Fan Engagement Score

Scores are not based on a single action but aggregate a weighted combination of signals. Common dimensions include:

• On-chain activity: NFT holdings, token staking, governance participation, transaction history.
• Social engagement: Content interactions, community contributions, event attendance.
• Financial commitment: Lifetime spend, recency of purchases, portfolio concentration. This multi-faceted approach prevents gaming and captures true loyalty.

Engagement decays over time. A robust scoring model applies temporal weighting, where recent interactions carry more weight than historical ones. This is often implemented using:

• Exponential decay functions to reduce the impact of old actions.
• Velocity metrics that measure the rate of recent engagement.
• Recency, Frequency, Monetary (RFM) analysis adapted for web3. This ensures the score reflects current, not just historical, fan status.

Raw counts (e.g., 'owns 5 NFTs') are meaningless without context. Scores are normalized to enable fair comparison:

• Percentile ranking within a specific community or cohort.
• Benchmarking against the broader user base.
• Tier-based classification (e.g., Diamond, Gold, Silver) based on distribution curves. This allows a project to identify its top 1% of fans, regardless of the absolute size of the community.

The primary utility of a score is to segment a user base for precise operations. High-fidelity scores enable:

• Reward distribution: Airdropping tokens or NFTs to top-tier fans.
• Governance power: Weighting voting rights based on proven engagement.
• Access gating: Providing exclusive content or events to specific score thresholds.
• Personalized experiences: Tailoring UI/UX or communications to different segments.

A sophisticated score is computed in a protocol-agnostic manner, ingesting data from multiple sources:

• Blockchain networks (Ethereum, Solana, Polygon).
• Social platforms (Farcaster, Lens, X).
• Off-chain data (event APIs, community platforms). The scoring logic abstracts away the underlying platform, creating a unified identity graph for a fan across the digital ecosystem.

In web3, an engagement score should be a verifiable credential tied to a user's wallet address or decentralized identifier (DID). Key properties include:

• Self-sovereignty: Users can potentially prove their score without an intermediary.
• Portability: Scores can be used across different applications that trust the scoring logic.
• Transparent methodology: The scoring algorithm's rules can be audited, building trust in the metric's legitimacy.

The most objective data layer, directly recorded on the blockchain. This includes:

• Transaction History: Purchases of NFTs, tokens, or digital goods.
• Holding Duration: The length of time assets are held (diamond hands).
• Governance Participation: Voting on proposals or using governance tokens.
• Contract Interactions: Frequency of interacting with a project's smart contracts.

Measures active participation in digital communities. Key sources include:

• Discord/Telegram Activity: Message frequency, reaction usage, and role attainment.
• Twitter/X Engagement: Likes, retweets, replies, and mentions related to the creator or project.
• Content Creation: Publishing blogs, memes, or tutorials that promote the community.
• Event Attendance: Participation in Twitter Spaces, AMAs, or live streams.

Quantifies the economic stake and support a fan provides. This goes beyond simple purchases.

• Total Value Locked (TVL): In DeFi contexts, assets staked in a protocol's pools.
• Royalty Payments: Secondary market fees paid to creators from NFT resales.
• Recurring Revenue: Subscriptions or recurring donations via mechanisms like Superfluid streams.
• Portfolio Concentration: The percentage of a user's wallet value dedicated to a specific project.

Considers the fan's verifiable identity and standing within the ecosystem.

• Soulbound Tokens (SBTs): Non-transferable badges for achievements or attestations.
• On-Chain Reputation: Scores from systems like Gitcoin Passport or Orange Protocol.
• Sybil Resistance: Proof of unique humanity (e.g., BrightID, Worldcoin) to filter bots.
• Credential History: Educational or professional credentials stored on-chain (e.g., verifiable credentials).

The algorithms that transform raw data into a single score. Common approaches include:

• Weighted Scoring: Assigning different importance (weights) to various activities (e.g., governance may weigh more than a simple like).
• Time Decay: Recent activity is often weighted more heavily than older actions.
• Cohort Analysis: Comparing a user's behavior against peer groups or top fans.
• Predictive Modeling: Using machine learning to forecast future engagement or churn risk.

How platforms and creators operationalize the score for tangible benefits.

• Access Gating: Unlocking exclusive content, channels, or NFT mint allowlists.
• Rewards & Airdrops: Distributing tokens or rewards proportionally to engaged users.
• Governance Power: Weighting voting power based on engagement, not just token holdings.
• Community Insights: Helping creators identify their most supportive fans and tailor engagement strategies.

The Fan Engagement Score acts as a reputation system that quantifies a user's contributions to a creator's ecosystem. It is calculated algorithmically based on on-chain and off-chain activities, such as:

• Collecting creator tokens or NFTs.
• Interacting with content (likes, comments, shares).
• Participating in community governance or events.
• Providing liquidity to creator pools.

