IAB's $32B Measurement Problem: Why Project Eidos Validates Earned Media Attribution

When the industry's top buyers say measurement gaps are locking $32B in investment, it's time to pay attention to the channels being measured worst

The Interactive Advertising Bureau (IAB) just launched Project Eidos—an industry initiative to fix what CEO David Cohen calls "foundational issues" in media measurement. The consortium includes Amazon, Google, Meta, Havas, NFL, Paramount, Pinterest, Publicis, Trade Desk, TikTok, Unilever, and WPP.

The stated problem: "While advanced measurement is widely used across the industry, it's still falling short of its core promise."

The business impact: IAB's State of Data 2026 report reveals that buyers believe AI can unlock $32 billion in media investment within 1-2 years—if measurement improves.

That's not a rounding error. That's a third of the digital advertising market sitting on the sidelines because attribution is broken.

And here's the part that should alarm earned media professionals: of all marketing channels, earned media is the most under-measured.

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Project Eidos isn't about inventing new measurement science. It's about acknowledging that the current system—cobbled together from channel-specific attribution models, inconsistent tagging, and walled-garden data—doesn't actually work.

Cohen's diagnosis is blunt:

> "We're dealing with a patchwork of channel-by-channel measurement, inconsistent attribution, and MMM [marketing mix modeling] gaps."

Translation: Brands are flying blind.

Here's what that looks like in practice:

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Paid channels have robust attribution because they're designed for it:

- Facebook Ads Manager tracks impressions, clicks, and conversions

- Google Ads connects search queries to landing pages to sales

- Programmatic DSPs measure viewability, frequency, and lift

When a CMO asks "What's the ROI on paid social?"—the answer is a dashboard with conversion funnels, cost-per-acquisition, and ROAS down to the creative variant.

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Owned channels (website, email, blog) are trackable via analytics platforms:

- Google Analytics shows sessions, pageviews, and goals

- Email platforms report open rates, click rates, and conversions

- CRM systems tie web behavior to accounts and deals

The limitation: owned media measurement only captures known visitors. It doesn't show how prospects discovered your brand, what external validation influenced them, or why they trusted you enough to engage.

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Then there's earned media—press coverage, byline articles, podcast appearances, expert citations, third-party reviews—which is systematically under-attributed.

Why? Because earned media creates influence before it drives traffic.

When a prospect reads your CEO's byline in TechCrunch, they don't click through immediately. They remember your brand. They Google you later. They mention you in a Slack channel. They cite you in an internal deck.

By the time they visit your website, the attribution trail is cold. Google Analytics logs them as "direct traffic" or "organic search"—and your earned media program gets zero credit.

This isn't a niche problem. It's structural.

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IAB's State of Data 2026 report surveyed media buyers on what would unlock more investment. The top answer: better attribution.

Here's the feedback loop:

1. CMOs can't prove ROI on channels with weak attribution

2. Budget flows to channels with clear, real-time measurement (paid ads)

3. Earned media gets labeled "brand building" and deprioritized

4. Competitors who do invest in earned media gain citation authority

5. AI search engines cite earned sources 5x more than brand sites

6. Brands with weak earned media lose AI visibility

7. Traffic declines, and CMOs double down on paid ads to compensate

This is how attribution failure creates strategic blindness.

And it's why Project Eidos matters—not because it will solve earned media attribution overnight, but because it acknowledges the system is broken at the foundation level.

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Let's be precise about why earned media is uniquely disadvantaged in current measurement frameworks:

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Paid ads are designed for direct response. You see an ad, you click, you convert. The attribution model works.

Earned media works differently. A Forbes byline or Wall Street Journal mention builds awareness and credibility. Prospects don't click the backlink—they remember your brand and research you later via Google, ChatGPT, or word-of-mouth.

By the time they convert, the earned media touchpoint is invisible in your analytics.

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Most attribution models—whether first-touch, last-touch, or linear multi-touch—prioritize known touchpoints (web visits, email opens, ad clicks).

Earned media often occurs before a prospect enters your funnel. It's the reason they searched for you. It's the credibility signal that made them care.

But if that earned placement didn't pass a UTM tag or referral string, it's not in your attribution model. It's ghost influence.

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When a prospect discovers your brand via an earned media placement, then later sees your paid ad on LinkedIn or Google, the ad platform takes credit for the conversion.

Facebook Ads Manager says: "This user converted because of our ad."

Your attribution report agrees: "Last-touch attribution: LinkedIn ad."

The earned media article that introduced them to your brand? Nowhere in the data.

This isn't Facebook's fault—it's how walled-garden attribution works. But it systematically erases earned media's contribution.

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The PR industry's answer to attribution has been Earned Media Value (EMV)—the estimated ad-spend equivalent of press coverage.

Example: A TechCrunch article with 500k readers is "worth" $50k in advertising value.

The problem: EMV doesn't measure actual business impact. It's a proxy metric invented by PR firms to justify retainers.

Real attribution requires connecting earned placements to:

- Brand awareness lift

- Consideration intent

- Website traffic

- Lead generation

- Pipeline influence

- Closed revenue

EMV measures none of these. It's ad equivalency theater.

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Project Eidos aims to:

1. Standardize cross-channel measurement - Create common frameworks for attribution

2. Improve MMM accuracy - Fix statistical models that underweight top-of-funnel impact

3. Close walled-garden data gaps - Force platforms to share attribution-friendly data

This is directionally correct. If successful, it would help earned media by:

- Valuing early-stage touchpoints more fairly

- Creating consistent tagging standards across channels

- Reducing last-touch attribution bias

But here's what Eidos doesn't address:

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Even with perfect cross-channel measurement, earned media will still create influence that occurs outside your measurement stack.

