The metrics we built are breaking.

There’s a version of this quarter’s report where organic traffic is flat or slightly down, the room gets uncomfortable, and you spend twenty minutes explaining why the number your board is looking at doesn’t reflect the actual impact of the work you’ve been doing.

You’re quietly sweating because you feel it’s the only lever you have to justify your salary. They’re questioning your value, their trust, your expertise. Lose lose on both sides.

It’s not that the work isn’t valuable or necessary for a business in the age of the Internet. It’s that the metrics we’ve been using by default to prove it are no longer up to the job. It’s not sessions, or clicks, impressions or even revenue. Those never were the right metrics. As an industry we’ve fundamentally misunderstood what search engine optimisation actually is as a channel.

We are marketers. Full stop.

I don’t have a clean, finished framework. Anyone who says they do is lying. What I do have is a set of replacements I’ve been testing, some borrowed from marketing disciplines that have dealt with attribution problems for far longer than we have.

What stopped working

A few things broke at roughly the same time. It broke a lot of our brains, and some of our jobs.

AI Overviews and LLMs are eating informational clicks, visibly and quickly

Pew Research tracked nearly 69,000 real searches, and click rates on traditional links dropped to 8% when a summary appeared, down from 15% without one. Ahrefs put the hit at 58% for position one by February 2026.

The Bermuda triangle of measurement:

• Search Console lumps AI impressions in with regular search
• Bing folds Copilot data into standard web metrics
• Lily Ray showed this concretely: 363K Discover clicks on one article, practically invisible in GA4

And as an early canary, GA4 itself stripped out what teams relied on. Page value disappeared. Events replaced sessions. The visibility exists. Unified, clear measurement doesn’t.

LLM visibility is growing but mostly invisible

93% of AI Mode searches don’t produce a click, though remember the law of small numbers — we’re still early. The visits that do come through convert at 4.4x standard organic with much lower bounce. A high-quality stream that sessions-based reporting treats as noise. Organic visibility is fractured across surfaces our standard reporting was never built to see.

We don’t actually know what made anyone buy. We never did. AI just stopped letting us pretend the dashboard knew.

Where the replacements come from

Digital growth as a whole is heading to directional measurement. More estimation. Less certainty about which action caused which outcome. Not just because of AI, either — privacy laws, JS-blocking, app-to-web journeys, dark social. All of it has been quietly eroding attribution for years. AI just made it impossible to ignore.

My first piece of advice has been consistent for years: go talk to finance. Ask how the business measures TV, radio, sponsorship — channels that have never had clean attribution. They’ve been working with approximations for decades, and businesses still invest, because directional measurement is better than none. And if you have no other recourse, set the value of the goal to $1. Start the conversation.

Anchor one: share of voice

Binet and Field established decades ago that brands whose share of voice exceeds their market share tend to grow. Theirs was a TV-and-print rule, and the translation to organic isn’t airtight — but the logic holds. The cleanest implementation right now is share of search via Google Trends and a watch on share of category visibility in your top 20 SERPs. Pick one and stick to it. Consistency matters more than the choice.

Anchor two: branded search trend

If your work is actually building anything — recognition, salience, the next-purchase consideration set — branded search should grow. Slowly, lumpily, but it should grow. It’s the simplest leading indicator we have, free in Search Console, and your stakeholders already understand what it is. If branded search is flat across a year of sustained content and PR, something isn’t landing. That’s worth knowing.

Anchor three: everything underneath the surface

The first two anchors are above the surface. The third is what’s actually moving them. Jono Alderson’s framework of six structural capabilities — experience integrity, physical availability, mental availability, distinctiveness, reputation, commercial proof — is the cleanest articulation I’ve seen.

Most of those six aren’t SEO’s job to drive. They’re product, support, brand, PR, sales, distribution. They are SEO’s job to measure — and to make legible enough that machines can read them. Jono calls that legibility coherence: making sense everywhere, not just on your site.

Measure the movement, not the position

One thing connects all of those metrics: they’re useless as absolutes. “Our share of voice is 14%” doesn’t mean anything. 14% of what? Compared to whom? What works is incremental change, year-on-year, against the market.

“We grew excess share of voice by 10% while our closest competitors held at 2%.” That’s a story. That’s defensible. That’s how every other department already reports — sales talks about revenue growth, not revenue. Product talks about adoption rate, not install count. SEO has been weirdly resistant to this. We report positions and traffic totals as if the snapshot is the point. It’s not. The trajectory is.

The industry’s wrong answer is already being sold

You’ve seen the dashboards. AI visibility scores. Citation rate in ChatGPT. Confidence intervals on probabilistic outputs, sold as deterministic numbers your CMO can put in a slide. Most of it is the same trick we played on ourselves with last-click.

Rand Fishkin and Patrick O’Donnell at SparkToro actually ran the experiment — 2,961 runs across ChatGPT, Claude and Google AI. Rank position in AI? The same prompt gives back the same list less than 1 in 100 times. But brand presence frequency — how often you appear across many runs — held up. Rank tracking in AI: theatre. Presence frequency at sample size: actually informative. Most of what’s being sold is the first thing dressed up as the second.

The harder conversation

Most of the trouble with new measurement isn’t the measurement. It’s that someone’s bonus is tied to the old one. Changing the protocol mid-financial-year tells someone the number they’re paid on no longer counts. Time the change with the financial year, or run the new metric in parallel for a quarter first.

Ian Lurie’s progressive detail framework is the cleanest version: three layers. The board gets the one or two metrics that map to business outcomes. The VPs get that plus their function’s supporting metrics. Your team gets everything underneath. Don’t make one dashboard serve everyone.

Less sexy. Also less wrong.

This is harder work. You won’t have the clean “we ranked #1 and revenue went up 20%” story. You’ll have a directional trend, a couple of proof-of-concept tests, and a stakeholder conversation that takes longer than you’d like. You’ll also be telling the truth.

The dashboards we built in the last decade weren’t really measuring what we said they were. We dressed up correlation as causation, called it attribution, and rode the numbers as long as they kept going up. AI didn’t break that. It revealed it. So when someone asks why the number isn’t doing what it used to — tell them the number wasn’t doing what we said it was in the first place.