Total Value Locked (TVL) is a vanity metric that inflates with yield farming incentives and collapses when they end, as seen in the boom-bust cycles of protocols like Compound and Aave. It measures capital, not commitment.
airdrop-strategies-and-community-building
Blog
Why On-Chain Engagement Scores Are the New Marketing KPI
Protocols are moving beyond simple transaction volume to measure user quality. This post argues that multi-dimensional engagement scores, built on behavioral graphs, are the only reliable KPI for predicting long-term value accrual and designing effective airdrops.
introduction
THE DATA
Introduction: The Vanity Metric Trap
On-chain engagement scores are replacing TVL and transaction counts as the definitive KPI for measuring protocol health and user loyalty.
Transaction volume is equally misleading because it fails to distinguish between a genuine user and a bot spamming swaps on Uniswap for airdrop farming. Activity does not equal engagement.
On-chain engagement scores solve this by analyzing behavioral patterns—like frequency, diversity, and protocol loyalty—to create a persistent identity. This is the core thesis behind Ethereum Attestation Service (EAS) and Gitcoin Passport.
Evidence: Protocols with high engagement scores, like Optimism's RetroPGF recipients, demonstrate sustained ecosystem contribution beyond mere capital deployment, directly correlating with long-term network effects.
key-trends
FROM SPRAY-AND-PRAY TO PRECISION TARGETING
Executive Summary: The Three Shifts
Legacy Web2 marketing metrics are failing in crypto. Engagement scores built on on-chain data represent a fundamental shift in how protocols and dApps measure, understand, and monetize their users.
01
The Problem: Vanity Metrics Are Bankrupt
Twitter followers and Discord members are meaningless. They don't predict wallet activity, TVL contribution, or protocol loyalty. This leads to inefficient ad spend and inability to identify power users.
- $1B+ wasted annually on influencer campaigns targeting empty wallets.
- 0 correlation between social metrics and on-chain retention rates.
0%
Predictive Power
$1B+
Wasted Spend
02
The Solution: The On-Chain Engagement Graph
A composite score derived from immutable, verifiable on-chain behavior: transaction volume, protocol interactions, asset holdings, and governance participation. This creates a universal KPI for user quality.
- Enables precision airdrops (see: EigenLayer, Starknet).
- Powers risk-adjusted lending (see: Arcade.xyz, Goldfinch).
- Drives hyper-efficient growth loops.
50x
ROI Improvement
100%
Verifiable
03
The Shift: From Clicks to Capital Flows
Marketing success is no longer measured in impressions, but in capital attracted and retained. The new funnel is: Attention -> On-Chain Action -> Engagement Score -> Targeted Incentive -> Protocol Loyalty.
- LayerZero's sybil detection for airdrops.
- Uniswap's fee switch governance gauging voter quality.
- GMX's trader tiering based on volume and profitability.
TVL
Primary KPI
LTV/CAC
New North Star
thesis-statement
THE NEW KPI
The Core Thesis: Engagement Graphs > Transaction Ledgers
On-chain engagement scores, derived from behavioral graphs, are replacing transaction volume as the definitive metric for user value and protocol health.
Transaction ledgers are obsolete for measuring user value. They record volume, not intent, failing to distinguish a Sybil farmer from a loyal protocol user. This data gap creates inefficient capital allocation in airdrops and marketing.
Engagement graphs capture behavioral intent by mapping relationships between wallets, dApps, and assets over time. This reveals user sophistication and loyalty, metrics that protocols like Aave and Uniswap now prioritize for governance and rewards.
The shift is from quantity to quality. A user bridging via LayerZero to farm a new L2 and exit is noise. A user consistently providing liquidity on Curve across chains is signal. Engagement graphs filter the signal.
Evidence: Protocols analyzing engagement, like RabbitHole and Galxe, achieve 5-10x higher user retention post-airdrop compared to volume-based distributions. This proves behavioral data drives sustainable growth.
