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Google AI Brief may be the replacement keywords never had

June 8, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

People have been calling the keyword dead since at least 2010. Yet here we are in 2026, still using keywords to show ads on Google.

Advertisers weren’t wrong to equate the loss of control with the death of the keyword. The keyword simply couldn’t disappear until Google had something better to replace it.

At Google Marketing Live (GML) last month, we may have seen that replacement. AI Brief is a Gemini-powered control layer that lets you steer AI Max using prompts-first language.

At first glance, AI Brief may seem like just another AI Max feature. AI Max is still trying to gain traction among advertisers. So couldn’t advertisers simply ignore it and stick with keywords?

Probably not.

When users shifted to mobile, Google eventually pushed advertisers toward Enhanced Campaigns. The conditions may now be in place for a similar transition, this time from keywords to prompts.

Consider the other announcements from GML. AI Mode surpassed 1 billion monthly users. The search box is getting its biggest redesign in 25 years. Users in AI Mode are also submitting queries that are, on average, three times as long as traditional searches.

Whether advertisers like it or not, people are increasingly using prompts instead of keywords to find information.

With AI Brief, the replacement for the keyword finally exists. We can now target prompts with prompts. Combined with the consumer-driven shift away from keyword-based searches, that makes the keyword’s obituary much easier to believe.

The keyword is dying because users stopped using it

Most “keywords are dead” arguments over the past decade were supply-side stories. Google reduced broad match’s control, made RSAs decide the best ad variation, and let Smart Bidding set bids to help any keyword deliver on its underlying financial goals. They also stopped showing every query in search terms reports, all steps framed as Google taking the keyword away.

Now it’s different. The pressure is coming from the demand side.

People are asking Google longer, more conversational questions because Google built a search experience that invites them to. The new search box, the biggest upgrade in 25 years, dynamically expands as you type. You no longer pick a “mode” before you ask. The interface itself is telling consumers that “running shoes” is no longer the only way to ask for what they really want.

If you’re an advertiser, the question stops being “Do I want to use keywords?” It becomes “How do I show up in a query a keyword can’t possibly match?” Trying to capture a paragraph of context with three positive match types and one negative is, let’s be real, increasingly absurd.

Optmyzr’s 2026 Match Type Study shows the same pattern from the spend side. We analyzed 30,000 Google Ads accounts in February 2026 across all Search campaigns with active keyword spend. (Disclosure: I’m the cofounder and CEO of Optmyzr.)

Exact match has lost nearly 10 percentage points of spend share since 2022, while broad match has climbed steadily to become the dominant match type by budget.

Phrase match, meanwhile, consistently punches above its weight, holding the largest share of non-branded spend and leading on conversion rate in both ecommerce and lead-gen segments.

Advertisers are clearly growing more comfortable trusting Google’s AI with broader targeting, a shift attributed to Smart Bidding’s maturation rather than exact match losing its performance edge.

The other tell is that Google isn’t alone here. We recently started managing ads on ChatGPT, and OpenAI’s ad surface is keyword-optional from day one.

When the company that invented keyword advertising and the company reinventing search both ship a keyword-optional product, that means something. At this point, we’re just arguing about how fast the keyword is dying.

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AI Brief is a technical replacement for keywords

Unsurprisingly, AI was the topic that drove nearly every announcement at GML 2026. At I/O the day before, Sundar Pichai, Google’s CEO, even said that Google’s migration to become an AI-first company was nearing completion, with AI agents providing the final push and rewriting the last remaining code. Downstream from all the talk about AI is the realization that consumers now prompt rather than search with keywords.

AI Brief is one way to operationalize the required evolution for advertisers to keep up with consumer behavior. Powered by Gemini, it lets you describe, in your own words, what your business is, what your messaging should and shouldn’t say, the searches you want to capture or avoid, and the audience you’re trying to reach.

Google calls these messaging guidelines, matching guidelines, and audience guidelines. Internally, I think of it as: tell the model what you’d tell a new media buyer on their first day.

Then AI Brief echoes back how it understood your requests and shows preview samples of the assets and queries it thinks you meant. You push back if it’s off. You iterate. When you’re happy, you lock in the brief.

That’s a meaningfully different interaction model than a keyword list. A keyword list is a static artifact. A brief is a negotiation. It can adapt as your business changes without you reuploading hundreds of new keywords.

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There’s a parallel in the world of coding, where AI has arguably had the biggest impact with agentic code writers and vibe-coding systems like Lovable.dev. The idea is that the code we write to have software achieve an outcome should be merely a temporary artifact reflecting the current abilities of the tech.

Coders should focus on writing the prompts that describe the goals of the web page rather than the code needed to achieve those goals. The prompt instructs the software what it should do and how to do it safely. AI can then write the code that executes the task on demand, using the latest capabilities while staying grounded in the prompts that define its purpose.

This is what Sam Altman called “software on demand” at the GPT-5 launch, the idea that AI can “instantaneously create an entire piece of computer software for you.”

Google echoed the same vision at I/O 2026, where Pichai described Search using Gemini and Antigravity to build custom experiences, dynamic layouts, and persistent mini apps on the fly. Software generated in response to what each user needs, in the moment they need it.

