AI-Based Web3 AdTech: How to Cross the Chasm and Slash Customer Acquisition Costs

X Space #15 — AI-Based Web3 AdTech: How to Cross the Chasm and Slash Customer Acquisition Costs. Watch the full recording on YouTube ↗ · Listen on X ↗

X Space #15 is ChainAware’s most complete session on the Web3 AdTech thesis — the argument that the same two-step targeting infrastructure that took Web2 from niche technology to multi-billion-dollar market dominance is now required by Web3, can be built on blockchain data, and will produce even better conversion outcomes than Web2 ever achieved. Co-founders Martin and Tarmo cover the mechanics of how Web2’s $30 billion real-time bidding ecosystem actually works, why Web3’s customer acquisition costs are 50-100x higher than necessary, and precisely how blockchain financial transaction data closes the gap — with Tarmo’s prediction that blockchain-powered AdTech will push Web3 conversion ratios to 40-45%, exceeding Web2’s 30% ceiling.

The Two Unit Costs Every Web3 Project Must Innovate

Martin opens X Space #15 with a framework that cuts through the noise of Web3’s growth crisis: every business has exactly two categories of unit cost that determine whether it survives, and Web3 has only innovated one of them.

The first unit cost is the cost of the business process — how cheaply the company executes its core product or service. Web3 has achieved extraordinary innovation here. DeFi protocols automate lending, borrowing, trading, and settlement through smart contracts with zero human back-office involvement. Martin draws on his decade at Credit Suisse to quantify the contrast: traditional banking requires 8-12 back-office employees for every front-office employee. Every single one of those back-office roles represents a cost that DeFi’s full automation eliminates. The unit cost of a DeFi financial transaction is a fraction of its traditional finance equivalent — genuinely revolutionary progress.

The Second Unit Cost Nobody Is Solving

The second unit cost is the cost of customer acquisition — how much the company spends to convert a new visitor into a transacting user who generates revenue. Web3 is producing almost zero innovation here. While DeFi protocols iterate relentlessly on smart contract efficiency and yield optimisation, they rely on 1930s-era mass marketing to acquire users. The result is a catastrophic imbalance: state-of-the-art business process costs paired with pre-industrial customer acquisition costs. As Martin states directly: “There are two types of innovation. The one is the business process innovation. The other is the customer acquisition innovation. We have to bring both down. Both unit costs. You bring down the unit cost of the business process — genius. But you have to bring down as well the acquisition cost. Because if you do not bring down the acquisition cost, how do you want to compete with the status quo platforms?” For how this connects to the full Web3 growth picture, see our intention-based Web3 marketing guide.

The Web3 CAC Mathematics: How $1,000 Per User Destroys Every Business Model

Martin and Tarmo build a step-by-step customer acquisition cost (CAC) calculation from first principles, using real market data for each input. The result is a number that explains why so many technically excellent Web3 projects fail to generate sustainable revenue despite strong product-market fit.

Start with cost per click (CPC). For high-quality traffic in OECD countries, $5 per click is a realistic benchmark for Web3 projects using crypto media or banner placements. From 20 paid clicks, one user connects their wallet — a 5% wallet-connection rate. From 10 wallet-connected users, approximately one completes an actual transaction — a 10% transaction rate. The arithmetic: $5 × 20 clicks = $100 to get one wallet connection; × 10 wallet connections needed for one transacting user = $1,000 per transacting user acquired.

The Web2 Comparison: $15-20 Per User

Web2, by contrast, achieves $15-20 per transacting user. The same $5 CPC produces a vastly different outcome because Web2’s intention-based targeting brings users to platforms that already resonate with their behavioral profile — and then adaptive user interfaces convert them with messaging matched to their specific intentions. Conversion rates reach 30%: one in three visitors who arrive at a well-optimised Web2 platform via targeted advertising completes a transaction. As Tarmo explains: “Cost per click is $5. You pay $50 to get ten and you get one third of them. So it is around $15-$20. Customer acquisition cost. Variable cost. And this is enormous — $15-20 to acquire a new user. It is unbelievably effective.” The 50x gap between Web3 and Web2 acquisition costs is entirely attributable to the absence of intention-based targeting infrastructure in Web3. For the full analysis, see our crossing the chasm in Web3 guide.

