Every wallet address looks identical on the blockchain — a string of 42 hexadecimal characters. Behind each one, however, sits a completely different person: a sophisticated DeFi veteran with five years of complex protocol interactions, a curious newcomer trying their first swap, a yield farmer running capital across twelve chains simultaneously, or a speculative memecoin trader chasing the next 100x. Your DApp receives all of them with the same landing page, the same onboarding flow, and the same call to action. That is why 90% of connected wallets never transact. In 2026, there is a better approach.

ChainAware’s Web3 Personas solve the identity problem that has limited Web3 growth since the beginning. By analyzing the complete on-chain behavioral history of any wallet address, ChainAware calculates who the person behind that address actually is — their behavioral intentions, experience level, risk appetite, and predicted next actions. With 18M+ Web3 Personas already calculated across 8 blockchains, the intelligence layer needed to run 1:1 personalized growth at scale already exists. This guide explains how it works and, more importantly, how to use it.

What Is a Web3 Persona?

A Web3 Persona is ChainAware’s calculated behavioral profile of who is behind a wallet address. It answers the question that every DApp, protocol, and growth team needs answered but currently cannot: who is this user, what do they want, and what are they likely to do next?

In Web2, understanding your user requires cookies, form submissions, survey data, and demographic proxies — none of which work in a pseudonymous blockchain environment. Web3, however, provides something far more powerful: a complete, immutable, publicly verifiable record of every financial decision that wallet has ever made. Every protocol interaction, every token swap, every liquidity provision, every leverage position, every NFT purchase — all of it is permanently recorded on-chain. ChainAware reads that history across 8 blockchains, applies its predictive AI models trained on 18M+ wallet profiles, and produces a rich behavioral persona that describes the real person behind any address.

Why Personas Are More Powerful Than Web2 User Profiles

Web2 user profiles are constructed from inferred data — cookies approximate browsing behavior, purchase history suggests interests, demographic segments proxy for individual preferences. Web3 Personas, by contrast, come from actual financial decisions made with real money at real cost. A wallet’s on-chain history is not browsing behavior — it is a complete record of consequential actions. Every transaction cost gas fees to execute. Every protocol interaction required the user to actively sign a transaction. Every leverage position involved real capital at real risk. Consequently, the behavioral signal quality in on-chain data is dramatically higher than any Web2 proxy — and it requires no cookies, no KYC, and no privacy invasion to access. For the full comparison of Web2 and Web3 data as marketing intelligence, see our Behavioral User Segmentation guide and our Web3 User Segmentation guide.

The Dimensions: What ChainAware Calculates for Every Wallet

A Web3 Persona is not a simple score or category — it is a multi-dimensional profile that captures distinct aspects of a wallet’s behavioral identity. ChainAware calculates the following dimensions for every address across its supported blockchains.

Behavioral Intentions (High / Medium / Low)

The intentions dimension is the most powerful for growth use cases because it answers “what is this user most likely to do on your platform next?” ChainAware calculates probability levels — High, Medium, or Low — for each of the following intention categories:

• Borrow — probability of taking a DeFi loan in the near future
• Lend — probability of providing capital to a lending protocol
• Trade — probability of executing token swaps on DEXes
• Gamble — probability of engaging with high-risk speculative positions
• NFT — probability of purchasing, minting, or trading NFTs
• Stake ETH — probability of ETH staking activity
• Stake Yield Farm — probability of yield farming across protocols
• Leveraged Staking — probability of leveraged staking positions
• Leveraged Staking ETH — probability of leveraged ETH-specific staking
• Leveraged Lending — probability of leveraged lending strategies
• Leveraged Long ETH — probability of leveraged long ETH positions
• Leveraged Long Game — probability of leveraged long gaming/metaverse positions

These intention probabilities are calculated from behavioral patterns in the wallet’s full transaction history — not from the most recent transactions alone, but from the complete pattern of engagement across all supported chains. A wallet that has borrowed on three lending protocols and repeatedly repaid and reborrowed has a High Borrow intention. A wallet that has never touched a leverage product and consistently holds conservative positions has a Low Gamble intention. These signals are objective, verifiable, and far more reliable than any self-reported preference data. For how intentions drive personalization in practice, see our Intention-Based Marketing guide.

Experience, Risk, and Identity Dimensions

Beyond intentions, ChainAware calculates the following profile dimensions that together describe who this wallet owner is as a Web3 participant:

• Experience Level — overall sophistication from blockchain transaction patterns (Beginner / Intermediate / Advanced / Expert)
• Willingness to Take Risk — behavioral risk appetite derived from historical position sizes and protocol complexity
• Categories Used — which DeFi categories this wallet has engaged with (Lending, DEX, Staking, Gaming, NFT, Bridges, etc.)
• Protocols Used — specific protocols interacted with across all supported chains
• Wallet Rank — ChainAware’s composite reputation score reflecting the overall quality and trustworthiness of the address
• Wallet Age — how long the address has been active on-chain
• Transaction Numbers — volume of on-chain interactions indicating engagement depth
• Balance — current asset holdings as a proxy for capital capacity
• Predicted Fraud Probability — AI-calculated likelihood of this address engaging in fraudulent activity (98% accuracy, backtested on CryptoScamDB)
• AML / OFAC / Sanctions Attributes — compliance screening flags for regulatory requirements

Together, these dimensions paint a complete picture of the person behind any wallet address — their capability, their history, their intentions, and their trustworthiness. For the complete Wallet Rank methodology and what each dimension represents, see our Wallet Rank guide and our Wallet Auditor guide.

The Spider Chart: Visualizing Identity on a Multi-Dimensional Map

The most intuitive way to understand a Web3 Persona is to imagine every Web3 user plotted on a spider chart — sometimes called a radar chart — where each axis of the spider web represents one of the persona dimensions. Experience sits on one axis. Risk willingness sits on another. Each intention category occupies its own axis. The result is a unique geometric shape for every wallet address — no two wallets produce identical spider charts, and the shape immediately communicates who this person is as a Web3 participant.

Why the Spider Chart Makes Differences Visible

Consider two wallets arriving at the same DeFi lending platform. Wallet A has a spider chart that extends far out on the Borrow, Lend, and Experience axes — and barely registers on Gamble or NFT. Wallet B has a completely different shape: high on NFT and Trade, low on Lend and Stake ETH, medium on Gamble. Both wallets look identical from the platform’s perspective if you only see “wallet connected.” Their spider charts tell a completely different story. Wallet A is an experienced DeFi lending user who will likely convert if shown relevant lending content immediately. Wallet B is an NFT-focused trader who may be exploring lending for the first time — and needs a completely different first experience if they are going to convert at all. Serving identical content to both produces low conversion for both. Serving persona-matched content produces dramatically higher conversion for each. For the SmartCredit.io case study documenting exactly this result, see our SmartCredit Case Study.

Real Examples: sassal.eth and vitalik.eth

Abstract explanations of multi-dimensional behavioral profiles become concrete the moment you apply them to real, well-known wallet addresses. ChainAware has calculated Web3 Personas for both sassal.eth (prominent Ethereum educator and content creator) and vitalik.eth (Ethereum co-founder). The resulting spider charts illustrate how dramatically different two highly experienced Web3 participants can be in their behavioral profiles — and why treating them identically as “experienced DeFi users” misses the most important distinctions.

sassal.eth — Experienced Educator Profile

sassal.eth’s persona reflects an experienced, education-focused Ethereum participant. The profile shows strong engagement with ETH staking and established lending protocols — consistent with a long-term Ethereum holder who interacts with the ecosystem thoughtfully rather than speculatively. The Gamble and Leveraged Long dimensions are notably low, reflecting a risk-conscious behavioral pattern that matches public content about measured, educational DeFi engagement. If sassal.eth connects to a DeFi protocol, the Growth Agent serving their session should immediately surface staking options, established lending pools, and educational content — not high-risk leverage products or speculative memecoin exposure.

vitalik.eth — Unique Founder Profile

vitalik.eth’s persona shape is unlike any other — reflecting the singular nature of the Ethereum co-founder’s on-chain behavioral history. Maximum experience level across every dimension reflects a wallet that has interacted with virtually every category of DeFi, NFT, and ecosystem activity since the earliest days of the network. The specific intention distribution, however, shows clear behavioral patterns that distinguish this address from a generic “experienced user” classification. The spider chart makes those distinctions immediately visible in a way that a simple score or category label never could. For each of these addresses, a one-size-fits-all content experience would be significantly worse than a persona-matched one.

See Any Wallet’s Full Persona — Free

ChainAware Wallet Auditor — Complete Web3 Persona in Under 1 Second

Paste any wallet address and get the complete persona: experience level, risk appetite, all intention probabilities, fraud probability, AML status, Wallet Rank, and behavioral categories. Free. No wallet connection. No signup. Try your own address or any address you’re curious about — including the examples above.

Audit Any Wallet Free Wallet Auditor Guide

The Web3 Growth Problem Personas Solve

Web3 growth is broken. The numbers are stark: acquiring one transacting DeFi user costs between $300 and $1,000 — ten to twenty times the equivalent cost in Web2. For every 200 visitors who reach a DeFi protocol, roughly ten connect their wallet. Of those ten, only one transacts. That 0.5% end-to-end conversion rate is not an anomaly — it is the Web3 industry average. The standard response is to spend more on acquisition: bigger airdrop budgets, more KOL campaigns, higher liquidity mining emissions, more aggressive paid ads. None of these tactics address the actual problem.

Why Standard Growth Tactics Fail

Airdrops attract wallet farmers who claim tokens and leave. KOL campaigns generate traffic from audiences that have no behavioral affinity for the protocol. Liquidity mining attracts mercenary capital that exits the moment a better rate appears elsewhere. Paid ads deliver undifferentiated traffic with no targeting precision beyond basic demographic proxies. All four approaches share the same fundamental failure: they bring wallets to a platform that then treats every single one identically. A sophisticated DeFi veteran and a first-time wallet holder arrive at the same landing page. Both see the same headline, the same features list, the same call to action. The DeFi veteran finds nothing compelling enough to action immediately. The newcomer finds the experience confusing. Both leave without transacting. The acquisition spend is wasted on both. For the full analysis of why Web3 marketing channels fail and what the alternative looks like, see our Why Web3 KOL Marketing Fails guide and our DeFi Onboarding guide.

The Conversion Gap Personas Close

Web3 Personas shift the intervention point from acquisition to conversion — the moment immediately after wallet connection when the user is on the platform and engaged. The moment a wallet connects, ChainAware calculates their full persona in under a second. That persona determines everything about the experience they receive: which product the platform highlights first, which CTA appears in the hero section, which risk level is shown by default, which educational content is surfaced, which social proof is relevant. A High Borrow intention wallet arriving at a lending platform immediately sees borrow rates, available collateral options, and a “Borrow Now” CTA. A High Stake Yield Farm intention wallet arriving at the same platform sees yield options, APY comparisons, and “Start Earning” messaging. Neither wallet needed to self-identify or complete a survey — their behavioral history told the platform everything it needed to know. For the detailed conversion mechanics and how resonating content produces measurable results, see our Web3 Personas Personalized Marketing guide.

Growth Agents: Deploying Personas as 1:1 Personalization

Understanding personas is the intelligence layer. ChainAware’s Growth Agents are the deployment layer that translates persona intelligence into personalized user experiences automatically, at scale, without any manual configuration per user.

How Growth Agents Work — Like Google AdWords for Your DApp

Think of Growth Agents as the Web3 equivalent of Google AdWords — but running inside your own DApp interface rather than on Google’s ad network. Google AdWords works by matching ad content to user intent signals (search queries) and serving the most relevant ad automatically. ChainAware Growth Agents work by matching DApp content to wallet behavioral signals (the Web3 Persona) and serving the most resonating content and CTAs automatically. The mechanism integrates directly into your DApp UI with a lightweight JavaScript snippet — comparable to adding Google Tag Manager or any analytics pixel. When a user connects their wallet, the agent reads the wallet address, queries ChainAware’s Prediction MCP for the full persona in milliseconds, and dynamically adjusts the content visible to that specific user before they see anything. The user sees a platform that feels built for them. They never know personalization is happening. Conversion rates increase because the content resonates. For the SmartCredit.io documented case of this working in production, see our case study.

What the Agent Personalizes

Growth Agents can personalize any content element that is driven by the DApp’s frontend: hero section headlines and sub-copy, featured product or pool recommendations, CTA button text and destination, risk level displayed by default, educational content surfaced in onboarding flows, notification messaging, and promotional banners. Every element responds to the wallet’s persona dimensions. A wallet with High Experience and High Leverage Long ETH sees advanced product options immediately. A wallet with Low Experience and Low Risk sees simplified entry-level options with educational context. Neither wallet had to tell the platform anything — their blockchain history told the agent everything. For the technical architecture of how Growth Agents integrate with DApp frontends, see our AI Agent Personalization guide and our Web3 Agentic Economy guide.

Autonomous, Continuous, Self-Learning

Growth Agents run autonomously once deployed — no manual configuration per user, no campaign management overhead, no A/B test scheduling. The agent handles every wallet connection independently, calculating and serving persona-matched content in real time. As ChainAware’s behavioral models update with new on-chain data, the persona calculations improve automatically. This means the personalization quality improves continuously without requiring the DApp team to do anything. Founders and growth teams redirect the time they previously spent manually configuring targeting rules toward higher-value strategic work — exactly the founder bandwidth argument that drives Web3’s coming innovation wave. For the unit economics of why this reduces effective acquisition cost, see our Unit Costs guide and our Crossing the Chasm guide.

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Wallet Auditor: Free Persona for Any Address

The Wallet Auditor is ChainAware’s free individual-user tool for accessing the full Web3 Persona of any wallet address. Paste any Ethereum, BNB, BASE, POLYGON, TON, or HAQQ address and receive the complete persona output: experience level, risk willingness, all intention probability scores, behavioral categories used, protocols interacted with, Wallet Rank, wallet age, transaction count, balance context, fraud probability, and AML/OFAC screening status. No signup required. No wallet connection needed. The full persona appears in under a second.

Who Uses the Wallet Auditor

The Wallet Auditor serves multiple audiences. Individual users check their own wallets to understand what their on-chain history says about them — and to verify their Wallet Rank before using it as a trust signal. DeFi participants check counterparty wallets before large transactions, partnerships, or delegate decisions. KOL teams audit influencer wallets before paying for promotions — a KOL whose wallet shows no genuine DeFi engagement is a mass marketer, not a genuine community builder. DAOs audit delegate and governance participant wallets to verify that voting power holders have meaningful on-chain experience. Security teams check sender wallets when receiving unexpected tokens or unusual transaction requests. For the complete Wallet Auditor feature breakdown, see our Wallet Auditor guide. For how Wallet Rank functions as a portable Web3 reputation credential, see our Wallet Rank guide. According to CoinMarketCap’s Web3 wallet overview , the number of active Web3 wallets continues growing rapidly — making persona-based wallet intelligence an increasingly critical layer for navigating interactions with unknown addresses.

Web3 User Analytics: Persona Distribution of Your DApp Users

While the Wallet Auditor provides individual persona lookups, Web3 User Analytics scales the same intelligence to the entire connecting user base of a DApp. The setup requires adding two lines of JavaScript to your DApp via Google Tag Manager — comparable to installing any analytics pixel. Within 24 hours, ChainAware’s analytics dashboard shows the complete persona distribution of every wallet that has connected to the platform: what percentage are High Experience vs Beginner, what the dominant intention profiles are, what risk appetite distribution looks like, which behavioral categories are most common among your users.

From Blindness to Clarity in 24 Hours

Most DApp teams know how many wallets connected but nothing about who those wallets represent. Web3 User Analytics answers every question that wallet count cannot: Are most of your users experienced DeFi participants or newcomers? Do the majority have High Borrow intentions — or are they primarily yield farmers who will never use your lending product? What fraction carry fraud probability flags that suggest low-quality traffic? Are your KOL campaigns bringing genuinely high-quality users or airdrop farmers whose behavioral profiles show no long-term engagement patterns? These questions currently require expensive manual research — or remain permanently unanswered. ChainAware’s free analytics layer answers them automatically, continuously, with no engineering overhead beyond the initial GTM snippet. For the full analytics platform capabilities and what the dashboard shows, see our Web3 Marketing Analytics guide and our complete analytics guide. For why understanding your existing user base matters before optimizing acquisition, see our User Segmentation guide.

Token Rank: Personas Applied to Token Holder Quality

Token Rank applies Web3 Persona intelligence to a specific and critical investment problem: distinguishing genuine token communities from artificially inflated holder bases engineered to attract investment before a coordinated exit. Every token holder is a wallet address with a Web3 Persona. The Wallet Rank dimension of that persona reflects the quality and depth of that holder’s on-chain engagement history. Token Rank aggregates the Wallet Ranks of all token holders and produces a composite score for the token itself — reflecting the genuine quality of its community rather than the raw count of addresses holding it.

Why Token Rank Exposes Long Rug Pulls

The most sophisticated rug pulls in 2026 are not the obvious liquidity-drain-in-24-hours variety. Long rug pulls build artificial communities over months: they distribute tokens to thousands of freshly created wallet addresses with no transaction history, manufactured Telegram groups fill with paid shills, and the price chart looks healthy because the holder count is growing. Token Rank pierces this illusion because freshly created wallets have near-zero Wallet Ranks — they have no on-chain behavioral history, no protocol engagement, and no demonstrated DeFi participation. A token showing 50,000 holders but a low median Wallet Rank is not a genuine community — it is a network of dust wallets bought to manufacture the appearance of adoption. By contrast, a token with 5,000 holders but a high median Wallet Rank represents an authentic community of experienced, engaged Web3 participants who chose this token based on their own research. That distinction is the single most powerful signal for separating genuine projects from sophisticated fraud. For the complete Token Rank methodology and how to use it for due diligence, see our complete product guide. According to Immunefi’s Web3 security research , exit scams remain the largest category of DeFi losses annually — and Token Rank directly addresses the pattern recognition that catches them.

Developer Access: MCP and Open-Source Agents

DApp teams and developers who want programmatic access to Web3 Persona data for building custom agent workflows have two primary integration paths: the Prediction MCP and the open-source pre-built agent library.

Prediction MCP: Natural Language Access to All Persona Dimensions

ChainAware’s Prediction MCP is an SSE-based Model Context Protocol server that exposes all persona dimensions to any AI agent or LLM via natural language queries. An agent asks “What is the behavioral profile of 0x123…abc?” and receives the complete persona — all intention probabilities, experience level, risk score, Wallet Rank, fraud probability, and AML status — in a single structured response in under a second. The MCP works with Claude, GPT, and any open-source LLM. Integration requires adding the MCP server configuration to the agent’s tool list — no custom API integration code, no blockchain parsing, no data pipeline. For the complete MCP integration guide and all five exposed tools, see our Prediction MCP guide and our 12 Blockchain Capabilities guide. For context on how the MCP standard is transforming AI agent data access across Web3, see our Blockchain Data Providers guide.

