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How signal decay hurts your top-of-funnel performance

Thu, 21 May 2026 15:00:00 +0000

Conversion signals are disappearing from your marketing data, and it’s probably costing your business money.

Ad blockers, aggressive privacy laws, cookie deprecation, and a host of other converging factors have combined to mask significant conversion data, costing businesses up to $203 million in revenue annually, according to one Deloitte study.

For most brands, the path from discovery to purchase is no longer clear.

This signal decay is more than an annoying data quirk. Left unchecked, it can make it harder for new customers to discover your brand.

Most marketers don’t realize they’re making decisions based on incomplete data. Instead, they see top-of-funnel campaigns that aren’t pulling their weight and reallocate those budgets elsewhere.

The algorithm inevitably responds by pulling traffic back further, investment continues to shrink, new customer acquisition dries up, and suddenly, a brand is in a downward spiral that’s difficult to correct.

The solution to avoiding the negative feedback loop isn’t better creative or larger budgets. Instead, data hygiene will be the competitive advantage in 2026. By feeding better information to Google’s hungry algorithm, you’ll transform your top-of-funnel activities and drive new customers through their purchase journey.

Why signal loss hurts discovery channels first

YouTube often sits near the top of the funnel, where attribution is weakest, and budget scrutiny is highest. That makes it one of the easiest channels to cut when performance data looks incomplete, even though it plays an important role in product discovery and brand research.

According to Google research:

“YouTube is the No. 1 platform viewers turn to when they want to research, vet, or make a decision about a brand or product.”

Despite this popularity, conversion signal decay has an outsized impact on YouTube’s perceived performance as a marketing channel. It’s often the first touchpoint for product discovery, but users then leave the platform to make purchases, breaking the signal chain.

Google’s own advertising tools underreport YouTube’s true marketing impact by 70% or more, a Haus Research study found. Fortunately, advertisers can recover some of those missing signals with a better measurement setup, making it easier to more fairly evaluate YouTube and other discovery channels.

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Closing the cross-device gap with enhanced conversions

You’ve probably watched TV while holding your phone in your hand. You’ve also probably seen a commercial on TV, looked up the product on your phone, and then made the purchase on your desktop three days later. This new device-spanning purchase journey is a common way to buy, but it’s also impossible to track under the standard cookie-based tagging that most brands still rely on to measure conversions.

Enhanced conversions help close that gap. They add a layer of hashed first-party data, like an email address or phone number, to every captured conversion. Google then securely matches that hashed data against its own user information to connect the conversion to an ad interaction.

Including enhanced conversions in your analytics unlocks insights into purchase journeys that began on YouTube and continued off the platform to the final purchase. Without this information, you’d never really understand how effective YouTube is at driving conversions down the line.

Training the algorithm with offline conversions

Here’s another scenario you’ve probably experienced: You see a YouTube Ad for a high-ticket item you’ve thought about purchasing, like a car or a new piece of furniture. It costs more than you’re comfortable spending online, so you close the ad, pick up the phone, and call the seller directly. Cookie-based tagging has no way to track these valuable conversions back to their source.

This tracking blind spot also applies to lead-generation campaigns because standard conversion tracking cannot provide insight into the full path from completed form to purchase. This is the gap offline conversions fill.

Offline conversions connect customer relationship management software and call data back to Google. This data layer trains the algorithm on which leads actually close rather than just tracking who filled out a form and disappeared. With this information, smart bidding can then optimize for revenue outcomes rather than just top-of-funnel actions.

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Defining new top-of-funnel signals with micro conversions

Enhanced conversions and offline tracking recover existing signals you haven’t had the ability to see before. However, sometimes top-of-funnel activities like YouTube don’t generate enough conversion data for the algorithm to learn from. In those instances, micro conversions can feed the algorithm the data it needs to optimize your ads.

Micro conversions send intermediate signals — such as watching half of a video, adding a product to cart, or lingering on a landing page — into campaigns that wouldn’t otherwise generate enough purchase-level data to effectively optimize. You can weight these signals as primary or secondary, depending on where the campaign sits in the funnel. Engagement signals like view times might feed prospecting data, while add-to-carts inform remarketing.

Without these intermediate signals, it becomes much harder to separate productive upper-funnel activity from wasted spend. Micro conversions will enable you to treat your top-of-funnel activities like any other campaign and make data-backed decisions on what’s really working.

Recovering lost signals with Google Tag Gateway

The final piece of the data-hygiene puzzle is recovering conversion signals that get blocked before they ever reach Google. Browsers like Safari and Firefox aggressively restrict third-party tracking, which contributes to the massive signal decay found in online purchases.

Google recently introduced a new tool, Google Tag Gateway (GTG), that can help you reclaim some of this lost data. GTG is a server-side technology that loads tracking tags from your website’s domain rather than Google’s. The gateway acts as a proxy, converting third-party requests into first-party requests, thereby bypassing some ad blockers.

Google reports that GTG users “saw an 11% uplift in signals” over advertisers who did not use the technology. GTG also offers advertisers important secondary benefits, including faster page load speeds, which improve Google’s landing page experience score and can lower costs per click.

While server-side tracking may sound like it’s difficult to implement, setting up GTG is actually a straightforward process if you’re on a content delivery network like Cloudflare.

Track, optimize, and win in Google and AI search from one platform.

Your data infrastructure is your competitive advantage

Every brand selling online today is affected by conversion signal decay. Most won’t recognize the real problem: Cross-device browsing, offline conversions, ad blockers, and low top-of-funnel signal volume are combining to distort our view of actual purchase behavior.

With inaccurate data in hand, most will respond by changing creative directions, reducing budget, or — even worse — cutting channels like YouTube that are secretly driving discovery. The downward spiral begins.

The advertisers that will win in 2026 won’t work around the edges. Instead, they’ll implement sophisticated data-hygiene layers that feed all that lost data back into Google’s algorithm, effectively outsmarting their competitors.

If you want to run more successful ads this year, focus on fixing your data first. Everything else will quickly follow.

Why AI adoption may look bigger than it really is: Data

Thu, 21 May 2026 14:31:22 +0000

AI adoption appears to be diverging between professional and consumer audiences, according to Rand Fishkin’s new analysis of Datos desktop-panel data and SparkToro audience comparisons.

The data highlights a sharp divide in how people talk about AI use: broader consumer adoption may be slowing, while professional and B2B audiences appear far more likely to use tools like Claude, ChatGPT, and Gemini.

Why we care. There’s no one-size-fits-all AI strategy. Your audience may behave very differently from broader AI trends, so you need to understand whether your audience is actually using these tools — and which ones.

ChatGPT desktop growth slowed. Fishkin, SparkToro’s cofounder and CEO, said Datos’ U.S. desktop data showed ChatGPT and OpenAI usage had largely plateaued over the past six to seven months, even as Claude and Gemini continued to grow.

At its peak, about 37% of U.S. desktop users visited OpenAI or ChatGPT in September 2025, according to the data Fishkin cited. That figure fell to 34% by March.
Fishkin said the same general pattern appeared in the EU and U.K., though desktop usage there was roughly 10% higher than in the U.S.

Claude gained with professionals. Claude showed the strongest recent momentum in the Datos data, with four straight months of growth from December through March. Fishkin said the trend supports his theory that consumer AI adoption may be plateauing while professional and business use continues to grow.

To test that idea, Fishkin used SparkToro audience comparisons to analyze business professionals and, separately, a broad consumer audience centered on retail shopping behavior.
The business-oriented audience showed substantially higher overall AI tool usage. Claude usage overindexed especially strongly among B2B professionals, with SparkToro showing a 373% lift versus the average U.S. population, according to Fishkin’s analysis.

Consumer audiences look different. Fishkin said ChatGPT was 15% less likely to be used by the retail-shopping consumer audience than by the average American. Claude didn’t rank among the top four AI tools for that group.

This may help explain why AI usage can feel far more dominant in professional online communities like LinkedIn than in broader consumer behavior, according to Fishkin.

The research. Watch Fishkin’s LinkedIn video here.

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What multilingual regions reveal about the future of AI search

Thu, 21 May 2026 14:00:00 +0000

AI search doesn’t just translate or localize results. It decides which sources, institutions, and versions of reality get surfaced in the first place.

Catalonia offers a useful stress test for that system. Two languages share the same geography, which makes retrieval patterns easier to spot.

When the same queries are run in Catalan and Spanish across Google AI Overviews and ChatGPT, the differences go far beyond wording — and reveal broader problems that extend well beyond multilingual regions.

Catalonia as a stress test for AI search

Did you know that if you search for Tradicions de Sant Jordi — Saint George’s Traditions, written in Catalan — Google Translate will identify the source language as Occitan?

Probably not. Most Catalan speakers don’t know it either, partly because Translate’s language guess isn’t exactly wrong: Catalan and Occitan share a common Romance ancestry, and some classification systems group them together.

The answer is technically defensible. It’s also, statistically, an odd call — and the kind of small anecdote that points at a much larger problem in the infrastructure underneath.

Google Translate showing “Detectado: Occitano” with input “Tradicions de Sant Jordi” and output “Tradiciones de San Jorge”

Occitan has roughly 200,000 speakers, mostly in southern France. Catalan has roughly 9 million speakers and is the co-official language of Catalonia, one of Europe’s wealthier regions and home to a city Google has operated in for over 20 years.

Asked from a Barcelona IP, Google’s translation product decides that the more plausible source language is the one with more than an order of magnitude fewer speakers, in another country. Translate then renders Sant Jordi into Spanish as San Jorge — castilianizing the proper name of the patron saint of Catalonia, a name that doesn’t need translating in the first place.

This single Translate quirk is anecdotal. What it points at isn’t. It’s a language-identification problem that has lived inside Google’s infrastructure for years — and Google itself has publicly acknowledged it.

In January 2023, the company’s Search Liaison account responded to a wave of complaints from Catalan-speaking users about Catalan results being downgraded in favor of Spanish ones. Google called the issue “a priority” and committed to keep investigating. The acknowledgment was even posted in Catalan — a tacit admission that the affected audience was real and large enough to warrant a direct response.

Google later pushed updates that year that measurably improved Catalan visibility in classical SERPs. But the underlying language-identification layer was never structurally repaired. When a Catalan speaker today watches Google’s AI Overview answer a Catalan-language query in Spanish, it isn’t a new bug. It’s an old bug now sitting underneath a synthesis layer that propagates it.

AI search, when it arrives, inherits the assumption that the language of the query is unreliable in the first place. The retrieval pipeline that flattens Catalan into Spanish today is the same pipeline that will, in modified forms, flatten sub-national jurisdictional context in markets where the surface language never changes.

I have spent the last several months documenting how AI search collapses Hispanic markets — treating 20+ Spanish-speaking countries as a single statistical default. That work is severe in its consequences, but at least the geography is clean: Spain is one country, Mexico is another, the model just fails to tell them apart.

What happens inside Catalonia is more revealing because the geography doesn’t change. Two languages share one territory, and the system produces two parallel realities — when it can identify the languages at all.

Multilingual regions are where the architectural defaults of retrieval become visible, because users in those regions can switch languages and watch the system reassign meaning, authority, and sometimes even the answer’s language.

The same defaults will surface inside markets that look monolingual on the surface, in different forms and with different mitigations. Catalonia is a leading indicator.

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How I tested this

The patterns I’m about to describe are familiar to any practitioner who has worked on Catalan-language SEO over the last decade — my own experience, and the experience of many colleagues working under similar conditions.

Anyone who has tried to do keyword research in Catalan has watched Google Keyword Planner report essentially zero volume for terms catalan-speakers query daily, or return volumes that are clearly mixed with Spanish-language data and impossible to use cleanly.

Anyone who has run multilingual sites has watched their Catalan variants underperform their Spanish ones for reasons standard tooling can’t explain. The small experiment I describe below is one specific, reproducible illustration of this broader, well-known systemic situation — not the foundation of the claim.

The setup was deliberately simple. From a residential IP in the Barcelona metropolitan area, I ran a set of paired queries in Catalan and Spanish across two surfaces:

ChatGPT (logged out, fresh session, no personalization).
Google web search with its AI Overview enabled when the system chose to generate one. (Google doesn’t generate an Overview for every query — itself a signal worth noting.)

Sessions ran in incognito mode. I ran the queries twice, roughly a week apart, to test whether what I was seeing was a stable pattern or a single-session artifact. Both dates are documented. Screenshots are available with location footers visible.

I chose five intent pairs, each designed to test a different layer of the retrieval stack:

A politically loaded factual query about Catalan independence, chosen because it has academic precedent in Walker and Timoneda’s 2025 study (Department of Political Science, Purdue University) of language-conditioned LLM output, published in Cambridge University Press’s Political Science Research and Methods. The replication of their method on a Barcelona IP gives the section editorial cover.
A transactional commercial query about local accountants for freelancers, chosen because it sits squarely inside the everyday SEO economy and is identical in intent across languages.
A cultural-tradition query about Sant Jordi, chosen because it has clear native authority (regional government, municipal authorities), low political temperature, and centuries of documented history independent of any particular brand.
A regulatory query about Catalan rental subsidies, chosen because it requires hyper-local jurisdictional precision and is administered by the Generalitat de Catalunya directly.
A language-identification stress test — a mix of casual and formal Catalan queries — to see whether the surface even recognized the input as Catalan.

The findings below are reproducible existence proofs rather than statistical evidence. These specific failures occur on these specific platforms today — from this specific location — and any practitioner can replicate them in under 15 minutes.

The broader claim — that these patterns generalize — rests on the community evidence the Google Search Liaison acknowledgment implicitly confirmed three years ago, and the lived experience of practitioners working in Catalan and other minority languages over the last decade.

Four patterns emerged. The first three describe retrieval. The fourth describes identification, and it underpins the other three.

Finding 1: Vocabulary and source plurality diverge

I asked both ChatGPT and Google’s AI Overview about the main arguments around Catalan independence.

In Spanish, both surfaces produced a legalistic frame anchored in the 1978 Constitution and the 2017 referendum’s illegality. In Catalan, both surfaces foregrounded dret a decidir (right to decide) and autodeterminació as named conceptual blocks, with historical references to the loss of institutions after the Decrees of Nova Planta.

The Catalan output wasn’t more ideological. It was more complete. It retained anti-independence arguments, including framings absent from the Spanish version.

The divergence sharpens in the citations. The Spanish AI Overview pulled from BBC, Wikipedia (ES), Fundación Espacio Público, and France 24. The Catalan AI Overview added El Punt Avui, VilaWeb, Reddit r/catalunya, and Wikipedia (CA), while still citing BBC and El País.

Same engine, same geography, same question. Two non-overlapping retrieval pools, triggered by the language string. The language isn’t labeling the answer. It’s filtering the corpus.

Finding 2: Commercial retrieval shifts, and the engine doubts the minority language

The transactional pair was simple: Millors gestories per a autònoms a Barcelona / Mejores gestorías para autónomos en Barcelona. Best accountants for freelancers in Barcelona, in two languages, from the same city.

ChatGPT recommended largely the same physical firms in both versions, but the online providers diverged: the Catalan response surfaced Openges and Gestasor; the Spanish response surfaced Gestoría Online and Gestorum. Same intent, same geography, two parallel commercial universes for the digital-first segment.

Google’s organic SERP showed a more pronounced split. The Catalan version elevated locally bilingual sites (Gremicat, Calders Assessors, Gestumm, barcelona.cool). The Spanish version led with aggregators and generalist directories (Legify, Zaask, bcngest).

Two secondary signals matter more than the rankings.

First, Google autocorrected the Catalan query. Above the results, the engine offered: Quizás quisiste decir: Millors gelateries per a autònoms a Barcelona. Did you mean ice cream shops? The system, sitting on a Barcelona IP, declined to believe a commercial query in Catalan was genuine and proposed a homophone-adjacent alternative.

Google SERP showing the autocorrection “Quizás quisiste decir: Millors gelateries per a autònoms a Barcelona”

Second, the Spanish results carried paid ads — Talenom, Declarando, Horus Firm. The Catalan results carried zero. The SEM market treats Catalan as territory without bidders, and the absence of a commercial signal is itself a signal. Models trained on click and engagement data read that absence as evidence that the language isn’t commercially serious and weight retrieval accordingly.

The mechanism teaches itself. Less commercial bidding produces less commercial visibility. Less commercial visibility produces less commercial signal.

The language is steadily deprioritized for transactional intent — even though every user typing in Catalan from Barcelona shares the same geography as a user typing in Spanish. This will become relevant again when we look at language identification itself.

Dig deeper: How AI search defines market relevance beyond hreflang

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Finding 3: Cultural authority gets reassigned

The Sant Jordi pair shows it most clearly, and the specific reassignment changes between sessions in a way that is itself revealing.

In the first session, the Spanish-language AI Overview for Tradiciones de Sant Jordi led with two hotel chains as primary citations — Casa Llimona Hotel Boutique and Sumus Hotels. The Catalan version cited the Ajuntament de Barcelona, the city council that has formally stewarded the tradition for centuries.

In the second session, a week later, the same queries returned a different reassignment. The Spanish version now cited the Ajuntament alongside Spain.info, the state tourism portal aimed at foreign visitors. The Catalan version moved up the institutional hierarchy entirely — its primary citation became the Generalitat de Catalunya, the regional government, with a footer link to the Guia Oficial de la Diada de la Generalitat de Catalunya. The Ajuntament was absent.

What stays stable across both sessions is the structural pattern: the cultural custodian credited by the system changes with the language. Catalan-language queries surface regional and municipal government, the institutions native to the tradition. Spanish-language queries surface state tourism, commercial entities, or municipal government framed as a tourist destination.

ChatGPT reinforces the same pattern in its prose. The Spanish version describes Sant Jordi externally: Día del amor “a la catalana,” oportunidad para conocer el patrimonio cultural catalán. The Catalan version uses native terminology without distance. The same 600-year-old tradition is described as exotic-from-outside in one language and as tradition-from-inside in the other.