DeFi Llama’s Power Law: Why the Long Tail Is Dying

Martin directs listeners to a specific exercise that makes Web3’s customer acquisition crisis visible in real data: open DeFi Llama, navigate to the fees and revenues section, and sort all protocols by annual revenue. The resulting distribution tells the story of the entire Web3 ecosystem’s health — and the news is not encouraging for the vast majority of projects.

The revenue distribution across Web3 protocols follows a sharp power law: a tiny number of established protocols — Uniswap, Aave, MakerDAO, and a handful of others — capture the overwhelming majority of total ecosystem revenue. The long tail consists of thousands of projects generating minimal revenue, many of them burning through treasury funds without any realistic path to cash flow positive. As Martin explains: “In the DeFi Llama, on the left if you scroll down, you see fees, revenues. Sort by yearly revenues and look how much companies are generating revenues. You will be very surprised. It is a full power law distribution. A very little number of companies are earning very much. And then you have a very, very, very long tail.”

Why the Long Tail Cannot Survive on Current Marketing

Projects in the long tail face an arithmetic trap. Their products may be technically innovative and genuinely useful — but at $1,000+ per transacting user acquisition, the revenue generated per user never recovers the acquisition investment. Paying KOL fees that produce negative returns compounds the problem: the project simultaneously destroys treasury and fails to acquire users. Martin is clear about the logical conclusion: “These are the founders who are fighting to get their revenues. But to get the revenues, you need customer acquisition. To get customer acquisition, you need AdTech. And the founders are fighting — they just do not know which means to use.” The power law distribution is not inevitable. It is the product of a missing infrastructure layer that, once added, will redistribute user acquisition efficiency across the entire ecosystem. For the power law analysis and its connection to the AdTech solution, see our AI marketing for Web3 guide.

The Hidden €30B Engine: How Web2 Real-Time Bidding Actually Works

Tarmo introduces the specific technological infrastructure behind Web2’s $15-20 CAC — a mechanism that most Web2 marketers themselves don’t fully understand, despite it being the engine that drives hundreds of billions in annual ad spend globally.

Real-Time Bidding (RTB) is the programmatic advertising technology that determines which advertiser’s creative reaches which specific user when they load any web page. Every time a user visits a publisher website, an automated auction completes in milliseconds: dozens of advertisers simultaneously submit bids for the right to show their ad to that specific user, with each bid reflecting how much that advertiser values reaching a person with that behavioral profile at that moment. The highest bidder’s creative is served before the page finishes rendering. The entire process is invisible to both the user and most advertisers who use it through intermediary platforms.

The Scale That Nobody Talks About

Tarmo emphasises the market scale specifically because it is so dramatically under-discussed relative to its economic importance: “RTB — you probably have not heard about these things. It is a 30 billion euro business in Europe, okay? Have you heard about any other 30 billion euro business in Europe? But you do not know it. Real-time bidding is one of them.” Europe alone generates €30 billion annually from RTB, growing 10-15% per year. The global RTB market is multiples larger. Every Google display ad, every programmatic banner on news sites, every social media retargeting campaign — all of these run on RTB infrastructure that processes billions of auctions per day. This is the mechanical engine behind Web2’s low acquisition costs, and it is entirely absent from Web3’s current marketing infrastructure. For the broader context, see our Web3 crossing the chasm analysis.

Google’s 2,600 Attributes: Why You Are the Product

RTB doesn’t operate on demographic data — it operates on intention data, assembled from thousands of behavioral signals that Web2 platforms collect about every user. Martin cites a specific figure that illustrates the depth of this data collection: Google maintains approximately 2,600 attributes per user, the majority of which relate to behavioral intentions and predicted next actions rather than static demographic facts.