People need to be purposeful about work. Your purpose at work isn’t to write emails and work with spreadsheets. It’s to achieve certain outcomes, and writing emails and using spreadsheets is how that gets done. Stop worrying about how and start thinking about the real goal: growing your ad revenue by 10% while maintaining similar margins.

Keywords are the “how,” not the “why.” AI Brief is actually closer to letting us manage the “why” while letting AI figure out the “how.”

How to try AI brief now

AI Brief is rolling out in English for AI Max for Search first, then Performance Max and AI Max for Shopping. Existing text guidelines will migrate into AI Brief automatically as messaging guidelines.

So yes, this is starting as an AI Max feature, and you may not be using AI Max because several practitioners note that AI Max can pull in junk traffic on lead-gen accounts, competitor-heavy verticals, and new campaigns with thin signal. Some veteran marketers have been turning AI Max off in those situations.

The practical playbook shared during a recent PPC Town Hall is solid: start new campaigns in Phrase, promote the winners to Exact, and layer Broad and Smart Bidding on top once you have data.

With the advent of AI Brief’s matching guidelines, advertisers can further tweak their targeting by saying, “prioritize searches for X, avoid Y.” But this strategy still requires a human who knows the account to pull that lever. So don’t unplug your keyboard just yet.

The new funnel, and why short keywords still have a job

Andrew Lolk and Kirk Williams pushed me on a real edge case in the LinkedIn discussion that led to this piece: the newborn photographer whose entire business depends on someone in their city typing “newborn photographer” and converting on the first ad that shows.

Short, transactional queries won’t disappear. So why not keep traditional search campaigns with keywords around to handle these types of queries? I think it’s reasonable to have two campaign types for different jobs. But their relationship is a funnel, not a parallel.

Here’s how I see it shaping up:

AI prompts for discovery: “I just had a baby and I want to remember this period. What are some ideas?”
AI prompts for research: “Compare lifestyle newborn photographers to studio newborn photographers in the Bay Area.”
Short keyword to buy: “Newborn photographer Los Altos.”

If you only show up at the bottom of that funnel, you’re betting your entire business on being the first short-keyword click. If you’re not present in the discovery and research prompts above it, you’re not in the consideration mix when the short query happens.

The reason a user may do that short query is that they already know more or less who they’d buy from, and they’re now looking for the best offer from a shortlisted set of options. The conversational layer feeds the transactional layer. Ignore it, and the transactional volume eventually stops coming to you.

This is also why I don’t think Google maintains two parallel systems forever. The short-keyword volume will keep shrinking relative to AI prompt volume, and at some point, the economics of supporting both stop working.

Further, AI-first campaign types will soon be great at converting agentically, using the Universal Commerce Protocol and other new methods being developed to allow agents to transact for their humans.

What AI Brief does to the four human PPC roles

I’ve argued for years that PPC pros take on four roles in an automated world:

Teacher.
Doctor.
Pilot.
Restaurateur.

These roles continue to explain the PPC manager’s world quite well, but with some new nuance.

The teacher

This role is the most direct analogy. You used to teach the machine what to target by handing it the end result: a keyword.

The funny part is that for many of us, that keyword was already generated by feeding an LLM a prompt and cleaning up the output.

AI Brief lets you skip the lossy translation step. Hand the machine the prompt itself, not the artifact it produced. The teaching gets richer because nothing gets lost.

The doctor

The shift is from “prescribe Drug X” to writing down, in structured language, what the patient actually needs.

The treatment can then evolve as the patient’s condition and the available solutions change. Keywords were restrictive: one symptom, one prescription.

Briefs and prompts allow freedom and evolution. That’s what good medicine looks like, and that’s what good targeting looks like now.

The pilot

We need a new instrument panel. If we’re not aiming at keywords anymore, the search query report stops being the right gauge of how well Google is matching intent.

We’ll probably see more search themes (buckets of intent that AI Brief is mapping into) replacing the line-by-line query list.

The restaurateur

You write the menu and the concept brief so the chef (the AI) cooks. AI Brief is almost literally the concept brief.

You define the cuisine, the values, the things the chef must never serve, and the kind of guest you’re cooking for. Then you taste, correct, and iterate. The kitchen runs.

If you want the longer-form version of where I think digital marketing automation is heading, I wrote it up earlier this year as AI skills, the next layer of marketing automation.

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Why AI Brief feels different

The keyword isn’t dying because Google decided to kill it. It’s dying because consumers stopped phrasing their needs in a couple of words.

AI Brief is the first structural replacement that seems to allow advertisers to express their intent in as rich a manner as consumers can now express theirs to a chatbot. That’s why this GML announcement felt like a more serious nail in the coffin of the keyword than the last several.

Control was about dictating keywords to Google. Leverage is about feeding the engine the right brief and letting the auction execute at a scale no human team can match.

We don’t have to escape automation. We have to coexist with it on better terms. AI Brief is a great eventual replacement for the keyword. Hand it your prompt. Watch what it does.

Push back. Lock it in. Then you can move on to the parts of the job a machine can’t do, like knowing your customers and working on the goals that move their business in the direction they want.

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Stop looking for the perfect PPC budget split

June 8, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

Most PPC budget discussions focus on finding the right split between brand awareness and conversion-focused campaigns. That’s usually the wrong goal.

The optimal balance changes constantly based on business stage, market saturation, seasonality, competitive pressure, and revenue objectives.