The model isn’t lying in either language. It’s producing the most statistically plausible synthesis given its retrieval pool. But the retrieval pool itself is constituted differently by language — and one constitution treats government as the cultural custodian, while the other treats tourism marketing as the cultural custodian.

For brands, this isn’t a translation problem. It’s a question of who the model thinks owns the answer.

Finding 4: Language identification was already broken before LLMs touched it

This is the finding that reframes the rest. The reassignment patterns above all depend on the system correctly identifying the language of the query in the first place. Often, it doesn’t.

The Google Translate finding — Catalan misclassified as Occitan from a Barcelona IP — is one face of it. Another is what happens when you type a query that is unambiguously Catalan into Google Search.

The query receptes de calçots — recipes for calçots, a vegetable that exists only in Catalonia and retains its Catalan name in every other language — produces a banner above the results: Sugerencia: Mostrar resultados en español. También puedes consultar más información sobre cómo filtrar por idioma.

The system suggests that the user filter Catalan results out. No AI Overview is generated for the query. The infrastructure has decided that a recipe search for a Catalan-only vegetable, in Catalan, is more usefully answered in Spanish.

Google Search showing the suggestion “Sugerencia: Mostrar resultados en español” for the query “receptes de calçots”

In Google’s AI Overview, the query Tradicions de Sant Jordi sometimes returns a Spanish-language answer despite being written entirely in Catalan, citing Spain.info. In other sessions, the same query is correctly identified and answered in Catalan.

The behavior is inconsistent across sessions, which is worse than consistently wrong: it is undiagnosable. A site owner can’t fix something that breaks intermittently for reasons the system itself doesn’t surface.

The failure isn’t universal. Queries like festivitats de Catalunya or poetes catalans contemporanis — slightly more formal or erudite phrasings — are correctly identified as Catalan and answered with Catalan-language synthesis, citing regional sources (Pimec, Gencat, El Temps, Lletra UOC).

The system can identify Catalan. It just doesn’t do so reliably for commercial or popular queries, which is where the cost of getting it wrong is highest for site owners.

This is where Findings 2 and 4 close a loop. The same commercial categories that show zero SEM bidding in Catalan are the categories where language identification fails most often. A language with no commercial signal teaches the system that it doesn’t need to be treated as commercially serious — and so, for commercial queries, the system permits itself to identify it less reliably. The two failures reinforce each other.

None of this is new. Google Search Liaison publicly acknowledged the Catalan demotion problem in January 2023 and later that year pushed visible improvements to classical SERPs.

The synthesis layer that now sits on top has not inherited those fixes. AI search is built on these pipelines. It inherits their defaults, their training-data composition, and their decisions about when a language deserves to be treated as the language of the answer.

The slop loop closing on minority languages

A second, slower mechanism makes all of this worse over time, and it is worth flagging because it is starting to be visible elsewhere.

LLMs trained on web-scale corpora are now generating significant quantities of low-quality content in minority languages — both directly (via translation features) and indirectly (via downstream tools that produce SEO content, social posts, and automated articles).

That generated content gets indexed, gets crawled, gets fed back into the next generation of training data. The model that doesn’t understand Catalan well produces the Catalan content that trains the next model.

This isn’t theoretical. A 2024 Princeton study by Brooks, Eggert, and Peskoff found that over 5% of newly created English Wikipedia articles showed signs of being AI-generated, with lower but still measurable rates in German, French, and Italian editions.

By extension — though outside the Princeton team’s measurement scope — minority-language editions with thinner editorial oversight are likely to absorb a greater proportional impact.

The minority-language damage is now well-documented. MIT Technology Review reported in September 2025 on a linguistic “doom loop” in vulnerable-language Wikipedias.

Volunteers working on four African-language editions estimated that between 40% and 60% of their articles were uncorrected machine translations.
The Inuktitut edition contained machine-translated portions in more than two-thirds of substantive pages.
Some Hawaiian-language entries had 35% of their words flagged as incomprehensible by native speakers.
The Greenlandic edition, where virtually no articles had been written by actual speakers, was ultimately recommended for closure in 2025, with the Wikipedia Language Committee citing AI tools that had “frequently produced nonsense that could misrepresent the language.”

Wikipedia was estimated in 2022 to be the sole easily accessible source of online linguistic data for 27 under-resourced languages — meaning these errors don’t stay on Wikipedia. AI systems train on them next.

This is the loop. Bad language identification produces bad retrieval. Bad retrieval surfaces bad content. Bad content gets generated at scale by LLMs that don’t fully understand the language. Bad content gets indexed. The next model trains on it.

The mechanism doesn’t need malice to degrade quality — it needs only volume. And volume in minority languages has never been easier to manufacture.

What Wikipedia decided to do about it

The clearest institutional signal that this problem is real comes from one of the few platforms with both the experience and the incentive to take it seriously.

On March 20, the English Wikipedia community formally voted to prohibit LLM-generated article content across its 7.1 million articles. Editors are still permitted to use LLMs for basic copyediting and for supervised translation of articles from other-language editions, but generating or rewriting article content with LLMs is prohibited outright.

The decision was a response to years of mounting concern: ChatGPT-era articles were appearing with the “as a large language model” prompt left in the text, with entirely nonexistent citations, and with the kind of fluent-but-empty prose that reviewers were spending disproportionate volunteer time cleaning up.

Wikipedia isn’t a typical SEO concern. It’s a curated knowledge platform with strong volunteer governance and explicit neutrality policies. If a platform with that level of structural defense against low-quality content has concluded that AI-generated text damages knowledge integrity, the SEO industry should not assume that retrieval pipelines downstream of Wikipedia will produce better answers than Wikipedia itself was willing to publish.

The institutions building defenses against AI-generated content in minority languages — Wikipedia, the Aina Project in Catalonia, the Latxa models in the Basque Country — aren’t being defensive for ideological reasons. They are responding to a measured degradation in quality. That degradation is now part of the training data of the next generation of AI search.

Dig deeper: How to use Google and LLM insights to improve international SEO

Why this happens, mechanically

Motoko Hunt has documented how AI systems collapse geographic boundaries by treating language as a proxy for markets, a phenomenon she calls geo-identification drift. The mechanism is the same here, with one extra constraint that exposes it more clearly.

When two languages share one geography, the system can’t quietly default to “the country the language belongs to.” It’s forced to choose something else. The choice usually goes to whichever corpus is larger, more recent, or more commercially tagged.

The Walker and Timoneda study above grounded this empirically. Their finding — that anti-independence framings appeared roughly twice as often in Spanish output as in Catalan — wasn’t a finding about politics. It was a finding about how training-data composition determines output. Catalan-language texts in the training corpus carry one distribution of perspectives; Spanish-language texts carry another. The model inherits both and surfaces whichever it is currently reaching for.

This compounds with what researchers call semantic collapse: when retrieval embeddings can’t reliably separate sub-national signals, the system flattens them into the dominant variant. In monolingual countries, the dominant variant is the country itself. In a region like Catalonia, the dominant variant is the larger linguistic neighbor — Spain — pulling Catalan-specific meaning toward a generic Spanish default unless something explicit pulls back.

Sub-national governments have noticed. The Aina Project and the Latxa models aren’t isolated efforts: they are direct attempts to build language-resource sovereignty because standard global LLMs perform measurably worse on Catalan and Basque than on Spanish. When governments start training their own LLMs, the SEO industry should treat that as evidence the underlying mechanism is real and structural.

The pattern isn’t unique to Catalonia.

Quebec users querying in French routinely receive Parisian-French defaults and answers anchored in French regulatory frameworks rather than Quebec’s distinct civil law and provincial tax regime.
Belgian users get conflated French and Dutch jurisdictional defaults inside a country whose three regions operate under genuinely different legal and linguistic rules.
Swiss users see retrieval flattened toward German or French national defaults rather than Switzerland’s own conventions.

The Catalan case is the easiest to test from a single IP in a single session, but the structural finding generalizes to every region where two or more languages share one geography.

The leading-indicator argument

The interesting question isn’t what this means for Catalonia. It’s what Catalonia means for everyone else.

Multilingual regions are the canary. The architectural flaw exposed when two languages share one geography — a vector space that can’t reliably separate jurisdiction from meaning, sitting on top of a language-identification layer that already gets things wrong — will show up in other forms as AI search matures and attempts genuinely sub-national answers.

This is where I want to be careful with the parallel. In monolingual markets, AI search does have access to localization signals that the Catalan case partly removes: IP geolocation, GPS context, browser locale, and structured local pack data.

A query from Austin about contractor licensing isn’t as ambiguous to the system as a query in Catalan from Barcelona, because the system has more non-linguistic context to lean on. The Catalonia–Texas parallel isn’t a direct equivalence.

It’s a hypothesis worth testing, though. The same mechanisms that flatten Catalan into Spanish — corpus-weight defaults, semantic collapse, training-data composition — are present in synthesis pipelines regardless of the language pair.

As AI Overviews and chat-style search increasingly answer queries by synthesis rather than by surfacing localized links, the protective effect of IP-based localization weakens. The system has to make a decision about which corpus represents “the answer,” and the corpus weight tends to win.

The places this is most likely to surface inside monolingual English markets: State-level regulation with significant corpus asymmetry. California’s CCPA and Texas’s data privacy regime are written in the same language but represent different jurisdictional realities.

The privacy literature is heavily California-weighted. When an AI Overview synthesizes a generic “what privacy rights do I have” answer, the defaults tilt toward whichever jurisdiction has more authority signals. Localization helps, but only when the query itself is jurisdictionally explicit.

Sub-national regulatory granularity in any large country. Liquor licensing, contractor licensing, real estate disclosure rules, alimony calculations, school district policies, zoning codes — jurisdiction-specific, all in English, with wildly different corpus weights between jurisdictions. As more queries are answered by synthesis rather than links, jurisdictional defaults become consequential in ways traditional SEO never had to worry about.

I don’t want to oversell this. The clean Catalan demonstration isn’t directly replicable in Texas. What is replicable is the underlying observation: when the retrieval system collapses signals, it collapses them in favor of the larger, better-represented corpus. That is true whether the signals being collapsed are linguistic, jurisdictional, or both.

The brands that figured out how to operate across Spain and Mexico have already learned a version of this lesson. The brands operating across Texas and California will likely learn a related one, in a form that will not look identical and will require its own diagnostics.

What to do about it

The principles that work for multilingual fragmentation transfer, with adaptation, to multi-jurisdictional fragmentation. Same family of medicines, different patient.

Treat sub-national jurisdictions as distinct entities. If your business operates in regulated verticals across multiple U.S. states, those state versions need their own authority signals — not just a folder structure. Each variant should canonicalize to itself, not to a national parent page that would invite collapse.

Encode jurisdiction explicitly in structured data and copy. Schema.org’s areaServed operates at any geographic granularity; use it down to the state, county, or municipality where it matters. Pair it with explicit copy markers: regulator names, state-specific identifiers, region-specific currencies or formats. The model needs deterministic hooks. Without them, it improvises.

Reinforce sub-national grounding through Wikidata. Most SEO programs stop at on-site schema, but knowledge graphs are reading what other graphs say about you. Wikidata’s jurisdiction property (P1001) and explicit language properties let you encode jurisdictional and linguistic boundaries at the knowledge-graph level — exactly the layer where AI systems pull entity context. If you operate in a sub-national jurisdiction that matters commercially, your entity should be modeled there with the granularity that matters.

Audit for sub-national authority gaps the same way you’d audit for international ones. Run the diagnostic prompts you would run for Spain versus Mexico, but for Texas versus California, or Ontario versus Quebec inside Canada, or any pair of jurisdictions where your business operates. If the model conflates them, your content has a fragmentation problem inside what looked like a single market.

Watch the secondary signals. In Catalan, the absence of SEM bids was a signal, and the system learned from it. The same applies to underserved monolingual jurisdictions: if no one bids on Texas-specific terminology, Texas-specific content gets deprioritized in synthesis. If your knowledge-graph presence, local citations, and authority signals all point to the dominant jurisdiction, the model has no reason to surface the underrepresented one.

This isn’t a new playbook. It’s the cultural SEO framework applied below the country line: market segmentation, transcreation, retrieval constraints, and entity reinforcement, but at sub-national granularity.

Track, optimize, and win in Google and AI search from one platform.

What this means for your AI search strategy

The Sant Jordi answer didn’t fail because of bad translation. It failed because the language-identification layer beneath the translation has never consistently distinguished Catalan from Occitan, Catalan from Spanish, or Catalan-the-language-of-the-query from Catalan-as-irrelevant-noise.

Google said so itself, in Catalan, three years ago. The retrieval pipeline built on top of that layer inherits every one of those decisions, and now produces synthesized answers that quietly propagate them.

Wikipedia, looking at the same generative-AI ecosystem from a different angle, decided in March 2026 that the risk of degradation was severe enough to prohibit LLM-generated content outright. The Aina Project and the Latxa team reached the same conclusion in advance by funding their own foundation models. The institutions closest to multilingual knowledge integrity are pulling away from generic AI. The SEO industry should at least notice the pattern.

Multilingual regions reveal a structural assumption baked into AI search: that language and market are the same thing, and that language is reliably knowable from a query string. Neither is true. Hreflang made the geographic distinction operational for traditional search. Nothing has yet made it operational for generative retrieval.

The brands that operate well across Spain and Mexico already know how to fix this for languages. The same techniques — explicit jurisdiction signals, market-specific authority, retrieval constraints, transcreation rather than translation, entity grounding in knowledge graphs — are now table stakes for operating well across any pair of jurisdictions, in any language combination.

If you operate across multiple jurisdictions, the question to ask isn’t whether your content is localized. It’s whether the model can tell.

Your customers search everywhere. Make sure your brand shows up.

Reddit’s AI search influence goes beyond training data

Thu, 21 May 2026 13:00:00 +0000

As the race to optimize content for AI consumption and citation continues, clients keep reaching out, confused about the web’s favorite genderless alien doodle, Reddit, and what it means for their near-term SEO and AI Overview strategy.

Questions usually sound something like this:

Should I be actively responding or posting about my brand on Reddit?
If AI is trained on Reddit, should we be running paid ads on Reddit?
Our CEO wants us to create a subreddit for each of our product lines. What do we do?
Why is Google’s AI Overview citing a Reddit thread that calls my product slow and difficult?

The problem is that people often lump together three distinct concepts:

Training data.
Licensed or real-time access.
Citation and retrieval systems.

They’re all related, but they aren’t interchangeable. And if you care about SEO, AI citations, or why Reddit is suddenly appearing in AI Overviews about your brand, understanding the difference between the three matters.

AI training vs. AI access vs. AI citation

Let’s differentiate between three concepts that are often lumped together. People read sentences like:

“ChatGPT was trained on Reddit.”

…and imagine that means every Reddit post gets fed directly into ChatGPT’s memory, waiting to be repeated later in response to a relevant query. That’s not really how training works.

Training

Training an AI is a lot more like going to school than memorizing an encyclopedia. After years of education, kids learn patterns, relationships, and use cases. They don’t remember the answer to question 8b on a seventh-grade math test, but they do understand:

“When I know two sides of a right triangle, I use the Pythagorean theorem to calculate the third.”

They learned the concept, not every example.

Similarly, AI models do not simply memorize all Reddit posts. They absorb patterns across millions of conversations. The model doesn’t necessarily “remember” a specific thread debating the best rock tumbler, but it can learn from scanning r/RockTumbling that buyers consistently care about things like:

Noise level.
Ease of cleaning.
Availability of replacement parts.
Drum size.
Long-term durability.

In other words, AI models trained on Reddit aren’t necessarily learning facts from Reddit so much as they’re learning how humans compare products, weigh tradeoffs, complain, recommend, and share lived experiences.

Licensed access

Now we get to the part that changed more recently.

In 2024, Reddit signed major partnership agreements with both Google and OpenAI, giving them licensed access to Reddit content. Since then, those relationships have evolved beyond static training datasets toward ongoing API access, meaning continued access to new Reddit posts and comments.

Or phrased differently: an avenue for AI systems to keep up with human conversations in near real time.

If training an AI model is like sending someone to school, then licensed access is like giving that graduate a newspaper subscription after they finish school.

Imagine two adults:

Adult A	Adult B
Graduated from high school 10 years ago	Graduated high school 10 years ago
Never reads the news	Checks the news every morning

Both received the same formal education. Both understand the Pythagorean theorem. But only one knows what happened this week.

That’s the difference between training and access. Training shapes broad understanding, while access helps keep information current.

Citations

AI citing a Reddit thread doesn’t automatically prove the model prioritizes Reddit over the rest of the web. It also doesn’t prove Reddit was part of the original training data.

Often, it simply means the system judged that specific source useful for answering the question.

Continuing our school analogy, an AI citing Reddit is less like a graduate reciting something they learned years ago in class and more like someone pulling out their phone during a conversation and saying:

“Hang on, I saw a discussion about this yesterday.”

The citation reflects what the system found helpful at the moment, not necessarily what it learned during training. That difference may be one of the most important things you need to understand when people say, “AI is trained on Reddit.”

Dig deeper: How to build an organic Reddit strategy that drives SEO impact

The SEO toolkit you know, plus the AI visibility data you need.

Why Reddit performs so well in AI outputs

So why does Reddit show up in Google’s AI Overviews when you search for your brand?

I’ve seen plenty of fantastical conspiracy theories tied to misunderstandings about Reddit’s partnership deals with Google and OpenAI. But those deals alone don’t explain Reddit’s visibility. The more useful question is why multiple AI systems repeatedly surface on Reddit at all.

I’d argue that Reddit is one of the largest sources of content relevant to the kinds of conversations people want to have with AI systems.

Here’s what Reddit has that your website probably doesn’t.

Context and lived experience

Reddit users rarely stop at facts. Your website says, “Battery for this fitness tracker lasts 30 hours.”

But a Reddit user says: “Mine lasted all day unless I tracked workouts. Then I had to charge it every day, and it drove me nuts because I was so used to a competitor’s longer battery life.”