These attributes accumulate from multiple data streams. Google search queries reveal what users are actively considering. Browsing history — collected via reCAPTCHA verification processes, where accepting terms and conditions transmits browsing data to Google — reveals passive interests and recent research. YouTube watch history reveals entertainment preferences and learning interests. Gmail content analysis (for users who have not opted out) reveals purchase intentions, travel plans, and financial activity. All of these streams feed into intention prediction models that determine what any given user is likely to do next and therefore what advertising will be relevant to them. As Martin explains: “Google has 2,600 attributes about you, most of them being your intentions, your next steps. Facebook the same, Twitter the same, maybe even more.”

GDPR as a Competitive Moat

Tarmo adds a pointed observation about GDPR’s actual competitive function in the AdTech market: “GDPR is a perfect tool for Google to avoid others’ entrance into the AdTech market. So that is all the idea — plus it is an excellent market.” The regulation’s compliance cost creates a barrier that Google, with its legal and engineering resources, absorbs far more easily than potential competitors. The result is that GDPR strengthens the incumbents’ oligopoly rather than democratising data usage — a regulatory outcome that benefits the existing Web2 AdTech giants while making competition harder. Web3 sidesteps this entire dynamic because blockchain data is public and free, requiring no data collection agreements, no GDPR compliance infrastructure, and no platform relationships. For the data quality comparison, see our predictive AI for Web3 guide.

The Two-Step AdTech Formula That Took Web2 Mainstream

With the mechanics of Web2 AdTech established, Martin and Tarmo articulate the two-step formula that enabled Web2’s mainstream crossing. Both steps are necessary; neither alone is sufficient. Understanding both is the prerequisite for building the equivalent system in Web3.

Step one is bringing resonating users to the platform. This means calculating each potential user’s behavioral intentions from available data and routing only those whose profile matches the platform’s value proposition toward it. A DeFi lending platform should attract users with borrower and yield-optimization intention profiles — not NFT collectors or casual browsers who will never interact with lending products. The RTB infrastructure executes this routing automatically at the millisecond level, showing the lending platform’s advertising only to users whose 2,600-attribute profile predicts high conversion probability.

Step Two: Resonating Experience on the Platform

Step two is delivering a resonating user experience to the visitors who arrive. This is where adaptive user interfaces — the second major Web2 AdTech innovation — operate. Rather than showing every visitor identical content, an adaptive interface serves different messages, different feature highlights, and different calls-to-action to different users based on their calculated intention profile. An experienced leverage trader visiting a lending platform sees advanced collateralisation ratios and looping strategy content. A newcomer visiting the same platform sees security information and step-by-step getting-started guidance. As Tarmo explains: “For every user, a different UI is shown which resonates with the user. We know intentions, and we know what we have to show to this user to convert him to a transacting customer.” Together, these two steps explain Web2’s 30% conversion ratio — and their absence explains Web3’s below-1% baseline. For the implementation guide, see our personalisation in Web3 guide.

Adaptive User Interfaces: Why the Perfect UX Designer Cannot Solve Conversion

Martin and Tarmo address a common misconception that leads Web3 founders to spend considerable resources on the wrong solution: the belief that a sufficiently talented UX designer can create a user interface good enough to convert anyone who visits. This belief is empirically false, and the reason explains why intention-based AdTech is the actual solution.

Martin describes the typical Web3 founder reaction: “If you are a founder and you go to a VC and the VC asks — how do you make sure users like your user interface? You will tell the VC: hire the best UX designer in the world. I will give him a lot of gold, okay? Will it work? In Web2, everyone knows it will not work.” The reason is structural: different users have fundamentally different intentions, needs, and behavioral profiles. A single interface designed for one ideal user is automatically wrong for the majority who have different profiles. There is no optimal static design for a heterogeneous user population.