Yet many teams still treat the funnel split as a fixed decision: 40% upper funnel, 60% lower funnel, set it and forget it. That might be the right ratio today and completely wrong in six months.

Every budget conversation eventually comes down to the same argument. Someone wants to cut brand awareness spend because it doesn’t convert directly. Someone else warns that if you only chase conversions, the pipeline dries up in 12 months.

Both are right, which is what makes this so difficult.

The lower-funnel case is easy to make

When most PPC managers talk about the lower funnel, they mean Shopping, Performance Max, and high-intent Search.

Someone typing “buy running shoes new york” has already decided they want the product. Shopping shows the right SKU at the right price. PMax chases the conversion signal across every Google surface. The attribution is clean, the ROAS is visible, and the CFO is happy.

The problem is that this demand already exists. These campaign types harvest intent. They don’t create it. Every conversion you get from a high-intent search term or a Shopping click is the result of awareness that was built somewhere else:

A YouTube pre-roll.
A friend’s recommendation.
A social post.
Years of brand presence in the market.

You’re collecting fruit from a tree you didn’t plant.

Search is worth treating separately here because it doesn’t sit neatly at the bottom of the funnel. A query like “best running shoes for marathon training” is informational.

The person is researching, not buying. AI Max and broad match expansion in Google Ads are pushing Search campaigns further into this territory, meaning Search can serve both ends of the funnel depending on how it’s configured and which queries it actually captures.

It’s worth auditing your Search terms regularly through this lens: How much of your Search spend is closing existing demand versus reaching people earlier in their decision-making process?

This works until it stops working. And the signal that it’s stopping usually arrives too late.

When branded search volume flatlines, CPCs on your core terms keep climbing because the same pool of high-intent users is getting more expensive to reach, and new customer acquisition starts to plateau while retention holds steady. These are the symptoms of a brand that’s been living off existing demand without replenishing it.

Lower-funnel efficiency is real. But it’s also borrowing against the future.

Dig deeper: PPC budget planning: Aligning business goals, ad spend, and performance

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The reseller trap: When your lower funnel depends on someone else’s brand

There is a version of this problem that’s specific to resellers and multi-brand ecommerce, and it doesn’t get discussed enough.

If you sell branded products you don’t own, your lower funnel can work extremely well in the short term.

Shopping and Search campaigns for established brands convert efficiently because the brand owner has already done the awareness work. You’re harvesting demand that Nike, Adidas, or whoever else has spent years and significant budgets building.

The structural risk is that you have no control over that demand. If the brand owner reduces its marketing investment, pulls out of a market, or simply fades in relevance, your Shopping and Search volume follows.

You can’t counter it with your own PPC spend because the underlying interest isn’t there to harvest. The tree stops producing fruit, and you never owned it.

This creates two strategic imperatives that are easy to deprioritize when the lower funnel is performing well.

Own-brand development: products or lines that you control, where you own the brand equity and can invest in awareness independently.
Reseller brand building: investing in the upper funnel to make your own name well known, so customers think of you as the destination regardless of which brands you carry. A consumer who searches for your store name rather than a specific brand is much more resilient than one who only finds you through a branded product query.

Both require some form of upper-funnel investment. Own-brand development needs awareness campaigns to build product recognition from scratch. Reseller brand building needs a consistent presence across Demand Gen, YouTube, and Display to make your name synonymous with the category, not just the brands within it. That’s only within Google’s ecosystem.

To complete the picture, you might also include SEO, word of mouth, pop-up events, local advertising, and more. Brand building has no limits.

Neither of these investments shows up in this month’s ROAS report. Both show up in next year’s business resilience.

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Upper funnel is inventory management

Brand awareness spend is often framed as the soft, hard-to-measure part of the budget. The part you do when you have money left over. That framing gets it exactly backward.

Upper-funnel investment is how you build the pool of future converters. Every person who sees a Demand Gen ad on YouTube or Google Display today and doesn’t click isn’t a failed impression. They’re a potential high-intent searcher in three weeks. You’re filling the top of the pipeline that your Shopping and Search campaigns will harvest later.

Google’s Demand Gen campaigns make this dynamic particularly visible within a single platform. You can run Demand Gen to reach in-market audiences who don’t yet know your brand, then watch Search impression share and branded query volume respond over the following weeks. The lag is real and measurable.

Upper-funnel spend today shows up in lower-funnel performance next month, not this week. That delay is why it gets cut first when budgets tighten, and why cutting it tends to hurt six to eight weeks later rather than immediately.

Teams that manage this well think of Demand Gen not as brand spend, but as pipeline investment. The question isn’t “What is the ROAS on this campaign?” It’s “How much qualified demand am I creating for my Shopping and Search campaigns to close?”

Dig deeper: Paid media efficiency: How to cut waste and improve ROAS

Why a fixed split is the wrong answer

The 70/30 or 60/40 rules you read about are averages across many businesses in many contexts. They’re useful as a starting point and useless as a long-term policy.

Consider what changes the optimal split.

A new product launch needs heavy upper-funnel investment upfront because awareness is zero.
A mature product in a saturated category needs it, too, because every competitor is also harvesting the same pool of high-intent searchers, and the only way to grow is to expand the pool.
A seasonal business approaching peak needs to have already done its upper-funnel work before the peak hits because awareness doesn’t respond fast enough to be built in-season.