Those two statements contain similar information. But the second, though anecdotal, adds context and real-world usage — the kinds of details people actually use to make decisions and the kinds brands rarely include in official copy.

Disagreement

For the past decade, you’ve been taught to create polished content: concise, authoritative, no nuance, no chance for misinterpretation. We publish Ultimate Guides and Top 10 Benefits of X.

Reddit’s user-generated content does almost the exact opposite.

Reddit threads can contain:

Conflicting opinions.
Caveats.
Unexpected use cases.
Frustration.
Humor.
Devil’s advocates.
Users changing their minds halfway through a discussion.

In other words, all the messy, unpolished parts of having a human brain.

For better or worse, disagreement makes information more useful, and that’s nothing new. It’s been around since Ancient Greece. A polished product page is great, but it won’t help AI systems answer subjective questions.

Authenticity (or at least the appearance of it)

The beauty of Reddit is that its comments are usually written by people who aren’t being paid to persuade you. And as the biggest content creators become increasingly monetized and sponsored, that counts for a lot more than it did even five years ago.

Being unsponsored doesn’t automatically make these users correct, unbiased, or trustworthy. But users often perceive firsthand experience as more credible than polished marketing copy or sponsored influencer posts, and perception matters a lot.

Especially when AI systems are essentially trying to combine unlimited viewpoints into a single answer.

A note about other platforms

It’s worth mentioning that Reddit isn’t the only source of human authenticity and disagreement on the web. It simply happens to be one of the largest examples, and the one I most often see cited and misunderstood when it comes to optimizing for AI.

Human context exists across forums like Stack Exchange, review platforms like Yelp, professional groups, and social networks like Facebook.

Dig deeper: A smarter Reddit strategy for organic and AI search visibility

Get the newsletter search marketers rely on.

See the complete picture of your search visibility.

How to make content more useful in AI search

If we go back to the beginning, where we discussed the differences between training, licensed access, and retrieval, we reviewed the idea that AI systems appear to learn from broad patterns, benefit from fresh information, and retrieve sources they judge useful in context.

Whether that context comes from Reddit, forums, reviews, or professional communities is far less important than the fact that it exists at all. The takeaway here isn’t that everyone needs a Reddit strategy.

The more useful question is: Where do people in my industry naturally discuss frustrations, disagreements, and lived experiences?

For many businesses, that answer is Reddit. But for others, it may be forums, professional communities, Facebook groups, Discord servers, product reviews, or places you rarely spend time. Once you understand where human context lives, you can prioritize your platform optimizations in a way that makes sense.

After you’ve identified those spaces, here are a few things worth borrowing.

1. Capture lived experience and make it visible

Reddit performs well in AI outputs partly because it contains what polished brand content often lacks: context after the purchase, implementation details, decision-making processes, and even buyers’ remorse.

We can’t — and shouldn’t — manufacture our own “authentic” discussion threads. But we do have access to our customers, and user data remains a massively underutilized source of information.

So instead of relying solely on internal expertise and picture-perfect case studies, pull more real perspectives into your content:

Customer interviews.
Reviews and support tickets.
Sales objections.
Community discussions.

If AI systems are trying to retrieve contextual information, part of our job is to make that context easier to find.

2. Stop trying to sound authoritative and start trying to be useful

If Reddit threads contain:

Uncertainty.
Disagreement.
Limitations.
Frustration.
Caveats.

Your content can contain more of that, too.

Acknowledging who your product or service isn’t for, or where it falls short, can help you create content that feels more credible to both humans and AI systems synthesizing perspectives.

3. Show your work

To quote my sixth-grade math teacher: show your work.

AI summaries are often adequate at distilling sources into conclusions, but humans are still much better at explaining reasoning.

Instead of your content only presenting, “This is the best option, check out all these great features,” try explaining:

Why customers chose you.
What alternatives they considered and why.
Tradeoffs or ituations where your product or service fails.

Reasoning provides context, and context increasingly appears to be one of the web’s most valuable commodities.

4. Optimize for decisions

Traditional SEO often focused on answering factual questions with objective answers.

Increasingly, users ask AI systems nuanced questions with subjective answers that change depending on which AI they ask.

They ask:

Is it worth it?
Which option is better?
What do people regret?
What happens after six months?

Those are decision-making questions.

Decision-making requires experience. Experience creates context, and context is turning out to be the connective tissue between what AI learns, what it accesses, and what it ultimately retrieves.

Dig deeper: Stop chasing Reddit and Wikipedia: What actually drives AI recommendations

Track, optimize, and win in Google and AI search from one platform.

Context is becoming the differentiator

We started with what makes AI training, licensing, and citations different, but we ended with what seems to connect all three — and what polished “optimized” content is usually missing: context.

It’s the difference between:

“This rock tumbler has a 3-pound drum capacity and operates at 75 decibels.”

And:

“This was too loud to have in my basement as I planned, so I had to move it to the garage. The replacement belts were easier to find than I expected, but by the third batch, I was really wishing I’d spent more upfront on a larger drum.”

One is the kind of fact you might find on a company website. The other is an experience that feels genuine.

Outcomes matter more than features is nothing new. AI may be forcing a similar realization: Being accurate, comprehensive, or keyword-optimized won’t be enough anymore.

More and more, the content that gets ahead is the content that helps people make decisions by adding context, tradeoffs, and lived experience around the facts.

Your customers search everywhere. Make sure your brand shows up.

The search everywhere optimization pyramid: How to build visibility before search

Thu, 21 May 2026 12:00:00 +0000

The customer journey used to start on the SERP. But that’s no longer the case. By the time a buyer types a search query into Google, they usually already have a few potential brands in mind. They’ve:

Seen the same product recommended across multiple Instagram Reels over the course of days or weeks.
Read a Reddit thread where five strangers agreed the same tool was the best option.
Watched peers recommend a specific service inside a Facebook group.

Google has become the confirmation step, not the starting point. Nobody searches with a blank mind. Buyers arrive focused on confirming assumptions and gathering more specific information, not browsing for options.

The question that matters is whether your brand made it onto that mental shortlist before the search happened. In most categories, getting on the shortlist means being visible on the platforms where buyers compare options.

Where is the shortlist actually built?

Peer-driven decisions happen across a handful of environments specific to each industry. For example:

In Facebook groups where peers recommend the same three brands again and again.
On Reddit threads where the same product keeps surfacing as the community pick.
On Instagram Reels and YouTube videos where independent creators and paid influencers endorse the same brand and model, and algorithms keep showing users the same product repeatedly.
On LinkedIn posts where an expert the buyer already follows names a tool or brand.
On podcasts where a trusted host endorses a specific person, brand, or product.
In AI answers that keep naming the same brands for similar questions.

By the time one of these interactions triggers a Google search, the scope is usually narrow, often limited to “brand X review,” “brand X vs. brand Y,” or a direct navigational query.

Ranking for the head term usually doesn’t decide the buyer. Being mentioned in those off-SERP conversations does.

Reddit is booming right now. That won’t always be the case. Platforms rise and fade in cycles, and visibility on any single platform is temporary by nature.

What doesn’t change is the underlying behavior: People ask peers before they ask search engines. The takeaway isn’t to chase whichever platform is hot this quarter. It’s to be part of the conversation wherever your category comes up.

Dig deeper: Why your brand isn’t making the AI recommendation set

The SEO toolkit you know, plus the AI visibility data you need.

The two objectives of search everywhere optimization (SEvO)

Every campaign in this space has two objectives:

Direct visibility: Show up where the shortlist is being built while buyers are narrowing down their options. This is the more obvious objective, and the easier one to measure through signals like direct search traffic and increases in specific branded queries.
Engine comprehension: Every time your brand appears next to a relevant problem, audience, or solution, you increase the likelihood of being recommended later by AI systems. This work is difficult to measure in the moment and usually only becomes visible in hindsight.

It might remind you of a famous quote from Steve Jobs:

“You can’t connect the dots looking forward; you can only connect them looking backward. So you have to trust that the dots will somehow connect in your future.”

You can’t see the system working while it’s being built. Only after enough signals accumulate does your brand start appearing in AI responses and in the conversations shaping buyers’ shortlists.

Where the shortlist lives today: SERP evidence

Pull any buyer query in your niche and count how many Page 1 results come from Reddit, Quora, YouTube, LinkedIn, Instagram, Medium, Substack, or industry-specific publications. The mix has already shifted.

Here are five recently verified examples pulled from live SERPs in Ahrefs.

SaaS and CRM

Query: “best CRM for small business” (U.S.)

YouTube at Positions 1 and 8.
Reddit at Positions 2 and 6.
Quora at Position 6.

Before buyers reach a traditional listicle, they’ve already watched a YouTube review and read multiple Reddit threads.

Consumer fitness

Query: “best home gym equipment” (U.S.)

Multiple Reddit threads on Page 1.
YouTube at Position 7.

Community recommendations inside home gym subreddits are shaping the consideration set.

Ecommerce platforms

Query: “Shopify vs. WooCommerce” (U.S.)

YouTube at Positions 1 and 4.
Reddit at Position 2, plus another result at Position 8.

The comparison decision is often shaped through video content and Reddit discussions before any vendor page gets a click.

Consumer electronics

Query: “best noise-canceling headphones” (U.S.)

YouTube at Positions 1 and 6.
Instagram at Position 1.
Reddit at Positions 3 and 5.
Facebook at Position 6.

Five of the top six results are social or user-generated. Brands that only show up on their own websites are missing the broader conversation.

Running and apparel

Query: “best running shoes” (U.S.)

YouTube at Positions 1 and 7.
Reddit at Positions 4 and 6, with multiple threads.
Quora at Position 6.

Even in highly commercial categories, community and video content dominate a large share of page one.

If your strategy ends at “rank on Google,” you’re optimizing for the last slide of a deck the buyer already watched.

In many categories, the SERP now acts more as a confirmation layer than a discovery layer.

Dig deeper: How to build a context-first AI search optimization strategy

The search everywhere optimization pyramid

How can you make sure your brand gets discovered?

To organize this work without overwhelming your team, I developed a framework called the Search Everywhere Optimization Pyramid.

From the bottom up, each layer supports the one above it. Skip a layer, and the structure above it becomes harder to sustain.

Layer 1: Audience platform research (APR)

This is the foundation. Before you touch a single platform, you map where your ICP researches, compares options, and makes decisions. The output is a prioritized list of platforms, along with specific sub-communities and engagement strategies.

This isn’t a generic “be on social” plan.

Problem: Most teams skip APR and default to whichever platform is trending internally. We’ve seen B2B consultants trying to build visibility on Pinterest instead of LinkedIn, and DTC brands focusing on LinkedIn even though their audiences were clearly on TikTok and YouTube.

Solution: Conduct one deep research pass per ideal customer profile (ICP), documenting the exact subreddits, LinkedIn creators, niche Slack communities, YouTube channels, and publications your buyers actually consume. Without this, every decision above this layer becomes a guess.

Audience research tools like SparkToro can help narrow your focus and identify the right platforms more quickly.

Practical steps: In SparkToro, enter a description of your ICP. The output is a ranked list of platforms by audience concentration, topics, social profiles they follow, and more, all exportable.

The deliverable from an APR sprint is a one-page brief per ICP that includes:

The Top 3 platforms,
The Top 5 sub-communities, such as subreddit names, LinkedIn hashtags, and Facebook group names, and
The exact phrases buyers use to describe their problems.

Layer 2: Alert systems and making them usable with AI

Once you know where your audience is, you need to know when they’re talking about topics relevant to your business. That means setting up alerts whenever someone mentions a competitor, asks a relevant question, or surfaces a problem your product solves.

Google Alerts exists. It underdelivers. Three tools worth considering are:

Semrush Brand Monitoring.
AlertMouse.
Firehose.

But picking the right tool is only half the problem. Volume is the other half.

Alerts can become messy very quickly. While you want broad coverage across conversations, it’s equally important to add enough exclusions so decision-making conversations don’t get buried in noise.

Fifty notifications a day quickly becomes unmanageable, and unmanageable systems get ignored.

The solution: Layer AI on top of your alert stream to filter and prioritize what actually deserves your attention. Two criteria matter most:

Recency: Someone is asking the question right now, which means you can join the conversation while attention is still high.
Ranking strength: The thread is already ranking for keywords relevant to your business, which means your response can live on a page that keeps surfacing over time.

Quick win: Beyond creating a smart alert setup, run each day’s alerts through an AI prompt that scores them against these two criteria and ranks the top three to five opportunities. Respond only to those.

Too often, teams set up 50 alerts and abandon the system within a month because it becomes impossible to manage. Prioritization is what makes the system sustainable.

The following prompt can serve as a starting point for your own alert workflow. You can expand it using APIs from Semrush, Ahrefs, DataForSEO, or SE Ranking to factor in ranking data more easily:

"You are helping a B2B SaaS company prioritize daily monitoring alerts. Their ideal customer is a founder or operations lead at a 10-50 person company, evaluating tools like CRMs, project management software, or productivity platforms. The company wants to show up in conversations where that buyer is actively comparing options or asking for recommendations. Here are today's alerts: [paste list]. 

Score each one from 1 to 10 on two criteria: Recency (is this thread active in the last 24 hours?) and Ranking potential (does this thread appear to rank or have the structure of a ranking thread - high engagement, authoritative domain, keyword in title?). 

Return only the top 3 to 5 alerts, ranked from highest to lowest priority. 

For each one, provide:
 - The alert title or link
 - Recency score (1-10) with one sentence of reasoning
 - Ranking potential score (1-10) with one sentence of reasoning
 - Combined priority score (average of the two)
 - A one-line suggested angle for how to respond (useful answer, not a pitch) 

Ignore alerts that are news articles, press releases, or brand mentions with no question or conversation attached."

Swap the ICP definition in the example prompt for your own brand’s ICP

When it comes to the alerts that actually matter, show up with a useful answer, not a pitch. The goal is to become a recognized voice in the spaces where your buyers already spend time.

Eventually, your audience may start mentioning your brand before you even receive the alert and join the conversation yourself.

Dig deeper: Social and UGC: The trust engines powering search everywhere

Get the newsletter search marketers rely on.

See the complete picture of your search visibility.

Layer 3: Industry publications

You can’t build third-party credibility by publishing only on your own blog. A byline in a publication your ICP already reads carries more weight. When someone searches your name after seeing you in a Reddit thread or LinkedIn comment, seeing a trusted publication in the results changes the dynamic entirely.

There’s a second reason industry publications should come before frequent publishing on your own platforms: distribution.

Many sites don’t have a strong distribution network in place. At a time when new articles can struggle to drive clicks or visibility despite solid SEO, publishing often makes more sense on platforms that already have distribution figured out. Here are a few practical tips:

Pitch angles that spark interest: Lead with data or a contrarian position, not a broad topic. “Why Reddit is outranking your blog for your own keywords” gets attention. “I’d like to write about SEO trends” does not.
Start with accessible publications: Focus first on publications with contributor portals or clear guest article guidelines. Cold-pitching editors at top-tier outlets without an existing relationship usually has a low success rate. Build momentum with mid-tier publications first.
Volume benchmark: Two to four bylines in relevant publications are often enough to change the dynamic when someone Googles your name. You don’t need 20.
Prioritize relevance over scale: One strong placement on a site your ICP already reads beats 10 placements on general marketing blogs they’ve never heard of.

Layer 4: Distribution

This is the most underestimated layer, and usually the reason great content dies quietly.

Producing content is the straightforward part. Getting the right people to see it at the right time is where most teams quietly fail.

Before you scale content production or invest heavily in studies, surveys, or large-scale experiments, build the necessary distribution infrastructure.

Distribution infrastructure means:

An email list you own.
A LinkedIn audience you’ve built.
3-5 partners or collaborators who will share your content when it publishes.
A repurposing system that automatically turns one article into 5-7 social posts.

You should also consider amplifying key pieces of content through paid ads to reach a wider audience.

A good test before publishing anything new is to ask yourself: “Where will this be seen by 500 people in the first 48 hours?”

If you can’t answer that, go back to the drawing board. Your distribution layer isn’t ready.

Every content piece you publish afterward becomes more effective simply by moving through a distribution framework you’ve already built.

Layer 5: Your own publications

After audience platform research, smart alert systems, third-party credibility, and strategic distribution, it’s finally time for your own content.

Most SEO teams treat their blog as layer one. The pyramid places it at layer five for a reason.

Content in 2026 needs to be highly relevant to your brand’s core business or topic, and it needs to add value that isn’t already widely available elsewhere on the web.

If you can create that kind of content and leverage your existing distribution channels and third-party placements, it will reach your target audience through multiple paths.

Dig deeper: Why social search visibility is the next evolution of discoverability

The day-to-day execution model

Here’s what this looks like in practice without overloading your team.

Phase 1: APR sprint

Conduct one deep research pass per ICP. Document platforms, sub-communities, and the exact language buyers use when describing problems.

Phase 2: Alert setup and AI prioritization

Configure 20 to 50 alerts across your ICPs’ primary challenges and conversation topics. Add enough exclusions so you don’t have to sift through excessive noise, and use an AI-based filter to help identify which conversations deserve attention.

Assign a daily 10-20 minute engagement block to maintain consistency. Track brand mention volume as your baseline metric.

Phase 3: Industry publications and distribution

Pitch topics to the publications your ICP already reads. In parallel, or shortly afterward, build your own distribution layer so every piece of content has a promotion plan before it publishes.

Phase 4: Owned content at scale

At this point, your LinkedIn posts, Reddit contributions, and blog articles all sit on top of a system designed to amplify them.

Important: This isn’t a “replace SEO” program. Technical SEO, keyword targeting, internal linking, Core Web Vitals, and all the fundamentals still matter. Search everywhere optimization sits atop traditional semantic SEO.

How do you measure something that happens before the click?

You can’t measure pre-click influence with perfect precision, but you can track the signals that suggest buyers already know your brand before they search.