What Actually Makes an Interface “Perfect”

The correct definition of a perfect user interface is not an interface that is beautifully designed — it is an interface that resonates with the specific user currently viewing it. Resonance requires knowing that user’s intentions and adapting the displayed content accordingly. As Tarmo explains: “There is nothing like a perfect user interface. A UX designer cannot create it. The user interface will become perfect when it is resonating with the user who is on the website.” Practically, this means the same basic interface architecture serves as a framework, but the data, messages, offers, and emphasis points that populate it change for each user based on their calculated intention profile. It is not the layout that needs to adapt — it is the content. For more on this distinction and how ChainAware implements it, see our behavioral user analytics guide.

The Network Effect Myth: What Business Schools Get Wrong

One of X Space #15’s most pointed arguments is the critique of how business schools and investment analysts explain Web2’s success. The standard explanation attributes Web2 platforms’ dominance to network effects — the self-reinforcing dynamic where a platform becomes more valuable as more users join it. Martin and Tarmo argue this explanation is backwards: it describes a consequence of AdTech rather than its cause.

Network effects are real and powerful. A social media platform with 1 billion users is more valuable than one with 1 million users because the connections available are greater. A marketplace with 10 million buyers and sellers is more liquid than one with 10,000. These effects compound growth dramatically once they engage. However, they cannot engage until a critical mass of the right users reaches the platform — and that critical mass only arrives when AdTech efficiently routes relevant users to the right platforms. As Tarmo argues: “To achieve the network effect, you need AdTech first. There is no point to have a super innovative process if you cannot bring users to this super innovative process. You need AdTech. The network effect switched on after AdTech. Not before.”

The Actual Sequence: AdTech First, Then Network Effects

The historical sequence is AdTech → resonating users → engaged community → network effects → dominance. Business school curriculum describes the end state (network effects) without explaining the enabling condition (AdTech) that triggered it. As Martin observes: “Business schools speak about network effects, but they should speak about AdTech. And business schools should tell students — the Web2 technology platforms started to grow, their wheel crossed the chasm. It happened when the AdTech emerged. Because AdTech was the key for Web2 till this moment, till Web2 took AdTech, it was just something innovative. And AdTech brought it to the broad masses, crossing the chasm.” Understanding this sequence is critical for Web3 founders who are waiting for network effects to spontaneously emerge without first building the AdTech infrastructure that would enable them. For more on the crossing the chasm framework, see our Web3 crossing the chasm guide.

KOL Marketing Reality: 40 Out of 750 Produce Positive Returns

Rather than relying solely on theoretical arguments about mass marketing’s inefficiency, Martin provides a specific, verifiable data point from AlphaScan — a KOL performance tracking tool — that quantifies the failure rate of Web3’s dominant marketing approach.

AlphaScan tracks 750 crypto KOLs and measures the average token return for projects they promote within a 30-day window. When Martin checked the platform before X Space #15, 40 of the 750 tracked influencers had produced positive 30-day returns. That means 710 — 94.7% of the tracked KOL pool — produced neutral or negative outcomes for the tokens they promoted. Projects paid these 710 KOLs their upfront fees and received negative price action in return. Martin describes the outcome bluntly: “You pay them, but they generated negative returns. Just imagine again, you are a founder, you pay the calls, you get negative returns. Double waste of money — double ruin.”

Mass Marketing Everywhere: Calls, Media, Banners

Martin notes that KOL marketing is not uniquely problematic — it is representative of the entire Web3 marketing channel mix. Crypto media placements (CoinDesk, Cointelegraph, Bitcoin.com) deliver the same article to all readers regardless of their individual relevance to the featured project. Banner advertising on Etherscan, CoinGecko, and CoinMarketCap delivers the same creative to every page visitor. All three channels are mass marketing with varying cost structures but identical structural flaws. Tarmo summarises: “It is mass marketing, Martin — nothing. Full mass marketing. And this full mass marketing results in $1,000 plus conversion cost for one transacting user.” For the complete channel analysis, see our KOL vs AdTech comparison guide.