High scores often unlock exclusive benefits, creating a gamified loyalty loop.

The score is derived from a multi-faceted analysis of user behavior. Primary data sources include:

• On-chain Data: Verifiable transactions like NFT mints, token swaps, and governance votes recorded on a blockchain (e.g., Ethereum, Solana).
• Off-chain Data: Social interactions from platforms like X (Twitter), Discord, or Farcaster, often verified via cryptographic signatures.
• Temporal Weighting: Recent activity is typically weighted more heavily than older actions to reflect current engagement levels.
• Monetary & Non-Monetary Signals: Both financial support (e.g., purchases) and social support (e.g., content curation) are factored in.

A high Fan Engagement Score grants users tangible utility and status within a protocol. Common rewards include:

• Access: To private channels, token-gated content, or early product releases.
• Governance Power: Increased voting weight in community decisions.
• Airdrops & Allocations: Priority or larger shares of token distributions and NFT drops.
• Badges & Roles: Visual on-chain credentials that serve as social proof.

This transforms passive followers into active, vested stakeholders.

For the protocol itself, aggregate engagement data is a critical ecosystem health metric. It enables:

• Sybil Resistance: Distinguishing genuine users from bots by analyzing depth of engagement.
• Personalized Economics: Dynamic reward curves and incentive calibration based on user tiers.
• Creator Discovery: Algorithms can surface creators with highly engaged, rather than just large, audiences.
• Collateral & Credit: In decentralized finance (DeFi) contexts, scores can inform underwriting for social capital-based loans.

Friend.tech implements a direct form of engagement scoring through its bonding curve model. Keyholders of a creator's "keys" (shares) are essentially scored by their:

• Financial Commitment: The price paid for keys, which increases with demand.
• Holding Period: Long-term holders contribute to creator stability.
• Transaction Activity: Buying, selling, and tipping within the app.

The protocol uses this implicit score to facilitate exclusive chat access and distribute fees, directly linking economic stake to engagement privileges.

Understanding Fan Engagement Scores requires familiarity with adjacent Web3 concepts:

• Social Graph: The network of relationships between users and creators; engagement scores enrich this graph with quantitative data.
• Proof of Stake & Delegation: Similar to how validators are chosen based on staked assets, creators can delegate rewards to top fans based on their engagement stake.
• Soulbound Tokens (SBTs): Non-transferable tokens that could represent a permanent record of a user's engagement score with a creator or community.
• Decentralized Identity (DID): A portable identity framework that could aggregate engagement scores across multiple platforms.

Creators and platforms use scores to grant exclusive access to content, communities, or events. This creates a merit-based system where the most engaged fans receive the best perks.

• Examples: Private Discord channels, early ticket sales, behind-the-scenes content, or special NFT mints.
• Mechanism: Smart contracts or platform logic check a user's score against a predefined threshold to unlock access.

Scores enable hyper-targeted reward distribution, moving beyond simple token holdings. Rewards are allocated based on proven engagement, not just wealth.

• Loyalty Programs: Higher scores yield better airdrop multipliers or unique digital collectibles.
• Community Incentives: Reward actions like content creation, event attendance, or governance participation with tokens or NFTs proportionate to one's engagement tier.

Scores can power dynamic pricing models in fan economies. More loyal fans might receive discounts, special offers, or the ability to purchase limited items first.

• Use Case: A musician's store could offer merch discounts scaled to a fan's engagement score.
• Mechanism: E-commerce integrations or smart contracts adjust prices or availability in real-time based on the verified score passed from an oracle or API.

Scores provide a sybil-resistant metric for weighting voting power in community governance. This ensures decisions are influenced by genuine contributors, not just token whales.

• Application: A DAO might use a combined metric of token holdings and engagement score to determine proposal voting power.
• Curation: High-score holders could be granted moderation privileges or roles in curating community-generated content.

For creators and brands, aggregate score data provides actionable analytics on their community's health and behavior.

• Key Metrics: Identifying top supporters, measuring campaign effectiveness, and understanding which engagement drivers (e.g., commenting vs. collecting) correlate with high-value fans.
• Outcome: Data-driven decisions on where to allocate resources, which fan segments to target for campaigns, and how to improve engagement strategies.

In decentralized finance (DeFi) contexts, a high Fan Engagement Score can serve as a form of social collateral or reputation. It signals trustworthiness and long-term alignment with a community.

• Potential Use: Protocols might offer uncollateralized microloans or better terms to users with high scores from reputable communities.
• Concept: This extends the score's utility beyond direct creator interactions into the broader on-chain economy, leveraging social capital.