Marketing Data & Customer Signals
The most valuable marketing data is not the data most vendors sell. The highest-signal indicator for finding and attracting ideal customers is the one closest to proven behaviour — not predicted intent.
Signal Hierarchy
Not all data is equal. This hierarchy reflects what practitioners have validated produces measurable CAC reduction and LTV lift — not vendor claims.
| Rank | Signal | What It Is | Conversion Impact | How to Acquire |
|---|---|---|---|---|
| 1 | First-party behavioural data | Your own audience's actions: pages visited, content consumed, time on page, return visits, scroll depth | Highest — 15% CAC reduction, 20% LTV lift when unified into profiles | Analytics (GA4), email engagement, site behaviour |
| 2 | Intent signals | Topic surge data indicating active research phase — audience is comparing options now | High — aligns outreach with buying window | Bombora, G2 intent, search query data |
| 3 | Job change signals | New decision-maker entered a role — authority reset, open window for vendor change | High — moment of maximum receptivity | LinkedIn alerts, data enrichment pipelines |
| 4 | Technographic data | What software stack a company runs — determines fit, timing, and displacement opportunity | Medium — identifies structural fit before outreach | BuiltWith, Clearbit, data enrichment APIs |
| 5 | Attention data | Where your audience actually spends time — not where intent vendors say to target | Medium — captures the 98% who never show up as intent signals | Audience intelligence tools, social listening |
| 6 | AI citation signals | Whether your brand or content is cited in ChatGPT, Perplexity, AI Overviews | Emerging — low competition, compounding | Manual AI surface monitoring, emerging AEO tools |
| 7 | On-chain signals | Wallet behaviour, token holdings, governance participation — Web3-native audiences | Niche but verifiable — highest fidelity for crypto-native segments | Blockchain explorers, on-chain analytics |
The practitioner tension: Intent-data vendors (signals 2-4) sell the bottom of the funnel. Attention data (signal 5) captures the 98% who never form an intent query. The berley trail is built on this logic — attract before intent forms, be there when it does.
The Feedback Loop
Data without a feedback loop is just storage. The loop that compounds:
Signal captured → Audience profiled → Content prioritised → Published
↑ |
└─────────── Engagement measured ◄───── Distributed ──────┘
Each cycle narrows the gap between who you think your audience is and who they actually are. The loop degrades when:
- Signals are captured but never scored
- Engagement is measured but never fed back to content prioritisation
- Content is created from instinct rather than ranked opportunity
The dollar-value discipline: Every content opportunity should have a revenue estimate attached. Volume × conversion rate × average deal value = content priority score. Without this, the calendar is driven by what feels interesting, not what compounds.
On-Chain Data
Leverage blockchain-verified customer data and social protocols to build authentic relationships with true fans.
Human Role: Privacy decisions, community strategy, relationship building AI Role: On-chain analysis, pattern recognition, engagement scoring Spectrum: AI-Led
Overview
| Attribute | Value |
|---|---|
| Purpose | Build verifiable customer relationships using Web3 |
| Trigger | Community launch, token program, or data strategy |
| Frequency | Continuous monitoring, weekly analysis |
| Duration | Initial: 2-3 weeks setup; Ongoing: automated |
| Owner | Community Lead / Growth |
| Output | Verified customer segments, true fan identification |
Prerequisites
Tools Required
| Tool | Purpose | Access |
|---|---|---|
| Farcaster | Social protocol, frames | Free |
| Blockchain explorer | On-chain analysis | Etherscan etc. |
| Wallet analytics | Customer behavior | Nansen, Dune |
| Community platform | Fan engagement | Discord, Telegram |
| AI Assistant | Pattern analysis | API |
Knowledge Requirements
- Understanding of Web3 social protocols
- Basic blockchain data interpretation
- Community management principles
- Privacy and consent best practices
Inputs
What you need before starting:
| Input | Source | Required? |
|---|---|---|
| Community presence | Platform accounts | ✓ |
| Token/NFT strategy | Product strategy | Optional |
| ICP definition | Marketing strategy | ✓ |
| Privacy policy | Legal | ✓ |
Upstream Dependencies
| Upstream Workflow | What It Provides | Link |
|---|---|---|
| ICP Definition | Who are our true fans | ICP |
| Token Strategy | Incentive structure | Token Economy |
Process
Phase 1: Platform Setup
Duration: 1 week Responsibility: Human-led
Step 1.1: Choose Social Protocol
| Platform | Strength | Use Case |
|---|---|---|
| Farcaster | Decentralized, frames | Web3-native audience |
| Lens | Portable profiles | Creator economy |
| Traditional + Wallet | Broad reach | Hybrid approach |
Step 1.2: Define Data Strategy
- What on-chain data will you track?
- What off-chain data do you need?
- How will you respect privacy?
- What's your consent mechanism?