THE NEW MARKETING KPI
Volume vs. Engagement: A Post-Mortem
Comparing traditional volume metrics with modern on-chain engagement scoring for protocol growth analysis.
| Core Metric | Raw Volume (Legacy) | Active Addresses (Basic) | Engagement Score (Chainscore) |
|---|---|---|---|
Primary Signal | Capital flow | User count | User intent & loyalty |
Manipulation Resistance | |||
Predicts Future TVL Growth | R² < 0.2 | R² ~ 0.4 | R² > 0.7 |
Identifies Sybil Activity | 0% accuracy | ~30% accuracy |
|
Time to Signal Degen Exodus |
| 3-5 days lag | <24 hours lead |
Integration Complexity | Simple API call | Moderate filtering | Custom model via API |
Key Use Case | Exchange listings, headlines | Basic community dashboards | VC due diligence, airdrop targeting, partnership ROI |
deep-dive
THE NEW KPI
Deconstructing the Behavioral Graph
On-chain engagement scores are replacing vanity metrics as the definitive measure of protocol health and user value.
Engagement Scores Reveal Intent. Legacy marketing KPIs like daily active wallets are gamed by airdrop farmers. A behavioral graph analyzes transaction patterns—like liquidity provision depth on Uniswap V3 or governance participation on Arbitrum—to isolate genuine user intent from noise.
The Graph Is the Product. Protocols like Goldfinch and Aave use on-chain history for underwriting and risk scoring. Your transaction graph becomes a portable reputation system, enabling features like gasless onboarding via ERC-4337 account abstraction without centralized data brokers.
Counterpoint: Data Silos Persist. A user's composite identity fragments across chains. Without standards like Ethereum Attestation Service (EAS) or Chainlink's DECO, a score on Optimism remains isolated from their activity on Base, crippling cross-chain user profiling.
Evidence: Sybil Attack Cost. The 2022 Optimism airdrop identified 17K sybil addresses. Advanced graph analysis, as employed by projects like Hop and Gitcoin Passport, increases the capital and coordination cost of such attacks by orders of magnitude, protecting treasury allocations.
protocol-spotlight
ON-CHAIN ENGAGEMENT INFRASTRUCTURE
Who's Building the Graph?
Protocols are competing to build the canonical scoring layer that turns raw on-chain data into actionable marketing intelligence.
01
The Problem: Vanity Metrics Are Bankrupt
TVL and transaction counts are lagging indicators that fail to measure user quality or loyalty. A whale's one-time swap is not equal to a user's sustained protocol engagement.
- Key Insight: A user's transaction graph and asset velocity are stronger predictors of future behavior than raw balance.
- Market Gap: No standard exists to quantify lifetime value (LTV) or acquisition cost (CAC) from on-chain activity.
0
Standard LTV Metric
High
CAC Waste
02
The Solution: EigenLayer's On-Chain Attestations
EigenLayer's Attestation Service (EAS) provides a decentralized registry for scoring and reputation, allowing any protocol to issue verifiable credentials about user behavior.
- Mechanism: Acts as a universal scoreboard where protocols like Galxe or RabbitHole can stamp achievements.
- Outcome: Creates portable reputation that reduces sybil attacks and enables targeted airdrops based on proven engagement, not just wallets.
15B+
TVL Securing
Universal
Registry
03
The Solution: Karate Labs & The Engagement Graph
Karate Labs builds protocol-specific engagement graphs that map user interactions across functions, measuring depth beyond simple transactions.
- Core Metric: Tracks feature adoption rate and retention cohorts directly on-chain.
- Application: Enables hyper-targeted rewards and governance power distribution to users who actually use the product, not just farm it.
Protocol-Specific
Graphs
Retention
Cohort Analysis
04
The Solution: Goldsky & Real-Time Data Pipelines
Goldsky provides sub-second streaming of on-chain events, allowing marketing engines to trigger actions based on live user behavior.
- Infrastructure: Replaces batch-based analytics with real-time event streams.
- Use Case: Enables instant reward distribution for completing specific on-chain tasks, turning engagement into a measurable, immediate feedback loop.
<1s
Latency
Real-Time
Triggers
05
The Aggregator Play: Degen Scores & Layer3
Platforms like Degen Score and Layer3 aggregate activity across multiple chains and protocols to generate a composite on-chain identity score.
- Approach: Synthesizes data from Ethereum, Solana, Base to combat chain-specific myopia.
- Result: Provides a holistic KPI for user quality that VCs and protocols use to identify alpha and allocate capital.
Multi-Chain
Aggregation
Composite
Identity
06
The Endgame: Programmable Marketing Stacks
The convergence of these tools creates a programmable marketing stack where smart contracts autonomously segment users and allocate incentives.
- Vision: If-Then logic based on on-chain scores (e.g.,
if user_score > X, send NFT). - Impact: Transforms marketing from a cost center into a verifiable, on-chain growth engine with measurable ROI.