Equally, a business in financial distress or facing a short-term revenue target can’t afford to wait eight weeks for upper-funnel investment to mature. The right answer in that moment is to focus on the lower funnel, accept the trade-off consciously, and plan to reinvest in awareness as soon as the pressure lifts.

The point is that both of these decisions are correct in context. A fixed split ignores context entirely.

Building a dynamic split logic

Rather than a fixed ratio, the most useful framework is a set of conditions that trigger a shift in either direction.

Shift budget toward upper funnel when:

Branded search volume is flat or declining quarter over quarter.
New customer acquisition cost is rising while retention metrics hold.
You’re entering a new market or launching a new product.
Competitors are visibly increasing their brand presence.
You’re approaching a peak season with at least six to eight weeks of runway.
You’re a reseller whose top brands are showing declining search interest or reduced marketing activity.

Shift budget toward lower funnel when:

You have a short-term revenue target that can’t wait.
Upper-funnel campaigns have been running long enough to build measurable awareness, and the conversion window is now.
Cost per acquisition on Shopping or Search is below target, and scaling makes sense.
Audience saturation on Demand Gen is high, meaning you’re reaching the same people repeatedly without expanding reach.

Within Google Ads, the data to monitor this is available without external tools. Branded query volume in Search Terms, impression share trends on non-branded terms, Demand Gen reach and frequency metrics, and new versus returning customer segmentation in conversion data together give you a reasonable picture of where the funnel is healthy and where it isn’t.

The review cadence matters as much as the metrics. Monthly is the minimum for a funnel split review. Quarterly is too slow. By the time a quarterly review catches a declining branded search trend, you’ve already lost several weeks of pipeline-building time.

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The conversation nobody wants to have

The reason funnel balance stays broken in most organizations isn’t analytical. It’s political.

Lower-funnel spend is easy to defend in a meeting. The ROAS is there, the conversion numbers are there, and the CFO can see a direct line between spend and revenue.

Upper-funnel spend requires a different kind of argument: “This investment will make our Shopping and Search campaigns work better in six weeks.” That argument is harder to make, easier to cut, and almost impossible to defend when someone asks for a quick win.

The answer isn’t to stop making the argument. It’s to change the evidence you bring to it.

Track branded search volume as a leading indicator.
Build a view that shows Demand Gen reach in month one and Search conversion volume in month two alongside each other.
Make the lag visible and the relationship concrete. Once the data tells the story, the conversation gets easier.

Budget allocation isn’t a one-time decision. It’s an ongoing signal about what kind of growth you’re building.

Optimizing purely for this month’s ROAS is a choice. So is investing in the demand that will drive next quarter’s revenue.

And if you’re a reseller, it’s also a decision about whether your business is built on a foundation you control or one you’re renting from brand owners who have their own priorities.

The best PPC teams do both, and they know when to lean in each direction.

Dig deeper: How to optimize B2B PPC spend when budgets and confidence are low

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Referral Traffic Is Declining for Smaller Publishers: What This Means and How to React

June 2, 2026/0 Comments/in Web Design/by Dubado Solutions

Key Takeaways

Chartbeat data tracking more than 2,500 news sites globally shows Google search referrals declined 33 percent in 2025, with small publishers (fewer than 10,000 daily page views) seeing 60 percent declines over two years.
AI platforms are compressing multiple sources into single answers, driving a rise in zero-click behavior that bypasses publisher sites entirely.
A top search ranking no longer guarantees a visit. AI summaries can satisfy the query without the user ever clicking through.
Building owned audiences through email, social, and direct relationships is now a core distribution strategy, not a supplement to search.
Content structured for AI discoverability (clear, well-organized, factually grounded) is the new version of ranking on page one.

Referral traffic is down, and smaller publishers are absorbing the sharpest declines. Some have seen traffic drop by as much as 60 percent over the past two years. That is not a temporary dip from an algorithm update. It is a directional change in how audiences find and consume content online.

The driving force is straightforward: AI-generated answers are satisfying queries that used to produce clicks. Users get what they need from a synthesized summary and never visit the source. The publisher who ranked for that query, optimized for it, and built content around it gets nothing.

Understanding why this is happening and what to do about it is urgent for any publisher or content-driven brand relying on search as a primary traffic source.

Why This Is Happening

It used to be that answering a search query meant earning a click. A user typed something into Google, saw a list of results, and visited a site. Publishers built their entire distribution model around capturing those visits.

AI Overviews, ChatGPT, Perplexity, and similar platforms have disrupted that chain. Instead of surfacing a list of links, they deliver a synthesized answer assembled from multiple sources. The user gets what they came for. The click never happens.

Source

The data on this is significant. According to Similarweb, zero-click searches increased from 56 to 69 percent between May 2024 and May 2025. For queries where a Google AI Overview appears, the zero-click rate hits between 80 and 83 percent. Pew Research found that users clicked on results only 8 percent of the time when AI summaries appeared, compared to 15 percent when they did not. That is a nearly 47 percent relative reduction in click-through from the presence of AI summaries alone.

Smaller publishers absorb the impact more severely than larger outlets. Chartbeat data reported in March 2026 breaks this down clearly: small publishers with fewer than 10,000 daily page views saw 60 percent declines in search referral traffic over two years. Medium publishers with up to 100,000 daily page views saw 47 percent declines. Large publishers saw 22 percent declines.