32 Open-Source Pre-Built Agents

For developers who want to deploy persona-powered agents without building from scratch, ChainAware publishes 32 MIT-licensed agent definitions on GitHub. Each agent integrates the Prediction MCP for persona access and implements a specific workflow — fraud detection, AML compliance, onboarding routing, marketing personalization, governance verification, DeFi intelligence, and more. Developers clone the relevant agent, configure it with their Prediction MCP credentials, and deploy. The growth agent that reads wallet personas and generates personalized DApp content is one of the 32 available agents — ready to integrate directly into any DApp’s frontend stack. For the full agent catalog and deployment instructions, see our Web3 Agentic Economy guide. According to Anthropic’s Model Context Protocol documentation , MCP has rapidly become the standard for connecting AI agents to external data providers — making ChainAware’s MCP server compatible with the widest possible range of agent frameworks from day one.

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Web3 Persona Dimensions Reference Table

Frequently Asked Questions

How does ChainAware calculate Web3 Personas without knowing who the person is?

ChainAware never attempts to identify the individual behind a wallet address — and does not need to. Instead, it analyzes the complete on-chain transaction history of the address across 8 blockchains, applying predictive AI models trained on 18M+ wallet profiles to classify behavioral patterns. A wallet that has borrowed, repaid, and reborrowed across multiple lending protocols produces a strong Borrow Intention signal — regardless of who owns it. The behavioral pattern is the signal; the identity is irrelevant. This approach preserves user anonymity completely while producing behavioral intelligence that is more accurate than identity-based profiling because it reflects actual financial decisions rather than demographic proxies.

How are 18M+ Web3 Personas already calculated?

ChainAware continuously analyzes the on-chain activity of wallet addresses across ETH, BNB, BASE, POLYGON, TON, TRON, HAQQ, and SOL — building and updating persona profiles for every address that has meaningful on-chain history. The 18M+ figure represents wallets with sufficient transaction history to produce reliable persona classifications. As blockchain activity continues growing and new wallets accumulate behavioral history, the covered population expands automatically. The models retrain continuously on new behavioral data, which means persona quality improves over time without requiring any action from DApp teams using ChainAware’s tools.

Can Web3 Personas be wrong or manipulated?

No behavioral model is 100% accurate — and ChainAware’s models are designed with specific accuracy metrics and confidence thresholds that reflect real-world performance. The fraud probability dimension, for example, carries 98% accuracy validated against CryptoScamDB using an independent test set. For intention dimensions, the models are trained on historical behavioral patterns and are regularly validated against observed user actions. Regarding manipulation: unlike Web2 profile data that can be easily fabricated with fake accounts or purchased behavioral data, on-chain transaction history requires real gas fees and real time to generate. Manufacturing a sophisticated behavioral profile is expensive and detectable — the cost and time required to fake extensive DeFi engagement patterns makes manipulation economically irrational at scale. According to a16z crypto’s research on on-chain behavioral data , blockchain transaction data provides unusually high-quality behavioral signal precisely because each action has real economic cost attached.

How do Web3 Personas differ from basic wallet analytics tools?

Basic wallet analytics tools show what happened — transaction history, token balances, protocol interactions, NFT holdings. Web3 Personas show who the person is and what they will do next — behavioral classifications, intention probabilities, risk profiles, and forward-looking predictions. The distinction is the difference between reading a bank statement and understanding a customer. A bank statement tells you what transactions occurred; a behavioral profile tells you what kind of financial actor this person is and what they are likely to need from your product. Web3 Personas convert raw on-chain data into actionable growth intelligence — the layer that makes 1:1 personalization possible without requiring wallets to self-identify. For how this compares to other analytics approaches, see our Web3 Analytics Tools comparison.

What is the fastest way to start using Web3 Personas for growth?

The fastest path is the free Web3 User Analytics tier — add two lines of GTM code to your DApp and see the full persona distribution of your users within 24 hours. This costs nothing and requires no engineering resources beyond the GTM snippet. The next step is integrating ChainAware’s Growth Agents into your DApp frontend to activate persona-driven personalization at wallet connection — this turns the analytics insight into a conversion improvement immediately. For teams building custom workflows, the Prediction MCP gives any AI agent instant access to all persona dimensions via natural language query. All three paths start with understanding who your users already are before optimizing how you talk to them.

Sources: CoinMarketCap — Web3 Wallets Overview · Immunefi — Web3 Security Research · Anthropic — Model Context Protocol · a16z Crypto — On-Chain Behavioral Data Research · FATF — Virtual Assets Recommendations

The post What Are Web3 Personas? How to Use Them to Enable Your Growth — Complete Guide 2026 first appeared on ChainAware.ai.

X Space #34 — Why Web3 Needs Intention Analytics, Not Descriptive Token Data. Listen to the full recording on X

X Space #34 tackles the analytics problem at the root of Web3’s growth crisis. Co-founders Martin and Tarmo open with a framework observation that most Web3 founders have never heard articulated clearly: every new technology paradigm requires two distinct innovations, not one. The first is business process innovation — building the product, the protocol, the smart contract logic. The second is customer acquisition innovation — developing the tools to find the right users, understand them, and convert them at sustainable cost. Web3 has invested enormously in the first and almost nothing in the second. The result is a DeFi customer acquisition cost of $1,000 or more per transacting user — a figure that makes every business model structurally unviable and drives founders toward token-based exit strategies instead of sustainable growth. The session explains why current Web3 analytics tools make this problem worse (by providing descriptive token data that looks like insight but enables no action), what intention analytics actually is and why blockchain data makes it more powerful than anything in Web2, and how any Web3 founder can get started with two lines of code in Google Tag Manager — free, today.

Two Innovations Every Technology Needs — Web3 Has Only One

Martin opens X Space #34 with a structural observation that reframes the entire Web3 growth debate. Every successful technology paradigm, he argues, requires two independent innovations to achieve mainstream adoption. Neither one alone is sufficient, and building only the first while ignoring the second will eventually kill even the most technically superior product.

The first innovation is business process innovation — the core technical contribution that the new paradigm enables. For Web3, this means smart contracts, decentralised protocols, non-custodial finance, trustless settlement, and all the genuine architectural improvements over legacy financial infrastructure. Web3 has invested billions in this dimension and produced real, valuable innovation: automated market makers, lending protocols, yield optimisation, decentralised governance, and more. The second innovation is customer acquisition innovation — developing the tools, methods, and infrastructure to find the right users, communicate with them effectively, and convert them to active participants at sustainable unit cost. Web3 has barely begun this second innovation. As Martin states: “Every new technological paradigm will need as well innovation of customer acquisition. You need always two innovations. There is innovation on the business process and there is innovation of customer acquisition. In Web3 there has been massive innovation with full heart in the business process innovation. But there has to be as well innovation in customer acquisition.”

Why Both Innovations Are Non-Negotiable

The reason both innovations are necessary is straightforward: a better product that nobody can find or afford to acquire is not a better business. Web3’s technical innovations are real, but they exist largely inside an ecosystem of 50 million technical enthusiasts. Reaching the remaining billions of potential users requires the second innovation — customer acquisition tools that make it economically viable to identify, target, and convert mainstream users. Without that second innovation, even genuinely superior products will remain trapped serving the early-adopter segment. For more on the growth dynamics, see our Web3 growth restoration guide.

Web3 Today Is Web2 in 2000: The Same Crisis, The Same Playbook

Martin and Tarmo anchor the entire session in a historical parallel that makes the current Web3 situation both less alarming and more solvable than it appears. Web3 in 2025 is not experiencing a unique crisis — it is experiencing the same crisis that Web2 experienced at the beginning of the 2000s internet era, with the same root causes and the same available solutions.

In the early 2000s, Web2 faced two specific barriers to mainstream adoption. First, fraud was rampant: credit card fraud was so prevalent that many consumers refused to enter payment details online, stifling e-commerce growth entirely. Second, customer acquisition costs were catastrophic: dot-com companies spent enormous sums on billboard advertising, TV spots, and mass media campaigns (the famous “pets.com” highway billboards became a symbol of the era’s marketing waste) with customer acquisition costs in the thousands of dollars — and no way to measure which half of the spend was working. As Martin recalls: “People were afraid to transfer their credit card as a payment means over Internet because the fraud was so high. And e-commerce companies, half of the developer power went into fraud detection. Acquisition costs of users were enormous.” Both problems were eventually solved: fraud through better detection systems, and CAC through Google’s AdTech innovations. Web3 faces identical structural challenges and has access to the same solution blueprint. For more on the fraud detection parallel, see our Web3 fraud and growth guide.

The Secret Everyone Knows But Nobody Admits

Martin makes a pointed observation about why the Web3 CAC crisis receives so little public discussion despite being universally known among founders. Admitting a $1,000+ customer acquisition cost to a venture capital investor essentially ends the conversation — it signals that the business model cannot become cash-flow positive regardless of how good the product is. Consequently, founders avoid discussing it publicly while silently dealing with the consequences: burning treasury on ineffective mass marketing, failing to hit growth targets, and eventually pivoting toward token-based revenue extraction rather than genuine product growth. As Martin puts it: “It’s a secret everyone knows but no one is speaking about this. No one wants to admit it — no one wants to say it loud — how difficult it is to acquire users in Web3.”

Descriptive Analytics vs Predictive Analytics: The Fundamental Difference

The core technical argument in X Space #34 is the distinction between descriptive analytics and predictive analytics — and the specific reason why Web3 analytics tools have remained stuck in the descriptive category while Web2 moved to predictive analytics over 15-20 years ago.

Descriptive analytics documents what happened. It tells you which tokens users held last month, which protocols they interacted with historically, and how transaction volumes changed over time. This data is backward-looking by definition. Crucially, it cannot tell you what a user will do next — which is the only information that matters for targeted acquisition and conversion campaigns. Predictive analytics uses behavioral pattern data to calculate forward-looking probabilities: what is the likelihood that this specific wallet will borrow in the next 30 days? Will this user stake, trade, or exit? Is this address behaviorally aligned with a high-leverage product or a conservative yield strategy? As Tarmo explains: “Today the most analytics in Web3 is descriptive — it just describes what happened in the past. The difficulty is past actions don’t predict what is going to happen. What is the user going to do in future?” For the full framework, see our behavioral analytics guide.

Why Web2 Made the Jump and Web3 Has Not

Web2 completed the transition from descriptive to predictive analytics in the early 2000s, driven by Google’s development of intention-based advertising technology. Google’s core insight was that search and browsing history, despite being lower-quality than financial transaction data, contained enough behavioral signal to calculate user intentions with sufficient accuracy for targeted advertising. The result was a dramatic reduction in customer acquisition costs: Web2 businesses that adopted Google’s AdTech moved from spending thousands of dollars per customer with no idea whether it was working, to spending $10-30 per transacting customer with measurable ROI at every step. Web3 has access to behavioral data that is qualitatively superior to anything Google uses — and has still not made the transition. That gap is precisely what ChainAware’s analytics tools address.

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Add ChainAware’s pixel to Google Tag Manager. No code changes to your application. Within 24-48 hours, see the real intentions of every wallet connecting to your platform — borrowers, traders, stakers, gamers, NFT collectors — aggregated and actionable. Not token holder data. Intention data. The difference between descriptive and predictive analytics, free.

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Why Token Holder Data Is Not Actionable

Martin introduces a specific critique of the most common form of “analytics” offered by current Web3 data platforms — token holder overlap analysis — and explains precisely why this data type, despite appearing informative, cannot drive any marketing or growth action.

Token holder analytics tells a protocol that, for example, 10% of their users also hold a specific token from another protocol, or that a percentage of their wallet addresses have previously interacted with a competing platform. This type of data describes the current composition of a user base at a superficial level. However, it answers none of the questions that matter for acquisition and conversion: What does this user intend to do next? Are they a borrower or a trader? Do they have the experience level to use this product? Are they likely to convert, or are they purely exploratory? As Martin challenges: “Let’s imagine you’re a founder and now you see this data — 10% of the people who hold your token have as well Uniswap. What do you do? How does it help you to get more users to your platform?” The honest answer is: it does not. Token holder data describes a static snapshot with no forward-looking signal. For more on what actionable data looks like, see our intention-based marketing guide.

Protocol Usage Data vs Token Holding Data

ChainAware deliberately focuses on protocol interaction patterns rather than token holdings. Protocol interactions reveal behavioral intentions: a wallet that has repeatedly used lending protocols is a behaviorally confirmed borrower or lender. A wallet that consistently interacts with high-leverage trading products has a demonstrated risk appetite. A wallet whose protocol history shows only simple swaps and staking is likely in an early lifecycle stage. These behavioral protocol patterns, combined with transaction frequency, timing, and counterparty analysis, produce the intention profiles that make targeting possible. Token holding tells you what someone owns. Protocol behavior tells you what someone does — and what they are likely to do next.

Why Blockchain Data Produces Better Predictions Than Web2’s Behavioral Data

Tarmo returns to the proof-of-work data quality argument that distinguishes blockchain behavioral data from the social media and browsing data that Web2’s AdTech systems rely on. The argument is foundational: Web3’s predictive analytics advantage is not just equivalent to Web2’s — it is structurally superior because the data quality is higher.

Web2’s behavioral data — search queries, page views, app usage — is generated at zero cost per interaction. A user can search for “DeFi borrowing” once because a friend mentioned it, then never engage with the topic again. That single search creates a behavioral signal that Google’s algorithms will interpret as a genuine interest, serving DeFi-related advertisements for weeks. The signal is noisy because the cost of generating it is zero. Blockchain transactions, by contrast, require real money (gas fees) and deliberate action. Nobody accidentally executes a DeFi lending transaction. Every transaction represents a considered, intentional financial commitment that reveals genuine behavioral priorities. As Tarmo explains: “When you have to pay cash for every transaction, you don’t just fool around. You think twice before you do your transactions. Financial transactions have very high prediction power because users think twice or three times before they submit.” For how this applies to prediction accuracy, see our predictive AI guide.

The User-Product Mismatch: Your Real Users Are Not Your Marketing Persona

One of X Space #34’s most practically useful arguments addresses a problem that many Web3 founders privately suspect but have no way to confirm: the users actually connecting to their platform may be fundamentally different from the users their marketing was designed to attract. This user-product mismatch is, according to Martin and Tarmo, one of the most common root causes of poor conversion rates — more common than actual product quality problems.

Every marketing team creates user personas — fictional representative characters who embody the ideal target customer. “Our persona is a DeFi-experienced borrower with 50+ on-chain transactions, comfortable with 150% collateralisation, seeking fixed-rate lending for predictable financial planning.” This persona guides all acquisition spend: the content, the channels, the messaging, the influencer selection. The problem is that there is currently no way to verify whether the marketing is actually attracting this persona or an entirely different audience. Without intention analytics, a protocol might spend $30,000 per month attracting traders who have no interest in borrowing, or attracting complete DeFi newcomers to a product designed for experienced users. As Martin explains: “Every founder is saying like oh I have 20,000 clicks a month. Cool. From which users? What is their profile? What are their intentions? And usually you don’t know it until now.” For the complete targeting methodology, see our AI marketing for Web3 guide.

The Reality Check: Persona R vs Persona P

Martin frames the user-product mismatch with a memorable shorthand. Founders design their product and marketing for “Persona R” — the imagined ideal user who perfectly matches the product’s value proposition. Analytics reveals that “Persona P” is actually arriving — a different behavioral profile with different intentions, different experience levels, and different risk tolerance. Neither outcome is necessarily catastrophic: sometimes Persona P represents a genuinely valuable market that the founder had not considered. However, it is impossible to respond to the mismatch — either by adjusting the product, refining the marketing, or deliberately targeting Persona R instead of Persona P — without first knowing it exists. Intention analytics creates this feedback loop, replacing the founder’s assumptions with market reality.

Risk Willingness: The Credit Suisse Model Applied to Web3 Audiences

Tarmo introduces the risk willingness dimension — a concept central to private banking client profiling at Credit Suisse and other major institutions — and explains why it is equally essential for Web3 platform design and user acquisition.

Risk willingness describes the level of potential loss a user is psychologically and financially comfortable absorbing. The spectrum is wide: some investors will sleep soundly through a 50% portfolio decline overnight, treating it as a normal fluctuation in a volatile asset class. Others cannot function effectively when facing even a 5% potential loss — the anxiety impairs their decision-making and leads to panic selling or avoidance behavior. Neither profile is wrong; they simply require different products, different communication styles, and different interface designs. As Tarmo explains: “In Credit Suisse, everything is based on the willingness to take a risk. Some people tolerate 50% loss overnight — they even don’t care. Other people cannot sleep if they have 5% possibility of loss.”

Matching Product Risk Profile to User Risk Willingness

The practical implication for Web3 protocols is direct: if a platform offers high-leverage products but its user base consists primarily of risk-averse wallets, the mismatch will produce poor conversion, high churn, and negative user experiences. Risk-averse users who encounter high-leverage products either avoid them entirely (reducing conversion) or engage inappropriately and suffer losses (damaging trust and creating churn). ChainAware’s analytics calculates risk willingness from transaction history — a wallet that has consistently taken large leveraged positions in volatile markets has a demonstrated high risk tolerance; a wallet that holds stable assets and rarely trades has a demonstrated risk-averse profile. Matching acquisition and interface design to these calculated risk profiles dramatically improves both conversion rates and long-term retention. For more on wallet behavioral profiling, see our wallet audit guide.

Mass Marketing in Web3: The 50/50 Problem Nobody Admits

Martin draws on a famous quote from the dot-com era that describes Web3’s marketing situation with uncomfortable precision: “We spend 50% of our marketing budget, but we don’t know which half is working.” This observation — originally attributed to department store magnate John Wanamaker in a pre-internet era — re-emerged as a central frustration of Web2’s early marketing phase, and it perfectly describes Web3’s current state.

Web3 marketing today consists primarily of KOL (Key Opinion Leader) campaigns, crypto media placements, loyalty programs, Discord community management, and airdrop campaigns. These channels all share one characteristic: they reach broad, undifferentiated audiences with identical messages and provide no meaningful feedback on whether the right users were reached. A protocol spending $30,000 per month on 20,000 clicks at $1.50 per click does not know whether those clicks came from wallets that will ever transact, wallets that are exclusively airdrop hunters, wallets that are completely misaligned with the product, or wallets that are genuine prospects. Without intention analytics providing the feedback loop, every optimization decision is guesswork. As Martin states: “At the moment, the Web3 marketing is something in the style — you spend 50%, but you don’t know which part worked.” For more on the mass marketing critique, see our Web3 KOL marketing guide.

How Web2’s $180 Billion AdTech Industry Solved the Same Problem

Martin and Tarmo contextualise the Web3 analytics opportunity by quantifying the industry that Web2 built to solve the identical user acquisition problem. Global AdTech — the technology infrastructure that enables targeted digital advertising based on user behavioral data — represents approximately $180 billion in annual revenue worldwide, with approximately $30 billion in Europe alone. This industry did not exist before Google’s AdWords innovation. It emerged specifically because the combination of user intention data and programmatic targeting reduced customer acquisition costs from thousands of dollars to tens of dollars, making digital business models viable at scale.

The mechanism was straightforward: by calculating user intentions from search and browsing behavior, Google could match advertisements to users whose behavior indicated genuine interest in the product being advertised. The result was dramatically higher conversion rates (users saw ads relevant to their actual intentions), lower cost per click needed for conversion, and measurable ROI that replaced the old 50/50 guesswork. Web3 has not yet built this infrastructure — but the data necessary to build it is available free of charge on every major blockchain. As Martin argues: “The first step, understand who your clients are. Not what you think, who they are, but who they really are. This is not possible without calculating user intentions and aggregating them.” For the complete AdTech framework, see our Web3 AdTech guide.