Why Blockchain Data Outperforms Google Search History

The most counterintuitive claim in X Space #15 is Tarmo’s assertion that blockchain financial transaction data is actually a higher-quality input for intention prediction than anything Google or Facebook has ever collected. This is not a marginal advantage — it is a fundamental data quality difference that Tarmo predicts will push Web3 conversion ratios above Web2’s ceiling.

Web2’s intention prediction relies primarily on search queries and browsing behaviour — signals that are noisy, easily faked, and only weakly correlated with actual purchasing or transaction intent. Tarmo acknowledges the limitation: “The data sources in Web2 are very not accurate. But even with these very non-accurate data sources, we get very high conversion ratio in Web2.” The implication is striking: Web2 achieved 30% conversion with mediocre data quality. Better data will produce better conversion.

The Gas Cost Filter

Blockchain financial transactions have properties that make them uniquely reliable as behavioral signals. Every transaction requires deliberate decision-making, wallet interaction, and real financial cost in the form of gas fees. As Martin explains: “Financial transaction is you really think about what you do. And Ethereum has a gas cost — you really think what you do plus you pay. In Google search, you can search anything. Facebook, you can pretend to be anything. In Twitter, you can create a lot of fake profiles. But in a blockchain, you will pay for the transactions. That means on the blockchain, you are really doing the transactions which you want to do.” Furthermore, blockchain data is completely public — accessible to anyone with the technical capability to process it, at zero cost. Google will not share its 2,600 user attributes. Facebook will not licence its social graph. Blockchain history is open and free. For the complete blockchain data quality analysis, see our behavioral user analytics guide.

Tarmo’s 40-45% Prediction: Why Web3 Can Exceed Web2 Conversion

Building on the data quality argument, Tarmo makes a specific, testable prediction about what Web3 AdTech will achieve once it matures: conversion ratios of 40-45%, exceeding Web2’s 30% ceiling. This is not an optimistic estimate — it follows logically from the data quality advantage.

Web2 achieves 30% conversion using data sources Tarmo characterises as “very not accurate” — search queries that reflect momentary curiosity, social interactions that reflect peer influence and presentation effects, and browsing patterns that include casual research. Blockchain transaction history, by contrast, records deliberate financial commitments made with real money at stake, filtered by gas cost requirements that eliminate casual or accidental signals. The signal-to-noise ratio is fundamentally better.

The Compounding Quality Advantage

If better data produces better intention predictions, better intention predictions produce better user routing, and better user routing produces higher conversion — the math suggests Web3 can exceed Web2’s current conversion ceiling. As Tarmo states: “My hypothesis is that conversion ratio goes even higher than Web2 because of very high quality data. We will soon have not 30% conversion ratio but even higher, maybe 40, maybe 45% conversion ratio in Web3, due to very high quality data source and very high prediction rate.” At 40-45% conversion with $5 CPC and realistic click-to-visit rates, the effective CAC drops to approximately $10-12 — better than Web2’s current benchmark. The practical consequence is that Web3 projects adopting ChainAware’s AdTech can potentially achieve acquisition economics that no Web2 company has ever reached. For ChainAware’s live performance data, see our analytics guide.

How ChainAware Implements Web3 AdTech Today

ChainAware’s AdTech implementation directly mirrors the two-step Web2 formula — intention calculation plus targeted messaging delivery — using blockchain transaction history as the data source instead of search and social data.

The foundation is ChainAware’s behavioral prediction models, developed originally for fraud detection and progressively extended to cover the full range of user intentions. The fraud detection system achieved 98% prediction accuracy (backtested against CryptoScamDB) by identifying behavioral patterns in wallet transaction histories that reliably preceded fraudulent activity. The same pattern-matching methodology, applied to non-fraud behavioral dimensions, produces intention profiles across multiple categories: borrower likelihood, trader likelihood, NFT activity, gaming engagement, experience level, and risk tolerance.