Scale and brand recognition provide a partial buffer, but even major names have not been immune. Business Insider saw organic search traffic fall 55 percent between 2022 and 2025. HuffPost lost half of its search referrals over the same period.

Ranking at the top of search results used to mean something close to guaranteed visibility. That relationship has broken down. Visibility no longer guarantees influence.

Why the Old Playbook Falls Short

The formula that drove publisher growth for the past decade was consistent: create content that ranks, capture organic traffic, monetize that traffic. SEO was the engine and search was the distribution channel.

That engine is still running, but far less reliably than before. Reuters Institute survey data from early 2026, covering 280 media leaders across 51 countries, found that most publishers now expect to put less effort into traditional Google search this year. Media executives worldwide fear search engine referrals will fall another 43 percent over the next three years.

The publishers navigating this period well are not the ones with the best keyword strategies. They are the ones with direct audience relationships that do not depend on any algorithm to survive. Strong email lists, consistent social presences, and loyal readerships keep them stable when search referrals drop. Publishers without those foundations are feeling the decline most acutely.

Continuing to optimize exclusively for traditional search while ignoring how AI discovery works is a compounding mistake. The channel has already shifted, and waiting for it to shift back is not a strategy.

What to Do Now

The response requires action on two fronts simultaneously: protecting your direct audience relationships and adapting your content for how AI surfaces information.

Build owned channels as your primary distribution. Email is the most durable investment you can make. A subscriber who gets your content in their inbox is completely insulated from AI summaries, algorithm changes, and shifts in how Google decides to handle any given query type. The data supports this: publishers sent 28 billion emails in 2025, reaching over 255 million readers, with average open rates exceeding 41 percent. That outperforms most social media content by a significant margin. Build your list. Send consistently. Give people a genuine reason to keep showing up.

Social media supports direct distribution, but the goal is consistent presence that builds recognition, not chasing reach. Regular posting across the platforms where your audience already spends time keeps you visible through channels that do not depend on search referrals. Chartbeat data shows social referrals were flat or slightly up in 2025, with X up 15 percent and Facebook up 9 percent year over year. Those are not transformative numbers, but they represent channels that are holding while search declines.

Earned media and press relationships matter here too. Coverage in credible third-party publications builds the kind of authority signals that make your content more likely to be cited in AI-generated responses, which is the new version of organic discoverability.

Optimize your content for AI citation, not just search ranking. There is a real upside to the AI traffic story that most coverage misses. Brands cited in AI Overviews earn 35 percent more organic clicks and 91 percent more paid clicks than non-cited brands for the same queries, according to Seer Interactive data.

A graph comparing Paid & Organic trends.

Source

Being cited by AI systems is not a consolation prize. It is becoming a primary visibility driver.

Clear structure, direct answers to specific questions, and accurate, current information make your content easier for AI systems to pull from and surface. Practical, utility-focused content (guides, how-to articles, explainers) generates more page views per article from AI referrals than other content types, suggesting that practical resource content is more likely to earn a citation from an AI system.

Think about what questions users in your category are asking AI tools right now. If your content is not appearing as a cited source for those queries, that is a gap to close through targeted content work. Google added dedicated AI search tracking to Search Console in mid-2025: use the Search Appearance filter to see your performance in AI Overviews specifically, and let that data guide your content priorities.

Source

Monitor your AI presence actively. Check regularly what major AI platforms say when users ask questions your content should be answering. Track changes over time. If you are being misrepresented, omitted, or replaced by less accurate sources, you have a visibility and reputation problem that content strategy needs to address. Platforms like Writesonic have a sentiment feature to help gauge how your brand or a client’s brand is being portrayed.

Thinking About The Bigger Picture

The 60 percent traffic decline some publishers have experienced did not happen overnight, and it has not reversed. AI platforms generated over a billion referral visits in mid-2025, a 357 percent year-over-year increase. Even so, AI referrals still account for less than 1 percent of total web traffic, because the volume of search traffic absorbed by AI is so large.

The brands and publishers that adapt their distribution mix now, investing in owned audiences while making their content AI-discoverable, will be in a far stronger position over the next two to three years than those holding out for a search traffic recovery that may not come.

FAQs

Is search traffic gone for good?

Not gone, but fundamentally changed. Certain query types will always generate clicks: transactional searches where users intend to purchase, navigational searches for specific sites, and research queries requiring depth beyond what AI summaries provide. The shift is in emphasis: optimizing for AI citation and direct audience relationships is now a higher priority than chasing organic keyword rankings, particularly for smaller publishers without the domain authority to compete in contested niches.

What types of content still drive clicks from AI-influenced searches?

Practical, utility-focused content generates more AI referrals than editorial or opinion content. Guides, how-to articles, and detailed explainers are more likely to earn AI citations. Transactional content tied to specific purchase intent also continues to drive clicks because AI summaries do not fully satisfy the need to complete a purchase.

How do I know if AI is affecting my traffic?

In Google Search Console, go to Performance, then Search Results, and use the Search Appearance filter to select AI Overviews. This shows impressions and clicks specifically for queries where AI summaries appear. Impressions holding steady while clicks decline is the clearest signal of AI Overview impact.

Should I be investing in Answer Engine Optimization (AEO)?