From Analytics to Automated Targeting

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Intention Analytics: The First Step Toward Sustainable Web3 Growth

Having established both the problem and its historical parallel, Martin and Tarmo turn to the specific solution that ChainAware provides. The solution architecture has two sequential steps — and X Space #34 focuses deliberately on Step 1, because attempting Step 2 without Step 1 is precisely the mistake that most Web3 marketing efforts currently make.

Step 1 is intention analytics: understanding who your users actually are, what they intend to do, and whether they match the profile your product is designed to serve. This step requires no immediate change to marketing strategy, creative, or spend. It requires only adding ChainAware’s tracking pixel to the platform and observing the aggregated intention data that emerges from actual wallet connections. Step 2 — which ChainAware also enables through its Marketing Agents product — is acting on that data: targeting acquisition campaigns at the right behavioral audiences, personalising on-site messaging to match individual wallet profiles, and converting matched users through intention-aligned calls-to-action. Step 2 is impossible to execute correctly without Step 1’s data. As Tarmo concludes: “What ChainAware offers is the key technology — a no-code environment to get a summary of your users of your Web3 applications. It’s free. It doesn’t cost anything. You get this feedback and with this feedback you can start doing actions, real actions which lead to user conversions.” For the complete analytics implementation, see our Web3 analytics guide.

Two Lines of Code: How to Get Started with ChainAware Analytics

Martin emphasises the implementation simplicity of ChainAware’s analytics pixel repeatedly throughout X Space #34, because the perceived complexity of analytics integration is one of the primary barriers preventing Web3 founders from adopting intention-based approaches. The actual integration requires no engineering resources and no changes to the protocol’s existing codebase.

The integration process uses Google Tag Manager — a standard no-code tag management platform that virtually every Web3 project already uses for analytics, tracking pixels, and conversion tools. Adding ChainAware requires two lines of code inserted as a new tag in the existing Google Tag Manager workspace. No application code changes. No engineering deployment. No smart contract modifications. No user-facing changes of any kind. Within 24-48 hours of adding the tag, ChainAware’s dashboard begins populating with aggregated intention profiles of the wallets connecting to the platform: experience levels, risk willingness scores, behavioral intention categories (borrower, trader, staker, gamer, NFT collector), protocol usage history, and predicted next actions. As Martin explains: “From the day after, you see the users, you see the weekly users, you see the monthly users. Two lines of code. If you don’t like it, delete them. You don’t have to change your application.” For the setup guide, visit chainaware.ai/subscribe/starter.

Free for Founders Who Build Real Products

ChainAware’s analytics tier is free. Martin clarifies the offering directly: founders who join before end of May 2025 receive the analytics product free permanently. After that date, ChainAware will revisit pricing — the infrastructure cost of running the intention calculations at scale requires eventual monetisation. However, the current offer represents a genuine opportunity for any Web3 founder to access enterprise-grade intention analytics at zero cost simply by integrating two lines of code. Martin is specific about the target user: founders who are building real products, want real users, and intend to generate real revenue — not founders whose primary goal is token price manipulation or exit strategies. For the complete pricing overview, see chainaware.ai/pricing.

The Feedback Loop: From Imaginary Persona to Real User Profile

Martin introduces a powerful framing for what intention analytics actually delivers to a founder who has been operating on assumed user personas. The moment a founder connects ChainAware’s analytics to their platform and sees real intention data for the first time, they experience what Martin calls a “moment of reality” — the point at which the imaginary persona the marketing team invented is replaced by the actual behavioral profiles of real users.

This reality check is often uncomfortable. Martin acknowledges this directly: “Oh, I designed this Persona R. But here I see totally a Persona P is using my application. And this is like a reality check. It’s very hard probably for all founders to see who really are the users.” However, this discomfort is enormously valuable. A founder who knows their actual user base can make rational decisions: adjust the product to serve the actual audience better, refine acquisition targeting to attract the intended audience instead, or recognise that a product-market fit exists in an unexpected segment worth pursuing. Without this data, every product decision and every marketing investment is based on untested assumptions. Intention analytics replaces those assumptions with market feedback — the most valuable input any product team can receive. For more on the analytics-to-action workflow, see our Web3 growth guide.

From Analytics to Action: Fully Automated Web3 AdTech

X Space #34 deliberately focuses on analytics as Step 1, but Martin briefly introduces the Step 2 product — ChainAware’s Marketing Agents — to give founders a view of the complete growth infrastructure available after establishing the analytics foundation.

ChainAware’s Marketing Agents take the intention profiles calculated from on-chain behavioral data and automate the entire content creation and targeting pipeline. The system analyses each connecting wallet’s behavioral profile, calculates their specific intentions, generates content that resonates with those specific intentions, creates appropriate calls-to-action matched to the user’s likely next action, and delivers the personalised experience automatically — without human intervention for each individual user interaction. The input required from the founder is minimal: a set of URLs describing the platform’s products and value propositions. The output is a fully automated, intention-matched marketing layer that converts identified prospects more effectively than any mass-marketing alternative. As Martin explains: “It is 100% automated. It analyzes users, it calculates their predictions, it creates the content which resonates with user intentions, it creates call to actions. The result is much higher user conversion, user acquisition. The dream of every Web3 founder.” For the complete marketing agent documentation, see our AI marketing guide.

The Role of Marketing Agencies Is Changing

Martin notes a parallel between Web3’s current marketing agency culture and Web2’s pre-AdTech marketing agency culture. In the dot-com era, marketing agencies controlled enormous budgets with no accountability infrastructure — the 50/50 waste was industry standard, and agencies benefited from the opacity. Google’s AdTech innovation changed that permanently: agencies that mastered the new tools thrived, while those who resisted were replaced by programmatic platforms. Web3 is at the equivalent inflection point. Founders who adopt intention analytics will gain the data needed to hold their marketing partners accountable, replace ineffective mass campaigns with targeted intention-based programs, and reduce CAC from the current $1,000+ to the $20-30 range that makes Web3 businesses viable. For more on this transition, see our high conversion without KOLs guide.

Comparison Tables

Descriptive vs Predictive Web3 Analytics: Full Comparison

Web3 Marketing Today vs Intention-Based Approach

Frequently Asked Questions

What is the difference between descriptive and predictive Web3 analytics?

Descriptive analytics documents what happened: which tokens users held, which protocols they used in the past, how transaction volumes changed over time. This data is backward-looking and cannot predict future user behavior. Predictive analytics uses behavioral pattern data from on-chain transaction history to calculate forward-looking probabilities: what is this wallet likely to do next? Are they a probable borrower, trader, or staker? Do they have the experience level and risk tolerance for this product? Predictive analytics is actionable — it directly informs acquisition targeting, on-site personalisation, and conversion strategy. Descriptive analytics, while informative, cannot drive any specific marketing or growth action.

Why is token holder overlap data not useful for marketing?

Token holder data tells you what users own, not what they intend to do. Knowing that 10% of your users also hold a competitor’s token does not tell you whether those users are active traders, passive holders, or protocol explorers. It does not tell you whether they are likely to borrow, stake, or trade. It provides no basis for targeting specific messages, creating personalised interfaces, or allocating acquisition budget to the right channels. Actionable marketing data requires intention data — what will this user do next, and what message or offer is most likely to convert them to a transacting customer? Protocol usage behavioral patterns produce this intention data; token holdings do not.

How does ChainAware’s analytics pixel integrate with a Web3 platform?

Integration requires two lines of code added to Google Tag Manager — a no-code tag management platform already used by virtually every Web3 project. No changes to the application’s codebase, smart contracts, or production deployment are necessary. After adding the tag, ChainAware begins calculating intention profiles for every wallet that connects to the platform. Within 24-48 hours, the ChainAware dashboard shows aggregated data: how many high-probability borrowers connected, how many traders, what the experience level distribution looks like, what the risk willingness profile of the user base is, and what intentions the majority of connecting wallets have signalled. To get started, visit chainaware.ai, navigate to Pricing, select the Starter tier (zero cost), and follow the five-step setup workflow.

Why is Web3 customer acquisition cost so much higher than Web2?

Web3 CAC is high for the same reasons Web2 CAC was high in the early 2000s: mass marketing to undifferentiated audiences with no feedback loop. When every marketing message reaches the same broad population regardless of intention alignment, the vast majority of contacts are not genuine prospects — meaning the cost is spread across mostly irrelevant interactions. Web2 solved this with Google’s micro-segmentation and intention-based AdTech, reducing CAC from thousands of dollars to $10-30 by reaching only users whose behavioral data indicated genuine interest in the product. Web3 has access to behavioral data that is qualitatively superior to Google’s (because blockchain transactions carry higher proof-of-work signal than search queries) but has not yet built the analytics and targeting infrastructure to exploit it. ChainAware’s analytics pixel is the first step in building that infrastructure.

What is risk willingness and why does it matter for Web3 user acquisition?

Risk willingness describes the psychological and financial tolerance for potential losses that a specific user has demonstrated through their transaction history. Users who have consistently made large leveraged positions in volatile markets have demonstrated high risk tolerance; users who hold primarily stable assets and rarely trade have demonstrated risk aversion. This dimension matters for Web3 acquisition because serving high-leverage products to risk-averse users — or conservative products to risk-tolerant users looking for high returns — creates fundamental product-user mismatches that prevent conversion and cause churn. Credit Suisse and other major banks have used risk willingness profiling for decades to match clients to appropriate products. ChainAware calculates equivalent profiles from on-chain behavioral history, making this private-banking-grade insight available to any Web3 protocol through the analytics pixel.

Analytics → Targeting → Conversion

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This article is based on X Space #34 hosted by ChainAware.ai co-founders Martin and Tarmo. Listen to the full recording on X . For questions or integration support, visit chainaware.ai.

The post Why Web3 Needs Intention Analytics, Not Descriptive Token Data first appeared on ChainAware.ai.

X Space with Klink Finance — ChainAware co-founder Martin in conversation with Philip, co-founder of Klink Finance, on AI-driven AdTech for Web3 finance platforms. Listen to the full recording on X

Two Web3 founders with very different perspectives on user acquisition sit down to map the honest state of Web3 marketing. Philip from Klink Finance brings three years of operating a 350,000-member crypto wealth creation platform — real experience running campaigns across Twitter, Telegram, and Discord through the full cycle of channel migration and community building. Martin from ChainAware brings the data layer: behavioral analytics across 18M+ wallets, AI-powered fraud detection at 98% accuracy, and the conviction that Web3 marketing is about to undergo the same AdTech transformation that Web2 underwent in the early 2000s. Their conversation covers the gap between traffic generation and user conversion, the 15x uplift that personalization delivers over mass marketing, why AI agents are not the next evolution of prompt engineering but something structurally different, and why the wallpaper analogy explains what resonating content actually means in practice. Together, they arrive at the same conclusion from different directions: the most important unsolved problem in Web3 growth is not reaching users — it is converting the right users at sustainable cost.

Klink Finance: Building Crypto Wealth Creation from Zero

Philip, co-founder of Klink Finance, opens the conversation with a platform overview that immediately establishes the scale of the Web3 user acquisition challenge from the operator’s perspective. Klink Finance is a crypto wealth creation platform — specifically designed to let anyone start building a crypto portfolio from $0 of personal investment. Rather than requiring users to bring capital, Klink enables participants to earn crypto rewards through completing quests, participating in airdrops, playing games, answering surveys, and engaging with various platform activities. Rewards are distributed in stablecoins (primarily USDT) as well as newly listed tokens and other airdrop opportunities.

Since launch, Klink Finance has grown to over 350,000 community members — accessible through a mobile app, a web app, and a Telegram mini app. That multi-platform presence reflects a deliberate strategic adaptation: Klink has observed firsthand how rapidly Web3 user communities migrate between channels, and has built infrastructure to follow users wherever they concentrate. As Philip explains: “The trends are changing so quickly in the crypto space and also user interest changes rapidly. Over the course of building Clink, we had different channels that worked better or worse over time.” For more on understanding Web3 user behavior patterns, see our behavioral analytics guide.

Web3 Marketing in 2025: 30 Years of Web2 Practice Meets Six Years of Web3 Native

One of the most practically useful observations Philip makes early in the conversation concerns the false dichotomy many Web3 founders hold about their marketing approach. Early in the crypto industry’s history, a significant faction believed that Web3 marketing was fundamentally different from Web2 marketing — that it required entirely new channels, tactics, and frameworks. Experience has proven this view too simple. As Philip puts it: “If you look at how it evolved over the years, it is very much a mixture of strategies that have worked extremely well in the Web2 space and adding things on top that are very much Web3 native.”

The asymmetry of the situation is significant: Web2 marketing has 30 years of accumulated best practices, tested frameworks, conversion rate data, and channel-specific expertise. Web3 marketing has approximately six years as a serious discipline. Rather than rejecting those 30 years, the most effective Web3 marketing operators layer Web3-native elements — wallet behavioral targeting, on-chain audience segmentation, token incentive structures — on top of the proven Web2 foundation. The projects that succeed are those that understand both layers and know which tool applies in which context. For how wallet behavioral data creates a Web3-native targeting layer, see our intention-based marketing guide.

Agility as the Core Marketing Competency

Beyond the hybrid approach, Philip identifies agility as the single most valuable marketing competency for Web3 operators. The speed at which trends, user concentrations, and effective channels shift in the crypto space is dramatically faster than in Web2. A marketing strategy that worked in Q1 may be significantly less effective by Q3 — not because the product changed, but because the ecosystem migrated. The operators who sustain growth are those who monitor channel effectiveness continuously and reallocate resources quickly when the data signals a shift. Rigidity — committing to a single channel because it worked previously — is one of the fastest ways to lose momentum in Web3.

Channel Migration: From Twitter Dominance to the Telegram Ecosystem

Klink Finance’s own channel history provides a concrete illustration of why agility matters. For an extended period after launch, Twitter (now X) was their primary user acquisition channel — leveraging the platform’s dense Web3 community and its culture of crypto discussion, alpha sharing, and community building. That approach worked well. Over the course of 2024, however, Klink’s primary acquisition channel shifted decisively toward Telegram — both the broader Telegram ecosystem and the specific advertising capabilities that Telegram provides to reach its 900+ million monthly active users.

This migration reflects a broader pattern visible across the Web3 industry: community infrastructure has been moving from Discord (which dominated the 2020-2022 era as the go-to community building platform for NFT and DeFi projects) toward Telegram as both a community platform and a distribution channel. Telegram mini apps have created an entirely new product category — lightweight applications running natively within Telegram that can reach users directly inside their primary communication environment. Klink’s Telegram mini app captures this opportunity directly. As Philip explains: “We also launched the Telegram mini app to leverage advertising on Telegram directly. Because you see a lot of migration also where Web3 communities are built up from being only on Discord initially to a lot more reliance on Telegram.” For more on channel strategy and conversion optimisation, see our Web3 marketing guide.

Mass Marketing Generates Traffic. Personalization Converts It.

Martin introduces the structural distinction at the heart of ChainAware’s approach to Web3 marketing — one that Philip quickly validates from Klink’s operational experience. The distinction separates two entirely different problems that most Web3 marketing discussions conflate: traffic generation and user conversion.

Mass marketing — banner ads, KOL campaigns, Telegram ads, Twitter promotions — is reasonably effective at generating traffic to a platform. It brings visitors to the website or application. However, it is almost entirely ineffective at converting those visitors into active, transacting users. The reason is structural: mass marketing sends the same message to everyone, regardless of their behavioral profile, experience level, risk tolerance, or actual intentions. People are different. A DeFi trader who arrives at a borrowing and lending platform has completely different needs, vocabulary familiarity, and conversion triggers than a crypto newcomer who arrived through the same campaign. Sending both of them an identical onboarding experience means neither gets a particularly relevant one. As Martin frames it: “Visitors are coming to your website. Everyone is seeing the same message. People are different. We have to give to people different messages.” For the complete framework on personalized Web3 marketing, see our AI marketing for Web3 guide.

Philip adds an important operational dimension to this framework. Reducing customer acquisition cost is not only about targeting better acquisition channels — it equally requires optimising the conversion from first landing to first transacting action. As he explains: “It’s not only about spending an amount of money and driving users into your platform. Because then you actually enter the next phase of facilitating a very easy onboarding towards the user. The simpler it is to use your product and to convert from first landing into becoming an actual user, the cheaper it will get also to grow your community.” The implication is clear: personalisation is the conversion layer that makes the acquisition spend worthwhile. Without it, the traffic generated by mass marketing leaks out of the funnel before reaching the transacting stage. For how behavioral segmentation enables the conversion layer, see our user segmentation guide.

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The Email Marketing Proof Point: 1% vs 15% — a 15x Conversion Multiplier

Martin introduces a specific data point that quantifies the personalization premium with enough precision to be immediately actionable for any Web3 founder evaluating their marketing strategy. The comparison comes from email marketing — a channel with decades of conversion rate data across millions of campaigns.

Mass email marketing achieves approximately 1% conversion across general audiences — dropping to 0.5% in the crypto sector, where inbox competition from project newsletters, airdrop announcements, and exchange promotions is particularly intense. Personalised email marketing — where message content is generated based on additional data about the recipient from LinkedIn, Twitter history, and behavioral signals — achieves open rates of approximately 15%. That is not a marginal improvement. At 15x the conversion rate of mass email, personalisation fundamentally changes the economics of every marketing investment. As Martin states directly: “Mass email marketing conversion ratio is 1%, in crypto 0.5%. Now if you go personalised, meaning the emails are generated based on additional information available about you via LinkedIn and Twitter, then you get open rates of 15%. And this shows how much personalisation impacts the conversion. 1% versus 15% — that’s 15x.” For the complete conversion framework applied to Web3 platforms, see our high-conversion Web3 marketing guide.

Blockchain Behavioral Data Outperforms LinkedIn and Twitter Signals

The 15x personalization premium in email marketing uses relatively shallow data sources — LinkedIn profile information, Twitter activity patterns, and basic demographic signals. Blockchain behavioral data is structurally richer and more reliable than any of those signals. Every on-chain transaction reflects a deliberate financial decision that cost real money (gas fees) to execute. The resulting behavioral profile captures actual financial behavior, not self-reported professional credentials or social media activity that may be entirely performative. A wallet with a three-year history of leveraged trading on multiple chains tells you far more about that person’s risk profile, experience level, and likely next action than their LinkedIn job title ever could. Consequently, the personalization premium that blockchain-based targeting enables is likely to exceed the 15x email marketing benchmark — because the underlying data quality is higher.

The Onboarding Aha Moment: How Klink Reduced CAC by Optimising the First Reward

Philip provides a concrete case study from Klink Finance’s own growth history that illustrates how onboarding optimisation directly reduces customer acquisition cost — without changing a single marketing channel or campaign budget. The concept centres on what product teams call the “aha moment” — the specific point in a new user’s first experience where they genuinely understand the product’s value, decide they like it, and commit to continued engagement.