Brand mention volume, measured against a baseline from your alert tool over a 90-day period.
Branded search growth in Google Search Console, which is often the clearest downstream signal that pre-click visibility is working.
Assisted conversion path length and entry sources in Google Analytics 4, specifically non-Google touchpoints that precede branded Google sessions.
Direct traffic, where users type your domain directly into the browser.
Self-attribution through lead form questions like “How did you find out about us?”

Attribution before the click is inherently fuzzy. You usually only see the full picture in hindsight once enough signals connect over time. What you’re building is compounding evidence, not a single-touch conversion path.

The real question is whether buyers are already arriving at your site familiar with your brand, and whether AI systems and other users across the web consistently mention you for relevant queries.

When those things happen, CTR on branded queries rises, sales cycles shorten, and paid CPCs on branded terms often decline.

Track, optimize, and win in Google and AI search from one platform.

The shortlist is built before the click

Buyers arrive at Google with a shortlist. Your job is to make sure your brand is on it and to give search engines and AI systems enough evidence across the open web to understand who you are and who you serve.

The search everywhere optimization pyramid organizes the work in order of leverage:

APR first.
Alerts second with AI prioritization.
Industry publications third.
Distribution fourth.
Your own content last.

Each layer supports the one above it. Platforms will rise and fade. The conversation itself is what you’re investing in.

Your campaigns span 12 channels. Why does it feel like 12 jobs? by AdPlus

Thu, 21 May 2026 11:00:00 +0000

Ask any paid media manager how their Monday morning starts, and you’ll hear some version of the same story.

Google Ads. Meta. LinkedIn. TikTok. Reddit. Pull the numbers, drop them into a spreadsheet, make them tell a coherent story, and send the report to your client or boss by 10 a.m. Somewhere in there, figure out what worked last week and why.

It’s a terrible use of a Monday morning.

I’ve been in performance marketing long enough to remember when “multi-channel” meant running Google Ads and maybe a Facebook campaign on the side. That was already hard enough to reconcile. Now you’re dealing with 10 or 11 networks, each with its own attribution logic, campaign structure, and definition of a conversion.

The data doesn’t just live in different places. It doesn’t even speak the same language.

And yet most teams still manage everything the same way they did five years ago: too many tabs, spreadsheets, and Monday mornings.

The Monday morning problem nobody talks about

What doesn’t get discussed enough is that most of the time paid media teams spend on “campaign management” isn’t actually campaign management. It’s

Data entry.
Reformatting.
Logging in and out of platforms.
Rebuilding the same campaign brief five different times because Google’s campaign structure doesn’t map to Meta’s, and neither of them map to LinkedIn’s.

Industry data puts the average paid media manager at 5 to 9 hours a week on administrative work alone. My sense from talking to practitioners — and from doing the job myself — is that’s probably conservative for anyone managing more than three or four active networks. Agencies handling multiple clients across multiple platforms can easily spend twice that.

Think about what 10 hours a week actually means. That’s 40 hours a month — five full working days.

If you’re billing that time to clients, a meaningful part of the retainer isn’t going toward the work they actually hired you to do. If you’re absorbing it internally, it’s a hidden cost that never shows up in your ROAS calculations but absolutely shows up in your margins.

Every week.

And that’s before you get to the errors. Manual data transfer is really just manual error introduction — there’s no way around it.

Budget caps get mistyped.
Negative keyword lists don’t get updated across platforms.
A campaign gets paused in Google while it keeps running in Meta because nobody caught it.

Small things, but small things compound.

What you’re actually losing (it’s not just time)

The time cost is real, but it’s not even the biggest problem. The bigger issue is the lag.

When your performance data lives in 12 different places and only gets consolidated once a week, you miss a meaningful optimization window between Monday and Friday.

The insight that LinkedIn is overspending while Google is underspending doesn’t surface until the budget’s already gone. The creative that stopped working on Wednesday doesn’t get flagged until Monday.

Another week of wasted spend.

There’s also a consistency problem that’s harder to see but just as expensive. When campaigns are built natively inside each platform — one brief rebuilt five times across five different UIs — the strategy starts to drift.

Audience definitions stop matching exactly.
Budget allocation logic becomes inconsistent.
Creative strategy changes not because you made a deliberate decision, but because you were tired on Thursday afternoon by the time you got to the LinkedIn build.

For agencies, there’s another layer. You’re not just managing drift across networks, you’re managing it across clients. Thirty native dashboards. Thirty credential sets. Thirty reporting exports to manually combine every week.

I’ve been that person. It doesn’t get easier.

It’s a lot. And if we’re being honest, most teams have just accepted it as part of the job.

Why native dashboards will never fix this

I want to be direct about something: Google, Meta, LinkedIn, and every other ad network aren’t going to solve the cross-network management problem. Not because they can’t, but because they won’t.

Every platform is incentivized to maximize your time inside its interface. Time spent in Google Ads is time you’re not questioning whether Google deserves that budget. Same with Meta. Same with LinkedIn.

The fragmentation isn’t an accident. It’s the product.

Yes, they’ve all built APIs. Yes, there are integration ecosystems. But use any of them and tell me this feels solved. Managing a multi-network buy in 2026 still means logging into 10 different tools. The gap hasn’t closed — it’s just been covered with more software.

Anyway.

The solution has to start from the opposite direction: not “how do we stitch together the outputs of 10 platforms,” but “what if you never had to build inside those platforms in the first place?”

What AI-native management actually changes

The tooling shift happening in performance marketing right now isn’t really about dashboards. Dashboards are the symptom fix. The real shift is about who — or what — is doing the operational work.

AI-native ad management platforms handle the upstream work that lives in your team’s heads and your team’s time.

Campaign planning from a plain-English brief instead of rebuilding logic for every platform.
Creative automatically sized to each network’s specs instead of manually reformatted.
Two-way sync on live campaigns so editing a headline in one place pushes the update across all 10 channels at once — no native dashboards required.

That last point matters because it changes the workflow itself. The old process for updating a live creative is: log into Google, pause the ad, upload the new version, publish. Then repeat the same process in Meta, LinkedIn, and TikTok. With two-way sync, you make one edit and the update propagates everywhere. The platform archives the old version and handles deployment.

That’s not a marginal improvement. That’s a different category of tool.

For agencies, the reporting side is probably the most immediately valuable. AI-generated client reports — normalized data, performance narrative, budget pacing — delivered in a branded format that’s ready to send. No more Sunday-night Excel ritual.

None of this is speculative. These platforms already exist, built specifically for teams that have been absorbing this operational overhead for years without a real alternative.

3 things worth doing this week

I’ll keep this practical:

1. Track where your hours actually go for one week.

Not roughly — actually track them. Before you evaluate a new tool or process, you need a real baseline.

Most teams I talk to underestimate their admin time by about 40%. Seeing the real number tends to motivate change faster than another article about it ever will.

2. Standardize naming conventions across every active account

Seriously. It’s unglamorous work, but the payoff is immediate. Inconsistent campaign names, ad set labels, and conversion event naming create a disproportionate amount of reconciliation pain in multi-network reporting.

Two hours of cleanup now can save hours every week going forward, no new tools required.

3. Evaluate what’s available now

This is the step most teams skip. The AI-native ad management space has moved quickly over the last 18 months.

If your mental model of “cross-channel management tools” is based on something you evaluated two or three years ago, it’s probably outdated. The gap between what the best tools can do today and what most teams are actually using is significant — and getting wider.

The operational edge is the performance edge

The teams winning in paid media right now aren’t necessarily the ones with the biggest budgets. They’re the ones that have compressed the cycle between data and action — teams that can see cross-network performance in real time, make changes across every channel at once, and get reporting out the door without losing half a day to manual work.

That’s an operational advantage. And operational advantages compound in ways that are hard to catch once another team has them.

The Monday morning spreadsheet reconciliation ritual isn’t inevitable. It’s just what the industry was stuck with until recently.

A modern admin experience Revisions are now more visual New blocks in the block editor Better responsive design controls Smarter pattern editing Connect to AI tools of your choice A new list filter for plugins Final thoughts

WordPress 7.0 is out: the 7 highlights of this release

Thu, 21 May 2026 08:07:59 +0000

On May 20th, 2026, the next major release of WordPress came out: WordPress 7.0. While previous releases focused on improving the block editor, this release takes it to a new level. It pushes the platform into the next phase of its roadmap with smarter workflows and a more app-like experience. So, let’s dive into what’s new and what features are interesting for you.

A modern admin experience

WordPress 7.0 introduces a refreshed admin interface. One thing that’s been changed is the new way to transition between pages in your backend. When navigating to another page, this now looks a lot smoother than before, thanks to the CSS View Transitions API. The new update also comes with a new addition to the menu bar at the top, called the Command Palette shortcut. When you click on this icon (or use the shortcut ⌘K or Ctrl+K), you get easy access to the command palette that allows you to navigate your backend or perform other actions from that bar.

Although it’s a seemingly small thing, another cool thing to mention is the new color palette. As you can see in the screenshot above, the default color scheme has changed. The palette previously known as ‘Modern’ is now the new default, better aligning the admin with the visual direction of the block and site editor. If you preferred the old look, don’t worry, it’s still available under your profile preferences, now listed as ‘Fresh’.

Overall, these improvements and others give a fresh look and feel to the backend of your website. With the intent of making WordPress feel less like a traditional CMS and more like a modern web app.

Revisions are now more visual

Whenever you need to check or restore an earlier version of a page, the revisions in WordPress help you do so. These give you an idea of what has been changed on your page and when. Now, WordPress 7.0 makes this even easier with visual revisions instead of the raw text shown until now.

The revisions feature can be found in the same spot as before, and now, when you click it, it takes you to a preview of your page, where you can use the slider at the top to view earlier versions. The slider also shows you the date and time of the change. When looking at an earlier version of the page, additions are shown in green, changed sections in yellow, and deleted sections in red. Allowing you to locate the changes made right away.

As before, this allows you to quickly restore previous versions of a page, find the source of layout issues and review updates. This visualization of the revisions makes it easier to do so, as you won’t have to dive into the text to figure out what changed. You’ll notice it right away when sliding between revisions.

New blocks in the block editor

As expected, the block editor has also gotten some new additions with the release of WordPress 7.0. For starters, the new Breadcrumbs block lets you add breadcrumbs to your pages, improving navigation on your site. When added, it automatically adds the correct breadcrumb path to the top of your page, but it also gives you options to customize it. The other new block in this release is the Icon block. This allows you to add icons to your pages from a directory of icons added to the backend.

There are also some improvements to existing blocks, such as the Grid Block and Cover block. The Grid block used to have an Auto/Manual toggle, but this has now been replaced by several options to help you set the responsiveness of the block and columns shown. The Cover Block now includes the option to use embedded videos as the background, so you can display videos from platforms like YouTube there. These new blocks and improvements continue to further reduce the need for plugins and custom work to achieve the desired design.

Better responsive design controls

Designing for mobile just got a little bit easier. This latest version of WordPress introduces viewport-based controls, allowing you to show or hide blocks depending on the user’s screen size. Simply go to the block, click ‘Show’ in the toolbar and select which devices should show the block (desktop, tablet, or mobile). This will automatically hide it on the devices that you don’t select. This allows you to fine-tune your design for different devices and build responsive designs without using custom CSS. A big win for anyone building sites without relying heavily on code.

Smarter pattern editing

Patterns and templates now come with different editing modes to make changes without accidentally messing up the design. When selecting a pattern, the List View will show you all the text and image elements in that pattern. This allows you to focus on the content-focused elements and change those where needed. However, when you click ‘Edit pattern’, it will also show you the remaining elements (design elements such as spacers), so you can still adjust those. This helps users focus on content optimization, while still giving the option to make changes to the design or layout if needed.

This new approach makes it a bit easier to customize patterns to fit specific use cases across your website.

Connect to AI tools of your choice

WordPress 7.0 doesn’t come with any AI-powered tools, but it is laying some groundwork. It comes with a Connectors section below Settings in your WordPress backend. Here you can connect to external integrations, including AI providers or agents. This allows you to connect to Claude, Gemini, OpenAI, and more. You can search the directory if the integration you’re looking for isn’t listed right away.

This gives you one central place to maintain any integrations that your website or plugins need to connect to by API keys or other credentials. In addition, this gives developers a future-proof ecosystem and standardized framework to work with.

A new list filter for plugins

WordPress 7.0 adds a filter that allows plugins to register custom tabs on the Plugins screen. This enables grouping plugins under a custom tab with a proper label. For example, thanks to this feature we were able to add a dedicated “Yoast” tab on the Plugins screen. This groups all Yoast plugins on that website in one view, making it easier for site admins to check versions, manage activation, and keep the overview of their Yoast suite.

Final thoughts

As always, these are just a few highlights. New blocks, smarter workflows, a modern admin and AI foundations. There’s a lot more we haven’t discussed here. For example, performance was not ignored in this release. Particularly, client-side media processing (faster uploads, less server strain), continued improvements to block rendering, and responsiveness. These changes help WordPress scale better, especially for media-heavy sites. It’s also worth noting that WordPress 7.0 raises the minimum PHP version to 7.4.

Still to come: real-time collaboration

Originally, the real-time collaboration feature was going to be shipped in this release. But a short while back it was decided to postpone the release of this feature to ensure the stability of this release. This feature will probably be part of a future release.

But for now, we can get going with the new features in WordPress highlighted above! So, go update to the latest version or dive into more details in the release post on WordPress.org.

The post WordPress 7.0 is out: the 7 highlights of this release appeared first on Yoast.

Google adds llms.txt check to Chrome Lighthouse

Wed, 20 May 2026 16:57:48 +0000

Google’s new Lighthouse “Agentic Browsing” audits now check for the presence of an llms.txt file. The new experimental Lighthouse documentation frames llms.txt as a discoverability and efficiency signal for AI agents, not a traditional crawling directive.

The audits are part of Chrome’s emerging “Agentic Browsing” category, which evaluates whether sites are structured for machine interaction.
This document comes less than a week after Google published new guidance on optimizing for AI search features like AI Overviews and AI Mode, in which it said you don’t need llms.txt files in a mythbusting section of its new guide on optimizing for generative AI features.

What Lighthouse now checks. Lighthouse’s Agentic Browsing category evaluates “how well your site is constructed for machine interaction” using deterministic audits, according to Google’s documentation. Among the checks:

WebMCP integration.
Accessibility tree integrity.
Layout stability through CLS.
Presence of an llms.txt file.

Lighthouse checks for “the presence of a machine-readable summary at the domain root.” Google also explained why the file matters for agents:

“Without llms.txt, agents may spend more time crawling the site to understand its high-level structure and primary content.”

The audit category doesn’t produce a traditional Lighthouse score (0-100). Instead, Google surfaces a fractional pass ratio along with pass/fail checks tied to agentic readiness signals.

The tension. The new Lighthouse documentation doesn’t directly conflict with Google’s advice on optimizing your website for generative AI features because these audits focus on AI agents and browser tools, not Google Search rankings. Still, seeing llms.txt mentioned in Chrome’s own readiness checks may cause some SEOs to rethink earlier doubts about the file.

Agentic engine optimization. The Lighthouse audits also align with ideas Google Cloud AI engineering director Addy Osmani outlined in April around Agentic Engine Optimization. Osmani said AI agents with limited context windows may cut off long pages or miss important information buried too deep in content. Among his recommendations:

Cleaner semantic structure.
Token-efficient content.
Markdown delivery.
llms.txt discovery layers.
Capability signaling files like AGENTS.md.

SEO vs. llms.txt. Here’s exactly what Google recommends in Mythbusting generative AI search: what you don’t need to do:

LLMS.txt files and other “special” markup: You don’t need to create new machine readable files, AI text files, markup, or Markdown to appear in generative AI search. Note that Google may discover, crawl, and index many kinds of files in addition to HTML on a website: this doesn’t mean that the file is treated in a special way.

Here’s what Google’s John Mueller said about Google using llms.txt, in response to Lily Ray asking him on Bluesky “Hey @johnmu.com – if you can answer, many folks are pointing out the irony that Google uses LLMs.txt files, plus markdown pages, despite also saying these things are not needed for performance in search. Could you share why Google might publish these files, if not to make crawling those pages/sites easier for agents? (I’m sure I’ll be getting this question a ton soon!)”:

The short answer is that it’s not done for search. There’s more to websites than just SEO :-).

The longer & nuanced version is that it’s worth separating “discovery” (finding the website or pages with a global search engine) vs “functionality” (there’s probably a more accurate term for this, but basically: once someone has found the page, helping them to best do the task they want to do).

Perhaps that’s similar to CTA’s on traditional pages? You don’t “do them” for SEO (to be found), but if you’re responsible for the website overall, ensuring a high “discovery rate” (SEO) together with a high conversion rate is useful to justify your work.

To get back to the developers.google.com site, AI coding has gotten very popular, and these coding systems can be (I think) efficient and accurate with the code they produce if they can easily read / parse reference material, such as developer documentation.

In those cases, it can help to give them a way to understand the context of the documentation they’re looking at, as well as a simplified version of the reference page (eg, in markdown). OF COURSE they can read HTML just fine, so this is imo more of a temporary crutch, perhaps to save some tokens.

For non-developer sites, I don’t think this makes much sense, even with more agentic traffic in the future (and if you check your logs, you’re not getting a lot of that at the moment). Making a markdown version of a shoe’s specs is not going to get you more sales (competitors appreciate it tho).

And (I know, nobody reads this far), if you think this is important to prepare for when agents are everywhere: your site (all sites) have much more important things to do for SEO than to prepare for a potential future situation that may or may not come. Prioritize needs before dreams.

What Google says agents rely on. Beyond llms.txt, Google’s new Lighthouse category strongly emphasizes accessibility and interface stability. The documentation says agents rely on the accessibility tree as their “primary data model.” Lighthouse specifically evaluates:

Programmatic labels for interactive elements.
Valid accessibility tree structure.
Whether interactive content is hidden from assistive systems.
Layout stability through CLS.