Yes. AEO and traditional SEO share significant overlap: content structure, technical optimization, and authority building all remain relevant. The shift is in emphasis. Clear structure, direct answers, factual accuracy, and third-party credibility signals are the factors that most influence AI citation.

Conclusion

The 60 percent decline in search referral traffic for smaller publishers is not a fluctuation. It is a signal of where information discovery is going. The publishers still performing have strong brands, direct audience relationships, and content that AI systems want to cite.

Building those same assets is the path forward for any content-driven brand. Diversify your distribution, optimize for AI discoverability, and treat owned channels as your foundation rather than your backup plan.

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How SEO turns customer success into AI-readable proof

June 2, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

SEO has expanded beyond conversion into the operational side of the business, because that’s where the signals AI engines increasingly rely on get created.

When AI systems decide whether to recommend a brand, they evaluate post-sale signals like onboarding accuracy, performance outcomes, integration depth, and customer advocacy. Most of that information lives inside sales, support, customer success, and delivery teams, not inside marketing calendars or publishing workflows.

That creates a major SEO opportunity. Much of the evidence that could influence AI visibility still dies in CRMs, support platforms, and quarterly retrospectives rather than being codified into machine-readable form.

Bots and algorithms need to understand your business: what you offer, how you deliver it, and what customers think about it, in as much detail as possible. Here’s how.

5 stages that turn customer success into SEO signals

OPIDC stands for onboarded, performed, integrated, devoted, and codified.

The first four stages map to the customer-success lifecycle most service and SaaS businesses already run: onboarding, adoption, retention, and advocacy.

Codified is the addition. It describes the work of turning post-sale experiences into machine-legible evidence that AI systems can evaluate, compare, and recommend.

My term	What everyone else calls it
Onboarded	Onboarding
Performed	Adoption, first value, time-to-value
Integrated	Retention, expansion, stickiness
Devoted	Advocacy, loyalty
Codified	No established term

The first four stages — onboarded, performed, integrated, and devoted — describe what the business already does as part of its operations. The fifth stage — codified — describes what SEO does with what the business produces.

Together, those five stages form the people phase, which sits after the first 10 gates of the AI engine pipeline: discovered, selected, crawled, rendered, indexed, annotated, recruited, grounded, displayed, and won.

Combined, the 15-gate sequence extends the AI assistive agent optimization approach I was exploring when I first coined AEO.

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OPID is the business, not a content opportunity

The four OPID stages are the active core of business operations, and they’re where the business actually makes money.

Onboarded is the operational practice of getting new clients from sale to delivery. Performed is the operational practice of achieving measurable outcomes against a baseline. Integrated is the operational practice of becoming structurally embedded in clients’ lives. Devoted is the operational practice of earning unprompted advocacy.

The people who run these stages are the sales, service, support, customer success, and delivery teams. Marketing shapes the message, but the raw material comes from the people doing the delivery. What’s changed is that SEO now has work to do inside that operational core: harvesting from it.

Frame the work as harvesting the output of other teams, and the service team turns from gatekeeper into collaborator. You walk away with large amounts of raw material to publish, codify, and distribute, where AI engines can read it.

Walk into a customer-success meeting saying, “I need content for my blog,” and nobody pays attention. Walk in saying, “The evidence your team produces every week influences whether AI recommends us to the next prospect, and I want to help you capture it,” and they’ll engage and help you.

Run OPIDC properly, and the work benefits the entire business. James Dooley told me his sales team now mostly fills in onboarding forms because AI has already done much of the selling before anyone picks up the phone. Inquiry volume is down, sales are up, and buyers often arrive already convinced.

That’s what OPID looks like once you harvest it, codify it, and distribute it.

AI-era business engineering - Assistive agent optimization in place

Your customer is now two customers, and only one of them can watch you work

Whether your next customer is a person or an agent, the work is the same: engineer the business to serve both, then make sure machines can see, ingest, and evaluate the quality of what you do.

Here’s the trap: OPID is some of the most persuasive evidence you can generate, and it’s invisible to everyone except the client being served in that moment. Every other prospect, and every agent weighing you against a competitor, stands outside the room while your best work happens inside it.

The agent is the exception. In agential mode, the agent sees the delivery, evaluates it against the promised terms, and decides whether to return. That means you now have a second audience to satisfy, and the agent may control repeat transactions.

Please the human and lose the agent, and you risk losing the repeat business the agent influences. Please the agent, and you may earn a customer who reselects you every cycle without a sales call.

Dave Davies at Weights and Biases has explored this idea through the lens of “my client is an agent, how do I provide after-sales service for a machine?”

The agent checks your story against the open web

The catch is that the agent sits inside a walled garden. It evaluates the quality of what you delivered, but when an experience disappoints, it may return to the open web to verify whether it got you wrong. It looks for public evidence that supports or contradicts its experience with your brand.

If the open web reinforces your credibility, the agent may treat the bad experience as an exception and continue recommending you. If the open web confirms weaknesses or inconsistency, the agent may conclude it backed the wrong brand and quietly switch to a competitor. You never see that decision happen.

An agent’s loyalty is shaped by its direct experience with you, but public proof still matters when it goes looking for validation.

And it goes deeper than that. The agent runs on a model trained on the open web, built from the same public record you’re either feeding or neglecting. Your digital footprint shapes what the machine thinks about you long before any individual query. It’s what the model learned from, what the agent checks against, and one of the few assets you can actively build.