Phase 1 Output: Platform presence + data strategy document
Phase 2: True Fan Identification
Duration: Ongoing Responsibility: AI-led analysis, Human interpretation
Step 2.1: Define True Fan Criteria
The "100 True Fans" model: with AI & Web3, the number may reduce to Dunbar's number (~150).
| Fan Level | Behavior Signals | Value |
|---|---|---|
| True Fan | Purchases everything, evangelizes | Highest LTV |
| Active Fan | Regular engagement, some purchases | High LTV |
| Casual Fan | Occasional engagement | Medium LTV |
| Follower | Passive consumption | Low LTV |
Step 2.2: On-Chain Signals
Track wallet behavior that indicates true fandom:
- Early minting activity
- Holding duration (diamond hands)
- Community participation (governance)
- Referral behavior
- Cross-collection engagement
Phase 2 Output: True fan identification criteria + initial list
Phase 3: Customer Profile Building
Duration: 2-3 hours initial, ongoing refinement Responsibility: Human insight, AI synthesis
Step 3.1: Profile Questions
Answer for your primary customer segment:
- What are their primary characteristics?
- What drives their behavior?
- What specific problem are you best at solving for them?
- What problems do you need to be better at solving?
Step 3.2: On-Chain Profile Elements
| Data Point | Source | Privacy Level |
|---|---|---|
| Wallet age | Blockchain | Public |
| Token holdings | Blockchain | Public |
| Transaction history | Blockchain | Public |
| Social connections | Farcaster/Lens | Opt-in |
| Content engagement | Platform analytics | Aggregated |
Phase 3 Output: Customer profile templates with on-chain enrichment
Phase 4: Engagement Strategy
Duration: Ongoing Responsibility: Human strategy, AI execution
Step 4.1: Content Production Funnel
- Attract — Publish content to educate
- Engage — Direct contact for group education sessions
- Convert — Packaged deal sales
- Retain — Community membership benefits
Step 4.2: Content Strategy for Web3 Audience
- Establish the emotional hook (why Web3 matters)
- Present research that educates on your solution
- Provide on-chain evidence of how it works
- Educate on long-term value capture
- Load site with verifiable testimonials (wallet addresses)
Step 4.3: Proof of Engagement
Leverage Web3-native engagement mechanisms:
| Mechanism | Purpose | Tool |
|---|---|---|
| Frames | Interactive content | Farcaster |
| Points | Engagement rewards | Custom/Blur-style |
| Airdrops | Community incentives | Token distribution |
| NFT Minting | Ownership proof | Native Web3 business model |
Phase 4 Output: Engagement playbook with Web3 mechanics
Phase 5: Analysis & Optimization
Duration: Weekly review Responsibility: AI analysis, Human decisions
Step 5.1: Weekly Metrics Review
- New true fans identified
- Community growth rate
- Engagement quality (not just quantity)
- Conversion from follower → fan
- On-chain activity trends
Step 5.2: Monthly Cohort Analysis
Track cohorts by:
- Entry point (how they found you)
- First engagement (what hooked them)
- Wallet behavior (on-chain activity)
- Retention curve (how long they stay engaged)
Phase 5 Output: Monthly insights report + strategy adjustments
Outputs
| Output | Format | Destination |
|---|---|---|
| True fan list | Database | CRM / Community |
| Customer profiles | Markdown | Strategy docs |
| Engagement playbook | Document | Team reference |
| Monthly analysis | Report | Leadership |
Downstream Consumers
| Downstream Workflow | What It Needs | Link |
|---|---|---|
| Loyalty Tokens | Fan segmentation | Loyalty Tokens |
| Airdrops | Wallet lists | Airdrops |
| Content Strategy | Audience insights | Article Copywriting |
Success Criteria
Quality Metrics
| Metric | Target | Measurement |
|---|---|---|
| True fan identification | Clear criteria | Checklist |
| Privacy compliance | 100% | Audit |
| Profile completeness | Key fields filled | Review |
Performance Metrics
| Metric | Target | Timeframe |
|---|---|---|
| True fans identified | 100+ initial | 90 days |
| Follower → Fan conversion | 5% | Monthly |
| Community retention | 70% | Monthly |
| On-chain engagement | +20% MoM | Monthly |
Failure Modes & Solutions
| Failure | Symptom | Solution |
|---|---|---|
| Privacy violations | User complaints, legal | Explicit consent, minimal data |
| Fake engagement | Bot activity, wash trading | Sybil resistance, quality signals |
| Over-quantification | Missing qualitative insight | Balance data with conversation |
| Platform dependency | Single point of failure | Multi-protocol presence |
| Analysis paralysis | Too much data, no action | Focus on actionable metrics |
The True Fans Model
Why 100 (or 150) True Fans?
Traditional: 1,000 true fans = sustainable creator business Web3/AI era: Reduced to ~100-150 (Dunbar's number) because:
- Direct monetization — No platform cut
- Verifiable ownership — Provable relationships
- Aligned incentives — Token-based participation
- Community compounding — True fans recruit true fans
Your first true fans lead the way
Listen to them carefully. They will tell you:
- What to build next
- How to position
- Who else to reach
- What's broken
Context
- Marketing Activities — The Work Chart
- Token Strategy — Token economy design
- Loyalty Tokens — Reward programs
- Airdrops — Distribution strategy
- Listening Capability — Customer research
Resources
Changelog
| Date | Change | Reason |
|---|---|---|
| 2024-12 | Upgraded to workflow template | Standardize with inputs/outputs |
Questions
What is the most important question this topic raises that current discourse tends to avoid or understate?
- Which assumption in the standard framing of this topic is most likely to be wrong in a 5-year horizon?
- How does the DePIN or agent-native lens change what matters most about this topic?
- Which first principle, if violated, would make the analysis of this topic fundamentally incorrect?