Google also warns that dynamically registered WebMCP tools and large DOM changes can affect audit results.

Why we care. Google says you don’t need llms.txt for Search, but Chrome is now checking whether the file exists. At the same time, Google’s agentic tools appear to favor sites that are easier for machines to read and use, especially sites with strong accessibility, stable layouts, and clear agent access.

Google’s help document. Lighthouse agentic browsing scoring

Dig deeper.

Google brings Meridian marketing mix modeling into Analytics 360

Wed, 20 May 2026 16:00:00 +0000

Google is expanding its measurement capabilities with new integrations between Google Analytics 360, Meridian and predictive AI reporting tools as announced today at Google Marketing Live 2026.

Driving the news. Meridian, Google’s open-source marketing mix modeling platform, will be integrated directly into Google Analytics 360.

The integration is designed to help advertisers:

Unify first-party and cross-channel data
Measure incremental performance
Forecast campaign outcomes
Optimize media mix investments

Google is also introducing Qualified Future Conversions (QFCs), a predictive reporting metric powered by Gemini.

QFCs connect current ad activity with future sales signals such as branded search behavior.

How it works. Meridian combines first-party data, media signals and cross-channel performance metrics inside Google Analytics 360 to model incremental impact and forecast outcomes. Qualified Future Conversions uses Gemini-powered predictive signals to estimate how current ad engagement may influence future purchasing behavior.

Over time, Google plans to integrate QFC insights directly into Meridian to improve predictive modeling accuracy.

The updates are part of Google’s broader push to simplify measurement and improve ROI forecasting in an increasingly fragmented media environment.

Why we care. Measurement and attribution continue to grow more difficult as customer journeys become less linear and privacy restrictions expand.

Google’s latest updates show the company investing heavily in predictive modeling, incrementality and AI-assisted forecasting to help advertisers better understand long-term performance.

The combination of Meridian and QFCs could also help marketers make stronger budgeting decisions by linking current campaign activity to future business outcomes.

What to watch. Predictive measurement and incrementality modeling are becoming more important as attribution grows more fragmented. Advertisers will likely test whether Meridian and QFCs provide more actionable forecasting compared to existing attribution and MMM solutions.

Availability. Meridian integrations are coming to Google Analytics 360 globally across all languages. QFCs are currently available in a restricted global pilot with broader beta access expected later this year.

Dig deeper. More Google Marketing Live 2026 news from today:

Google tests new conversational ad formats in AI Mode and Search
Google launches AI Performance Insights and Conversational Attributes in Merchant Center
Google expands Direct Offers with AI-generated bundles, native checkout and travel deals
Google expands Demand Gen with YouTube creator tools
Google upgrades Asset Studio with Gemini-powered creative generation and video tools
Google expands Universal Commerce Protocol and launches new agentic shopping tools
Google launches Ask Advisor across Ads, Analytics and Merchant Center

Google launches AI Performance Insights and Conversational Attributes in Merchant Center

Wed, 20 May 2026 16:00:00 +0000

Google is introducing new Merchant Center tools designed to help retailers improve visibility across AI-powered shopping experiences as announced today at Google Marketing Live 2026.

Driving the news. The company announced AI Performance Insights, a new reporting feature that helps merchants understand how their brand performs across AI surfaces.

The tool compares a brand’s share of voice against similar competitors and provides visibility into AI-driven discovery performance.

Google is also rolling out Conversational Attributes, a new product data capability that helps retailers optimize listings for more natural, conversational search behavior.

How it works. Retailers can add conversational product attributes and updated descriptions directly inside Merchant Center. Google’s AI systems then use that structured data to better match products with conversational shopping queries across AI Mode, Gemini and other AI-powered surfaces.

The updates are designed to help brands improve discoverability as shopping experiences become increasingly AI-driven.

Google also confirmed that Ask Advisor integrations are coming directly into Merchant Center.

Why we care. Structured product data is becoming more important as AI-powered shopping experiences expand across Search, Gemini and Maps.

Retailers that adapt product descriptions and feeds for conversational discovery may be better positioned to surface products in AI-generated recommendations and shopping flows.

The new reporting tools also provide advertisers with early visibility into how brands perform inside AI-powered experiences.

What to watch. As conversational search behavior grows, product feed optimization may become increasingly important for AI visibility. Retailers should also monitor how Google defines and measures “share of voice” across AI-powered shopping experiences.

Availability. AI Performance Insights is expected to roll out in Australia, Canada, India, New Zealand and the U.S. in the coming months. Conversational Attributes are rolling out globally.

Dig deeper. More Google Marketing Live 2026 news from today:

Google tests new conversational ad formats in AI Mode and Search
Google expands Direct Offers with AI-generated bundles, native checkout and travel deals
Google brings Meridian marketing mix modeling into Analytics 360
Google expands Demand Gen with YouTube creator tools
Google upgrades Asset Studio with Gemini-powered creative generation and video tools
Google expands Universal Commerce Protocol and launches new agentic shopping tools
Google launches Ask Advisor across Ads, Analytics and Merchant Center

Google expands Direct Offers with AI-generated bundles, native checkout and travel deals

Wed, 20 May 2026 16:00:00 +0000

Google is expanding its Direct Offers pilot with new AI-powered promotion formats, native checkout integrations and travel-focused deal experiences as announced today at Google Marketing Live 2026.

Driving the news. The company announced several updates designed to make promotional offers more discoverable inside AI-powered Search experiences.

Brands will soon be able to upload:

Discounts
Giveaways
Local coupons
Product bundles

Google says Gemini will help dynamically construct personalized offers based on search intent.

How it works. Advertisers upload eligible promotions, products and campaign guardrails into Google Ads. Gemini then assembles contextual offers — such as bundles, coupons or discounts — based on a shopper’s query and browsing behavior inside AI-powered Search experiences.

The company is also adding native checkout support for merchants using Universal Commerce Protocol (UCP), allowing users to complete purchases directly from AI-assisted shopping flows.

Travel partners including Booking and Expedia will also soon be able to surface travel offers directly inside AI-assisted trip planning experiences.

Why we care. Google is turning promotions into a more integrated part of conversational shopping experiences.

Instead of relying on traditional deal extensions or static offers, advertisers may increasingly need to optimize promotions for AI-assisted discovery and contextual recommendation engines.

The native checkout integrations could also reduce friction between product discovery and conversion.

What to watch. Google appears to be moving promotional commerce experiences closer to AI-assisted discovery flows. Advertisers should watch how native checkout and AI-generated deal recommendations affect conversion rates and shopping behavior.

Availability. Direct Offers remains in pilot for U.S. advertisers.

Dig deeper. More Google Marketing Live 2026 news from today:

Google tests new conversational ad formats in AI Mode and Search
Google launches AI Performance Insights and Conversational Attributes in Merchant Center
Google brings Meridian marketing mix modeling into Analytics 360
Google expands Demand Gen with YouTube creator tools
Google upgrades Asset Studio with Gemini-powered creative generation and video tools
Google expands Universal Commerce Protocol and launches new agentic shopping tools
Google launches Ask Advisor across Ads, Analytics and Merchant Center

Google expands Demand Gen with YouTube creator tools

Wed, 20 May 2026 16:00:00 +0000

Google is adding new creator, video and measurement capabilities to Demand Gen campaigns as it pushes YouTube further into performance advertising as announced today at Google Marketing Live 2026.

Driving the news. The company announced several new Demand Gen updates focused on creator partnerships, product discovery and cross-platform campaign optimization.

Advertisers will soon be able to:

Use multimodal video creation inside Asset Studio
Promote creator partnership videos directly within campaign setup
Upload Merchant Center product videos for dynamic distribution
Extend Demand Gen campaigns into Google Maps inventory

Google is also expanding checkout links into additional markets and extending product feed support into new verticals including automotive.

According to Google, advertisers with large product selections typically see a 33% increase in conversions when adopting product feeds in Demand Gen campaigns.

Additional measurement updates include:

Campaign Type Attribution
Uplift Experiments
Expanded third-party integrations with companies like TransUnion

Google is also introducing AI-assisted Demand Gen campaign creation, allowing advertisers to use settings from existing campaigns like Performance Max to streamline setup.

How it works. Demand Gen uses AI signals from YouTube, Discover, Maps and Shopping activity to dynamically distribute creative and product feeds across Google surfaces. Advertisers can also use creator videos and Merchant Center product assets to personalize campaigns based on user interest and engagement patterns.

Why we care. Google is increasingly positioning YouTube and Demand Gen as full-funnel performance channels rather than upper-funnel awareness products.

The integration of creator content, Maps inventory and dynamic product experiences reflects how discovery and commerce behaviors are converging across Google properties.

For advertisers, the updates could create new opportunities to connect creator-driven content with measurable conversion outcomes.

What to watch. Google’s continued investment in creator tools and Demand Gen suggests YouTube will play a larger role in performance advertising strategies. Advertisers should also monitor how Maps inventory and creator-led commerce campaigns impact conversion performance.

Availability. Many Demand Gen updates are rolling out globally in open beta.

Dig deeper. More Google Marketing Live 2026 news from today:

Google tests new conversational ad formats in AI Mode and Search
Google launches AI Performance Insights and Conversational Attributes in Merchant Center
Google expands Direct Offers with AI-generated bundles, native checkout and travel deals
Google brings Meridian marketing mix modeling into Analytics 360
Google upgrades Asset Studio with Gemini-powered creative generation and video tools
Google expands Universal Commerce Protocol and launches new agentic shopping tools
Google launches Ask Advisor across Ads, Analytics and Merchant Center

Google expands Universal Commerce Protocol and launches new agentic shopping tools

Wed, 20 May 2026 16:00:00 +0000

Google is expanding its Universal Commerce Protocol (UCP) initiative with new checkout features, AI shopping integrations and support for additional industries as announced today at Google Marketing Live 2026.

Driving the news. The company announced several new UCP-powered commerce capabilities designed to support what it calls the “agentic commerce era.”

A key update is the expansion of Universal Cart, which allows shoppers to save products across retailers and complete purchases using Google Pay or retailer checkout experiences.

Google says the experience will soon support brands including Nike, Sephora, Target, Walmart, Wayfair and Shopify merchants such as Fenty and Steve Madden.

The company is also integrating UCP into:

Direct Offers
Demand Gen campaigns
AI Mode shopping experiences
Shopping ads on YouTube

Google additionally announced buy-now-pay-later integrations with Affirm and Klarna directly inside Google Pay.

How it works. Universal Commerce Protocol (UCP) allows retailers to connect product catalogs, checkout and payment experiences across Google surfaces including Search, AI Mode and Gemini. Users can add products to a shared Universal Cart and complete purchases either through Google Pay or directly on the retailer’s website.

The company is also expanding UCP into additional verticals including hotel booking and food delivery.

Future experiences will allow users to book hotels directly from AI Mode or order food from conversations inside Google Maps.

To help brands improve discoverability across AI experiences, Google is introducing:

AI Performance Insights in Merchant Center
Conversational Attributes for product descriptions
Merchant Center integrations with Ask Advisor

Why we care. Google is laying the infrastructure for AI-assisted shopping and commerce discovery.

As conversational shopping experiences expand across Search, Gemini and Maps, brands may need to rethink how they structure product feeds, descriptions and commerce data.

The update also signals that AI-driven commerce is moving beyond discovery and into transaction workflows, with checkout, financing and promotions increasingly embedded directly into Google experiences.

What to watch. Retailers should pay close attention to how Google integrates checkout, financing and shopping actions directly into AI-powered experiences. The expansion into hotel booking and food delivery also suggests Google is preparing for broader transaction capabilities across Search and Maps.

Availability. Many UCP-powered features are rolling out in the U.S. first, with expansions planned for Canada, Australia and later the U.K. Additional vertical integrations are expected in the coming months.

Dig deeper. More Google Marketing Live 2026 news from today:

Google tests new conversational ad formats in AI Mode and Search
Google launches AI Performance Insights and Conversational Attributes in Merchant Center
Google expands Direct Offers with AI-generated bundles, native checkout and travel deals
Google brings Meridian marketing mix modeling into Analytics 360
Google expands Demand Gen with YouTube creator tools
Google upgrades Asset Studio with Gemini-powered creative generation and video tools
Google launches Ask Advisor across Ads, Analytics and Merchant Center

Google upgrades Asset Studio with Gemini-powered creative generation and video tools

Wed, 20 May 2026 16:00:00 +0000

Google is rolling out major updates to Asset Studio aimed at helping advertisers generate creative assets faster using Gemini as announced today at Google Marketing Live 2026.

Driving the news. Asset Studio will now support AI-powered generation across text, images and video using natural language prompts.

According to Google, the platform can understand:

Marketing briefs
Brand guidelines
Website content
Campaign goals

The system then generates creative assets across multiple themes and formats.

Google is also integrating Gemini Omni, its multimodal model, into Asset Studio to support video creation workflows.

Advertisers will additionally gain access to 1-Click Creative Testing, which helps identify high-performing assets based on campaign objectives.

How it works. Asset Studio uses Gemini models to interpret a marketer’s brief, brand guidelines, website content and goals. Advertisers can generate and refine assets using natural language prompts while Gemini Omni supports multimodal video creation workflows inside the same interface.

The company says the goal is to centralize creative production and reduce friction for advertisers building campaigns across Google and YouTube.

Why we care. Creative production remains one of the biggest operational bottlenecks for advertisers.

Google’s updates show how generative AI is increasingly becoming embedded directly into campaign production workflows rather than functioning as a standalone creative assistant.

For marketers managing campaigns across multiple surfaces, the ability to rapidly generate and test creative assets at scale could become a significant competitive advantage.

What to watch. As creative generation becomes more automated, advertisers will likely evaluate how AI-generated assets perform compared to traditional creative workflows. Brands may also need to rethink approval processes, governance and brand safety as AI production scales.

Availability. The new Asset Studio features are expected to roll out globally in English this summer.

Dig deeper. More Google Marketing Live 2026 news from today:

Google tests new conversational ad formats in AI Mode and Search
Google launches AI Performance Insights and Conversational Attributes in Merchant Center
Google expands Direct Offers with AI-generated bundles, native checkout and travel deals
Google brings Meridian marketing mix modeling into Analytics 360
Google expands Demand Gen with YouTube creator tools
Google expands Universal Commerce Protocol and launches new agentic shopping tools
Google launches Ask Advisor across Ads, Analytics and Merchant Center

Google tests new conversational ad formats in AI Mode and Search

Wed, 20 May 2026 16:00:00 +0000

Google is introducing a new generation of Gemini-powered ad formats across AI Mode and Search designed to make ads feel more conversational, contextual and helpful as unveiled today at Google Marketing Live 2026.

Driving the news. The company announced several new ad experiences for AI-powered Search, including Conversational Discovery ads, Highlighted Answers, AI-powered Shopping ads and Business Agent for Leads. The updates are part of Google’s broader push to integrate Gemini deeper into Search and advertising experiences.

Conversational Discovery ads are designed to answer a person’s specific question directly inside AI Mode. For example, someone searching for ways to make their home smell like a spa could see tailored creative generated with Gemini that highlights relevant product features.

How it works. Google’s Gemini models analyze the intent behind a user’s query and dynamically generate ad creative tailored to that specific conversation. The ads also include an independent AI explainer that evaluates and summarizes product or service information alongside the advertiser’s messaging.

Highlighted Answers will allow highly relevant ads to appear directly within AI-generated recommendation lists.

Google is also launching AI-powered Shopping ads for high-consideration purchases like TVs and appliances. Gemini will generate custom explainers highlighting why a product may fit a shopper’s needs.

Business Agent for Leads introduces an AI-powered chat experience directly within lead generation ads. Instead of filling out static forms, users can interact with a Gemini-powered brand agent trained on an advertiser’s website.

Google is also expanding its Direct Offers pilot with:

Promotion bundling
Native checkout for UCP merchants
Travel deal integrations
AI-generated offer recommendations

The updates will roll into AI Mode responses to help shoppers discover deals more naturally.

Why we care. Google is fundamentally changing how ads appear in AI-powered Search experiences.

Instead of relying on static creative and keyword matching alone, advertisers will increasingly need to optimize for conversational discovery, AI-generated explainers and intent-rich interactions.

The announcement also signals that AI Mode is becoming a larger monetization surface for Google. Advertisers that adapt early to conversational ad formats and AI-assisted shopping experiences may gain a competitive advantage as Search behavior evolves.

What to watch. Advertisers should closely monitor how AI-generated explainers and conversational ad placements affect click-through rates, conversion behavior and attribution. The rollout could also signal broader changes in how Search inventory is monetized as AI Mode usage continues growing.

Availability. Conversational Discovery ads and Highlighted Answers are currently being tested in the U.S. on mobile and desktop. AI-powered Shopping ads are expected to roll out later this year in the U.S. Business Agent for Leads is launching in open beta for U.S. advertisers.

Dig deeper. More Google Marketing Live 2026 news from today:

Google Marketing Live 2026: Everything you need to know

Wed, 20 May 2026 16:00:00 +0000

Google Marketing Live 2026 made one thing clear: Gemini is no longer just powering features — it’s becoming the operating system behind Google’s advertising, commerce and measurement ecosystem.

This year’s event focused heavily on agentic AI, conversational Search, automated creative production and AI-assisted shopping experiences. Across Search, YouTube, Merchant Center and Analytics, Google introduced new tools designed to make campaigns more autonomous, predictive and interconnected.

Here’s a recap of the biggest announcements from Google Marketing Live 2026.

Google introduces a new generation of AI-powered Search ads

Google announced several new Gemini-powered ad formats designed for AI Mode and conversational Search experiences.

The updates include:

Conversational Discovery ads
Highlighted Answers
AI-powered Shopping ads
Business Agent for Leads

These formats are designed to make ads feel more contextual and interactive by embedding AI-generated explainers and conversational experiences directly into Search journeys.

Google also expanded its Direct Offers pilot with AI-generated bundles, native checkout and travel promotions integrated into AI-assisted Search experiences.