From Intentions to Targeted Messages

These intention profiles connect directly to ChainAware’s targeting system — the component that completes the AdTech loop by delivering matched messages to each identified persona. When a user connects their wallet to a platform running ChainAware’s marketing agent, the system reads the wallet address, calculates the behavioral profile in real time, identifies the appropriate persona, and serves the corresponding message variant configured by the platform operator. A borrower-profile wallet visiting a lending platform sees loan terms and collateral information. A gamer profile visiting the same platform sees bridging content explaining how DeFi lending connects to their existing behavior. As Martin summarises: “Two product lines, but the common is always that it is the next. What will happen in the future. We are predicting what the user will do as next. We are focused on this very much in ChainAware.” For the full implementation details, see our behavioral analytics guide and our Web3 AI agents guide.

Crossing the Chasm: What Web3 Must Do That Web2 Already Did

Martin and Tarmo close X Space #15 by connecting the entire AdTech framework to the dominant metaphor for technology mainstream adoption: Geoffrey Moore’s Crossing the Chasm. The chasm describes the gap between early adopter usage (enthusiasts who adopt for the technology itself) and mainstream adoption (users who adopt for the utility). Web2 crossed it. Web3 has not yet crossed it. The reason in both cases is the same: AdTech.

Web2’s crossing happened when Google’s AdTech infrastructure began routing users to platforms that matched their behavioral intentions, and those platforms began serving adaptive content that converted visitors with unprecedented efficiency. Before AdTech, Web1 had thousands of innovative platforms and millions of early adopters but no mechanism for efficiently matching them. After AdTech, the right users reached the right platforms, converted, stayed, referred others, and triggered the network effects that eventually produced billion-user platforms. As Tarmo states: “AdTech was the secret sauce of Web2. The same AdTech is the secret sauce of Web3. Technology is here. Now we just have to see how technology adoption curve runs.”

The Path for Web3 Founders

For Web3 founders specifically, the message is actionable: stop waiting for network effects to spontaneously emerge and start building the AdTech layer that enables them. Stop spending budget on KOL campaigns with 94%+ negative return rates and start investing in intention-based targeting that routes relevant users to platforms and converts them with personalised experiences. Stop treating customer acquisition as a marketing problem to be delegated to agencies and start treating it as a technology problem to be solved with the same rigor applied to smart contract development. As Tarmo concludes: “The founders have options that they do not need to do pump-and-dump anymore. They can build sustainable businesses with positive cash flow and run Web3 companies as long-term successful companies.” For the complete ecosystem transformation analysis, see our guide to why AI agents will accelerate Web3.

Comparison Tables

Web3 Mass Marketing vs Web3 AdTech: CAC and Conversion Economics

Metric	Web3 Mass Marketing (Current)	Web2 AdTech (Benchmark)	Web3 AdTech — ChainAware (Target)
Conversion ratio	Below 1%	Up to 30%	Target 40-45% (Tarmo’s hypothesis)
CAC per transacting user	$1,000+	$15-20	Target $10-15
CPC basis	$5 (same)	$5 (same)	$5 (same)
Targeting method	Mass — KOLs, banners, media	Microsegments — RTB, behavioral	1:1 — wallet intention profiles
Data source	None — undifferentiated traffic	Search + browsing (noisy)	Blockchain transactions (deliberate)
Data quality	N/A	Medium — easily faked	High — gas-cost filter
Data access cost	N/A	Billions in infrastructure	Free — public blockchain
KOL positive rate	40/750 = 5.3%	N/A	N/A — not needed
Loyalty generated	None — herd moves monthly	Medium	High — resonating experience
Sustainable business possible?	No — CAC exceeds LTV	Yes — profitable at scale	Yes — higher margin than Web2