Neglect it, and you become invisible in training data and difficult to verify in the moment. Build it, and you’re known before the conversation starts and reinforced when it does. This helps with both humans and assistive engines: your digital footprint supports both discovery and trust.

Here’s the part that matters more than the labels themselves: OPID isn’t a marketing program bolted onto the business. It’s the business, the way companies operate to make money, whether they’re B2B, B2C, ecommerce, or SaaS. Every one of these companies onboards customers, performs against a promise, embeds itself into customer workflows, and earns advocacy, because that’s what operating a business requires.

The new requirement is codifying those experiences and distributing them back into the open web. That’s the flywheel, and it applies across business models.

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Onboarded: Getting the customer from sale to first success

Onboarded is what you do to take a customer from the moment they pay to the moment they get what they paid for, and get them there without the wheels coming off. Whatever you sell, the job is the same: close the gap between what you promised in the sale and what the customer actually experiences when delivery begins.

That’s the satisfaction gap. You close it before the contract is even signed by asking two questions many businesses skip:

What matters most to you here?
How will you know you’ve got it?

If you don’t ask the second question, your team and the customer end up measuring success against different scorecards, and the relationship starts breaking down in the first few weeks because you were working toward different outcomes.

So you get the answer up front, write it down, and carry it across every part of the business that touches the account. You’ve defined what success looks like in the customer’s own words before you deliver a thing. Get that right, and you can codify it and distribute it as proof of delivery.

Harvest: When the client tells you the first win landed, capture it in their words, include the date, then codify it and distribute it.

Performed: Delivering a measurable outcome against a baseline

Performed is doing the thing you were paid to do and proving it made a difference. You increase the client’s revenue, reduce their processing time, solve the problem they hired you to solve, and deliver the result they came in wanting. Then you do the part many businesses miss: show the difference from where they started.

“Reduced support tickets by 43% in six months against a baseline of 1,200 a month” is proof that a machine can evaluate confidently. “We helped them grow” is a claim every human and every engine will question.

The trap is measuring only what the customer happens to notice — the project finished, the order shipped, the feature launched — while never capturing the comparison against the prior state. That comparison is the proof. Capture it, and you have evidence machines can evaluate and support.

Harvest: Results only matter in context, so capture the before and after to create evidence instead of unsupported claims.

Integrated: When the customer makes you a repeatable use case

Integrated is earning a permanent place in how the customer operates, not by trapping them, but by becoming the answer they reach for every time the need comes around again. This is the customer who has stopped shopping. They have a recurring job, you’re the one they call, and they’re happy keeping it that way.

When you sell something ongoing, it’s the account that renews without a conversation because you’ve become how a particular thing gets done. When you sell something bought once, it’s the buyer who comes straight back without comparing, the brand an agent drops into the basket because it already ran the comparison and you won.

Different shape, same outcome: you become the use case they’ve assigned to you, and you keep earning it so they never feel the need to reopen the question. Win that, and the renewal happens before anyone thinks to reconsider.

Harvest: Listen for lines like, “I can’t imagine XYZ without them.” That’s the customer telling you you’ve become a repeatable use case worth keeping.

Devoted: When the customer sells you to the next customer

Devoted is turning a happy customer into one who says so publicly. It’s one of the strongest signals in the model because engines can distinguish earned advocacy from manufactured promotion. A manufactured testimonial carries little weight. A customer praising you independently carries much more.

The B2B client naming you on a panel, the SaaS user posting a workflow to their network, the ecommerce buyer leaving an unsolicited review, and the B2C customer recommending you to a friend are all doing the same thing: describing what you do in their own words, in language the next buyer actually needs to hear.

That phrasing often carries more weight than brand messaging because it serves as independent corroboration rather than self-description. The challenge is that customers rarely do it on their own, so part of the work is creating opportunities for them to share those experiences publicly.

Harvest: Encourage customers to share their experiences publicly, capture those stories, publish them on your own channels, and encourage customers to publish them on theirs.

The proof AI needs already exists

Here’s the thing many SEOs have been getting half-right for years. You create content to satisfy machines, and always have, but too much of it gets created at a desk instead of being extracted from how the business actually serves its customers. You end up talking to the machines without gathering the material they actually need.

That material doesn’t live in your head or your content calendar. It lives in the business: in sales calls, support desks, account managers, founders taking difficult calls, and the day-to-day reality of delivering the right thing to the right people. Your job is to extract it, codify it, and feed it back into the ecosystem.

That’s the foundation under everything else, because codifying isn’t about writing content and guessing what people want to hear. It’s about pulling sales calls, FAQs, success stories, and product attributes from a central source and consolidating them.

The unique marketing content you create still matters — the pieces where you demonstrate topical authority and show you know what you’re talking about — but that’s one stream, not the whole river.

This is where much of the SEO community has it backward. We overlook the bigger truth sitting in plain sight: businesses are already delivering the right products and services to the right people every day. That delivery is what convinces both machines and humans. You don’t have to invent it. You have to codify it and make it visible.

And this extends beyond AI assistants. The model we’re discussing includes assistive engines like Google, ChatGPT, Perplexity, and Copilot, but codifying isn’t an AI-specific tactic. It’s the discipline of making what you do legible to any machine that reads content, which is exactly what marketing teams already try to do on LinkedIn, Facebook, Instagram, and other platforms.