For Klink Finance, that aha moment is precisely defined: it is when a new user earns their first crypto reward starting from zero. Not when they register. Not when they download the app. Not when they complete a profile. The specific moment they see their first crypto balance appear — earned without any prior investment — is when they truly understand what Klink is and why it is valuable. As Philip explains: “For us, this key moment of being a Klink community member is when you earn your first crypto rewards starting from zero. Over time we more and more optimise this flow of getting someone to land on the website or application and getting them to earn their first rewards. And the more you understand how to optimise this onboarding flow, that will have a direct impact on your Web3 marketing strategy and the types of users you are targeting.” For how behavioral profiling enables personalised onboarding at scale, see our DeFi onboarding guide.

Personalisation Reduces Onboarding Noise

Philip makes a specific practical observation about personalised onboarding that connects directly to ChainAware’s approach. If a platform builds a single onboarding flow suitable for both complete crypto beginners and experienced DeFi natives, both groups receive significant irrelevant content. The beginner needs education about private keys and basic wallet concepts. The experienced DeFi user finds that same education condescending and time-wasting. As Philip explains: “If you understand they have been in the crypto space for years already, you don’t need to educate them about what a private key is or how to stake tokens. But you can get straight to the point of the key benefits of your specific solution.” ChainAware’s experience level parameter (1–5 scale derived from transaction history) enables exactly this distinction to be made at wallet connection — before the user interacts with any onboarding content at all. For how ChainAware calculates experience levels, see our wallet auditor guide.

The User Journey from CEX to DeFi: 90%, 10%, and Why It Matters

The conversation surfaces a data point that has significant implications for how Web3 platforms should think about their addressable market. Philip observes that Klink Finance’s community sits at the intersection of Web2 and Web3 — serving users who interact with crypto applications but are not necessarily DeFi natives. Martin provides the broader industry context: approximately 90% of crypto users conduct their activity exclusively on centralised exchanges, with only around 10% actively using DeFi wallets and interacting with on-chain protocols.

Rather than viewing this 90/10 split as a limitation, Martin frames it as a predictable stage in a user journey that is directionally clear and commercially important. New crypto users almost universally start on centralised exchanges — the user experience is familiar, the custodial model removes the complexity of key management, and the fiat on-ramps are straightforward. Over time, as users gain experience and confidence, they begin exploring Web3 applications. Typically, they encounter rug pulls or other fraud events on platforms like PancakeSwap or pump.fun, temporarily retreat to centralised exchanges, then return to DeFi with more caution and more knowledge. Eventually, experienced users often exit centralised exchanges entirely. As Martin describes the arc: “It’s like a personal development upon every Web3 user. It was as well my journey. I started on the central exchanges. I don’t want to use central exchanges anymore.” For more on the user journey and how behavioral analytics tracks it, see our Web3 growth guide.

The Commercial Implication: Protect New Entrants or Lose Them Permanently

The user journey analysis has a specific commercial implication that Martin emphasises throughout the conversation: new users who encounter fraud in their first DeFi experiences frequently leave the ecosystem permanently. They do not pause and try again — they associate the entire Web3 space with the negative experience and return to centralised exchanges as their permanent solution. Every fraudulent interaction that drives a new user out is not just a lost transaction — it is a permanently lost ecosystem participant who will never contribute to DeFi liquidity, governance, or growth again. Reducing fraud rates therefore directly expands the addressable market for every DeFi platform by keeping new entrants in the ecosystem long enough to become genuine participants. For the full fraud reduction argument, see our fraud detection guide.

Address History as Trust Infrastructure: Your Best Business Card in Web3

Martin introduces an underappreciated use case for on-chain behavioral data that extends beyond fraud detection and marketing personalisation: address history as a trust infrastructure for peer-to-peer and business-to-business interactions in the Web3 ecosystem. The argument is both practical and elegant — blockchain’s combination of transparency and pseudonymity creates a unique opportunity to project verifiable trustworthiness without sacrificing privacy.

In a traditional business context, trust is established through credentials — CVs, references, LinkedIn profiles, company registrations. All of these can be falsified. On-chain transaction history, by contrast, is cryptographically immutable and permanently public. A wallet with a five-year history of sophisticated DeFi interactions, consistent protocol usage, and zero fraud associations tells a more reliable story about its owner than any self-reported credential. Furthermore, the history cannot be retrospectively altered — it stands as a permanent, verifiable record. As Martin explains: “Address history is a way to create trust in the ecosystem. You can stay anonymous but you can still calculate the trust level — how much you can trust other persons. Your address history is my credit score, my business card, my visit card. I don’t need to pretend to be someone — I say that’s my address, look who I am, look at the predictions, look at my behavior. I am who I am.” For the complete Share My Wallet Audit implementation, see our Share My Audit guide.

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KOL Accountability: Why Share My Wallet Would Change Everything

The trust infrastructure argument leads Martin to a pointed application: Key Opinion Leaders (KOLs) — the influencers who shape investment decisions across the Web3 space — should be required to share their wallet audits alongside their investment calls and project promotions. The logic is direct: if a KOL claims to be an experienced trader who got into a memecoin at a specific early price, their on-chain transaction history either confirms or refutes that claim with cryptographic certainty.

Philip acknowledges the principle but highlights the practical barrier: most KOLs would resist because public wallet history would expose the gap between their public claims and their actual behavior. As Philip explains: “I think that would be beneficial but I also feel like there is still a very big barrier from creators in the economy to start sharing that. Because I personally believe that we would see a lot of false X tweets and Telegram posts of people saying I only bought it at this price, whilst they already got it a lot earlier or even didn’t even buy it but just got paid by projects to present.” The resistance to wallet-based KOL accountability is itself revealing — it confirms the extent to which the current KOL marketing ecosystem relies on unverifiable claims to function. For more on KOL marketing accountability, see our KOL marketing guide.

Address Clustering: Finding One Entity Across Many Wallets

Philip raises a challenge that represents one of the genuine technical limitations of wallet-based behavioral analytics: many sophisticated Web3 users deliberately distribute their activity across multiple wallet addresses — sometimes for privacy reasons, sometimes for tax management, and sometimes simply because different wallets serve different purposes. This multi-wallet behavior limits the completeness of behavioral profiles derived from any single address.

Martin’s response introduces address clustering — a technique that partially addresses this limitation by identifying circular dependencies between addresses that appear unrelated on the surface. Even when a user routes through centralised exchanges between DeFi interactions, or regularly creates fresh wallet addresses to separate their activity, they inevitably leave interaction patterns that connect those addresses: shared funding sources, common counterparties, timing correlations, or token flow patterns that form identifiable clusters. As Martin explains: “Even if you look on the first side that addresses are not interrelated, you will still find the circular dependencies. And then you realise — wow, it’s actually one person behind these addresses. So with the analytics, even if you have centralised exchanges between them, still many things can be calculated, much more than people think.” For more on the analytics capabilities across multi-wallet scenarios, see our blockchain analysis guide.

AI Agents Defined: What Separates Autonomous Agents from Prompt Engineering

As the conversation shifts toward AI agents — the topic Philip explicitly identifies as dominating X and generating enormous community interest — Martin provides one of the clearest definitions of what differentiates a true AI agent from the prompt engineering paradigm that preceded it. The distinction matters because “AI agent” has become one of the most overloaded terms in technology marketing, applied to everything from simple chatbot wrappers to genuinely autonomous systems.

Prompt engineering, which dominated the two years following the emergence of large language models, requires a human at every interaction. A prompt engineer designs clever input sequences that extract useful outputs from an LLM — but that process requires a person to initiate each query, evaluate the response, and decide on the next step. Furthermore, the LLMs available during that period operated on training data that was 18-24 months old, limiting their usefulness for time-sensitive applications. An AI agent, by contrast, removes the human from the loop entirely. It runs autonomously, operates continuously (24/7), learns from feedback loops without human intervention, and processes real-time data rather than static training datasets. As Martin defines it: “AI agent is not the next level of prompt engineering. Prompt engineering still needs a person who is creating the prompt. In the case of an AI agent, it means it’s autonomous, it runs from itself. You don’t need this person. There it’s continuous, it’s 24/7. It’s not like an employee who in the evening goes home. And it’s a continuous self-learning when they integrate the feedback loops.” For the complete AI agent taxonomy applied to Web3, see our Web3 AI agents guide.

How ChainAware Built Agents Without Knowing It

Martin’s account of how ChainAware arrived at its agent architecture is instructive precisely because it was not planned. The team built fraud detection, then rug pull detection, then wallet auditing, then AdTech targeting — each product emerging organically from the previous one. At some point, the combination of real-time behavioral prediction and automated content generation produced a system that ran continuously, learned from results, and required no human intervention per user interaction. That is, by any rigorous definition, an AI agent. As Martin puts it: “We got to the agent without knowing that we built an agent. We just kept building and then we realised other people are calling it AI agents and we were like — oh, we like the name, that’s great.” The organic emergence reflects both the genuineness of ChainAware’s agent architecture and the fact that most legitimate Web3 AI agents were built from solving real problems, not from top-down narrative construction.

Generative AI vs Predictive AI: Two Entirely Different Engines

Before explaining how ChainAware’s marketing agents work, Martin establishes the foundational distinction between the two types of AI that are frequently conflated in Web3 marketing discussions. This distinction is critical because the two types are not interchangeable — they solve different problems with different architectures and different value propositions.

Generative AI — the category that includes ChatGPT, Claude, Gemini, and most of the AI tools that became mainstream in 2022-2023 — is fundamentally a statistical autocorrelation engine. It processes enormous volumes of text and learns the probabilistic relationships between words, sentences, and concepts. When asked a question, it generates the statistically most probable response given its training data. This makes it extremely capable at content creation, summarisation, translation, and conversational interaction. However, it cannot make deterministic predictions about specific future events from numerical behavioral data, cannot classify fraud with 98% accuracy, and cannot calculate a specific wallet’s likelihood of borrowing in the next 30 days. As Martin explains: “Generative AI is just an autocorrelation engine. It produces the most probable answer based on the data that it has. It doesn’t think, it just gives you statistically the most probable response.” Predictive AI, by contrast, uses supervised learning on labeled behavioral data to classify future states — which wallets will commit fraud, which will borrow, which will trade. For the full generative vs predictive AI analysis, see our generative vs predictive AI guide.

The Marketing Agent in Practice: The Wallpaper Analogy

Having established the distinction between generative and predictive AI, Martin explains how ChainAware’s marketing agents use both in combination to create what he calls a “resonating experience” — a website interaction that feels personally relevant to each visitor without revealing why.

The operational sequence begins at the moment a wallet connects to a platform. If the wallet is entirely new with no transaction history, the platform shows its default messages — the same experience every user receives today. However, as soon as transaction history is available, the agent processes the wallet’s behavioral profile and generates matched content. An NFT collector arriving at a DeFi lending platform sees messages framed around the NFT ecosystem and how lending connects to it. A leverage trader arriving at the same platform sees messages about collateral usage and leveraged position opportunities. Neither visitor has explicitly requested this personalised experience — the agent inferred it from their transaction history and generated the appropriate content automatically. As Martin describes the mechanic: “You get an NFT guy at a borrowing lending platform — the NFT guy sees messages cut for him. You get a trader there — the trader gets messages like you can leverage up, you can use your funds as collateral, you can borrow more and go long trades.” For the detailed marketing agent implementation guide, see our AI marketing guide.

The Wallpaper Analogy: You Like It But You Don’t Know Why

Martin uses a memorable analogy to explain the user experience created by resonating content. Imagine walking into a living room where some guests see blue wallpaper and others see green wallpaper — each person sees the colour they prefer, but nobody explains this or draws attention to it. They simply feel comfortable in the space. Web3 marketing agents create the equivalent effect on a website: each visitor experiences content that resonates with their specific behavioral profile, generating a feeling of relevance and comfort without any explicit personalisation signal. As Martin explains: “Some people see blue wallpapers, other people see green wallpapers — they see a wallpaper what they like. And the same will be on the website. If you’re resonating with someone, you like them, you spend more time there. If you’re not resonating, probably you could have a website where you speak to someone else. It’s about resonance.” For how this resonance mechanism drives conversion, see our Web3 personas guide and our high-conversion guide.

The Transaction Monitoring Agent: Expert-Level Compliance Running 24/7

The second agent Martin describes in detail is the transaction monitoring agent — a fundamentally different use case from the marketing agent but sharing the same architectural characteristics of autonomy, real-time operation, and continuous learning. Where the marketing agent operates at the acquisition and conversion layer, the transaction monitoring agent operates at the compliance and security layer.

The agent’s function is straightforward to describe: it takes a defined set of wallet addresses — the connected users of a Web3 platform — and continuously monitors all of their on-chain transactions across every blockchain it has access to. When behavioral patterns emerge that match the fraud signature library (not just fund flow from blacklisted addresses, but forward-looking behavioral indicators of future fraud), the agent automatically flags the address and sends a notification to the relevant compliance officer via Telegram or the platform’s interface. The compliance officer then decides what action to take — shadow ban, full restriction, or further investigation. As Martin explains: “This agent is continuously, autonomously analyzing all these wallets all the time. If there’s a new transaction — not on your platform, but on any platform — it analyses these transactions and if it sees fraud patterns, it will automatically flag it. Then a compliance officer gets the notification: watch out this address, there’s a probability that something will happen there.” For the full transaction monitoring methodology and regulatory context, see our transaction monitoring guide and our AML and transaction monitoring guide.

Expert-Level Workers at a Fraction of the Cost

Martin frames both agents through an employment analogy that makes their commercial value immediately tangible. Both the marketing agent and the transaction monitoring agent perform work that would otherwise require expert human professionals — senior marketers who understand behavioral segmentation and personalisation strategy, and compliance analysts who monitor transaction activity and identify fraud patterns. Both roles typically cost significant salaries, operate only during business hours, require management overhead, and cannot physically monitor thousands of addresses simultaneously. The agents eliminate all of these constraints: they operate at expert level, run continuously 24/7, require no management beyond initial configuration, and can monitor unlimited addresses in parallel. As Martin puts it: “These are like expert workers who are doing work for you — transaction monitoring agents or marketing agents. Expert-level workers, 24/7.” For how these agents fit into the broader Web3 agentic economy, see our Web3 agentic economy guide.

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Marketing Agent: calculates each wallet’s behavioral profile at connection, generates resonating 1:1 content automatically. Transaction Monitoring Agent: continuously monitors your user address set, flags fraud patterns before damage occurs, alerts compliance via Telegram. Both run 24/7. Both integrate via Google Tag Manager. Both powered by 18M+ Web3 Personas across 8 blockchains.

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Amazon, eBay, and the Mechanism Behind Web2 Crossing the Chasm

Martin returns in the conversation’s closing section to the historical parallel that contextualises everything ChainAware builds: the mechanism by which Web2 crossed from 50 million technical early adopters to mainstream adoption affecting hundreds of millions of users and generating trillions of dollars of commerce annually. The crossing the chasm framework, popularised by Geoffrey Moore’s influential book on technology adoption, describes the phenomenon but does not fully explain the mechanism. Martin’s argument is that the mechanism is now identifiable in retrospect and directly applicable to Web3.

Web2 companies in the early 2000s faced the same cost structure Web3 faces today: catastrophically high customer acquisition costs from mass marketing, combined with user trust being eroded by credit card fraud. The crossing of the chasm happened when two specific technologies were deployed at scale. First, AI-based fraud detection — mandated by regulators for payment processors — reduced credit card fraud to the point where consumers felt safe transacting online. Second, and more structurally transformative, was AdTech: Google’s micro-segmentation and intent-based targeting, followed by the adaptive interface infrastructure deployed by Amazon, eBay, and eventually every major Web2 platform. As Martin explains: “If you go on Amazon.com, eBay, everyone is seeing his own version of a website. No two people are seeing the same website. Everything is super personalised, super calculated for you. And people think I can personalise the color — no, no, no. The platform provider personalises it for the visitor so that every visitor is getting the most resonating experience.” For the complete Web2-Web3 parallel analysis, see our ChainAware vs Google Web2 guide and Statista’s internet industry data for AdTech growth figures.

The CAC Reduction That Made Web2 Companies Viable

The reason adaptive interfaces and micro-segmentation mattered commercially was not just better user experience — it was the reduction in customer acquisition cost to levels that made business models viable. When Web2 platforms could target users whose behavioral signals indicated genuine intent to purchase, the conversion rate per dollar of marketing spend increased dramatically. Reaching a user who has already demonstrated relevant purchase intent costs the same advertising dollar as reaching a random mass audience — but the conversion from that targeted reach is ten or twenty times higher. Consequently, the effective CAC dropped from hundreds or thousands of dollars to tens of dollars. That reduction was what made it mathematically possible for Web2 companies to acquire users profitably and, as Philip frames it, “build ventures that can sustain themselves and generate revenue.” Web3 is standing at the equivalent inflection point. For more on the CAC reduction framework for Web3, see our unit costs and AdTech guide and the IAB Internet Advertising Revenue Report .

Comparison Tables

Mass Marketing vs Personalized Marketing: The Conversion Economics

Prompt Engineering vs AI Agents: What Actually Changed

Frequently Asked Questions

What is Klink Finance and how does it relate to Web3 user acquisition?

Klink Finance is a crypto wealth creation platform that enables users to start building a crypto portfolio from $0 of personal investment by earning crypto rewards through quests, airdrops, games, and surveys. With over 350,000 community members across mobile, web, and Telegram mini app platforms, Klink operates at the exact intersection of Web3 user acquisition and retention where the challenges Martin and Philip discuss are most practically felt. Klink’s experience illustrates both the effectiveness of multi-channel agility (migrating from Twitter to Telegram as community infrastructure shifted) and the importance of onboarding optimisation in reducing effective customer acquisition cost — specifically by identifying and optimising toward the aha moment when a user earns their first crypto reward.

What is the difference between mass Web3 marketing and personalised Web3 marketing?

Mass Web3 marketing sends identical messages to every visitor regardless of their experience level, risk profile, behavioral history, or actual intentions — exactly as Web2 billboard or TV advertising did in the 1990s. Personalised Web3 marketing uses each connecting wallet’s on-chain transaction history to calculate their behavioral profile and generate matched content automatically. The conversion rate difference is substantial: mass email marketing achieves 0.5-1% conversion in crypto, while personalised email marketing achieves approximately 15% — a 15x multiplier. ChainAware’s marketing agents extend this personalisation to the full website experience: each wallet sees different content, messages, and calls-to-action based on their behavioral intentions, without requiring any identity disclosure or cookie tracking.

How do AI marketing agents differ from prompt engineering?

Prompt engineering requires a human to write an input for every query and evaluate every output. AI agents run autonomously without human intervention per interaction. The key distinctions are: autonomy (agents run continuously without a human initiating each step), real-time data (agents process live blockchain data, not 18-24 month old training sets), continuous learning (agents improve performance through feedback loops), and scale (agents can process unlimited parallel interactions simultaneously). ChainAware’s marketing agent, for example, autonomously calculates each connecting wallet’s behavioral profile, generates matched content, and serves it — all without any human involvement beyond the initial configuration.

Why does blockchain transaction history make a better behavioral dataset than Web2 data?

