segmentation come into play. By leveraging Web3 user analytics, teams can turn raw, anonymous data into a powerful growth engine.
This guide will show you how to implement wallet labeling, create effective user segments, and use these insights to build better products and execute targeted growth campaigns. It’s time to move beyond guesswork and start making data-driven decisions.
What is Wallet Labeling and Onchain User Segmentation?
Wallet labeling is the process of categorizing blockchain addresses by analyzing their onchain activity, holdings, and behavioral patterns. These labels provide valuable context, turning a generic address into an identifiable persona like a "DeFi Degen," "NFT Whale," or "Airdrop Farmer." It's the first step toward understanding the who behind the wallet while protecting user privacy.
Onchain user segmentation takes this a step further by grouping wallets with similar labels and characteristics. This allows you to create targeted cohorts, such as "High-Value Traders" or "New Users on Base."
This approach is fundamentally different from Web2, which relies on demographics, cookies, and self-reported data that are often incomplete or unreliable. Web3's advantage is its foundation of real-time, transparent, and verifiable user data. Every transaction, swap, and mint is a data point that can be used to build a more accurate and dynamic user profile.
Wallet Label Examples Across Web3 Verticals
Labels can be tailored to any protocol or app, but some common examples have emerged across different Web3 sectors.
DeFi Protocol Labels
DeFi Degen: High-frequency, high-risk user interacting with multiple new protocols.
Perp Trader: Actively trades perpetual derivatives on exchanges.
Yield Farmer: Seeks to maximize returns by providing liquidity and staking assets.
ETH Staker: Staking ETH in protocols like Lido or Rocket Pool.
Liquidity Provider: Adds capital to liquidity pools on decentralized exchanges.
Net Worth Labels
Whale: Wallet holding over $10M in assets.
Dolphin: Wallet holding between $1M and $10M in assets.
Professional: Wallet holding between $10K and $1M.
Retail: Wallet holding under $10K.
Consumer & Social Labels
NFT Whale: Owns high-value or a large quantity of NFTs.
Gaming Enthusiast: Spends significant time and assets in Web3 games.
Social Token Holder: Actively holds and trades creator or community tokens.
Airdrop Farmer: Strategically interacts with protocols to qualify for airdrops.
Lens Influencer: Wallet with a connected Lens account and over 1,000 followers.
Farcaster Influencer: Wallet with a connected Farcaster account and over 1,000 followers.
Engagement Tiers:
Power User: Highly active and proficient user of your app.
Casual User: Interacts with the app infrequently.
Churned User: Has become inactive after a period of use.
Cross-Platform Labels
OG: Wallet created before 2021, indicating early adoption.
Multi-chain User: Active across multiple blockchain networks.
Bridge User: Frequently moves assets between different chains.
DAO Participant: Actively delegates or votes on proposals in one or more DAOs.
Note that this is not an exhaustive list of wallet labels.
User Lifecycle Labels
Lifecycle labels are essential for understanding user engagement and activity of your users across time. User Lifecycle Analysis helps web3 teams identify where users are in their lifecycle and guide strategic decisions to drive retention and growth.
New User: Recently onboarded and still exploring the platform. These users require an optimized onboarding experience to encourage early engagement.
Returning User: Regularly interacts with the app and contributes to consistent activity, indicating satisfaction with the platform.
Dormant User: Previously active but shows declining or minimal interaction, signaling the need for re-engagement efforts.
Ressurected User: A dormant user who has returned to active usage, often due to targeted campaigns or new features.
Churned User: Formerly active but has ceased all interactions, representing an opportunity for win-back strategies.
User lifecycle labels, when combined with user segmentation and other wallet labels create rich wallet personas, helping teams deliver targeted campaigns and product updates that maximize user retention.
Benefits of Wallet Labeling for Product Teams
For onchain teams, wallet labeling provides the context needed to build products that resonate with users.
Enhanced User Understanding: Go beyond anonymous addresses to see rich behavioral profiles. Understand what your users do both inside and outside your app to build a complete picture.