Web2 Data Sources vs Blockchain Data for Intention Prediction

Property	Google Search + Browse	Facebook Social	Blockchain Transactions (ChainAware)
Signal type	Passive curiosity + active search	Social performance + peer influence	Deliberate financial decisions
Fake signal risk	Medium — bots and fake searches	High — fake profiles widespread	Low — gas fees filter fakes
Financial commitment	Zero — free to search	Zero — free to post	Real — gas cost per transaction
Ownership	Private — Google owns it	Private — Meta owns it	Public — anyone can read
Access cost	Billions in ad spend	Billions in ad spend	Free
Tarmo’s data quality assessment	“Very not accurate”	“Very not accurate”	“Very high quality”
Achievable conversion	30% (Web2 maximum)	30% (Web2 maximum)	Target 40-45%
ChainAware prediction accuracy	N/A	N/A	98% (fraud) · High (intentions)

Frequently Asked Questions

Why is Web3 customer acquisition cost 50x higher than Web2?

Web3 customer acquisition costs are 50x higher than Web2 ($1,000+ vs $15-20) because Web3 uses mass marketing — KOLs, banners, crypto media — that delivers the same message to undifferentiated audiences. Web2 achieved its low CAC through intention-based targeting (routing users whose behavioral profile matches the platform) and adaptive user interfaces (serving matched content to each visitor). Both steps multiply conversion probability dramatically. Web3 uses neither. For the detailed step-by-step calculation, see our Web3 CAC breakdown guide.

What is real-time bidding and why does it matter for Web3?

Real-Time Bidding (RTB) is the programmatic advertising auction system that determines which advertiser’s creative reaches each specific user in milliseconds when they load a web page. It operates on top of microsegmentation — advertisers bid specifically for users in defined behavioral intention segments. RTB powers Web2’s efficient user routing, enabling $15-20 CAC. Europe alone generates €30 billion annually from RTB, growing 10-15% per year. Web3 projects cannot access this infrastructure directly due to publisher restrictions and licensing requirements for financial service advertisers. Blockchain-based Web3 AdTech provides the equivalent functionality using public on-chain data. See the full RTB overview on Wikipedia.

Why did network effects not occur spontaneously in Web2 before AdTech?

Network effects require a critical mass of the right users on a platform before they engage — users who find genuine value in the platform and therefore stay, engage, and refer others. Without AdTech routing relevant users to relevant platforms, early Web2 platforms reached broad but poorly matched audiences with low conversion rates. AdTech created the efficient matching that delivered the right users to the right platforms, generating the engaged communities that then triggered network effects. The correct sequence is AdTech first, then network effects. Business school curriculum typically presents network effects without explaining the AdTech prerequisite that enables them.

Why can blockchain data predict user intentions more accurately than Google’s data?

Google’s search and browsing data reflects passive curiosity and incidental activity — easily faked, often unrelated to actual purchase or transaction intent, and available at zero cost to the user. Blockchain transactions are deliberate financial decisions made with real money at stake, requiring conscious wallet interaction and gas fee payment. The gas cost filter means only transactions the user genuinely intended get executed — eliminating the noise that plagues Web2 data sources. ChainAware achieves 98% accuracy in fraud prediction from blockchain data, demonstrating the prediction quality available from this source. Tarmo’s hypothesis is that this data quality advantage will push Web3 AdTech conversion to 40-45% — exceeding Web2’s 30% maximum.

What does ChainAware’s Web3 AdTech implementation look like in practice?

When a user connects their wallet to a platform running ChainAware’s marketing agent, the system reads the wallet address and processes its complete on-chain transaction history across 2,000+ Ethereum and 800+ BNB Smart Chain protocols. The behavioral AI models generate an intention profile: borrower, trader, yield farmer, gamer, NFT collector, newcomer, experienced DeFi user. The targeting system then selects the message variant configured for that persona and delivers it on the platform in real time. Each persona sees content matched to their predicted next action — not the generic messaging every other visitor sees. The platform operator configures which messages map to which personas and refines the mapping as conversion data accumulates. For the complete setup guide, see our behavioral analytics guide.

This article is based on X Space #15 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.