Every blockchain transaction requires a gas fee — a real financial cost that forces deliberate action before execution. This proof-of-work filter ensures that every data point in a wallet’s transaction history represents a genuine, committed financial decision rather than casual browsing or search activity generated at zero cost. By contrast, Google’s behavioral data derives from search queries and page visits that anyone can generate without spending anything. The financial commitment filter embedded in blockchain data produces substantially higher behavioral signal quality, which is why ChainAware achieves 98% fraud prediction accuracy from transaction history alone — an accuracy level that would be significantly harder to achieve from Web2 behavioral proxies.

What is the resonating experience and why does it improve conversion?

A resonating experience is a website interaction where the content, messages, and calls-to-action precisely match what that specific visitor is looking for — without the visitor knowing why it feels relevant. ChainAware’s marketing agents create this by analysing each connecting wallet’s behavioral profile (experience level, risk willingness, intentions) and generating matched content automatically. An NFT collector sees content framed around NFT use cases; a leverage trader sees content about collateral and position management. Neither has explicitly requested this personalisation — the agent inferred it from their transaction history. The commercial result is increased time on site, higher engagement with key actions, and improved conversion from visitor to transacting user. This is the Web3 equivalent of the adaptive interfaces Amazon and eBay built in the early 2000s to drive Web2 adoption.

This article is based on the X Space between ChainAware.ai co-founder Martin and Philip from Klink Finance. Listen to the full recording on X . For integration support or product questions, visit chainaware.ai.

The post AI-Driven AdTech for Web3 Finance Platforms first appeared on ChainAware.ai.

X Space #24 — AI-Based Web3 Marketing Agents: How to End Mass Marketing and Start Converting Users. Watch the full recording on YouTube · Listen on X

Web3 marketing is broken — and most founders know it but can’t articulate exactly why. They spend significant portions of their treasury on KOLs, banners, media articles, and crypto ad networks. Traffic arrives. Wallets connect. Almost nobody transacts. Marketing agencies suggest doing more of the same. X Space #24 is ChainAware co-founders Martin and Tarmo’s most focused session on this problem: why Web3 marketing fails structurally, what solved the exact same problem in Web2, and how AI marketing agents deliver the Web3 equivalent of what Google AdTech did for the internet economy. The session connects twenty years of experience in financial services, startup product development, and predictive AI to the most pressing sustainability challenge every Web3 project faces.

Web3 Marketing Is Still in the 1930s — Literally

Martin and Tarmo open X Space #24 with a historical comparison that is simultaneously uncomfortable and precise. Web3 marketing in 2024 operates on the same principles as Madison Avenue advertising in the 1930s. Both use mass distribution of identical messages to everyone in the target population, with zero personalisation based on the recipient’s individual profile, needs, or intentions.

The 1930s version involved newspaper advertisements, travelling salespeople, and in-store displays at department stores like Macy’s. Every customer walking into Macy’s saw the same store layout. Every newspaper reader saw the same print ad. The communication was one-directional, undifferentiated, and incapable of adapting to the individual receiving it. As Tarmo describes: “1930s — there was a newspaper. The ads were printed in newspaper. People took the newspapers, went to Macy’s. Everyone saw the same newspaper, went to Macy’s separately, individually. Then there was Macy’s and everyone saw the same shopping flow.” Web3 in 2024: “Everyone sees the same banners. Everyone gets the same messages from KOLs. Everyone is reading the same articles. Everyone gets the same content. Like in the 1930s. Then they get to the application — and everyone sees the same application screen. Zero personalisation. Zero.”

The comparison is not a rhetorical flourish. It identifies a structural reality: 90 years of marketing evolution happened in Web2, producing micro-segmentation, intent targeting, and personalised user journeys. None of that evolution transferred to Web3. Consequently, every Web3 project that relies on mass marketing is operating with tools that Web2 abandoned decades ago. For the broader context on why this matters for ecosystem growth, see our guide to why AI agents will accelerate Web3.

The Three Pillars of Web3 Mass Marketing — and Why None of Them Work

Martin identifies the three primary marketing channels that Web3 projects currently use — and explains why all three are mass marketing with the same structural flaw.

KOLs — Key Opinion Leaders

KOL campaigns send the same message to an influencer’s entire follower base. The influencer’s audience may be large — millions of followers — but the message is identical for every person in that audience. An NFT collector and a yield farmer and a first-time crypto user all receive the same promotional content, regardless of their completely different needs and intentions. This is, by definition, mass marketing. The cost per follower reached may seem low, but the cost per converted transacting user is enormous precisely because undifferentiated messaging converts at near-zero rates.

Banner Advertising

Display advertising on platforms like CoinGecko, CoinMarketCap, and Etherscan shows identical banner creatives to every visitor. There is no targeting by wallet behavior, DeFi experience level, or behavioral intention. An experienced yield farmer visiting Etherscan sees the same banner as a complete beginner who has never used a DeFi protocol. Furthermore, projects pay enormous sums for these placements — on platforms where the same banner is shown to the entire user base without any intention-matching whatsoever.

Crypto Media Articles

Press releases and editorial coverage in publications like Cointelegraph and CoinDesk reach broad audiences but without any personalisation. Every reader of the same article gets the same content regardless of their specific interest, experience level, or likelihood to convert to the featured project. Media coverage generates awareness — which is valuable — but awareness alone does not produce converting users. Additionally, the cost of premium crypto media placement has escalated significantly, making the economics of media-driven acquisition increasingly unworkable for projects without substantial treasuries. For more on the structural economics of this problem, see our ChainAware AI agents roadmap.

The Conversion Crisis: 3,000 Visitors, 6 Transacting Users

Martin presents a real-world example from a ChainAware client that makes the conversion problem concrete. This DeFi platform had 3,000 monthly website visitors. Of those visitors, 600 connected their wallets. Of those wallet connectors, 6–8 completed actual transactions. That represents a 0.2% end-to-end conversion rate from visitor to transacting user.

The question Martin poses is simple and devastating: “If you get 3,000 visitors, 600 wallet connects, and 7–8 transactions — will you ever be cash flow positive? Actually never.” At $1,000–$2,000 per transacting user in DeFi acquisition costs (a realistic figure given the combination of KOL fees, banner placements, and media costs), acquiring 8 transacting users costs between $8,000 and $16,000. If each transacting user borrows $100 on a platform with a 0.5% fee, the revenue from those 8 users is $4. The unit economics are not marginal — they are structurally impossible.

The Two-Problem Structure

Tarmo clarifies that two distinct problems exist within user acquisition, and confusing them leads to wasted resources. The first problem is traffic — getting visitors to the website at all. Web3 has partially solved this through quest platforms, loyalty systems, token incentives, and community building. Projects can generate substantial visitor numbers. The second problem is conversion — turning visitors into transacting users. This problem remains almost entirely unsolved. Marketing agencies typically conflate the two, measuring success by traffic metrics while ignoring conversion rates. As Martin describes: “Marketing agencies are saying your website doesn’t convert. Your website is bad — keep giving us money, we’ll fix your website. Like a drug dealer: more of the same.” For the full analysis of why conversion remains broken, see our DeFi onboarding guide.

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Why the AIDA Framework Fails in Web3

Tarmo introduces the AIDA marketing framework — Attention, Interest, Desire, Action — to explain why the structural timeline of mass marketing makes Web3 conversion impossible, regardless of the quality of the product being marketed.

In a functioning personalised marketing environment, AIDA collapses to seconds. A user sees a message that immediately resonates with their specific intentions — attention is captured, interest is triggered, desire forms almost simultaneously, and action follows. The entire sequence completes within a single session. This is what makes personalised web commerce work: when a user encounters something that genuinely matches what they were looking for, the conversion happens naturally and quickly.

In Web3’s mass marketing environment, the sequence stretches over months. A user sees a KOL post (attention). Perhaps they visit the website briefly (interest starts, weakly). They leave without converting. Over the following weeks, they encounter more generic messaging that doesn’t specifically address their needs (desire fails to build). By the time they might theoretically convert, they have completely forgotten the initial attention signal — overwhelmed by the constant stream of identical mass marketing messages from hundreds of competing projects.

The Sensory Overload Problem

Tarmo identifies the neurological mechanism: “Our brains have cognitive limits. Our brains are not working in a way that we will remember some attention which happened four months ago because of the brain’s sensory overload. Like everyone is doing the mass marketing in Web3 today — everyone does the mass marketing and the potential clients get sensory overload.” When every project broadcasts to everyone simultaneously, users cannot retain or act on any individual message. Furthermore, the attention captured by one project’s mass marketing is immediately displaced by the next project’s mass marketing message. The solution is resonance — delivering messages so precisely matched to a user’s intentions that they generate instant desire rather than fleeting attention. For a deeper analysis, see our guide on why personalisation is the next big AI agent opportunity.

The Missing Invisible Hand: What Web2 Solved That Web3 Hasn’t

Martin introduces the economic concept that frames his entire analysis: the invisible hand. In classical economics, the invisible hand describes the market mechanism that allocates resources efficiently without central coordination — buyers and sellers find each other and transact at prices that reflect their respective values. The invisible hand is the matching technology underlying every functional market.

In technology markets, the invisible hand is not abstract — it is a specific piece of infrastructure. Web3 has extraordinary innovation on both sides of the market: 50,000–80,000+ projects creating valuable products and services, and millions of users who would benefit from those products and services. However, the mechanism that connects them efficiently — the technology that routes the right users to the right platforms at the right moment — does not exist in Web3.

Consequently, the market is deeply inefficient. Projects with good products cannot find their users. Users who would benefit from a protocol never discover it. The economic value of the innovation goes unrealised not because the product is bad but because the matching infrastructure is missing. Tarmo puts it directly: “What is the point of a pricing if a user doesn’t know about you? I have three offerings — Starter, Advanced, Premium — and the user doesn’t know you exist, although you will bring so much value.” For more on this dynamic, see our guide to how ChainAware is doing for Web3 what Google did for Web2.

The Google AdTech Innovation: How Web2 Crossed the Chasm

Web2 faced an identical problem in its early phase. E-commerce companies had genuine value to offer — lower prices, greater convenience, wider selection — but could not reach the users who would benefit from their products at sustainable acquisition costs. Web2 companies started with the same mass marketing approaches Web3 uses today: billboard advertising, print media, television commercials. The economics were equally broken: customer acquisition costs were too high for the unit economics of the internet to survive.

Google solved this with a specific technical innovation: micro-segmentation based on behavioral data. By analyzing search history and browsing patterns, Google calculated user intentions — what someone was actively looking for, what problems they were trying to solve, what products they were likely to purchase. This enabled targeted advertising that reached users at the moment of maximum receptivity, with messages specific to their demonstrated intentions rather than general demographics. User acquisition costs collapsed from hundreds of dollars to $15–35 per transacting user in mature markets. Web2 businesses finally had viable unit economics. As Martin notes: “Google is not a search engine. Google gets 95% of revenues via ad tech.” Similarly, Twitter, Facebook, and every large Web2 platform generates its core revenue through intention-based advertising technology.

The Technology Paradigm Law

Martin articulates a principle he calls the technology paradigm law: every technology paradigm requires its own targeting system. Web1 had its own approach. Web2 had Google AdWords. The physical retail economy before Web1 had Madison Avenue and travelling salespeople. Each paradigm creates new user behavior patterns — and matching technology must be purpose-built for those patterns. You cannot port Web2’s Google AdWords to Web3 and expect equivalent results, because Web3 users behave differently, interact through different interfaces, and leave different behavioral traces than Web2 users do. Web3 needs its own paradigm-native targeting technology — and that technology is AI marketing agents powered by blockchain behavioral data. For how this connects to the broader Web3 growth thesis, see our guide to the three levers that accelerate Web3.

Why Blockchain Data Is More Accurate Than Google’s Data

The comparison between blockchain data and Google’s search/browsing data reveals a crucial insight: Web3 actually has access to higher-quality behavioral data than Google had when it invented AdWords. This is a significant advantage that Web3 has not yet exploited.

Google’s targeting accuracy is limited by the quality of its data sources. Search queries reflect momentary curiosity more than settled behavioral patterns. Browsing history captures passive scrolling and incidental visits that carry weak signal about genuine intentions. Tarmo explains the fundamental limitation: “You can search anything. You get some little input, you speak with someone, you see something, a car is driving by, weather — and then you’re curious to search something. So actually search queries don’t really define who you are as a person.” The signal-to-noise ratio in search and browsing data is relatively low.

The Financial Transaction Signal

Blockchain transactions are fundamentally different. Every on-chain transaction required the user to consciously decide to commit real financial value to a specific action. Nobody accidentally borrows $500 on Aave or buys an NFT on OpenSea. The decision process involves real money, MetaMask signature confirmation, and often significant deliberation. As Martin describes: “Will I do this borrow transaction? Will I do this buy transaction? People are thinking. In the case of search, it’s pretty much arbitrary — the kind of searches people are doing during the day.” The deliberateness of financial transactions means that on-chain history reveals genuine behavioral commitments — not momentary curiosity — making it vastly more predictive of future behavior.

Furthermore, the data is permanent, tamper-proof, and publicly available at zero cost. Unlike Google’s data, which is proprietary and not accessible to third parties, blockchain behavioral data is a public good. Any organisation can build predictive models on this data — giving Web3 projects access to a targeting intelligence infrastructure that, in quality terms, surpasses what Web2’s richest ad tech platforms have. ChainAware’s fraud prediction achieves 98–99% accuracy precisely because blockchain data is so high-quality — and the same data quality advantage applies to behavioral intention prediction for marketing. For more on this data advantage, see our guide to predictive AI for Web3 and our comparison of forensic vs AI-based blockchain analytics.

How Web3 Marketing Agents Actually Work

With the problem and the data advantage established, Tarmo and Martin walk through the precise mechanism of ChainAware’s marketing agents — making clear that this is a live production system with actual clients, not a theoretical concept.

The process begins at wallet connection. The moment a user connects their wallet to a Web3 platform, the marketing agent accesses the wallet’s complete public on-chain transaction history and runs it through ChainAware’s behavioral prediction models. The output is a detailed profile: what is this wallet likely to do next? Are they a borrower, a yield farmer, an NFT collector, a trader, a complete newcomer? What is their experience level with DeFi? How risk-tolerant are they based on their historical behavior? What protocol categories have they used?

From Profile to Resonating Content

Based on this profile, the agent generates content specifically tailored to the wallet’s predicted intentions. The content is not just text — it encompasses layout, colour, messaging tone, and call-to-action framing. Tarmo’s example of personality types illustrates why this depth matters: there are at least 16 distinct personality types in standard psychometric frameworks, each of which responds to different visual and textual presentations. Additionally, cultural background and social environment shape aesthetic preferences. A single user interface cannot resonate with 16 different personality types simultaneously. However, a dynamically generated interface can present each user with the specific combination of visual and textual elements that matches their profile.

Martin describes the user experience outcome: “You come to the screen, you look on the screen, and the screen is cut for you. It feels for you at home. It resonates with you. You like some cafe, you like some website — they resonate with you.” When a user experiences genuine resonance, the AIDA framework collapses from months to seconds. Attention, interest, desire, and action all happen in a single session because the content the user sees is precisely matched to what they were already looking for. SmartCredit.io, ChainAware’s lending platform, was among the first to deploy this system — with measurable improvements in wallet engagement visible immediately upon deployment. For the full measured impact, see our SmartCredit case study.

Setup Simplicity

The technical integration is deliberately minimal. Deploying a ChainAware marketing agent requires four lines of JavaScript — the same complexity as adding Google Analytics to a website. Additionally, the marketing team provides URLs pointing to existing content (blog posts, product pages, announcements), and the agent uses these to generate intention-matched messages for each user profile. No custom development, no design team involvement, no ongoing campaign management. The agent operates continuously and autonomously — 24/7, across all time zones, without breaks. For the complete setup walkthrough, see our behavioral user analytics guide.

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The Self-Learning Loop: From 8x to 80x Cost Reduction

The most powerful aspect of the marketing agent architecture is not its initial performance — it is its trajectory. Every interaction with a converting or non-converting user generates feedback that updates the agent’s models. Did the content delivered to a borrower-intent wallet produce a transaction? If yes, that content-profile mapping is reinforced. If not, the agent adjusts its content selection for similar profiles in future interactions.

This feedback loop runs in real time — not in the monthly campaign review cycles of traditional marketing agencies, not in the quarterly retrospective analysis of enterprise marketing teams. As Martin emphasises: “The campaign is finished, it’s over, it’s finita, it’s gone — it’s too late. You need learning in the same moment.” The agent learns from each user interaction immediately, applying the lesson to the very next user it encounters with a similar profile. Consequently, the agent that has processed 10,000 wallet connections is demonstrably more accurate than the agent that processed 1,000 — because each of those 10,000 interactions has contributed to model refinement.

The Compound Improvement Projection

Martin’s quantitative projection illustrates the trajectory. At deployment, marketing agents reduce acquisition costs by at least 8x compared to mass marketing — through immediate behavioral targeting that eliminates the mismatch between message and recipient. After multiple self-learning cycles — six months, nine months, twelve months of continuous operation — the projected improvement reaches 80x or more. The model continues improving as long as it operates, because each user interaction adds to the training set from which it learns. Furthermore, an agent that has been running for 18 months on a specific platform has learned the unique behavioral patterns of that platform’s specific user base — knowledge that is not transferable to a competitor who deploys a generic agent without that training history. For the full theoretical framework, see our complete guide to how Web3 projects benefit from AI agents.

Breaking the Power Law: Why Best Innovation Should Win

Martin and Tarmo spend considerable time on the revenue distribution problem in Web3 — which they identify as both a symptom of broken marketing and a structural barrier to innovation. The revenue distribution across Web3 projects follows a power law, not a normal distribution. This is verifiable: go to DeFi Llama, navigate to the revenue section, sort by annual revenue, and observe the distribution. A small number of protocols capture the vast majority of revenue, while thousands of other projects generate insufficient revenue to sustain themselves.

The critical question is whether this concentration reflects the quality distribution of innovation — or simply the distribution of marketing reach. Tarmo argues, with conviction, that it does not reflect innovation quality: “Some technologies, some systems which don’t deserve to be so much in the focus have cannibalized the market. The real innovations have no chance because the others have created such strong brands. These real innovations coming on next and next — they have no chance.” In other words, the current power law rewards projects with existing brand visibility and shilling capacity, not necessarily those with the most genuinely valuable products.

Marketing Agents as a Levelling Force

Marketing agents address this directly by giving every project — regardless of treasury size or brand visibility — access to the same conversion efficiency. When a small, genuinely innovative DeFi protocol can deliver the same precision-targeted experience as a large, heavily-funded incumbent, the conversion advantage of the incumbent’s mass marketing spend disappears. Users make decisions based on which product actually resonates with their needs — which is which product’s marketing agent best identifies their intentions and delivers matching content. As Tarmo argues: “The best innovation will get the highest conversion of users. The best innovation wins — not some solution that maybe is not the best innovation but the best innovation. Marketing agents bring a kind of normality into the ecosystem. Innovation is incentivised.” For the detailed analysis of the power law mechanism, see our three levers guide.

Adaptive Applications: Beyond Text to Personalised Interfaces

The discussion in X Space #24 extends beyond marketing messages to a broader concept that Tarmo calls “adaptive applications.” This is the logical extension of personalised content: not just what a user reads, but how the entire application presents itself.