A prospect reads your CEO's byline on Bloomberg while drinking coffee. They don't click. They don't visit your site. But they remember your brand.

Three weeks later, they Google your company name and visit your site directly.

Google Analytics logs them as "direct / none" traffic. Even the best attribution model can't connect that visit to the Bloomberg article—because there's no trackable interaction.

This isn't a data tagging problem. It's a discovery model problem.

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Project Eidos is focused on traditional digital channels: paid, owned, and measured media. It's not (yet) addressing AI search.

But AI engines like ChatGPT, Perplexity, and Gemini are becoming primary discovery mechanisms—and they overwhelmingly cite earned media sources.

University of Toronto research found that AI engines cite earned media (news articles, expert bylines, third-party analysis) 5x more often than brand websites.

When a prospect asks ChatGPT "What are the best earned media platforms for B2B SaaS?"—and ChatGPT cites a Forbes article mentioning your brand—that's attribution-invisible influence.

Eidos doesn't have a framework for this yet. And until it does, earned media attribution will remain incomplete.

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If $32B in media investment is waiting for better measurement, earned media needs its own attribution layer—one designed for how influence actually works.

Here's what that looks like:

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Earned media attribution should measure:

- How often your brand is cited in AI responses (ChatGPT, Perplexity, Gemini)

- Which publications drive the most AI citations

- Whether competitors have higher citation authority in your category

Why this matters: AI visibility is the new SEO. If your attribution system only measures clicks, it's blind to where modern discovery happens.

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Instead of measuring immediate conversions, track:

- Awareness lift before/after earned campaigns

- Consideration intent changes

- Share-of-voice in category conversations

Why this matters: Earned media's value is influence, not clicks. The metrics need to match the mechanism.

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Ask prospects directly: "How did you hear about us?"

Log responses that mention:

- Specific publications (Forbes, TechCrunch, WSJ)

- AI research (ChatGPT, Perplexity)

- Word-of-mouth referrals

Why this matters: Qualitative data fills gaps that quantitative tracking can't reach.

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Tag every press mention, byline, and podcast link with:

- `utm_source=earned-media`

- `utm_medium=[press / byline / podcast]`

- `utm_campaign=[publication-name]`

Even if prospects don't click immediately, you'll see which placements drive eventual referral traffic.

Why this matters: Some earned media does drive clicks—but only if you tag it to capture the signal.

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Not all press is equal. A TechCrunch mention drives more authority (and AI citations) than a tier-3 blog.

Build a scoring model that weights earned placements by:

- Domain authority (Ahrefs DR, Moz DA)

- AI citation frequency (how often ChatGPT cites this publication)

- Audience quality (decision-maker readership, B2B relevance)

Why this matters: Attribution should reflect impact, not just volume.

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The IAB's $32B figure isn't theoretical. It represents real marketing budgets sitting in locked accounts because CMOs can't confidently allocate spend.

And here's the competitive dynamic:

Brands that figure out earned media attribution now will win AI visibility before their competitors.

Why? Because while everyone else waits for Project Eidos to solve measurement at the industry level, early movers are:

- Investing in earned media despite attribution gaps

- Building proprietary tracking systems

- Establishing citation authority in AI engines

- Owning the expert positioning that drives AI recommendations

By the time Eidos standardizes cross-channel measurement (2-3 years, realistically), these brands will already dominate AI search results. Their earned media engines will be citation infrastructure—defensible, compounding, and attribution-provable.

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You don't need to wait for Project Eidos to solve attribution. You need to build your own earned media ROI framework:

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- Track your brand's citation frequency in ChatGPT, Perplexity, and Gemini

- Identify which publications AI engines cite most in your category

- Benchmark against competitors

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- Use UTM parameters on every byline, press mention, and podcast link

- Create a master spreadsheet tracking publication, date, topic, and UTM

- Monitor referral traffic from earned sources in Google Analytics

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- Run quarterly brand awareness surveys with your ICP

- Ask: "Have you heard of [your brand]?" and "Where did you hear about us?"

- Track changes after major earned media campaigns

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- Add a "Discovery Source" field to your CRM (Salesforce, HubSpot, etc.)

- Include "AI Search (ChatGPT/Perplexity)" as an option

- Report on AI-sourced leads monthly

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- Pull all deals closed in the last 90 days

- Check CRM notes for mentions of press, bylines, or AI research

- Calculate the percentage of pipeline influenced by earned media

This won't be perfect. But it's better than flying blind.

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The IAB's Project Eidos is an admission: the industry's measurement infrastructure is fundamentally broken. And of all channels, earned media suffers most from attribution blindness.

But here's the paradox: earned media is also the most valuable channel for AI-era discovery.

AI engines don't cite ads. They don't cite landing pages. They cite earned sources—press articles, expert bylines, third-party analysis, peer reviews.

Brands that invest in earned media now—despite imperfect attribution—will own the AI visibility that drives future demand.

The $32B that IAB says is locked behind measurement gaps? A meaningful chunk of it will flow to brands that solve earned media attribution before the industry consensus emerges.

You can wait for Eidos to build the infrastructure. Or you can build your own.

The choice is whether you want to be cited—or ignored.

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Christian Lehman is Chief Growth Officer at AuthorityTech, where he leads performance-based earned media programs that guarantee AI visibility for B2B brands. Before AuthorityTech, Christian built growth engines at venture-backed SaaS companies and advised Fortune 500 CMOs on attribution strategy.