Improved Product Development: With clear user segments, you can build features that cater to the specific needs of your most valuable cohorts, like advanced trading tools for "Perp Traders."
Better User Experience: Personalize the user journey based on a wallet's history. A "New to DeFi" user might receive a simplified onboarding flow, while an "OG" gets direct access to advanced features.
Data-Driven Roadmapping: Prioritize your product roadmap based on actual user behavior, not assumptions. Analyze which features are most used by your "Power Users" and double down on what works.
Benefits of User Segmentation for Growth Teams
For marketers and growth leads, onchain segmentation unlocks hyper-targeted and efficient campaigns.
Precision Marketing: Target your campaigns to high-value segments. Run a VIP campaign exclusively for "Whales" or an educational series for "DeFi Beginners."
Optimized Acquisition: Focus your marketing spend on acquiring users who resemble your most valuable segments. Create lookalike audiences based on onchain data to improve your ROI.
Improved Retention: Identify users who are at risk of churning by monitoring their activity levels. Re-engage them with targeted incentives or notifications before they leave for good.
Practical Use Cases and Examples
Here’s how wallet labeling and segmentation translate into real-world growth strategies.
Incentive Design
Airdrops and token incentive programs are a powerful acquisition tool unique to crypto. However, they are often exploited by Sybil attackers and farmers. With wallet intelligence, you can design a more effective distribution.
Segment users by wallet score and activity level to filter out low-value, opportunistic farmers.
Differentiate rewards by targeting "Long-Term Holders" with a larger share than "Bots".
Product Feature Launches
Instead of launching new features to your entire user base, you can use segmentation for a strategic rollout.
Offer early access to advanced trading features for your identified "Power Users" to gather expert feedback.
Create a guided, simplified onboarding experience for users labeled as "New to DeFi" to reduce drop-off.
Marketing Campaign Targeting
Move beyond broad-stroke marketing and deliver personalized messages that convert.
Create whale-specific campaigns offering white-glove service or exclusive perks to attract and retain high-value users.
Develop educational content, like tutorials and guides, for users labeled as "DeFi Beginners" to help them get started.
How Wallets Are Labeled From Onchain Data
Wallet labels are generated by analyzing a combination of onchain data points.
Transaction Pattern Analysis: Analyzing the frequency, timing, and sequence of transactions to identify behaviors like day trading or yield farming.
Portfolio Composition: Examining the types of tokens and NFTs a wallet holds to understand their interests and risk appetite.
Network Activity: Looking at cross-chain behavior, gas spending habits, and interactions with specific types of protocols.
Time-Based Metrics: Using wallet age, holding periods, and activity consistency to distinguish between long-term believers and short-term speculators.
Social Signals: Leveraging onchain social data like ENS domains, POAP collections, and DAO voting records to add another layer of context.
Identity Resolution for Wallets
To get a truly complete picture, teams need to connect the dots between different wallets that may be controlled by the same entity. Identity resolution is the process of uncovering these relationships.
Wallet Clustering: Grouping wallets based on shared interaction patterns, such as funding from the same source or interacting with the same series of smart contracts.
Daisy Funding Chains: Tracing funds as they move from one wallet to another to build a trail of association and identify wallet hierarchies.
Behavioral Correlation: Matching wallets by identifying identical operational timing across different protocols, suggesting they are controlled by a single user or bot.
Public Metadata: Using publicly shared identifiers like ENS domains and profiles to link different addresses.
Offchain-Onchain Linking: With user consent, merging offchain data or social profiles with onchain activity to create a unified user profile.
Wallet Score: The Composite Reputation Metric
A Wallet Score is a numeric rating typically from 0 to 100 that serves as a composite reputation metric for a wallet. It's calculated from a wallet's onchain footprint, including its labels, attestations, and financial activity. This score provides a quick way to gauge a wallet's value and credibility.