Tarmo is direct in addressing the UX designer community’s objection: “Of course, now there are thousands of UX designers coming and saying — no, it’s not true, we design perfect UX. We are saying — guys, you cannot create perfect UX. Let’s think on this. We are all persons, we are different, we have different psychometrics.” The fundamental challenge of UX design is that it must serve an enormously diverse user population with a single interface — and average design, by definition, resonates with nobody in particular while approximately fitting everyone.

Adaptive applications solve this by generating interface elements dynamically based on the user’s behavioral profile. Colors, layouts, typography weight, call-to-action intensity, content hierarchy — all of these adjust to match the specific psychological and behavioral profile the marketing agent has calculated for the connecting wallet. A risk-tolerant trader gets a high-intensity, action-oriented interface with prominent position-taking CTAs. A cautious newcomer gets a gentler, more educational interface with lower-pressure progression. Both users interact with the same underlying protocol, but each sees an interface specifically calibrated to produce the resonance that drives conversion for their specific profile. For more on how ChainAware implements this, see our behavioral user analytics guide.

The Innovation Bandwidth Effect

X Space #24 closes with a reflection on what happens to Web3 innovation when marketing is no longer a manual, time-consuming, human-operated function. Martin identifies the founder time allocation problem: a significant proportion of every Web3 founder’s time goes to marketing coordination, community management, content production, and campaign management — all supplementary activities relative to product innovation.

When marketing agents automate these activities, founders recover bandwidth for the work that only they can do: identifying unmet user needs, designing innovative product mechanisms, iterating on user feedback, and building the features that create genuine competitive differentiation. This bandwidth recovery has a compounding effect: more innovation cycles produce better products, better products attract more users through marketing agents, more users generate more data for agent learning, better agent learning produces higher conversion, higher conversion generates more revenue, and more revenue funds more innovation cycles.

Martin’s conclusion in X Space #24 is a direct prediction: “AI marketing agents will be the new Google. What Google did for Web2, AI marketing agents will do for Web3. The crossing of the chasm for Web3 will happen because of this technology — the same way the Crossing the Chasm in Web2 happened because of Google technology.” The session is not abstract theorising — ChainAware’s marketing agent is live, running on client platforms including SmartCredit.io, generating measurable conversion improvements. For the ecosystem-level implications, see our full ChainAware AI agents roadmap and our guide on the Web3 Agentic Economy.

Comparison Tables

Web3 Mass Marketing vs AI Marketing Agents

Web2 AdTech vs Web3 Marketing Agents: The Parallel

Frequently Asked Questions

Why is Web3 marketing called “1930s marketing” in this X Space?

Because the underlying approach is identical: one message broadcast to everyone with zero personalisation. In the 1930s, this was a newspaper advertisement or in-store display at Macy’s — the same content seen by every customer regardless of their individual preferences or intentions. In Web3 in 2024, this is a KOL tweet, a banner ad on CoinGecko, or a Cointelegraph article — the same content delivered to every member of the audience regardless of whether they are an NFT collector, a yield farmer, a first-time user, or an experienced DeFi participant. The digital delivery mechanism is different; the absence of personalisation is identical.

What makes blockchain data better than Google’s search data for marketing?

Blockchain transactions require deliberate financial decisions. Before executing a transaction, users consciously evaluate whether to commit real money, confirm the transaction in their wallet, and accept the gas cost. This deliberateness means on-chain history reflects genuine behavioral commitments rather than momentary curiosity. Search queries, by contrast, are costless and often arbitrary — triggered by passing conversations, casual curiosity, or algorithmic prompts. As a result, behavioral predictions from on-chain data carry significantly higher accuracy than predictions from search data. ChainAware’s fraud detection achieves 98–99% accuracy specifically because blockchain data is so high quality — and the same quality advantage applies to intention prediction for marketing purposes.

How quickly does a ChainAware marketing agent start producing results?

Immediately. From the first wallet connection after deployment, the agent delivers personalized content based on that wallet’s behavioral profile. The initial 8x improvement in acquisition efficiency applies from day one — because personalised content targeting outperforms mass marketing regardless of how long the agent has been running. The self-learning improvement compounds over time: the longer the agent runs, the more accurately it learns which content variants convert which profiles on that specific platform. After six to nine months of continuous operation, Martin projects conversion improvements of 80x or more relative to mass marketing baselines. For deployment instructions, see our behavioral user analytics guide.

Why does the power law distribution in Web3 revenues persist?

Because marketing reach, not innovation quality, determines which projects acquire users at scale. Projects that secured early market positions through aggressive mass marketing — regardless of their technical merit — benefit from accumulated brand visibility and community trust that makes continued user acquisition easier. Smaller, potentially more innovative projects cannot compete for users using the same mass marketing tools because the economics are prohibitive. Marketing agents change this by giving every project access to the same conversion efficiency — making product quality, rather than marketing budget, the primary determinant of user acquisition success. Verify the power law yourself at DeFi Llama by sorting protocols by annual revenue.

Are marketing agents a replacement for all other marketing?

Marketing agents optimise the conversion of visitors who are already on a platform. They do not replace top-of-funnel awareness generation — some level of traffic acquisition (community building, content marketing, social presence) is still required to get visitors to the platform in the first place. However, marketing agents make every unit of traffic investment dramatically more productive: when 8x more visitors convert to transacting users, the effective cost per transacting user falls 8x, and the economics of awareness-generation activities improve proportionally. The combination — awareness generation to drive traffic, marketing agents to convert that traffic — produces sustainable acquisition economics that pure mass marketing never can.

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This article is based on X Space #24 hosted by ChainAware.ai co-founders Martin and Tarmo. Watch the full recording on YouTube · Listen on X . For questions or integration support, visit chainaware.ai.

The post AI-Based Web3 Marketing Agents: How to End Mass Marketing and Start Converting Users first appeared on ChainAware.ai.

X Space #20 — Intention-Based Web3 AdTech: The Invisible Hand That Will Take Web3 Mainstream. Watch the full recording on YouTube · Listen on X

X Space #20 is ChainAware’s most comprehensive session on the Web3 user acquisition crisis — and the most historically grounded. Co-founders Martin and Tarmo spend the full hour making a case that most people in Web3 have never heard articulated clearly: the reason Web3 can’t scale is not missing innovation. The innovation is extraordinary. The reason is missing user acquisition technology — and that gap has a precise historical precedent, a known solution, and a live implementation available today. The session covers the economics of why most Web3 projects will never reach cash flow positive, why Geoffrey Moore’s Crossing the Chasm never answered its own central question, and how blockchain data creates a higher-quality AdTech foundation than anything Google ever had access to.

The Two Unit Costs Every Web3 Founder Must Innovate

Martin opens X Space #20 with a framework that most Web3 founders have never applied to their own businesses: every successful company must innovate two distinct unit costs simultaneously, and failing to innovate either one guarantees failure regardless of how good the product is.

The first unit cost is the cost of the business process — how cheaply and efficiently the core product delivers its value to users. DeFi has achieved extraordinary innovation here. Lending, borrowing, trading, and staking through smart contracts costs a fraction of what equivalent services cost in traditional finance. Martin draws on ten years at Credit Suisse to make the contrast vivid: “Guys, just check what Credit Suisse’s business processes look like, how long they take. There is no comparison with DeFi.” The automation of financial processes that DeFi achieves is genuinely revolutionary in unit economics terms.

The Second Unit Cost Nobody Is Innovating

The second unit cost is the cost of customer acquisition — how cheaply the company reaches users who will transact with the product. Web3 is producing almost no innovation in this area. Founders treat customer acquisition as a necessary operational expense managed by external agencies rather than as a core technical problem requiring the same level of innovation as the product itself. As Martin states directly: “You have to innovate both processes. The unit cost of your business process and the unit cost of your customer acquisition. You cannot delegate one part — the customer acquisition — to marketing agencies with different motivations.” The mathematical result of failing to innovate customer acquisition while succeeding brilliantly at business process innovation is a business that delivers enormous value to the tiny fraction of users who find it while remaining structurally unable to scale. For the full analysis of how this plays out across the ecosystem, see our Web3 AI marketing guide.

The Web3 Acquisition Cost Mathematics: Why $1,000 Per User Kills Every Project

Martin walks through a specific, reproducible calculation that any Web3 project can run against its own marketing spend. The numbers are not hypothetical — they reflect real conversion rates from real campaigns.

Start with a banner ad campaign on Etherscan, CoinGecko, or CoinMarketCap. High-quality Web3 traffic costs approximately $5 per click. With a $1,000 budget, a project gets roughly 200 website visitors. Of those 200 visitors — each arriving through a paid click, each therefore showing some initial interest — approximately 10 will connect their wallets. Of those 10 wallet connections, approximately 1 will complete an actual transaction. The result: $1,000 spent to acquire one transacting user.

Why This Math Guarantees Failure

Now apply the customer lifetime value test. A DeFi lending platform earns revenue from transaction fees, spread, or interest — typically a fraction of a percent per transaction. If the average transacting user performs transactions totalling $10,000 and the platform earns 0.5%, the lifetime revenue from that user is $50. The project spent $1,000 to generate $50 in revenue. This is not a business — it is a loss mechanism. As Martin summarises: “This one transacting user should generate total lifetime revenues of at least $1,000 — will they generate that in DeFi? No, probably not. Maybe some whales. But 99% of users are not generating this.” The mathematics of current Web3 acquisition economics make cash flow positive essentially unachievable for all but the most established protocols. For more on how this connects to the broader Web3 growth crisis, see our guide on why AI agents will accelerate Web3.

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The Mass Marketing Trap: Why Projects Are Forced to Do What Doesn’t Work

One of the most insightful observations in X Space #20 is Martin’s analysis of why mass marketing persists in Web3 despite its demonstrable failure. The answer is not that founders are irrational — it is that they are in a prisoner’s dilemma where stopping is worse than continuing.

Consider the situation from a single project’s perspective. Mass marketing produces poor results — high cost, low conversion, cognitive overload for potential users. However, if competitors are all doing mass marketing and a project stops, it loses even the minimal attention that mass broadcasting generates. The project that opts out of the mass marketing race disappears from potential users’ awareness entirely. Consequently, every project is forced to participate in a system they know is inefficient because opting out is even worse than participating.

The Cognitive Overload Effect

From the user’s perspective, the result of 50,000+ projects all doing mass marketing simultaneously is catastrophic cognitive overload. Telegram channels fill with generic project announcements. Twitter feeds flood with KOL promotions. CoinGecko and CoinMarketCap banners rotate through identical calls-to-action. As Martin describes: “First you get this first mass marketing message, then the second, then the next 10,000 mass marketing messages. You will run away. You say these messages don’t resonate — you close your Telegram, your Discord, you stop going to Twitter. You say enough is enough.” The ecosystem is simultaneously over-broadcasting to potential users and under-converting them — spending enormous resources to generate a backlash. For more on this dynamic and its connection to the trust problem, see our guide to building trust in Web3 anonymous ecosystems.

The KOL Reality: 60 Out of 625 Generate Positive Returns

Martin cites a specific data point from Alphascreener that quantifies the ineffectiveness of KOL-based marketing more precisely than any qualitative criticism could. Using the 10-day delayed free version of the platform, he checked KOL call performance and found that approximately 60 out of 625 calls generate positive returns within 30 days. That is a 9.6% success rate — meaning over 90% of KOL promotional campaigns produce no positive price action for the project within a month.

Projects pay significant fees for these campaigns regardless of outcome. The payment structure is typically upfront — cost per million impressions or a flat promotional fee — with no performance accountability. The KOL receives payment whether the campaign generates users or not. As Martin notes: “You have to pay these KOLs and you have to pay them a lot. And if you don’t pay them, they are not tweeting about you — they tweet about someone else and generate sensory overload for someone else instead.” The incentive structure of KOL marketing is fundamentally misaligned with project success, yet it remains one of the largest budget line items in Web3 marketing spend.

Attribution vs Intention: Last Week’s Weather vs Tomorrow’s Forecast

Tarmo introduces a conceptual distinction that is essential for understanding why most Web3 analytics tools — and the marketing strategies built on them — are fundamentally limited: the difference between attribution and intention.

Attribution describes what a user has done in the past: which protocols they used, what transactions they completed, which tokens they held. This is descriptive data — it tells you what happened but says nothing reliable about what will happen next. Tarmo’s analogy is precise: “It’s like reading a weather forecast from five days ago. Okay, it was the weather. But you are interested in the future weather, not what the weather was in the past.” Attribution data tells you that a user interacted with Aave, Uniswap, and Compound. However, that tells you almost nothing actionable — it doesn’t tell you whether they’re currently looking to borrow, trade, or exit the market entirely.

What Intention Actually Is

Intention is forward-looking behavioral prediction. Tarmo defines the core dimensions: “Are you a high risk taker, low risk taker, medium risk taker? These are psychological preferences. It is your investment behavior — how you invest. What is your innovation attitude — are you an innovator, early adopter, late adopter? What is your experience?” These behavioral characteristics allow prediction of what the user will do next. A high-risk-tolerance, experienced DeFi user who has been staking for two years but hasn’t made a new position in three months is exhibiting pre-borrowing behavioral signals. Showing that specific user a targeted borrowing offer at that moment creates resonance — the offer matches what they’re already considering. Showing them a generic “join our community” banner creates noise.

Furthermore, Tarmo distinguishes intention from simple protocol attribution: “User uses protocols, but corresponding to his intentions. If a user wants something, he does it. It’s not that he did it in the past so he will repeat it in the future.” Behavioral intentions are deeper than usage patterns — they are psychological and economic states that drive behavior across multiple protocol categories. For the full analysis of what ChainAware calculates, see our behavioral user analytics guide and our personalisation guide.

The Invisible Hand: What Actually Took Web2 Mainstream

Economics textbooks describe the “invisible hand” as the market mechanism that coordinates buyers and sellers efficiently — the spontaneous coordination of supply and demand through price signals. Martin and Tarmo argue that this description obscures the actual mechanism, and that understanding the real mechanism is the key to understanding what Web3 is missing.

The invisible hand in economics is usually presented as self-generating — markets coordinate themselves without central planning. But Martin points out the practical reality: “Things never happen from themselves. There has to be something for things to happen.” In every functioning market at scale, a specific coordination technology exists that matches buyers with relevant sellers efficiently. Before digital markets, this was the travelling salesman, the newspaper classified section, and the local market stall. In Web1, it was banner advertising and directory listings. In Web2, it was AdTech — the technology that calculated user intentions and matched them to relevant products at the moment of maximum receptivity.

AdTech Is the Invisible Hand

Google’s AdWords, Facebook’s news feed targeting, and Twitter’s promoted tweets are all implementations of the same mechanism: calculate what a user wants, show them the relevant offer at the right time. As Tarmo summarises: “AdTech is the invisible hand. The invisible hand which brings right users to right platforms at the right time.” The Web2 ecosystem scaled from tens of millions of users to billions not because the underlying products got dramatically better (they were already good), but because AdTech created the coordination layer that made discovery efficient, acquisition economical, and conversion reliable. For more on this parallel, see our guide to how ChainAware is doing for Web3 what Google did for Web2.

Crossing the Chasm: The Question Geoffrey Moore Never Answered

Martin makes a pointed critique of one of the most influential business books ever written on technology adoption: Geoffrey Moore’s Crossing the Chasm. The book describes the transition from early adopters to mainstream users in technology markets — the “chasm” that most innovative technologies fail to cross — with considerable analytical sophistication. Martin recalls reading it during the early internet era: “I was so excited. I remember reading this book even during the night because it was so cool.”

However, Moore’s book describes the phenomenon without explaining its mechanism. He identifies that a chasm exists and that crossing it requires specific strategies, but he doesn’t answer the fundamental question of how the crossing actually happens at the market infrastructure level. As Martin notes: “He had maybe 200 pages. He was all the time speaking about the crossing the chasm — this transformation from early innovators to early maturity. And he never said how it happened.” The answer, which neither Moore’s book nor conventional business education explains clearly, is AdTech. The crossing of the chasm in Web2 happened specifically because Google created the coordination mechanism that matched products to users at scale — reducing acquisition costs from $500-700 to $15-30 and enabling the mass-market economics that made Web2 companies viable.

Web1 to Web2: The Exact Transition Web3 Must Now Make

The historical parallel that structures the entire X Space #20 discussion is precise: Web3 in 2024 is at the same stage as Web1 in approximately 2000-2002. The user numbers are similar (50 million), the project count is similar (tens of thousands), and the acquisition cost problem is identical in structure — high costs preventing viable unit economics, mass marketing failing to convert, and a coordination layer missing that would bring the right users to the right platforms.

Web1’s 50 million users and thousands of Web1 companies faced an almost unsolvable mapping problem: how do you get 50 million people to find the specific products relevant to their needs among thousands of options? The answer to the mapping problem was not better products, not more content, not more conferences. The answer was AdTech — technology that computed user intentions from available data and matched users to relevant products automatically. Once that technology existed, user acquisition costs collapsed, mass-market economics became viable, and Web2 scaled globally.

Web3 Has the Same Mapping Problem

Web3 currently has 50 million users and 50,000-70,000 projects. That means approximately 1,000 potential users per project on average — enough to build viable businesses if the matching worked efficiently. The problem is that matching doesn’t work. KOLs, banner ads, and crypto media broadcast to undifferentiated audiences. The right users never find the right platforms. Platforms that would create enormous value for specific user profiles waste their entire marketing budget on users who will never convert. As Martin frames it: “We need the mapping. We need to get the right people to the right platforms at the right time. So and Web2 solved this problem.” For how this applies to the DeFi sector specifically, see our DeFi onboarding guide.

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Google Is Not a Search Company: Every Big Tech Platform Is AdTech

Martin makes an observation that reframes the entire Web2 technology landscape: the companies that are typically described as search, social media, or communication companies are all, at their revenue core, AdTech companies. Understanding this is essential for understanding what Web3 needs to build.

Google generates approximately 95% of its revenues through advertising. Twitter (now X) generates essentially all of its revenues through advertising. Facebook/Meta generates essentially all of its revenues through advertising. None of these companies is primarily in the business they describe themselves as being in. Each is fundamentally in the business of calculating user intentions and matching those intentions to relevant offers — the definition of AdTech. As Martin states: “Google is not a search company. They call themselves a search company. Well, they are not. They’re an ad tech company. Twitter, Facebook — they call themselves social media. We are asking: how does Facebook generate revenues? With ad tech, with the targeting system.”

Why This Matters for Web3

If the central value creation mechanism in Web2 was intention calculation and user-product matching, then Web3’s missing piece is precisely the Web3-native equivalent of that mechanism. Building better DeFi protocols, launching more NFT collections, and creating more GameFi experiences all produce more supply — but supply without matching infrastructure doesn’t scale. Web3 needs its own AdTech layer, built on the data source native to its ecosystem: on-chain transaction history. As Tarmo summarises: “Each technological paradigm needs its own coordination mechanism. We cannot create a new technological paradigm based on the old coordination mechanism.” For the full parallel, see our complete Web3 AI marketing guide.

