What Actually Happened With Web3 Marketing
Between 2021 and 2023, a significant number of brands made substantive investments in Web3 marketing — NFT campaigns, metaverse activations, blockchain-verified loyalty programs, Discord communities positioned as "Web3-native" brand experiences.
Most of those investments produced very little.
Not because Web3 is inherently unworkable as a technology category. But because the brands that invested did so primarily for one of two bad reasons: FOMO (fear of being left behind by a trend that might matter) or optics (signaling innovation to investors, media, or internal stakeholders).
Neither of those is a good reason to invest in marketing innovation. The result was campaigns that were technically novel, genuinely confusing to most of their target customers, and impossible to attribute to any business outcome that the investing brands could defend.
I watched this cycle play out with several clients. The brands that declined to chase the trend — including some I worked with who were under real pressure to "do something with NFTs" — mostly look prescient in hindsight.
The Pattern Repeats
Web3 marketing was not a unique event. It was the latest iteration of a cycle that happens with every new technology category that attracts significant media attention and VC capital:
Phase 1 (Hype onset): Early adopters achieve genuine results. Breathless coverage follows. "This will change everything" becomes the dominant narrative. FOMO pressure builds.
Phase 2 (Rush to participate): Brands without genuine strategic rationale invest because the pressure to participate feels greater than the cost of participation. Most implementations are poor because they're not grounded in genuine user need.
Phase 3 (Disappointment): Most investments fail to produce attributable business results. Coverage shifts to the failures. "This was all hype" becomes the corrective narrative — usually overcorrecting.
Phase 4 (Consolidation): The genuine applications emerge, separate from the hype. The technology becomes useful to specific users in specific contexts — typically smaller and more specific than the hype suggested.
We've seen this cycle with virtual reality (the current wave is producing genuine results; the 2015-2018 wave mostly produced expensive demos), with social media commerce (still maturing after years of "this is the future of shopping" coverage), with voice search (useful, but the predicted "50% of searches will be voice by 2020" didn't materialize), and now with Web3.
The Questions to Ask Before Chasing the Next Trend
The framework I use when evaluating whether a new technology trend deserves a brand's marketing investment:
Question 1: Is there genuine consumer behavior to meet, or is the demand manufactured by the ecosystem around the technology?
The strongest signal that a new technology is real: people are using it to solve problems they had before the technology existed, and they're doing so in large enough numbers that the behavior is measurable. The weakest signal: coverage is primarily from investors, founders, and advocates who have financial stakes in adoption.
For Web3 marketing in 2021-2022: consumer adoption of the specific experiences being built (NFT ownership as a community access mechanism, metaverse social interaction) was extremely limited outside of specific sub-communities. The behavior wasn't there in any broadly accessible form.
Question 2: Would your specific target customer actually use this?
Not "could someone imagine using this" — would your specific customer, in your specific category, actually engage with this format or platform?
The brand selling premium kitchen appliances to 45-55 year old home cooks considering a Web3 loyalty program in 2022 should have been asking: how many of our customers own crypto wallets? The answer was: almost none.
Question 3: What's the specific business outcome you're investing toward?
New technology campaigns should be held to the same standard as any other marketing investment: what is the specific, measurable business outcome this investment is supposed to produce, and how will we measure whether it did?
The Web3 campaigns that failed most spectacularly were the ones with no clear answer to this question. "We want to be seen as innovative" is not a measurable business outcome.
Question 4: What's the cost of being wrong?
Some bets are worth making even at low probability if the downside is bounded and the upside is significant. The question is whether you've correctly estimated both.
For most brands in Web3: the downside was not bounded. Significant budget allocation to campaigns that couldn't be attributed to business outcomes, reputational exposure if the campaign became associated with hype rather than genuine value, and team time and organizational attention that had real opportunity costs.
Question 5: Is there a way to test at small scale before full investment?
The best technology investments in marketing start small, measure quickly, and scale only after evidence. The worst ones skip the test phase because the narrative around the technology creates urgency.
What This Means for AI Marketing Right Now
The obvious parallel: AI in marketing is currently in Phase 1-2 of the cycle I described above. Breathless coverage, significant VC capital, FOMO pressure on brands to participate.
This doesn't mean AI marketing is equivalent to Web3 marketing. AI has genuine, measurable applications that produce real business results right now. Automated ad creative testing, content drafting acceleration, personalization at scale, predictive analytics — these are producing real ROI in specific contexts.
But the hype layer is real too. The AI marketing applications that are purely about optics — "AI-generated campaigns" as a PR story rather than a genuine workflow improvement — are following the same pattern as Web3.
The questions are the same: Is there genuine consumer behavior to meet? Would your specific customer experience this differently than the alternative? What's the specific measurable outcome? What's the cost of being wrong? Can you test small?
The answers, applied honestly, will separate the AI applications worth investing in from the ones you're considering primarily because they'll generate a press release.
Key Takeaways
- Web3 marketing mostly failed because brands invested for FOMO and optics rather than genuine consumer behavior
- The hype cycle repeats: VR, voice search, social commerce, Web3, now AI — each follows the same pattern with different technology
- Five questions before chasing any new technology trend: genuine consumer behavior? Your specific customer? Specific measurable outcome? Cost of being wrong? Small-scale test possible?
- "We want to be seen as innovative" is not a business outcome — any campaign that can only be justified that way should be questioned seriously
- AI marketing is in Phase 1-2 right now — genuine applications exist, but the hype layer is real; apply the same framework
- Small-scale testing with clear measurement is the difference between learning and wasting — never skip the pilot phase because urgency feels real