The score is often based on a multi-dimensional framework that includes:
User Activity: Transaction frequency, gas fees paid, and diversity of protocol interactions.
Financial Metrics: Portfolio value, token holdings, and net worth.
Product Adoption: The range and depth of apps and protocols a wallet is interacting with onchain.
Wallet scoring can be applied to detect Sybil attackers, review incentive programs, and design loyalty programs.
Summary
The transparency of the blockchain is one of Web3's greatest strengths. By implementing wallet labeling and onchain user segmentation, you can turn this transparency into a competitive advantage. You can finally understand who your users are, what they want, and how to best serve them.
This data-driven approach leads to a powerful feedback loop: better products attract more valuable users, targeted marketing improves ROI, and a deeper understanding of your audience fuels sustainable growth.
Stop building in the dark. Harness wallet intelligence to drive growth for your Web3 project with confidence.
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Speedrun Wallet Labeling with Formo
While it's possible to build a custom data pipeline to analyze onchain data, specialized Web3 analytics platforms like Formo automate the process. These tools offer automated wallet labeling and segmentation, integrating directly with your existing data stack.
With a platform like Formo, you can:
Get a 360° view of any wallet interacting with your app.
Set up custom labels for protocol-specific behaviors.
Create dynamic segments that update in real-time as user behavior changes.
Unify offchain and onchain data to build a complete picture of your users.
Frequently Asked Questions
What is wallet labeling in Web3?
Wallet labeling is the process of tagging blockchain addresses with meaningful categories based on their onchain activity, transaction history, and asset holdings. Labels transform anonymous strings of characters into user personas such as “NFT Whale,” “Yield Farmer,” or “Early Protocol Adopter.”
How does onchain user segmentation work?
Onchain user segmentation groups wallets with similar behaviors or characteristics into defined cohorts. By clustering labeled wallets into segments (e.g., “High-Value Perp Traders on Hyperliquid”), teams can design personalized product experiences, optimize retention strategies, and run highly targeted marketing campaigns.
What are examples of wallet labels for DeFi protocols?
DeFi wallet labels often fall into three main categories:
Net worth tiers: Whale, Professional, Retail, Dormant Holder.
Behavioral labels: Yield Farmer, DeFi Degen, Perpetual Trader, Stablecoin Hoarder.
Protocol-specific tags: ETH Staker, Aave Lender, Uniswap Liquidity Provider, Curve Farmer.
These labels help protocols identify power users, casual participants, and emerging segments.
Can wallet labeling help prevent Sybil attacks?
Yes. By analyzing wallet histories, transaction flows, funding sources, and behavioral anomalies, wallet labeling can surface suspicious activity consistent with Sybil attacks. Teams can assign trust scores or exclude flagged wallets from airdrops and incentive programs to maintain fairness.
How do I implement wallet labeling for my Web3 app?
There are two main approaches:
Use a specialized analytics platform – Tools like Formo automate wallet labeling, scoring, and segmentation with minimal setup.
Build custom pipelines – Teams can pull onchain data from sources like Etherscan, Dune, or The Graph and enrich it with offchain context (e.g., Twitter activity) to generate tailored wallet insights.
What’s the difference between wallet labels and user segments?
Wallet label: A descriptive tag for a single address (e.g., “Perps Trader”).
User segment: A group of wallets that share one or more labels or traits (e.g., mobile wallets labeled “Perps Trader” who are also Whales).
Labels provide granularity, while segments create actionable audience groups.
What metrics should I track for wallet-based segmentation?
Important metrics include:
Transaction volume, frequency, and recency
Portfolio size and asset diversity
Wallet age and activity lifecycle
Protocol-specific interactions (staking, lending, swapping)
Cross-chain activity and bridge usage
Governance participation and voting power
How often should wallet scores and labels be updated?
For reliable insights, wallet scores and segmentation should be refreshed in near real-time or at least daily. Frequent updates ensure your analytics capture the latest market shifts, user behavior changes, and protocol interactions—critical for accurate targeting.