Full story: Google tests new conversational ad formats in AI Mode and Search

Google launches Ask Advisor across Ads, Analytics and Merchant Center

Google unveiled Ask Advisor, a new Gemini-powered AI collaborator that connects Google Ads, Analytics, Merchant Center and Google Marketing Platform.

The system acts as a unified assistant capable of helping marketers:

Build campaigns
Analyze performance
Surface recommendations
Automate operational tasks

Google says Ask Advisor can pull insights across platforms to help marketers move from planning to optimization more quickly.

Full story: Google launches Ask Advisor across Ads, Analytics and Merchant Center

Google expands Universal Commerce Protocol and AI shopping experiences

Google announced major updates to its Universal Commerce Protocol (UCP), Universal Cart and AI-powered checkout experiences.

New capabilities include:

AI-assisted checkout flows
Buy-now-pay-later integrations with Klarna and Affirm
Cross-retailer shopping experiences
AI-powered travel and food ordering integrations

Google is also expanding UCP integrations into Demand Gen campaigns, YouTube Shopping ads and AI Mode experiences.

Full story: Google expands Universal Commerce Protocol and launches new agentic shopping tools

Asset Studio gets Gemini-powered creative and video tools

Google upgraded Asset Studio with multimodal Gemini-powered creative generation capabilities.

Advertisers can now use natural language prompts to generate:

Images
Video assets
Text variations
Creative themes

Google also integrated Gemini Omni into Asset Studio to support video workflows and introduced 1-Click Creative Testing for asset optimization.

Full story: Google upgrades Asset Studio with Gemini-powered creative generation and video tools

Demand Gen expands with creator tools, Maps inventory and AI optimization

Google announced several Demand Gen updates focused on YouTube creators, AI-assisted optimization and cross-platform discovery.

The updates include:

Creator partnership tools
Google Maps inventory
Dynamic product video distribution
AI-assisted campaign setup
Expanded measurement integrations

Google says advertisers with large product feeds continue seeing stronger conversion performance inside Demand Gen campaigns.

Full story: Google expands Demand Gen with YouTube creator tools

Google upgrades measurement with Meridian and predictive AI tools

Google also announced new measurement and forecasting capabilities for Google Analytics 360.

The company is integrating Meridian, its open-source marketing mix model, directly into Analytics 360 while introducing Qualified Future Conversions (QFCs), a new predictive reporting metric powered by Gemini.

The tools are designed to help advertisers:

Improve media mix modeling
Forecast campaign outcomes
Measure incrementality
Connect current ad activity with future revenue signals

Full story: Google brings Meridian marketing mix modeling into Analytics 360

Merchant Center gets AI visibility and conversational commerce updates

Google announced new Merchant Center features designed to help retailers improve discoverability across AI-powered shopping experiences.

New tools include:

AI Performance Insights
Conversational Attributes
Merchant Center integrations with Ask Advisor

The updates aim to help retailers optimize product feeds and descriptions for conversational shopping behavior across Search, Gemini and AI Mode.

Full story: Google expands Direct Offers with AI-generated bundles, native checkout and travel deals

Your customers search everywhere. Make sure your brand shows up.

How to stand out in AI search when every business sounds the same

Wed, 20 May 2026 14:00:00 +0000

Most businesses sound interchangeable online, and AI search is making that impossible to ignore. When ChatGPT, Google AI Overviews, or other AI systems summarize your business, they build that understanding from your website, profiles, reviews, and content. If all of it sounds like everyone else in your category, the machine’s summary will too.

This is why AI visibility is becoming a positioning problem as much as a technical one.

Businesses that stand out in AI search are not necessarily the ones with the biggest budgets or the most aggressive tactics. They’re the businesses that clearly communicate who they serve, what they do differently, and why customers should care. Everything else — content, ads, SEO, PPC, schema, and optimization — only amplifies that underlying message.

Why businesses default to tactics instead of positioning

Sun Tzu said it first, and nobody has improved on it since:

“Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat.”

He was talking about warfare, but he might as well have been sitting in on a digital marketing meeting, watching a business owner ask their agency to “do something with the SEO” while their homepage still describes how they deliver exceptional results with great service.

There is a pattern here: something changes, whether it’s a Google update, a dip in traffic, or the dawning realization that AI is changing how people research and buy things. This creates an instinct to act. Immediately. Update the keywords, try a new ad format, and post more on LinkedIn. Be busy, busy, busy. Do something because at least action feels like progress.

This isn’t just a marketing folly. This is evolutionary behavior that is hardwired.

Daniel Kahneman spent a career documenting this. His framework from “Thinking, Fast and Slow” describes two cognitive systems: System 1, which is fast, automatic, and emotional, and System 2, which is slow, deliberate, and analytical.

The uncomfortable finding? System 1, basically heuristics, runs the show roughly 95% of the time. We’re not the rational strategists we imagine ourselves to be. We’re pattern-matching, reflex-driven creatures who take comfort in movement and action.

Psychologists call the specific reflex that drives all this tactical overactivity “action bias” — the largely unconscious urge to act in the face of uncertainty, even when a more considered approach would serve us better.

Think of a goalkeeper who dives left or right on a penalty kick, even though the data shows they’d save more by standing still. The diving feels more engaged, more committed, more in control. Standing still feels like giving up — even when it’s the smarter play.

Business owners do this constantly. They change their Google Ads targeting every week because the waiting feels unbearable. They add another service to their homepage because it feels more thorough. They try a new social platform because something must account for the slow results.

Meanwhile, the actual problem is often that their positioning is unclear, undifferentiated, and indistinguishable from every competitor, but it sits untouched because fixing it requires sitting with uncertainty long enough to think rather than just act.

This is why most businesses end up with tactics stacked on tactics: an ad here, a keyword tweak there, a new landing page, a social media calendar. This all floats on a foundation of generic, uninspiring positioning.

This is busy, expensive, and utterly unsuccessful. This is Sun Tzu’s noise before defeat.

The SEO toolkit you know, plus the AI visibility data you need.

AI removes the hiding places for generic marketing

I have been in this game a long time, maybe pushing 30 years of marketing something or another, and a good 26 digital and online, and maybe, just maybe, you could get away with this for a while.

Why? Well, the customer is also human.

Being first, just being there, also connects to the inherent laziness, the path of least resistance, that is common in all of us. If there is an easy option, take it. Don’t waste precious calories thinking.

This helped us survive as hunter-gatherers, but it exposes us now, so those ads nobody clicks on actually get all the clicks, and for a while, you could buy your way around this.

But AI is the flood. The entropic tide. The sudden calamity that will finally expose lazy marketing and wash away those businesses that bought their way out of the pit of undifferentiation.

Now, the businesses that win in SEO, PPC, and definitely AI, whether search or otherwise, are not the ones with the most budget or the biggest tactical reach. The businesses that will board the ark and survive the AI flood are the ones that did the harder, earlier strategic work: figuring out what they stand for, who they actually serve, and what makes them genuinely different.

They’re also the businesses that took the time to figure out how to articulate this clearly and communicate it to an increasingly fatigued audience that just wants things to be easy.

When AI summarizes you, what does it say?

Open ChatGPT. Ask it to recommend an accountant, an IT support company, or a marketing agency in your city. Read what comes back. Notice anything?

Probably not. And that’s precisely the problem.

Now visit five websites in any professional services sector. Count how many claim to be passionate, experienced, client-focused experts delivering exceptional results through tailored solutions backed by decades of expertise.

Yawn.

That’s marketing wallpaper. It covers every surface, blends into the background, and communicates absolutely nothing. The sad truth is that most businesses have spent years perfecting bland, insipid, boring messaging.

They may be winning customers because they’re nearby or competing on price, but if you don’t stand out and have something truly different, that is often the only reason they will.

AI has turned up the contrast on this problem in a way that’s hard to ignore. People are no longer just typing keywords. They’re handing entire decision-making processes to AI assistants. AI reads, synthesizes, compares, and recommends.

It doesn’t just find you. It forms an opinion of you.

And here’s the uncomfortable part: the machine only knows what it can find about you.

It reads your website, your Google Business Profile, your LinkedIn bio, directory listings, and reviews. It stitches all of that together into a summary, and if everything you’ve put out there sounds like everyone else in your category, that summary will sound like everyone else, too.

The human element matters here, too. Have a niche. Stand out. Target a subset of your broader customer profiles and specialize your service. This makes marketing, sales, and operations easier.

The technical side of this tends to dominate the conversation. Structured data. E-E-A-T. Citation signals. Schema markup. All of it matters eventually. But it all sits downstream from a much more fundamental question that almost nobody is asking:

What story are all those sources telling about you?

Spend an afternoon polishing your schema markup while your homepage still says “we deliver exceptional results for businesses of all sizes,” and you’ve polished a window that looks out to nothingness.

The sea of sameness: Why this happened

Most businesses sound alike because they made the same rational decision: look at what successful competitors are saying, then copy the tone, the structure, and the claims. Safe. Professional. Unremarkable.

I’ve always been a fan of classic marketing texts from before the chaos and constant change of the digital era, and what they can still teach us. One text that captured this deeper problem decades ago was “Positioning: The Battle for Your Mind” by Al Ries. The battle for customers isn’t fought in the marketplace. It’s fought in the mind.

And minds are crowded places with very limited space. If you can’t occupy a distinct position in someone’s thinking, you won’t occupy much position at all. You become a commodity, and the only competition left is price — the classic race to the bottom.

The result is what Blue Ocean Strategy calls a red ocean: a market where everyone competes on the same dimensions, in the same ways, for the same customers.

The good news is that most of your competitors are still swimming in that red ocean, and they have no intention of getting out. The water is bloody and exhausting, but at least it’s familiar.

You’re not the hero

Before we get to the frameworks, there’s one mindset shift worth making because it changes everything that follows.

Joseph Campbell spent a career documenting the Monomyth, the universal story structure that appears across cultures throughout human history. If you’re not familiar, George Lucas hired Campbell to help shape the story for the original Star Wars.

Subsequent work revealed that we tend to see the world through the lens of a story, and in those stories, we are the hero.

It goes something like this: the hero receives a call to adventure. The hero faces obstacles. The hero finds a guide. The hero transforms. It’s the backbone of every myth, every great film, and every story that actually sticks with us.

In Star Wars, Luke is bored and craves adventure. He meets Obi-Wan, but initially turns down the call until he goes home to find his adoptive parents dead. Then, with the help of his guide, he sets off, saves the universe, and transforms himself from farm boy to savior of the galaxy.

Donald Miller tied this framework to marketing in “Building a StoryBrand.” He recognized that outside of movies, this is also the structure of every great brand. And he noticed something even more important: almost every business gets this completely backward and makes itself the hero.

“We are the UK’s leading provider of…”
“Our award-winning team has been serving clients since…”
“We pride ourselves on delivering…”

The customer reads this and thinks: great story, but what about me?

The best client-facing messaging reframes this. The customer is the hero. You’re the guide. Think Gandalf, not Frodo. Yoda, not Luke. Your job isn’t to tell your story — it’s to help your customer save their own galaxy.

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Find your difference

From having thousands of conversations with business owners and marketing teams over the years, I firmly believe that most businesses do have something unique. They just don’t do a very good job of communicating it in their marketing.

Fortunately, there are some powerful tools you can use to aid you in this endeavor. The goal is to surface what makes you genuinely distinctive and identify the segments you can best help.

The Blue Ocean Strategy canvas

Blue Ocean Strategy’s central tool is the strategy canvas: a chart mapping the key factors businesses in your sector compete on — price, service speed, specialization, range, expertise, location, and industry focus — and plotting where you and your competitors score on each.

Do this honestly. Most businesses, when they complete it, find their lines almost perfectly parallel to their competitors. They’re competing on the same things, in the same ways. The marketing sounds the same because the strategy is the same.

Blue Ocean asks a different question:

Instead of competing better, what if you competed differently?
What could you eliminate, reduce, raise, or create that would genuinely move your line — not just rhetorically, but in practice?
Where is there uncontested space rather than bloody, contested ground?

A quick starting exercise: draw a simple table, list six to eight things businesses in your sector typically compete on, and score yourself and two competitors honestly from one to 10.

If the lines are broadly parallel, you don’t have a marketing problem. You have a positioning problem, and no amount of clever copy or website wizardry will fix it.

The Value Proposition Canvas

The Value Proposition Canvas works beautifully alongside Blue Ocean thinking.

On one side, you map your customer:

Their jobs (what they need to get done).
Their pains (what makes those jobs hard or risky).
Their gains (the outcomes they’re hoping for).

On the other side, you map your offering:

The pain relievers.
The gain creators.
The products or services you provide.

The insight comes from the fit — or the mismatch. Most businesses spend 80% of their marketing talking about features and 20% talking about the customer’s pains and gains. It should probably be the reverse because features are about you, and pains and gains are about them.

A useful test: if your ideal customer woke up at 3 a.m. worrying about something your business could solve, what would that worry be?

Are you speaking to that worry on your homepage?
In your Google Business Profile description?
In your LinkedIn bio?

If not, you’re solving a problem people don’t feel instead of speaking to one they’re already carrying.

Tell your story

Once you’ve identified what genuinely makes you different, you need to get it into words — usable, repeatable, tested words that anyone in your business can pick up and run with.

Again, there are a couple of simple, well-established tools you can use to achieve this. The cost of entry here is probably around $10 for each book.

The BrandScript

StoryBrand’s BrandScript is a one-page framework that maps out your complete brand narrative: who your customer is and what they want, the problem they face (external, internal, and philosophical), how you establish yourself as a trustworthy guide, the plan you offer them, the action you want them to take, and the transformation that awaits them if they do.

It takes an afternoon to do properly. It’s worth every minute because what comes out is a shared story that everyone in your business tells consistently, rather than a different version depending on who picks up the phone.

The internal problem deserves particular attention because it’s the most powerful and the least used.

External problems are practical: “I’m not getting enough leads.”
Internal problems are emotional: “I feel like I’m falling behind while my competitors grow.”
Philosophical problems are about values: “Good businesses shouldn’t have to be invisible online.”

Most marketing speaks only to the external problem.

We are far more emotional creatures than we like to admit, and the businesses that truly connect speak to all three.

The one-liner

This is a modern way to tackle the elevator pitch for the internet generation, where 60 seconds is a lifetime.

This comes from Donald Miller’s “Marketing Made Simple.” The one-liner is a single sentence that captures what you do in a way a distracted person at a networking event would actually register.

The structure is:

“We help [who] who [have this problem] so they can [achieve this result].”

Simple in theory. Genuinely difficult to nail in practice.

Compare these two:

“We’re a full-service digital marketing agency offering bespoke solutions for businesses of all sizes.”
“We help small businesses in competitive markets get found online so they can compete with companies 10 times their size.”

The test is simple: does someone respond with “Oh, interesting — how do you do that?” or do their eyes slide away?

Write five versions. Say them out loud to someone who doesn’t work in your industry. Watch their face, and you’ll know when you’ve found one that lands.

The three-layer soundbite

While the tactical masses are decrying the death of the marketing funnel because of AI, your messaging still needs to speak to customers throughout the purchase journey.

Think of your core message as needing to do three jobs at once.

It needs to grab attention by leading with the customer’s pain rather than your solution. “Struggling to stand out in a crowded market?” outperforms “Experts in brand positioning” because one is about them, and one is about you.

It needs to engage by showing you understand their world. Use their language, not your industry’s. “We help businesses that have outgrown their original marketing” lands differently from “we provide strategic marketing consultancy services.”

And it needs to convert by giving them one clear next step. Not “explore our full range of services.” One clear, consistent call to action.

Once you have these three layers working, they become the raw material for everything: your homepage, your Google Business Profile description, your social bios, and your elevator pitch. Written once, deployed everywhere.

Get it out there

Here’s where the positioning work meets the visibility work.

You now have the strategy to build the tactics that take you toward victory.

AI systems are assembling a picture of your business from every source they can find. According to Yext research, 86% of citations in AI-generated responses come from sources businesses can directly control: websites, listings, and owned content.

That’s actually good news. It means this is largely in your hands.

The question is whether all those controlled sources are telling the same distinctive story or a confusing, muddled, generic one.

Your website

Your homepage has roughly five seconds to answer the single question every visitor arrives with: Is this for me?

Most homepages fail this test spectacularly. They open with a company name, a tagline only the founders understand, and a stock photo of someone shaking hands in a glass-fronted office.

Lead instead with your customer’s problem. Make it immediately clear who you help and what life looks like once you’ve helped them.

Have one primary call to action, not five.

Then let the rest of your site consistently reinforce the proposition you established at the top, rather than drifting into “we also do…” territory that undoes all the clarity you’ve worked for.

Google Business Profile

If you serve local customers, your Google Business Profile is some of the highest-leverage, lowest-cost marketing real estate available, and most businesses treat it like an admin task, filling it in once and never going back.

Your business description is 750 characters. Use your one-liner. Speak to the customer’s problem. Don’t waste it on a list of services and the year you were founded.

The same logic applies to every directory listing, trade association profile, and industry registry your business appears in. AI systems read all of these, and they’re building a composite picture.

Consistency in messaging — not just in your name, address, and phone number — is what compounds over time into genuine authority.

These systems triangulate, so set a clear message and communicate it consistently.

Social channels and bios

Every platform gives you a bio field. Most people put their job title and a handful of emojis. That’s a missed opportunity.

Use the space to articulate who you help and why it matters. Same one-liner. Same language. Same story, adapted for each audience.

Then use your content to demonstrate your positioning rather than merely declare it because anyone can claim expertise, but consistently helpful, specific, clearly articulated content actually proves it.

For a content strategy that flows naturally from this, take a look at my earlier pieces on applying They Ask, You Answer to AI search and why customer personas sharpen everything.

Every other touchpoint

Email signatures. Speaker bios. Award entries. Press releases. Partner directories. Podcast show notes. LinkedIn About sections. Reddit. Forums. Everything else.