Why Blockchain Data Produces Better AdTech Than Google Ever Had

Martin and Tarmo make a counterintuitive but well-grounded argument: blockchain data is actually a higher-quality input for intention calculation than any data source Google or Facebook has ever had access to. This is not a marginal difference — it is a fundamental data quality advantage that makes Web3 AdTech more precise than anything Web2 built.

Google calculates user intentions primarily from search queries and browsing history. Search queries reflect momentary curiosity — they are triggered by passing conversations, random associations, and deliberate research in roughly equal measure. Browsing history captures passive content consumption with weak behavioral signal. Both data sources are noisy, easily influenced by context, and imprecise about actual behavioral intent.

Financial Transactions as Pure Behavioral Signal

Blockchain transactions are financial decisions. Every on-chain transaction required a deliberate choice, deliberate execution in a wallet interface, and real financial cost (gas fees). As Tarmo explains: “Blockchain data is not like social metadata or some browsing history. These are financial transactions where you pay gas. It is really very high quality data. And we can calculate very precise user intentions.” Nobody accidentally borrows $500 on Aave, stakes in a liquidity pool, or purchases an NFT. Each of these actions reveals deliberate behavioral commitments that Google’s data cannot match in precision. The result is that ChainAware’s intention calculations from blockchain data achieve accuracy that Web2 AdTech systems took years of data accumulation to approach — because the data source is inherently more signal-dense. For the full data quality analysis, see our predictive AI for Web3 guide.

How ChainAware’s Intention-Based Marketing Works

With the theoretical case established, Martin explains ChainAware’s specific implementation — which is live, scaling, and immediately available to any Web3 project.

The process starts at wallet connection. When a user connects their wallet to a Web3 platform, ChainAware reads the wallet’s complete on-chain transaction history across Ethereum (2,000+ protocols monitored) and BNB Chain (800+ protocols monitored). ChainAware’s AI models process this history to generate a behavioral profile: is this wallet likely to borrow? Trade with leverage? Provide liquidity? Buy NFTs? What is their experience level? What is their risk tolerance? What stage of the technology adoption cycle are they in?

From Profile to Resonating Message

Based on this profile, the marketing agent selects or generates content specifically matched to the user’s predicted intentions. A borrower-profile wallet visiting a lending platform sees messaging about the platform’s loan terms and benefits. A yield farmer profile visiting the same platform sees messaging about liquidity provision returns. A first-time DeFi user sees educational content about how to get started safely. Every user sees something different — content designed to resonate with what they were already planning to do.

The email marketing parallel makes the conversion difference concrete. Mass email in crypto achieves below 0.05% open rates — so degraded by cognitive overload that almost nobody reads it. Personalised email marketing — even using imprecise data sources like LinkedIn — achieves 15% open rates. That is a 300x improvement from personalisation alone. ChainAware’s intention-based targeting uses blockchain data that is more precise than LinkedIn profiles, applied at the highest-intent moment (wallet connection), to deliver content that matches the user’s demonstrated behavioral history. The conversion improvement compounds accordingly. For the full implementation guide, see our behavioral user analytics guide and the SmartCredit case study.

The Marketing Agency Problem: Misaligned Incentives

Martin delivers a pointed analysis of why delegating customer acquisition to Web3 marketing agencies systematically fails — not because agencies are incompetent, but because their incentives are structurally misaligned with their clients’ success.

Web3 marketing agencies charge upfront fees for traffic generation, campaign management, and KOL coordination. Their revenue model is fee-based, not performance-based. They receive payment whether or not the campaigns generate transacting users. Their business interest is to maximise their fee revenue, which means maximising the scope of campaigns and minimising accountability for conversion outcomes. As Martin states: “Founders think that marketing agencies will do their job. Marketing agencies have a different intention — their intention is to get money, a lot of money from founders. Their intention is not to acquire users.” This is not a moral critique — it is a structural incentive observation. Agencies operating under this model will always recommend more spending (more campaigns, more KOLs, more media placements) rather than diagnosing the fundamental problem of user acquisition technology.

The Web1 Marketing Agency Parallel

Critically, this situation is not unique to Web3 — it is a predictable feature of any technology paradigm that lacks an efficient coordination mechanism. Web1 had identical marketing agencies charging founders for “guerrilla marketing,” media placements, and conference presence — all of which generated noise without solving the mapping problem. When Google’s AdTech emerged and made intention-based targeting viable, the Web1 marketing agencies went through one of two fates: they disappeared, or they evolved into AdTech consultants who helped their clients use the new targeting tools effectively. Martin predicts the same transformation is coming for Web3 marketing agencies: “The same transformation will happen in Web3 with the marketing agencies. The ones who remain will start using advanced AdTech solutions for their clients — instead of telling founders stories, give us the money, we solve all your problems.” For the full context on where this transition stands today, see our guides on how Web3 projects benefit from AI agents.

Why Pump-and-Dump Is a Rational Response to Broken Economics

One of X Space #20’s most uncomfortable but analytically important arguments is Martin’s claim that pump-and-dump is not simply malicious behavior — it is a rational economic response to the impossibility of reaching cash flow positive under current acquisition cost conditions.

The logic runs as follows: VCs invest in Web3 projects knowing that the probability of the project reaching cash flow positive is extremely low given current acquisition costs. The mathematical outcome of $1,000 acquisition cost against $50-100 user lifetime value is negative regardless of product quality. Consequently, the rational exit for both founders and VCs is to capture value through token appreciation before the unit economics reality becomes undeniable. Token pump-and-dump is not a failure of ethics — it is a rational adaptation to a structural economic problem that has not been solved.

The Ecosystem Implication

This analysis has an important implication: attacking pump-and-dump behavior without solving the underlying acquisition cost problem will not change the ecosystem’s dynamics. The incentive to pump and dump exists because sustainable long-term business building is economically irrational under current conditions. Solving the acquisition cost problem — through intention-based targeting that reduces costs from $1,000 to $50-150 per transacting user — changes the incentive calculation. Sustainable business building becomes viable, and pump-and-dump loses its comparative advantage. As Martin argues: “VCs know the probabilities for Web3 companies to become cash flow positive are pretty low. And if you know this information, what do you do? We see pump-and-dumps. Because the long-term perspective is not there.” The solution is not regulation — it is innovation in user acquisition technology. For more on how this connects to the ecosystem trust problem, see our guide on AI-based predictive fraud detection in Web3.

The Ecosystem Cleanup: What Happens When AdTech Arrives

Martin and Tarmo close X Space #20 with a prediction about the market structure transformation that will follow when intention-based AdTech becomes widely adopted in Web3. The prediction is grounded in the Web2 precedent: the arrival of efficient targeting technology does not just improve individual company performance — it restructures the entire competitive landscape.

In Web2, the arrival of Google AdWords and its successors created a bifurcation between companies that adopted the new targeting capabilities and companies that continued relying on mass marketing. Companies that adopted AdTech gained sustainable acquisition economics, could iterate on products with reliable user feedback, and accumulated the user bases needed for network effects and defensibility. Companies that didn’t adopt died — not from bad products but from unsustainable acquisition costs. The same selection dynamic is coming to Web3.

First-Mover Advantage Is Significant

Tarmo is direct about what this means for projects that adopt ChainAware’s intention-based marketing early: “Message to other founders — use this opportunity. Be the first. You get competitive advantage and you get your acquisition costs down. Innovative solutions that you have built find their real users — users who are proud to use these innovative solutions.” The competitive dynamics of AdTech adoption in Web3 will mirror Web2: early adopters gain sustainable economics while competitors continue burning capital on mass marketing. The gap compounds over time as early adopters reinvest acquisition savings into product development, generating better products that convert even better. As Martin summarises: “This leads to a kind of market cleanup, ecosystem cleanup, where the focus goes away from pump-and-dump over to sustainable positive cash flow generating businesses. And AdTech is the key — it was the key in Web2, it is the key in Web3.” For how ChainAware’s agents support this ecosystem transformation, see our full AI agents roadmap.

Comparison Tables

Web3 Mass Marketing vs Intention-Based Marketing

Web1 to Web2 vs Web2 to Web3: The Parallel Transition

Frequently Asked Questions

Why is Web3 user acquisition so much more expensive than Web2?

Web2 built an efficient coordination layer — AdTech — that matched users’ demonstrated intentions to relevant products at the moment of maximum receptivity. This matching mechanism reduced acquisition costs from $500-700 (Web1-era mass marketing) to $15-30 (mature Web2). Web3 currently uses Web1-era mass marketing tactics (KOLs, banner ads, crypto media placements) without any intention-matching layer, producing Web1-era acquisition costs of $1,000+ per transacting user. The gap is not a product quality issue — it is a missing infrastructure layer. For the full analysis, see our Web3 AI marketing guide.

What is the difference between attribution and intention in Web3 marketing?

Attribution describes what a user has already done — which protocols they used, what transactions they completed, what tokens they held. Tarmo’s analogy: it is like reading last week’s weather forecast. Intention predicts what a user will do next — based on their behavioral patterns, risk profile, experience level, and investment psychology. Showing a user content matched to their attribution history is marginally better than mass marketing. Showing a user content matched to their behavioral intentions — what they are actively considering doing — creates genuine resonance and drives conversion. ChainAware calculates intentions, not just attribution.

Why can’t founders delegate customer acquisition to marketing agencies?

Because marketing agencies have structurally different incentives from their clients. Agencies earn fees based on campaign scope and upfront payments — not on acquisition outcomes. Their business interest is to maximise fee revenue, which means recommending more spending rather than solving the underlying acquisition technology problem. Additionally, customer acquisition is one of two unit costs that determine whether a business becomes cash flow positive — delegating either unit cost to a party with misaligned incentives is a structural mistake. Founders must own acquisition cost innovation the same way they own product development.

Why does blockchain data produce better AdTech than Google’s data?

Google’s targeting relies on search queries and browsing history — signals that reflect momentary curiosity, passive consumption, and incidental exposure. Blockchain transactions are deliberate financial decisions made with real money at stake. Every on-chain action (borrowing, trading, staking, purchasing an NFT) required conscious evaluation and execution. This deliberateness makes blockchain history a substantially higher-quality behavioral signal than browsing patterns, producing more accurate intention predictions. ChainAware achieves 98% accuracy in fraud prediction from blockchain data — a precision level that reflects the inherent signal quality of the underlying data source.

How does solving the acquisition cost problem affect pump-and-dump behavior?

Pump-and-dump is a rational economic response to a situation where sustainable long-term business building is mathematically impossible — when acquisition costs ($1,000+) permanently exceed user lifetime value ($50-100 in DeFi). When intention-based AdTech reduces acquisition costs to $50-150, sustainable business building becomes viable. Founders and VCs who previously had rational incentives to pump-and-dump now have rational incentives to build. The ecosystem cleanup follows naturally from the economics, without requiring regulatory intervention or changes in founder behavior. For more on the complementary role of fraud reduction in this ecosystem transformation, see our guide on AI-based predictive fraud detection in Web3.

The Web3 Invisible Hand — Live and Available

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This article is based on X Space #20 hosted by ChainAware.ai co-founders Martin and Tarmo. Watch the full recording on YouTube · Listen on X . For questions or integration support, visit chainaware.ai.

The post Intention-Based Web3 AdTech: The Invisible Hand That Will Take Web3 Mainstream first appeared on ChainAware.ai.

X Space #16 — Do You Still Believe in Web3 KOL Marketing? Why Mass Marketing Fails and Web3 AdTech Wins. Watch the full recording on YouTube · Listen on X

X Space #16 is ChainAware co-founder Martin’s most comprehensive solo breakdown of the Web3 marketing crisis. With Tarmo experiencing connection difficulties, Martin delivers an extended analysis covering every major Web3 marketing channel, the data on KOL effectiveness from AlphaScan, a deep dive into how Web2 real-time bidding actually works, why Web3 projects cannot access Web2 advertising infrastructure, and precisely how blockchain history enables the Web3 AdTech alternative. The session frames everything around one central question: if the goal of marketing is to reduce user acquisition cost, are any of the tools Web3 projects currently use actually achieving that?

The Purpose of Marketing: User Acquisition, Not Hype

Martin opens X Space #16 by establishing the single purpose that all marketing should serve — a definition that most Web3 founders never explicitly articulate but that determines whether any marketing activity is money well spent or money wasted.

Marketing is not a purpose in itself. It is a tool for user acquisition. Every channel, every campaign, and every budget allocation should be evaluated against one question: does this activity bring down the cost of acquiring a transacting user? As Martin states: “It’s not just marketing — this is not self-glorification. It’s all about user acquisition. We need to acquire users. We need to get users to the platform. Marketing is a tool for user acquisition.” The implication is immediate and uncomfortable: if a marketing activity generates impressions, engagement, and community noise without producing transacting users at an acceptable cost, it is not marketing — it is an expensive entertainment purchase.

The Two Unit Costs Every Project Must Optimise

Martin connects marketing purpose to unit economics. Every sustainable business has two critical unit costs that must both be optimised: the cost of the business process itself, and the cost of customer acquisition. DeFi protocols have achieved extraordinary innovation on the first — smart contracts eliminate intermediaries, automate settlement, and reduce transaction costs to a fraction of traditional finance equivalents. However, achieving near-zero business process costs is irrelevant if the cost of acquiring users who actually transact remains prohibitively high. As Martin explains: “You need both. You need both processes and you need to bring your user acquisition cost down. That is the challenge for most Web3 founders.” For the full unit economics framework, see our intention-based Web3 marketing guide.

The KOL Landscape: Why Call Marketing Dominates Web3

Understanding why KOL marketing became Web3’s dominant promotional approach requires understanding the structural constraints that pushed projects toward it. Martin identifies the core issue: Web3 projects cannot access the marketing infrastructure that Web2 companies use, so they built a parallel universe of alternatives — with KOLs at the centre.

KOL marketing, as it currently operates in Web3, involves paying influencers to post messages about a project to their followers. The project pays upfront, the influencer broadcasts promotional content, and the project hopes that a percentage of the influencer’s audience visits the platform and transacts. This model became standard because it is one of the few options available: crypto advertising is banned from most mainstream publisher platforms, DeFi projects cannot obtain Google ad accounts, and the Web2 targeting infrastructure that enables microsegmentation is entirely inaccessible for non-compliant financial services.

The False Security of KOL Ubiquity

Because every Web3 project uses KOL marketing, its use creates a false sense of legitimacy. Launch pads offer special KOL packages. VCs ask about KOL relationships. Exchanges evaluate project Twitter scores partly based on which influencers engage with the project. This systemic embedding of KOL marketing in Web3’s evaluation infrastructure makes opting out feel dangerous even when the data shows it is ineffective. Tarmo — before his connection issues — frames it precisely: “It is a kind of escape from reality. It is wishful thinking. It is the last hope. People think that if they cannot use real AdTech, then let’s use this virtual call marketing. It is the last hope for all Web3.” The problem is not that founders are irrational. The problem is that the rational-seeming alternative — doing what everyone else does — is collectively destroying value across the entire ecosystem. For more on why the ecosystem is trapped in this cycle, see our crossing the chasm in Web3 analysis.

The AlphaScan Reality: Max 10% of KOLs Produce Positive Returns

Rather than relying on qualitative critique, Martin checks AlphaScan — a KOL performance tracking tool — immediately before X Space #16 and reports the results live. AlphaScan tracks 650 crypto influencers and measures the average token return for projects they promote within a defined measurement window. Sorting all 650 by 30-day positive return reveals a striking data point.

Of 650 tracked KOLs, 29-30 produced positive 30-day token returns at the time of the session. That represents approximately 4.5% of the total. Martin notes that he checks AlphaScan regularly and that the positive count fluctuates between 30 and 60 — meaning the upper bound is approximately 10% of tracked influencers producing positive outcomes. As he explains: “Max 10% of them are producing positive returns for you. So projects are paying money, paying quite some money. But somehow it is standard now in Web3 that everyone is doing call marketing. Everyone is doing call marketing.”

The 90% Problem

The inverse of the 10% positive rate is a 90% neutral-or-negative rate. Projects that hire KOLs from the majority of the tracked pool are paying upfront fees for campaigns that produce either no measurable positive effect on token price or an actively negative effect. Martin notes that AlphaScan uses a 10-day delay in its free version, making the data slightly lagged but still directionally reliable. The key takeaway is not that all KOLs are ineffective — 10% genuinely produce positive results. Rather, without the analytical tools to identify which 10%, projects default to hiring from the full pool and get the weighted average outcome: mostly negative, occasionally positive, never reliably predictable. For the deeper analysis of KOL economics, see our comprehensive KOL vs AdTech comparison.

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The Hype Addiction: Why KOL Spend Compounds Without Compounding Results

Beyond the static performance data, Martin identifies a dynamic problem with KOL marketing that makes it structurally unsustainable even for the minority of projects that see initial positive results: hype is an addiction that requires ever-increasing doses to maintain the same effect.

Hype, by definition, is a temporary elevation above baseline attention. Generating it requires novelty — the first announcement of a project creates hype; the third announcement of the same project creates considerably less. Maintaining elevated attention therefore requires escalating inputs: more KOLs, more frequent posts, larger paid promotions. As Martin explains: “For hype to become stronger, you need more of hype, and then you need again more of hype, and then you need even more hype. It is a drug, it is an addiction. So that means if you start, you have to do it more and more and more.”

The Herd Movement Problem

KOL followings behave as herds — they move as a collective toward the most engaging current narrative and away from yesterday’s story. A project that paid for KOL promotion in month one has no residual audience attention by month three. The influencer’s followers have moved on to four other narratives since then. Stopping KOL payments means immediate disappearance from the herd’s attention entirely. As Martin observes: “One day you stop. One day you stop paying. And the KOLs, they have their own followers — this herd is going somewhere else. They were one day following you and next day they will follow someone else.” This means KOL marketing produces no compounding value: every month of spend delivers exactly one month of attention, with nothing carrying forward into subsequent months. The economics are permanently linear — while the goal of user acquisition requires compounding growth. For the broader strategic analysis, see our Web3 AI marketing guide.

All Web3 Marketing Is Mass Marketing — KOLs, Media, Banners, Guerrilla

Martin’s most important structural argument is that KOL marketing is not a unique problem — it is just the most expensive symptom of a broader disease. Every major Web3 marketing channel shares the same fundamental failure: it is mass marketing that delivers one message to many recipients regardless of their individual needs, intentions, or likelihood to convert.

Crypto media — CoinDesk, Bitcoin.com, Cointelegraph, and dozens of others — charges projects for articles that reach the publication’s entire readership. Every reader receives the same content regardless of whether they are a DeFi power user, a complete newcomer, or someone whose interests have no overlap with the featured project. The publication’s credibility transfers to the project through association — a genuine but fleeting benefit that fades without ongoing spend. Martin’s assessment is direct: prices are “ridiculous, especially for startups.”

Guerrilla Marketing: A Nice Term for the Same Problem

Beyond KOLs and media, agencies sell “guerrilla marketing” to Web3 projects — a term that Martin identifies as primarily a rebranding exercise. “Some agencies are selling guerrilla marketing, whatever it means. It is always a nice term to sell. Like — we do guerrilla marketing. It is a guerrilla. And some projects are paying for this in the hope they get results.” Guerrilla marketing in this context typically means creative social media stunts, community infiltration, and non-conventional promotional activities — all of which share the mass marketing flaw: undifferentiated audiences receiving undifferentiated messages. Martin’s recommendation is memorable: “If you hear guerrilla marketing, you better run — not do guerrilla marketing, but away.” For the full landscape analysis of what does and doesn’t work, see our crossing the chasm in Web3 guide.