Autonomous
Allocation
Verifiable
ROI
counter-argument
THE DATA
The Sybil's Rebuttal (And Why It Fails)
On-chain engagement metrics render traditional Sybil arguments obsolete by providing a verifiable, multi-dimensional proof of user intent.
Sybil attacks are a cost function. The argument that engagement scores are easily gamed ignores the prohibitive capital lockup required. Protocols like EigenLayer and Ethereum's restaking ecosystem demonstrate that credible, long-term stake is the ultimate Sybil defense.
On-chain history is immutable proof. A wallet's transaction graph across Uniswap, Aave, and ENS creates a non-forgeable behavioral fingerprint. This multi-protocol footprint is more expensive to fake than any off-chain marketing metric.
Engagement scores measure intent, not identity. The goal is not to find a 'real person' but to identify capital-weighted commitment. A Sybil farmer's low-liquidity, high-volume pattern is trivial for Dune Analytics dashboards to flag and filter.
Evidence: The failure of airdrop farmers on Arbitrum and Starknet proves the point. Despite massive Sybil campaigns, protocols that weighted scores for transaction depth and asset diversity successfully filtered noise, allocating rewards to genuine users.
takeaways
WHY ON-CHAIN SCORES ARE THE NEW KPI
Actionable Insights for Builders
Forget vanity metrics. On-chain engagement scores built with protocols like EigenLayer, Karak, and Hyperliquid provide a deterministic, composable, and sybil-resistant foundation for growth.
01
The Problem: Airdrop Farming is a Sybil-Ridden Arms Race
Legacy airdrop models attract mercenary capital, not real users. You spend $10M+ in token incentives to acquire wallets that exit immediately post-drop, destroying token value and community health.
- Sybil clusters inflate user counts by 10-100x
- Zero retention post-airdrop; TVL evaporates
- High cost per fake user with no long-term alignment
>90%
Churn Rate
$10M+
Wasted Incentives
02
The Solution: EigenLayer & Karak's Restaking Primitive
Use restaked capital as a proxy for high-intent, high-stake engagement. Builders can design scoring models that weight actions by the EigenLayer restake amount or Karak points, creating a cost-to-sybil barrier.
- Capital-at-stake creates real economic alignment
- Composable reputation across AVSs and L2s like Arbitrum and Optimism
- Native yield for engaged users, replacing inflationary token dumps
$15B+
Restaked TVL
100x
Sybil Cost
03
The Problem: Off-Chain Data is a Black Box for Composability
Twitter followers and Discord roles are siloed, unverifiable, and impossible to permissionlessly integrate into on-chain logic. This stifles innovation in loyalty programs, credit scoring, and cross-protocol incentives.
- No shared state between dApps like Uniswap and Aave
- Fraudulent attestations and easy-to-game social proofs
- Missed opportunities for programmable user journeys
0
On-Chain Utility
High Risk
Data Integrity
04
The Solution: Hyperliquid's On-Chain Orderbook as a Behavior Graph
Hyperliquid's L1 demonstrates how granular, high-frequency on-chain actions (trades, leverage, liquidity provision) create a rich, real-time behavior graph. This data layer is a superior KPI for DeFi protocols than simple TVL.
- Real-time intent signaling via order flow
- Quantifiable risk appetite and sophistication level
- Native composability for perpetuals DEXs, options vaults, and lending markets
~1s
Update Latency
High-Fidelity
User Profile
05
The Problem: CAC is Unmeasurable in a Multi-Chain World
You can't optimize what you can't measure. User acquisition costs are obfuscated across dozens of chains and bridges (LayerZero, Axelar). Did your user come from a Coinbase campaign, a friend's referral, or a liquidity mining pool on Polygon?
- Fragmented user journey across Arbitrum, Base, Solana
- Impossible attribution for cross-chain campaigns
- Blind spending on growth initiatives
Unmeasurable
CAC
10+
Chain Fragmentation
06
The Solution: Chain-Agnostic Engagement Scores as a Universal Ledger
Build a canonical engagement score—powered by attestation networks like EAS or verifiable credentials—that persists across chains. This becomes the single source of truth for user value, enabling precise LTV calculation and targeted incentives.
- Portable identity from Ethereum mainnet to any L2 or alt-L1
- Provable attribution for grants and marketing spend
- Dynamic reward curves based on cumulative cross-chain contribution
1 Source
Of Truth
Precise LTV
Calculation
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