Every one of these is a signal.

We have worked with colleges, universities, societies, and many other organizations, and every time, the breakthrough came from focusing on the core audience, stripping positioning down to a single clear category, and aligning every channel around that one message.

When all your signals tell a consistent, distinctive story, you start to become the obvious answer to a specific question.

When they’re inconsistent or generic, you remain part of the noise, and AI dutifully summarizes you as such — or ignores you altogether.

The framework in brief

If you want to make this actionable, here’s the sequence.

Start with your difference

Run the Blue Ocean Strategy Canvas honestly, and map your customers’ real pains and gains through the Value Proposition Canvas.
Ask the 3 a.m. question. Find where you have genuine white space, not just claimed differentiation that your competitors could say just as easily.

Document your story

Work through the BrandScript. Write your one-liner in multiple versions and test them on real people.
Build your three-layer soundbites. Get it all captured somewhere that everyone in your business can access and use.

Audit every touchpoint

Website, Google Business Profile, social bios, directory listings, email signatures — everywhere you appear.
Rewrite each one through the lens of your customer’s problem and your genuine answer to it. Remove anything that could have been written by any of your competitors.

Build content that proves it

Answer the real questions your customers ask for the real people asking them.
Not generic, keyword-stuffed articles, but specific, useful, persona-driven content that demonstrates you understand their world better than anyone else.

Stay consistent

Stay consistent long enough for it to work. The temptation will be to broaden the message, add more services, and cover more ground. Resist it.

Specificity isn’t a limitation. That’s the whole point.

Track, optimize, and win in Google and AI search from one platform.

The gap between who you are and what AI sees

Most businesses already know what makes them different. Ask a founder over coffee, and they’ll tell you exactly why customers stay loyal, what frustrations they solve, and what competitors still don’t understand.

Then ask where any of that appears on their website. Usually, there’s a long pause. Their marketing has become an echo of their competitors instead of a reflection of who they actually are.

Today, that gap — between who you are and what the web thinks you are — is becoming a serious risk. AI systems are reading your digital footprint right now, forming a summary of your business, and presenting it to people making buying decisions.

What story is it telling? The answer usually isn’t a new tool, a bigger budget, or another tactical adjustment. It starts with clarity: understanding what you do differently, expressing it in language your customers actually use, and communicating it consistently everywhere.

When that happens, the website gets easier to write. SEO becomes more effective. AI systems start to understand you. Customers do, too.

The noise disappears, and what remains is a clear signal.

Microsoft rolls out AI-powered bidding, reporting and import updates for advertisers

Wed, 20 May 2026 13:29:41 +0000

Microsoft is rolling out a slate of new advertising updates aimed at simplifying campaign management and improving AI-powered optimization across its ad platform.

What’s new. Microsoft introduced a new Import Center designed to simplify importing campaigns from Google Ads and Meta Ads into Microsoft Advertising.

The new hub allows advertisers to:

Search and filter imports
Edit or pause imports
Access imported campaigns
View troubleshooting guidance
Get performance recommendations after imports complete

Microsoft says the goal is to reduce manual troubleshooting and simplify cross-platform campaign management.

Bidding capabilities. AI-powered bidding capabilities is being expanded with cross-account portfolio bidding for Search and Shopping campaigns.

The feature allows advertisers to manage portfolio bid strategies across multiple accounts, helping automated bidding systems pool signals and optimize budgets more efficiently.

Microsoft also added new bid strategy reporting metrics including:

Avg. Target ROAS
Avg. Target CPA
Avg. Target impression share

The company says advertisers can use the updated reporting tools to better understand bid performance, conversion delays and target adjustments directly inside the UI.

Improved reporting. Reporting flexibility is being expanded with new custom column capabilities.

Advertisers can now:

Access all conversion metrics in custom columns
Segment reports by goal name
Analyze additional metrics including CPA, ROAS and All Conversions

The company says the changes are designed to give advertisers more transparency and faster optimization insights directly inside the platform.

Why we care. Microsoft is making it easier to manage campaigns across Google, Meta and Microsoft Ads while expanding AI-powered automation and bidding capabilities. The new Import Center reduces operational friction for teams running multi-platform campaigns, while cross-account portfolio bidding and enhanced reporting tools could help advertisers optimize budgets and performance more efficiently.

Catch up. Microsoft confirmed two previously announced AI-powered bidding updates are now rolling out broadly:

Seasonality adjustments for portfolio bidding and shared budgets
Data-driven attribution for automated bid strategies

The attribution model assigns conversion credit across the customer journey for campaigns using Maximize Conversions, Maximize Conversion Value and Enhanced CPC bidding strategies.

Bottom line: Microsoft is leaning further into AI-assisted campaign management while trying to reduce operational friction for advertisers managing campaigns across Google, Meta and Microsoft ecosystems.

Your customers search everywhere. Make sure your brand shows up.

Google Ask Maps: How to optimize for visibility

Wed, 20 May 2026 13:00:00 +0000

Understanding how to optimize for visibility in Google Ask Maps starts with recognizing what’s changing. Instead of returning a long list of businesses, Ask Maps narrows the field, interprets the user’s intent, and explains why certain businesses are a good fit.

That shift has an important implication. Visibility in Ask Maps now depends on how a business is understood and positioned within the response. If Ask Maps is more recommendation-driven, what should businesses and SEOs do differently?

At a high level, the answer is not to treat Ask Maps as a separate tactic. It’s to make your business easier for Google to understand, easier to match to real-world situations, and easier to trust. The fundamentals of local SEO still apply, but the way those signals come together matters more.

Visibility in Ask Maps is a filtering problem first

One of the most noticeable differences in Ask Maps is how limited the result set is.

Across testing, around 3-8 businesses were shown, depending on the query. That’s a very different experience from traditional Maps, where users can scroll through dozens of options and do their own comparisons.

Here, that comparison happens earlier in the process. Instead of presenting a wide set of options and letting users filter them down, Ask Maps does that filtering up front. It narrows the field, interprets the request, and presents a smaller group of businesses, along with an explanation of why each fits.

That changes what it means to be visible.

It’s no longer enough to show up somewhere in a longer list, preferably in the top three. The goal becomes making that smaller set of recommended businesses. That introduces a second layer beyond ranking: Google needs enough confidence to include your business and explain why it belongs there.

A useful way to think about this is that Ask Maps is solving two problems at once.

First, it decides which businesses are eligible.
Then, it decides which of those businesses it can confidently recommend.

Dig deeper: Google Ask Maps is moving from listings to recommendations

The SEO toolkit you know, plus the AI visibility data you need.

Ask Maps needs enough information to explain your business

Another consistent pattern is that Ask Maps doesn’t just list businesses. It interprets and describes them.

Even for relatively simple queries, businesses are described in terms of qualities like responsiveness, experience, specialization, or the types of situations they seem well-suited for. As queries become more specific or more tied to trust and decision-making, that framing becomes more central to the response.

This creates a different kind of requirement for optimization.

It’s not enough for Google to know that a business exists or even to understand its basic services. The system needs enough information to answer a more practical question: When should this business be recommended?

That includes understanding things like:

The types of jobs the business handles.
The situations it commonly deals with.
The concerns customers typically have.
How the business approaches those situations.

If that information is unclear or inconsistent, the system has less to work with. And when it has less to work with, it becomes harder to confidently position the business as a fit.

Another way to look at it is this: If Google can’t clearly explain why your business fits a situation, it becomes much less likely to recommend it.

Google Business Profile becomes the identity layer

At the foundation of this is the Google Business Profile.

In earlier-stage queries, Ask Maps relies heavily on GBP data, including business descriptions, services, reviews, ratings, and operational details. That makes the profile the primary source of how your business is understood.

Most businesses treat their profile as something to fill out with basic details and keep updated. That’s still necessary, but it’s nowhere near sufficient in this context. The profile needs to communicate a clear and specific identity.

A general profile might say that a business offers plumbing, HVAC, or electrical services. A more useful profile goes a step further and clarifies the kinds of work and situations the business handles.

For example, instead of only listing broad services, the profile can reinforce context, such as:

Emergency calls and response times.
Specific types of repairs or installations.
Experience with older homes or complex systems.
Common problems the business solves.

This added specificity gives Google more ways to match the business to different types of queries. It also helps the system move beyond basic categorization and into more situational understanding.

When profiles lack detail, the system has to infer more. When they’re specific, the system has more direct evidence to work with.

Dig deeper: The local SEO gatekeeper: How Google defines your entity

Reviews help shape how your business is positioned

Reviews have always played a central role in local search, but their function here appears to be more structured.

In the earlier analysis, review language consistently showed up in how businesses were described. Themes like responsiveness, honesty, professionalism, and clear communication weren’t just present in reviews. They were reflected in how Ask Maps framed the business itself.

That suggests reviews are doing more than supporting credibility. They’re helping define positioning.

This changes the way reviews should be evaluated. It’s still very important to look at rating, volume, and recency, but those are only part of the picture. The actual language within reviews carries more weight in Ask Maps than many businesses might realize.

A vague review provides limited value. A detailed review that describes the situation, the service, and the outcome gives the system much more context to work with.

For example, a general statement like “great service” reinforces satisfaction, but doesn’t say much about what the business does. A more detailed review that mentions a same-day response to a drain backup, clear communication about options, and a repair-focused solution provides multiple signals about how that business operates.

Over time, those patterns accumulate. Those patterns can influence how the business is interpreted and described.

In that sense, reviews are more than feedback. They’re one of the primary ways Google learns what your business is known for.

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Website content plays a bigger role when decisions get harder

The role of website content appears to expand as queries become more complex.

For simpler searches, such as basic service lookups, Google Business Profile and reviews carry much of the weight. But as queries move into more specific, higher-cost, or more uncertain situations, the system appears to look for additional supporting evidence.

This is where the website becomes more important.

Many service pages are built to describe what a business offers and why it’s qualified. That’s still necessary, but it doesn’t fully align with how users search in more complex scenarios.

As queries become more situational, users aren’t just looking for a service. They’re trying to understand a problem, evaluate options, and decide what to do next.

Content that reflects that process tends to be more useful.

Instead of focusing only on service definitions, stronger pages also address:

The situations that lead to the service.
How to recognize the problem.
What options are available.
How to think about the decision.
What outcomes to expect.

For example, a page about furnace repair can be expanded to include common symptoms, when repair is appropriate, when replacement might be considered, and how to evaluate that decision. That type of content aligns more closely with the kinds of prompts Ask Maps is interpreting.

This is also where job-to-be-done pages can be effective. Instead of organizing content only around services, these pages are built around the situation the customer is trying to solve and the decision they’re working through. I covered this approach in more detail in my article on jobs-to-be-done pages.

Dig deeper: If your local rankings are off, your map pin may be the reason

Trust signals matter more as risk increases

As queries move beyond basic service needs and into decision-making, the emphasis in Ask Maps shifts.

One of the clearest patterns is how trust-oriented queries changed what was highlighted. When users expressed concern about cost, honesty, or making the wrong decision, Ask Maps began organizing businesses around qualities like transparency, fairness, and careful workmanship.

That shift is important because it reflects how people actually think in those moments.

When someone is dealing with a higher-cost repair, an unexpected issue, or a recommendation they’re unsure about, they aren’t just asking who can do the work. They’re asking who they can trust to handle it correctly.

That means the signals that support trust become more important.

These signals can take different forms, but they tend to reinforce similar ideas:

That the business explains options clearly.
That it doesn’t push unnecessary work.
That it has handled similar situations before.
That customers felt confident in the outcome.

Some of this comes through reviews, but it should also be supported on the website and within the broader business presence. Process explanations, “what to expect” sections, examples of completed work, and clear communication around how decisions are handled all help reinforce this.

As the perceived risk of the job increases, the amount of supporting evidence matters more. Google appears to reflect that by expanding the types of sources it draws from and by placing more weight on how businesses are described in those contexts.

External signals help reinforce the same story

As queries become more complex or more tied to trust and decision-making, Ask Maps appears to expand the range of sources it draws from.

In addition to Google Business Profile data, reviews, and website content, it may incorporate information from third-party platforms, directories, and other publicly available sources when those help reinforce understanding or trust.

This doesn’t mean that every external mention carries equal weight. It does suggest that consistency across sources becomes more important.

If a business is described one way on its website, another way in reviews, and differently across directories or social platforms, the overall picture becomes less clear. When those signals align, they reinforce each other.

From a practical standpoint, this is less about expanding into every possible platform and more about ensuring that the core information is consistent and credible wherever it appears.

That includes:

Business descriptions.
Services offered.
Types of work handled.
Customer experiences.
Overall positioning.

When Google looks beyond a single source, it’s not just looking for additional information, but also confirmation.

Dig deeper: Local SEO sprints: A 90-day plan for service businesses in 2026

Think in terms of evidence, not just keywords

Taken together, these patterns point to a broader shift in how optimization should be approached.

Traditional local SEO often centers on keywords and rankings. Those still matter, but they don’t fully capture what Ask Maps is doing.

A more useful way to think about this is in terms of evidence.

For a business to be recommended, Google needs enough information to support several things at once:

What the business does.
What types of jobs it handles.
What situations it’s a good fit for.
How customers experience working with it.
Whether it can be trusted in higher-stakes decisions.

Each source contributes part of that picture.

The Google Business Profile establishes the baseline. Reviews add real-world context. Website content provides deeper explanations. External sources help confirm and reinforce what’s already present.

Individually, none of these elements tells the full story. Together, they form a more complete and consistent understanding.

This is where the shift from ranking to recommendation becomes more apparent. Keywords help establish relevance, but evidence supports being recommended.

A practical framework for Ask Maps optimization

To bring this together, it can be helpful to think in terms of a simple framework.

Instead of focusing on individual tactics, consider whether your business is clearly supported across five areas:

Identity: Can Google clearly understand what the business does and where it operates?
Relevance: Is it easy to match the business to specific services and situations?
Trust: Is there enough evidence that customers feel confident choosing the business?
Context: Does the content reflect the types of decisions customers are trying to make?
Consistency: Do different sources reinforce the same understanding of the business?

This isn’t a checklist to complete once. It’s a way to evaluate how clearly and consistently the business is represented across the sources Ask Maps appears to use.

What not to do

With any new development, there’s a tendency to overcorrect or treat it as a completely separate channel. That isn’t what this calls for.

Trying to optimize specifically for Ask Maps in isolation can lead to the wrong kinds of changes, such as:

Creating thin content aimed only at AI systems.
Forcing unnatural language into profiles or reviews.
Duplicating generic service pages at scale.
Focusing on volume over clarity.

These approaches may add more content, but they don’t necessarily add more useful information.

The more effective approach is to align more closely with how customers actually search and decide. That tends to produce the same types of signals Ask Maps appears to rely on.

What we still don’t know about Ask Maps

Everything covered here is based on observed patterns, not a fully documented system.

Ask Maps is still being tested and refined, and there are several areas where the direction isn’t yet clear.

First, the feature itself is still evolving. The way results are structured, how businesses are grouped, and how much explanation is provided can vary depending on the query and the test environment.

Second, usability is still in flux. In many cases, Ask Maps presents businesses without making it immediately easy to take action. For example, users need to click into the Google Business Profile to call or engage, rather than interacting directly from the response. That creates an extra step in the process, which may change over time.

Third, tracking and measurement are limited. At this stage, there’s no clear way to isolate Ask Maps visibility or performance within standard reporting tools. That makes it difficult to directly attribute traffic, calls, or conversions to this experience.

Finally, the weighting of different signals isn’t fully understood. While patterns suggest that Google Business Profile, reviews, website content, and external sources all play a role, the relative importance of each likely shifts depending on the query and the level of decision complexity.

Because of this, it’s important to treat these insights as directional rather than definitive.

Dig deeper: 5-step Google Business Profile audit to improve local rankings

Track, optimize, and win in Google and AI search from one platform.

The shift from ranking to recommendation

Ask Maps is a version of local search where retrieval, evaluation, and decision-making are happening much closer together. Instead of searching, comparing, researching, and then deciding across multiple steps, users are guided through that process within a single experience.

That changes what visibility means. For Ask Maps, it’s no longer just about being present in the results. It’s about being understood well enough for Google to explain why your business fits the situation and trusted enough to be recommended.

For businesses and SEOs, the response isn’t to chase a new tactic. It’s to build a clearer, more consistent, and more complete representation of the business across the sources that shape that understanding.

The businesses that benefit from this shift are likely to be the ones that are easiest to interpret, easiest to trust, and easiest to match to real-world customer needs.

Your customers search everywhere. Make sure your brand shows up.

How Google Discover publisher profiles work and why they matter

Wed, 20 May 2026 12:00:00 +0000

Google introduced publisher follows and profile pages to Discover last year but the feature remains lightly documented and poorly understood.

Since then, publisher profiles have started appearing more broadly across Discover for publishers, creators, and social-first accounts.

Here’s how Discover publisher profiles appear to function today, how they connect to social accounts and the Knowledge Graph, and why some publishers are already getting access to more customizable profile experiences.

Why publisher profiles matter more now

As a technical SEO, I’m used to Google leaving plenty unsaid in its documentation. Discover publisher profiles take that even further.

Google’s official Discover documentation barely mentions profile pages, despite the growing role they appear to play in how publishers and creators surface across Discover.

The timing is notable. As publishers reassess visibility in AI-driven search experiences, Discover profiles give users more direct control over the publishers they follow while aggregating content across websites and social platforms.

Because Google has shared so little about how these profiles work, I’ve spent a lot of time analyzing them across publishers, creators, and social-first accounts. Several patterns are starting to emerge around:

How Google builds these profiles.
How they connect to the Knowledge Graph and social platforms.
Which publishers appear eligible.
Why some publishers are already getting access to more customizable profile experiences.

Dig deeper: Inside Google Discover: 20 pipelines, 42 million cards, and what they mean for publishers

The SEO toolkit you know, plus the AI visibility data you need.