The Banner Problem: $8 CPM for Untargeted Impressions

Banner advertising on crypto platforms — Etherscan, CoinGecko, CoinMarketCap, BSCScan — represents the clearest illustration of what Web3 mass marketing costs relative to what it delivers. Martin provides a specific price point that frames the inefficiency precisely.

The standard banner CPM (cost per thousand impressions) on major crypto platforms is approximately $8. This means a project pays $8 for every 1,000 times its banner appears to a visitor — regardless of whether that visitor is a DeFi power user, a trader looking for price data, a developer checking a contract, or someone who accidentally clicked a link. Every visitor to Etherscan or CoinGecko sees the same banner creative regardless of their individual profile, current needs, or likelihood of ever using the advertised platform. Martin describes the pricing directly: “The banner prices are like $8 CPM — $8 per 1,000 impressions — which are, using an English word, ridiculous, very high prices.”

Why $8 CPM Is Actually Expensive

At first glance, $8 per 1,000 impressions might seem affordable. However, the cost-per-acquisition calculation reveals the problem. If a banner generates a 0.1% click-through rate (optimistic for an untargeted banner), $8 CPM produces approximately 1 click per $8 spent — or $8 per click. From those clicks, if 5% connect a wallet (generous), and 20% of those transact (also generous), the effective acquisition cost is $8 / (0.001 × 0.05 × 0.20) = $8,000 per transacting user. Mass marketing economics make the nominal CPM irrelevant — what matters is conversion rate, and untargeted mass marketing achieves conversion rates that make every apparent cost metric misleading. For the complete acquisition cost calculation showing how Web3 compares to Web2’s $30-40, see our user acquisition cost breakdown.

How Web2 AdTech Actually Works: RTB, Microsegmentation, and €30B Markets

To understand what Web3 AdTech needs to build, Martin explains how Web2 actually reduced user acquisition costs — not through better creative or more media spend, but through a technological infrastructure that most Web2 marketers themselves don’t fully understand.

The foundation of Web2 AdTech is microsegmentation: the division of users into extremely precise audience clusters based on thousands of behavioural attributes. As Martin explains: “Microsegmentation means that when I am sending messages to my users, I am sending to specific segments. The segments are very, very specifically calculated — like the company shows Nike shoes to a lot of technology companies. We are speaking like zillions of different segments and people are assigned to these segments.”

Real-Time Bidding: The €30B Market Most Marketers Don’t Know About

On top of microsegmentation sits RTB — Real-Time Bidding — the technology that determines which advertiser’s creative reaches which user in real time. When a user visits a publisher website, an automated auction runs in milliseconds: multiple advertisers simultaneously bid to show their ad to that specific user based on their segment membership. The advertiser willing to pay the most to reach that specific microsegment wins the impression. The entire auction completes before the page finishes loading. Martin emphasises that this market is enormous and almost invisible to most practitioners: “RTB is a real-time bidding market — Europe alone, annual 30 billion euro. 30 billion euro. That is this market. It is a data market where technology is running. It is an ad technology. That is where it is decided which customer is getting which ad. You probably never heard about it.” The implication is that Web2’s $30-40 per user acquisition cost was not achieved by better banners or smarter KOL choices — it was achieved by a technological infrastructure that matches specific users to specific offers at the millisecond level. For the broader historical context, see our Web3 crossing the chasm guide.

Web2’s $30-40 Per User: What Microsegmentation Achieves

The concrete output of Web2’s microsegmentation and RTB infrastructure is a user acquisition cost that makes sustainable business building possible. Martin cites the Web2 benchmark: $30-40 per transacting user. This compares directly with Web3’s current reality of hundreds to thousands of dollars per transacting user from mass marketing approaches.

The mechanism behind the Web2 cost advantage is precision: showing the right message to the right user at the right moment dramatically increases conversion probability. A user who searches “DeFi lending rates” and then sees a targeted lending platform ad is far more likely to click, visit, connect their wallet, and transact than a user who sees the same ad banner while checking their portfolio value on CoinGecko. The same ad creative, the same landing page, and the same product produces radically different conversion rates depending entirely on how well the targeting matches the message to the recipient’s current intentions.

Where Web2 Gets Its Intention Data

Web2’s microsegmentation relies on three main data inputs. Google uses search history and browsing history — the latter collected partly through reCAPTCHA, which transmits browsing data to Google as part of bot verification. Facebook uses social interactions, content consumption patterns, video watch time, and the explicit data users provide through their profiles. Twitter uses engagement patterns and dwell time. Each platform builds a virtual identity for every user consisting of hundreds to thousands of behavioural attributes, which then feeds both the microsegmentation and the RTB bidding logic. As Martin notes: “In Web2, we have browsing history, search history. Google is using a lot of browsing history. This identity — some virtual identity somewhere — with the microsegmentation and with the intention calculations, with hundreds slash thousands attributes about each of us.” For how blockchain data compares to these sources, see our predictive AI for Web3 guide.

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Why Web3 Projects Cannot Use Web2 Ad Technology

The natural question following any description of Web2’s superior targeting infrastructure is: why don’t Web3 projects simply use it? Martin addresses this directly, explaining two structural barriers that prevent Web3 DeFi projects from accessing Web2 ad platforms — barriers that are not technical limitations but regulatory and policy constraints.

The first barrier is publisher access. Approximately 99% of Web2 publishers — news sites, content platforms, social networks outside of Twitter — do not accept cryptocurrency advertising. The closed ecosystem of “crypto media” that Web3 projects use for banner advertising and sponsored content exists precisely because mainstream publishers reject crypto ad spend. Martin frames it clearly: “The number of publishers who accept crypto ads at all is very limited. The amount of publishers is limited, plus you need an ads account.”

The Google Ad Account Problem for DeFi

The second barrier is the ad account requirement. Google Ads requires financial service advertisers to hold a relevant license — a reasonable requirement for consumer protection in regulated financial markets. Centralised exchanges like Binance, OKX, and Coinbase can obtain these licenses and therefore qualify for Google ad accounts. Decentralised Finance protocols, by contrast, have no legal entity operating the protocol in most cases and therefore cannot obtain the required financial services licence. No licence means no Google ad account. No ad account means no access to Google’s targeting infrastructure, RTB participation, or search advertising. As Martin explains: “If you want to get an ads account from Google, of course you can make some little steps, but Google is probably telling you to show them a licence. But there is no licensing for DeFi. There is only licensing for centralised finance companies.” The result is that the most powerful and cost-effective marketing infrastructure ever built is structurally inaccessible to the most innovative financial sector currently operating.

The Twitter Exception: When Web3 AdTech Access Is Possible

Within the broadly inaccessible Web2 ad landscape for crypto projects, Twitter/X represents a meaningful exception — with important conditions that determine which Web3 projects can benefit. Martin notes that ChainAware itself uses a Twitter ad account, using it to promote X Space announcements.

Twitter’s policy on crypto advertising is more permissive than Google’s or Facebook’s, but it still draws a line at financial services. Projects that are not classified as financial service providers — AI tools, developer infrastructure, analytics platforms, community tools — can obtain Twitter ad accounts and use Twitter’s targeting capabilities. Projects that provide direct financial services — lending, borrowing, trading, or investment products — face the same licence requirements that block Google access. As Martin explains: “In Twitter, it is a little bit easier if you are not doing financial transactions. If you are doing advertisements for AI and Web3, you can — you will get an answer from Twitter, and in ChainAware we have an ads account. We are using it and it is very effective.” For Web3 projects that qualify, Twitter’s targeting represents a genuine partial alternative to the fully closed mainstream ad infrastructure.

Blockchain History as the Web3 Data Source for Microsegmentation

With Web2 ad infrastructure inaccessible, Martin establishes the data source that makes Web3-native microsegmentation possible: blockchain transaction history. This data source is not only accessible — it is public, free, and arguably more accurate for predicting financial behaviour than anything Google or Facebook has ever collected.

Web2 AdTech uses browsing history, social interactions, and search queries to infer what a user is likely to do next. These are indirect signals — someone who searches “DeFi lending” might be a researcher, a journalist, a curious student, or an active lender looking for better rates. The signal is noisy because the same query serves many different purposes. Blockchain transaction history, by contrast, records actual financial decisions made with real money at stake. A wallet that has borrowed on Aave, provided liquidity on Uniswap, and staked on multiple protocols over two years is not ambiguously interested in DeFi — it is an active, experienced DeFi participant with a specific behavioral profile that predicts future actions with high confidence.

Pattern Matching at Scale Enables Prediction

ChainAware’s approach to intention calculation from blockchain history mirrors the pattern-matching methodology behind all predictive AI: train models on historical data from wallets with known outcomes, identify the patterns that reliably preceded those outcomes, and apply the identified patterns to new wallets to predict their likely next actions. Martin explains the process: “You create the models, you train them with your data, training with negative data, training with positive data. It is a very iterative process. Most interestingly — we can predict fraud 98% before it happens, because there are some patterns in addresses which are saying there are other addresses with the same patterns that committed fraud. This address here, which has not yet committed fraud, probably will commit fraud.” The same pattern-matching logic applies to non-fraud intentions: borrower patterns, trader patterns, gamer patterns, NFT collector patterns — all extractable from transaction history with high confidence. For the full methodology, see our behavioral user analytics guide.

The Two-Step Web3 AdTech Framework: Calculate and Target

Martin distils the entire Web3 AdTech approach into a two-step framework that mirrors the structure Web2 AdTech already uses — but replaces Web2’s browsing and social data with blockchain transaction history as the input.

Step one is calculating user intentions from blockchain history. This produces a behavioral profile for each wallet address: what is the wallet owner likely to do next? Are they a likely borrower? A potential liquidity provider? An active NFT trader considering their next purchase? A newcomer who has never used DeFi protocols? Each profile represents a different set of needs, motivations, and messages that will resonate. As Martin explains: “What is the web three AdTech? It is the same as we have in Web2. From one side, we need to predict user behavior. We have to do this microsegmentation. And from the other side, we have to place messages for the users.”

Step Two: Matching Messages to Intentions

Step two is connecting the calculated intentions to a targeting system that delivers matched messages to each persona. This is the component that transforms static user profiles into dynamic, conversion-optimised interactions. A project defines which messages to show each persona — not a single message for all visitors, but a matrix of persona-message pairings that ensures every user receives content relevant to their specific behavioral profile and likely next action. Martin describes the mechanics: “From one side, we calculate who is this user, what is his behavior. And from the other side, we are connecting the calculated intentions with the messaging. Two parts: we calculate user intentions, and we connect it with a targeting system so that you can target users with proper messages.” For the implementation guide, see our personalisation in Web3 guide and the Web3 AI agents guide.

Persona Examples: NFT Collector, Gamer, Leverage Staker on a Lending Platform

To make the abstract framework concrete, Martin walks through a specific scenario that illustrates why persona-based messaging produces fundamentally different conversion outcomes than mass messaging. The scenario involves a lending and borrowing platform — one of the most common DeFi product types — receiving three different types of visitors.

Visitor type one is an NFT collector. Their blockchain history shows active trading in NFT marketplaces, token holdings associated with NFT communities, and minimal interaction with lending protocols. The right message for this visitor is not the lending platform’s general interest rate — it is the possibility of borrowing against NFT collateral to fund new purchases without selling existing holdings. Without personalised targeting, this visitor sees a generic lending pitch that doesn’t connect to their actual use case. Consequently, they leave without converting.

Gamer and Leverage Staker

Visitor type two is a gamer whose blockchain history shows GameFi token holdings, in-game asset transactions, and play-to-earn protocol interactions. Their lending platform use case is different from the NFT collector’s: they may want to borrow stablecoins against GameFi assets to fund game purchases or amplify in-game earnings. Generic lending messaging misses this framing entirely. Visitor type three is a leverage staker — an experienced DeFi participant whose history shows repeated loop borrowing strategies on multiple protocols. For this visitor, the technical details of the platform’s leverage mechanics, collateralisation ratios, and yield optimisation features are exactly what they need to see. As Martin states: “For all these three personas, you give fully different messages. If he is an NFT dealer on the borrowing platform, we give him fully different messages. If he is a gamer, fully different. If he is a leverage taker, of course — then it is easy, he is used to borrow-lend and looping.” For more on persona calculation and marketing strategy, see our behavioral analytics guide.

ChainAware On-Site Targeting: Personas from Blockchain History

ChainAware implements the two-step framework as a live product that Web3 platforms can integrate in minutes. When a user connects their wallet to a platform running ChainAware’s targeting system, their blockchain address is immediately evaluated against ChainAware’s behavioral models to generate a persona assignment. The platform then displays messaging configured for that specific persona rather than the generic content every other visitor sees.

Martin describes the persona development process as iterative: “You calculate, you start maybe five personas, you get more experience, you have ten personas, you get even more experience, twenty personas. And you just define which messages you are showing to different personas.” Projects begin with a small number of broad persona categories and refine them over time as more conversion data accumulates. Each iteration produces more precise persona definitions and better-performing message variants, creating a compounding improvement cycle that mass marketing can never achieve.

The Conversion Impact

The conversion impact of switching from generic messaging to persona-matched messaging is significant. When each visitor sees content that matches their behavioral profile and addresses their specific use case, the proportion who take the target action increases substantially. Martin frames the outcome: “Then the wonders will happen because the conversion starts to change. It is not anymore that one magic message is converting every possible user. One magic message is converting the NFT dealer and the gamer and the leverage taker. No — if you are this platform, everyone is getting his own magic message. And that is how you start to convert the users.” For the specific conversion rate benchmarks — and how Web3 personalisation compares to Web2’s 10-15% AI-segmented conversion — see our full AdTech comparison guide.

The Unit Cost Conclusion: Why Personalisation Is Not Optional

Martin closes X Space #16 by returning to the unit economics framework that opened the session, tying the entire analysis together into a conclusion about business sustainability.

Innovation in the business process — the technology that powers a DeFi protocol, the smart contracts, the automated settlement — is necessary but not sufficient for sustainable business building. Every innovative Web3 project also needs innovation in user acquisition. Without both, the business process innovation produces value that cannot reach the users who need it, the project burns through capital, and the logical outcome is closure regardless of product quality. As Martin states: “You need both. One is your cost of business process, other is cost of user acquisition. You need both processes and you need to bring your user acquisition cost down. And that is the challenge for most Web3 founders.”

The Web2 Crossing of the Chasm — Repeated for Web3

The transition Martin describes is not unprecedented. Web2 faced the identical situation: thousands of innovative platforms, limited user budgets, and mass marketing as the only available tool. The moment Web2 solved user acquisition through AdTech — microsegmentation, RTB, intention-based targeting — was the moment Web2 crossed from niche technology to mainstream adoption. As Martin summarises: “Web two had exactly the same situation. There were all these technology innovators who created all these beautiful new platforms. But how do you get the right people to the right platforms? We have two steps: get the right people to the right platform, and then on the platform, convert them. When Web2 solved this, that was the moment when Web2 crossed the cosmos.” Web3 is at the same inflection point now, and blockchain data provides the foundation for the same transition. For the complete historical analysis and what it means for Web3 in 2025, see our crossing the chasm in Web3 guide.

Comparison Tables

Web3 Mass Marketing Channels vs Web3 AdTech (ChainAware)

Web2 AdTech Data vs Blockchain Intention Data

Frequently Asked Questions

Why do only 10% of KOLs produce positive returns?

Because KOL marketing is mass marketing — the same message delivered to an undifferentiated audience regardless of individual intentions, needs, or likelihood to convert. The 10% who produce positive results likely have audiences with higher concentrations of users whose profiles happen to match the promoted project, or the timing of their promotion coincides with positive broader market sentiment. Without a systematic way to identify which KOLs have relevant, authentic audiences for a specific project, the majority of campaigns will miss their target entirely. AlphaScan’s data — 29-30 positive outcomes out of 650 tracked — reflects this structural mismatch. For the full analysis, see our KOL vs AdTech comparison.

Why can’t DeFi projects use Google Ads?

Google requires financial services advertisers to hold a relevant jurisdiction-specific licence. Centralised exchanges and regulated crypto brokers can obtain these licences. Decentralised Finance protocols — which typically operate without a central legal entity and are not regulated as financial services in most jurisdictions — cannot obtain them. Without the required licence, DeFi projects cannot get a Google Ads account, which means no access to Google’s search advertising, display network, or YouTube targeting infrastructure. Twitter/X is more permissive for non-financial-service Web3 projects.

What is Real-Time Bidding and why does it matter for Web3?

Real-Time Bidding (RTB) is the auction technology that determines which advertiser’s creative reaches which specific user when they load a web page. Advertisers bid simultaneously for each impression in milliseconds, with the highest bidder’s ad displayed. RTB operates on top of microsegmentation — advertisers bid specifically for users in defined micro-audience segments rather than for generic page impressions. This combination produces the $30-40 per transacting user acquisition cost that makes Web2 businesses sustainable. Europe’s RTB market alone is €30 billion annually. Web3 projects are currently structurally excluded from this infrastructure — which is why blockchain-based Web3 AdTech is the necessary alternative. For more, see the RTB Wikipedia overview.

How does ChainAware create user personas from blockchain data?

ChainAware’s AI models analyse a wallet’s complete transaction history across 2,000+ Ethereum protocols and 800+ BNB Smart Chain protocols to identify behavioral patterns that reliably predict future actions. Pattern matching against known outcomes — the same technique that achieves 98% fraud detection accuracy — produces behavioral profiles: NFT collector, gamer, leverage staker, yield farmer, newcomer, experienced DeFi user. These profiles are then connected to a targeting system that delivers matched messages for each persona when users connect their wallets to integrated platforms. The entire process runs in real time at wallet connection. For the implementation guide, see our behavioral user analytics guide.

Is blockchain data actually better than Google’s data for targeting?

For Web3 use cases, yes — substantially. Google’s data reflects browsing and search behaviour, which includes passive curiosity, research, and incidental exposure. A user who searches “DeFi lending rates” might be a journalist, a student, or an active DeFi participant — the search query alone doesn’t distinguish them. Blockchain transactions are financial decisions made with real money, requiring deliberate evaluation and action. They leave behind high-confidence behavioral signals that predict future financial actions with far greater precision than browsing history. Additionally, blockchain data is completely public and free to access — it doesn’t require building a massive data collection platform or paying licensing fees to a data marketplace.

The Web3 AdTech Alternative — Live and Available Now

ChainAware Prediction MCP — Intentions, Fraud, Credit. One API.

Intention calculation + 1:1 targeting + fraud detection + credit scoring — all via one MCP API. The microsegmentation and persona targeting Web2 achieves with billions in infrastructure — now available for Web3 using free public blockchain data. 98% accuracy. 14M+ wallets. 8 blockchains.

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This article is based on X Space #16 hosted by ChainAware.ai co-founder Martin. Watch the full recording on YouTube · Listen on X . For questions or integration support, visit chainaware.ai.

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