What Discover publisher profiles actually are

In September 2025, Google rolled out one of the most significant Discover updates in years, introducing publisher follows and dedicated profile pages.

The update changed Discover in several ways:

Publishers received landing pages that aggregate their content.
Users gained a more direct way to influence which publishers appear in their feeds.
Social content became more integrated into the Discover experience.

This update came against the backdrop of the shift in sentiment toward AI’s impact on publishers’ visibility. Paired with preferred sources in Google Search, this gave users more control over which publishers they wanted to see while giving publishers another way to surface relevant content to the right audiences.

For the most part, publishers can’t control the layout of their profile pages. But in March, people began noticing a small group of publisher profile pages with a more premium-looking layout and extra features not found on typical Discover profile pages.

It’s since been confirmed to me that these pages are part of a limited beta group where publishers can control and edit their profiles themselves.

Dig deeper: Google quietly gave 54 publishers control over their Discover profiles. Here’s what they did with it.

The features inside Discover publisher profiles

Liverpool FC’s publisher profile

Aside from the brand name and “Follow on Google” button, publisher profiles typically include the following features:

Profile photo: These are based on the Knowledge Graph first, but if there’s no Knowledge Graph, the photo often comes from the YouTube account’s profile photo.
Total followers: The total number of followers across social media platforms. This is not related to followers on Google Discover.
Social profiles: The various social media accounts associated with the publisher. So far, the following social media platforms are supported:
- YouTube.
- TikTok.
- Instagram.
- Facebook.
- X.
- LinkedIn.
About: A brief description of your brand. In most cases, this comes from a Wikipedia page or another source connected to the Knowledge Graph, such as your About Us page.
Latest posts: These come from your articles and social posts across various channels.

Get the newsletter search marketers rely on.

See the complete picture of your search visibility.

Features on editable publisher profiles

Fox News’ publisher profile

Publishers in this testing group have access to several features with some level of control:

Customized banner image: At the top of these publisher profiles, there is a horizontal banner image.
Pinned posts: Publishers can pin Discover cards for articles and social posts to the top of their profile.
Links: These are general links publishers can add to their profile. They do not need to be articles or social posts, but can be any general links on your site. For example, in the screenshot above, Fox Weather links to an app and a livestream, which would otherwise have a hard time getting visibility in Discover.

Dig deeper: How to increase Google Discover traffic with technical fixes

The two types of Discover publisher profiles

Functionally, there are two types of publishers that receive publisher profiles: those who publish on websites and social media, and those who publish only on social media.

Profiles for web publishers

Publishers who publish on websites include pretty much any brand that publishes on a website:

Legacy publishers.
Local news publishers.
Trade publishers.
Company websites.

If an organization has a Knowledge Graph and regularly publishes content, it’s generally eligible for a profile page. Because Google doesn’t currently provide a direct way to find publisher profiles, there’s a decent chance your brand already has one, even if you’ve never seen it. Tools like Damián Taubaso’s Profile Page Finder tool and 1492’s Vision can help you find it.

Generally speaking, these profiles are often more enriched than those for social media publishers. They’re more likely to include an About section, a logo, links to other social media accounts, and links to the website.

The most common issue with web publisher profiles is missing social profile links. To combat this, there are three main steps:

Add the social media account as a sameAs entry in your organization schema.
Link to your site’s homepage from the website section in your account bio.
On your profile page, select the three dots and click Send feedback, then link to the relevant social media accounts you would like linked there.

Profiles for social media publishers

Profile pages for social media publishers are for brands or people who aren’t publishing primarily on a website where they are the primary entity. Glenn Gabe, for example, is treated as the same entity as his organization GSQi, and his publisher profile is linked to the GSQi site.

This primarily includes journalists, people of note, and publishers that only publish on social media channels.

My rule of thumb is that, to be eligible for this, a social media account needs roughly 50,000 followers, although sometimes it can be less, and it generally needs to be safe for work.

publisher profiles for social media publishers can be much less enriched than those for web publishers. Because these aren’t linked to a website, they’re less likely to connect to a Knowledge Graph, which serves as a source of truth for various profile elements. As a result, these profiles are often less complete.

It’s most common for these to be built around X accounts, leading to profile pages with no profile picture and no description. The best way to get a profile picture added is to connect the account to YouTube as well.

Without being connected to a site, you’re limited to submitting feedback on the profile page and getting your other social media accounts added. But without a connected Knowledge Graph, there’s currently no way to add a description to your profile page.

Dig deeper: How Google Discover qualifies, ranks, and filters content: Research

Track, optimize, and win in Google and AI search from one platform.

Where Discover publisher profiles may be heading

Based on the testing already happening, it seems likely that Google will eventually announce editable profile pages as a beta feature for publishers.

That said, I don’t expect the current templates to remain unchanged. From my perspective, the existing layouts aren’t especially user-friendly, and I’d expect Google to continue iterating on how these pages are structured and customized.

I also suspect access to editable profiles will remain somewhat gated. While hundreds or even thousands of publishers may eventually gain access, profile editing is unlikely to become universally available. At least for now, the feature appears more likely to remain limited to established publishers and creators.

Why some teams launch faster by Storyblok

Wed, 20 May 2026 11:00:00 +0000

New data from the recent Storyblok Global Speed-to-Market Benchmark Report reveals the biggest causes and costs of slow go-to-market (GTM) delivery today — with tech limitations at the center of the problem.

The world has shifted gears in recent years, and the pace of change has accelerated beyond anything we’ve experienced before. Rapid advances in AI and technology, combined with the constant emergence of new digital channels and trends, have transformed GTM delivery.

Customer and organizational expectations are clear: deliver great work fast or lose out.

The problem is that while speed-to-market expectations are skyrocketing, only 22.5% of teams say they consistently deliver at the pace the market demands. That gap between intention and execution is clear.

So, what’s slowing teams down?

In the Global Speed-to-Market Benchmark survey, hundreds of GTM teams shared where the go-to-market process is slowing down — or, in some cases, stalling completely — what’s causing the delays, and what it takes to achieve true speed-to-market today. Here’s what they had to say.

The bottlenecks sabotaging GTM velocity

Four major bottlenecks stood out in the survey findings — and each one traces back to a technology limitation or dependency.

1. The approval process

The approval process is the single biggest bottleneck in the GTM workflow, cited by more than 50% of teams. More than half go through three or more rounds of content revisions, and nearly one in five endure five or more.

This rigorous review process isn’t necessarily driven by high standards. More often, it’s the result of stacks fragmentation.

When feedback is scattered across tools, with no single source of truth, unclear sign-off ownership, and no firm deadlines, reviews become the bottleneck that quietly kills momentum. It’s as much a technology failure as a process failure — and one that adds significant unnecessary time to the GTM process.

A well-configured CMS is often the most effective fix here — and for GTM campaign delivery, that increasingly means a headless CMS. Because content is decoupled from presentation, a headless CMS stores everything in a single structured repository every stakeholder (marketers, developers, legal, and others) can review. No version confusion. Just one content record for every reviewer.

Pair that with a built-in visual editor and in-app commenting, and you remove two of the biggest friction points in content review. Stakeholders can comment on specific elements while seeing exactly what will go live, and feedback stays centralized and trackable instead of scattered across inboxes and tools.

The takeaway

The report data supports that conclusion: only 50% of teams say their CMS even somewhat supports speedy go-to-market. For MarTech leaders auditing their stack, the content review layer deserves the same scrutiny as automation platforms and analytics infrastructure if faster, more accurate delivery is the goal.

2. Overreliance on developers

Thirty-eight percent of marketing and digital teams need developer support for most or every campaign, according to the survey. More than a third of developers spend between a quarter and half of their working time supporting GTM campaigns. And 42% say their tech platform makes that support more complex than necessary.

When publishing a landing page, updating campaign assets, or changing a simple content block requires a developer ticket, two things happen: marketers lose speed and developers lose focus. Neither team can work at its best, and every launch takes longer than it should.

The takeaway

The solution isn’t to give marketers and other teams access to the entire tech stack and codebase. It’s to build a stack that lets each team operate independently where they should.

For marketing teams, that means owning the work that’s fundamentally theirs: building campaign pages, updating content, publishing launches, and managing assets. For developers, it means getting time back to focus on high-value technical work.

Once again, this is where a headless CMS proves its value for teams trying to improve speed-to-market. Decoupling content from code gives marketing teams a dedicated space to create, edit, and publish without touching the underlying codebase. Developers define the structure, marketers own the content, and launches no longer depend on developer support every time.

3. Compounding tech limitations

This finding comes as no surprise based on the data so far. Nearly a third of GTM teams cite tech limitations as a major cause of slow delivery. The main issues are complex deployment processes (39%), tool integration problems (25%), and fragmented or outdated systems (14%).

What makes this bottleneck especially costly is how invisible it can be. Missed deadlines get flagged. Approval delays get escalated. But tech limitations build quietly as developers create workarounds, marketers wait on tickets, and teams start accepting that “this is just how long it takes.”

The takeaway

A thorough audit of where tech limitations are slowing speed-to-market — especially in the areas above — is critical for GTM teams today, along with evaluating alternatives that can support modern delivery demands.

The data is clear: a team can only move as fast as its technology allows.

4. Post-launch firefighting: The hidden speed tax

Even when teams get campaigns out the door, the work often isn’t finished. Post-launch fixes affect 79% of teams at least some of the time. When projects go live with errors, the fallout is immediate and public: broken user experiences, compromised results, and developers pulled away from planned work to firefight.

For teams already over-reliant on developers to publish anything, this is where the damage compounds. Fragmented, outdated tooling often makes developers the critical path for every GTM launch, and when post-launch fixes pile onto that workload, ticket queues back up fast.

The takeaway

The teams that break the cycle address the source of the problem: the tech stack — and especially the CMS.

A headless CMS reduces risk by letting teams implement and preview content changes independently of the codebase, so code updates can’t accidentally break page layouts, and what gets approved is exactly what goes live. The result is fewer surprises, faster delivery, and more time spent running campaigns instead of fixing them.

The cost of slow GTM delivery

The Global Speed-to-Market Benchmark survey examined the causes and costs of slow speed-to-market. The data is clear:

Lost revenue (22%)
Missed market opportunities (18%)
Reduced marketing effectiveness (15%)

These three leading consequences are already affecting most GTM teams, with nearly half saying their competitors are moving faster than they are. At the same time, only 22.5% feel they consistently deliver at the speed today’s market demands.

The data shows teams are already feeling the impact of these issues — and the cost of slow delivery doesn’t stop there. It affects your people, too.

The gap between what teams are capable of and what their processes and tech stacks allow them to deliver can become a quiet, persistent source of disengagement. It often leads to lower morale and growing frustration across teams, creating real retention risks — reflected in the 58% of developers considering leaving their jobs because of inadequate or outdated tech stacks.

An emerging leadership alignment gap

A key conclusion from the report was the clear misalignment between leadership priorities and the investment needed to execute them.

Fifty-six percent of executives rate speed-to-market as important or mission-critical to growth. Yet only 36.5% of respondents believe senior leadership is doing enough to support and improve it. The urgency is there, but the commitment to enabling delivery often isn’t.

But data changes that conversation.

When bottlenecks are identified, quantified, and tied directly to revenue risk and competitive disadvantage, speed-to-market stops being an abstract priority and becomes a concrete business case.

For leadership, that means knowing where to act. For GTM teams, it means having the evidence and focus needed to drive meaningful improvements.

How to close the speed-to-market gap

Most of the bottlenecks and consequences in this article and report aren’t new. What’s changed is the cost of ignoring them.

In a market where AI has compressed timelines, audience expectations are sky-high, and competitors are moving faster, a slow or fragmented tech stack is no longer just an inconvenience — it’s a strategic liability.

The teams consistently hitting the market at the right speed aren’t doing it by working harder or hiring bigger teams. They’ve built a tech foundation that creates confidence, autonomy, and efficiency across the organization. Marketers own their work, developers are freed from constant GTM tickets, stakeholders know exactly what will go live, and the entire process becomes easier.

The full Storyblok Global Speed-to-Market Benchmark Report details the complete data, analysis, and a practical framework for closing the speed-to-market gap.

Google launches Ask Advisor across Ads, Analytics and Merchant Center

Wed, 20 May 2026 04:00:00 +0000

Google is introducing Ask Advisor, a new Gemini-powered AI collaborator designed to work across Google Ads, Google Analytics, Merchant Center and Google Marketing Platform as announced today at Google Marketing Live 2026.

Driving the news. Ask Advisor acts as a unified agent that connects insights, workflows and recommendations across Google’s marketing ecosystem.

According to Google, the tool can help marketers launch campaigns, analyze performance and surface optimization recommendations without needing to switch between products.

For example, a marketer could ask Ask Advisor to “find new customers for my hair care products,” and the system would automatically pull product details from Merchant Center and help build a campaign in Google Ads.

How it works. Ask Advisor connects Google Ads, Analytics, Merchant Center and Marketing Platform through a shared Gemini-powered interface. The system can access campaign, audience and product data across products to generate recommendations, automate setup tasks and surface insights based on a marketer’s goals.

Ask Advisor also integrates reporting insights from both Google Ads and Google Analytics to explain campaign performance and recommend next steps.

Google says the goal is to make advanced campaign management and analysis more accessible, even for marketers without deep technical or analytics expertise.

The launch expands Google’s growing portfolio of in-product AI agents and positions Gemini as a central operating layer across advertising and measurement tools.

Why we care. Ask Advisor represents one of Google’s clearest moves yet toward agentic advertising workflows.

Instead of manually navigating between reporting dashboards, campaign tools and optimization settings, marketers may increasingly rely on AI agents to execute operational tasks and surface strategic insights.

The bigger shift is structural: Google is positioning Gemini as the connective layer across its ad stack, which could reshape how campaigns are built, optimized and measured.

What to watch. The biggest question will be how much operational control advertisers are willing to hand over to AI agents. Marketers will also likely watch closely for transparency around recommendations, automation decisions and reporting accuracy as Ask Advisor expands.

Availability. Ask Advisor is currently available in beta for English-language accounts, with additional capabilities expected to roll out later this year.

Dig deeper. More Google Marketing Live 2026 news from today:

Google tests new conversational ad formats in AI Mode and Search
Google launches AI Performance Insights and Conversational Attributes in Merchant Center
Google expands Direct Offers with AI-generated bundles, native checkout and travel deals
Google brings Meridian marketing mix modeling into Analytics 360
Google expands Demand Gen with YouTube creator tools
Google upgrades Asset Studio with Gemini-powered creative generation and video tools
Google expands Universal Commerce Protocol and launches new agentic shopping tools

Google lets you build your own app within Google Search with agentic coding

Tue, 19 May 2026 17:45:00 +0000

Google is now letting searchers build their own apps directly into Google Search. This enables searchers to set up a search feature that delivers the information they need, in the format they want, from the sources they want.

Liz Reid, the head of Google Search, announced at Google I/O, “Search can build the ideal response, in the right format for your question – completely on the fly. So you can get custom generative Ul, including visual tools and simulations, tailored precisely to your needs.”

Examples. Here are a few examples of what you can code yourself into Google Search:

(1) Whether you want to wrap your mind around astrophysics or visualize how your watch works, Search can design custom layouts, assembling components (like interactive visuals, tables, graphs or simulations) in real-time, Google wrote.

(2) Ongoing tasks widgets, like planning a wedding or managing a home move. Search can go a step further, building you custom dashboards or trackers that you can continue to come back to and make progress on. You can think of these like mini apps for your own specific tasks, Liz Reid explained.

(3) Fitness tracker in Search, where you can ask Google Search to build you a custom fitness tracker. Search will code it for you, tapping into fresh, real-time sources including reviews, live maps and local data like the weather, so you get a tracker that works for you, helping you stay on track week after week.

What it looks like. Here are some examples of what this looks like in Google Seaerch.

Generative UI example:

Custom tracker example:

Availability. The generative Ul capabilities will be available for everyone in Search this summer, free of charge.

The custom experiences with Antigravity, like mini apps, right in Search in the coming months, starting first for Google Al Pro and Ultra subscribers in the U.S.

Why we care. Google Search will not just answer your questions but you can code your own mini apps within Google Search to give you the answers you want, in the format and style you want.

This is really a unique way for search and likely can only be done with generative-AI features and tooling.

Google’s new intelligent Search box – its biggest change to the search box in 25 years

Tue, 19 May 2026 17:45:00 +0000

Google unveiled the biggest change to its search box in 25 years. It is calling the new search box the “Intelligent Search box.” The new search box aims to bring easier access to the AI search features in Google Search to Google’s users.

And yes, this is all powered by the latest Gemini release, Gemini 3.5 Flash.

What it looks like. Google redesigned this search box to give searchers more space to ask longer, deeper queries. The search box will continue to expand as the user enters the query or prompt. There is an AI-powered suggestion that Google’s Head of Search, Liz Reid, said “goes beyond autocomplete.”

Plus, you can search with text, images, files, videos or your Chrome tabs.

Here is what the new intelligent search box looks like:

This puts Google’s “most powerful AI tools right at your fingertips, making it easier to ask your questions,” Liz Reid of Google said.

Seamless Google Search to AI Mode. Google also said it made the AI Overviews seamless link approach to AI Mode live today globally both on desktop and mobile. This is something that launched to many back in January but is now fully live.

Here is how this works:

Why we care. The Google Search box looks and feels different and that might be a big deal to how it leads to how users search on Google. It might impact the type of search traffic Google has been sending you and will send you in the future. It might lead to more people jumping to AI Mode sooner from Google Search and it might lead to more AI Overviews with deeper answers. It might lead to fewer clicks to your web site than before.

Change is not always easy, but it is inevitable, especially when it comes to Google Search.

Sundar Pichai, Google’s CEO told us that the extraordinary thing about Search is how people search and expect more from Google Search. Search is evolving, from individual queries to ongoing conversations and now to agentic workflows. Search is the most used product in the world, Sundar said and Google will evolve super hard to stay a step ahead of where our users want to be.