The moment they codify content for those channels, they’re feeding assistive engines too, because those systems read many of the same sources. One discipline supports every machine, and marketing teams have already been laying much of the groundwork.

So stand where your audience is looking. Show them how well you serve people they recognize as themselves, invite them down the funnel by demonstrating you can solve their problem, and let them see the proof in your delivery.

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Codifying is your job, and every channel depends on it

Codifying gives SEO a coordinating role across the business. The business creates value every day, serves customers, and delivers results. Someone has to extract that evidence, turn it into something machines can read, and distribute it into the world. Increasingly, that responsibility falls to SEO.

And here’s the broader shift: machine-driven distribution now shapes nearly every major platform. Google, ChatGPT, LinkedIn, YouTube, Facebook, and Instagram all rely on systems deciding what gets surfaced. That means every platform increasingly depends on structured, machine-readable content.

Marketing teams can publish raw posts and hope they land, but machines can’t reliably interpret unstructured information. Distribution works better when someone codifies the message first, turning it into structured proof that can travel across search, assistive engines, and social platforms.

That content has to come from the business itself: real delivery, real customer feedback, and real proof, not marketing copy invented to fill a calendar. That’s why business operations, marketing, and SEO increasingly depend on each other. Business teams generate the evidence. Marketing shapes the message. SEO codifies and distributes it in ways machines can understand.

Because increasingly, once communication moves through a screen, a machine helps determine whether people see it. Codify for that machine, and you do more than feed search and AI systems. You organize information in a way that also makes it easier for humans to understand. The structure that helps algorithms interpret content also helps people process it.

The takeaway is simple: codify the real business. Use real delivery, real customer feedback, and real proof, then distribute it where your audience is already looking. Machines increasingly mediate what people see online, so feeding those systems has become part of reaching humans in the first place. That’s why codifying matters, and why SEO is well positioned to lead it.

This is the 16th piece in my AI authority series.

Part 1, “Rand Fishkin proved AI recommendations are inconsistent, here’s why and how to fix it,” introduced cascading confidence.
Part 2, “AAO: Why assistive agent optimization is the next evolution of SEO,” named the discipline.
Part 3, “The AI engine pipeline: 10 gates that decide whether you win the recommendation,” mapped the full pipeline.
Part 4, “The five infrastructure gates behind crawl, render, and index,” walked through the infrastructure phase.
Part 5, “5 competitive gates hidden inside ‘rank and display’,” covered the competitive phase.
Part 6, “The entity home: The page that shapes how search, AI, and users see your brand,” mapped the raw material.
Part 7, “The push layer returns: Why ‘publish and wait’ is half a strategy,” extended the entry model.
Part 8, “How AI decides what your content means and why it gets you wrong,” covered annotation, the last gate where you’re alone with the machine.
Part 9, “Why topical authority isn’t enough for AI search,” opened the competitive phase proper with topical ownership.
Part 10, “The funnel flip: Why AI forces a bottom-up acquisition strategy,” named the process.
Part 11, “The framing gap: Why AI can’t position your brand,” exposed the gap between evidence and recommendation.
Part 12, “The 10-gate AI search pipeline: Find where your content fails,” showed you how to find (and repair) your F grades in the AI engine pipeline.
Part 13, “The delegation boundary: How AI decides which brands win,” mapped how delegation moves between user and engine across Search, Assistive, and Agent modes.
Part 14, “The funnel query pathway: A framework for measuring AI visibility,” built the measurement instrument.
Part 15, “The micro-macro shift: How to measure AI visibility now that precision is gone,” moved measurement from micro precision to macro trend.
Up next: How you turn harvested operational evidence into machine-legible content.

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Why high-ROAS campaigns don’t always deserve more budget

June 2, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

It’s one of the best “problems” you can have in paid media.

You’re running a campaign that delivers on every front. Cost per acquisition is strong. Return on ad spend is exceptional. Lead quality meets expectations. Average order value is exactly where it should be.

Then the ask comes in: Double the budget and keep the momentum going.

Before you take that step, pause. Increasing budget can unlock more performance, but only if there’s real room for that budget to be productive. If you’ve already maximized what the campaign can deliver on its own, adding budget can lead to higher costs without meaningful incremental revenue gains.

There are times when increasing budget is the right choice, and those are covered later. First, it’s important to understand when not to increase spend.

(Disclosure: I’m a Microsoft Ads employee, and while I’ll share some Microsoft insights, this is intended to be a platform-agnostic piece.)

What to evaluate before increasing budget

Before you increase spend, make sure the campaign can support more scale without sacrificing efficiency.

Learning periods matter

Any meaningful change to budget, target CPA, or target ROAS can trigger a learning period.

In Microsoft Advertising, changes exceeding approximately 15% are likely to introduce performance volatility. This can result in short-term fluctuations in efficiency and volume while the system recalibrates.

If you increase budget too aggressively, you risk disrupting a high-performing campaign. A more stable approach is to increase budgets incrementally week over week. It’s also important to set expectations with stakeholders that growth will be gradual rather than immediate.

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Validate that performance is real

High return on ad spend only matters if it reflects real business value. Before increasing investment, confirm that:

Conversion tracking is accurate and complete.
Lead quality aligns with downstream outcomes.
Revenue signals reflect actual profitability.

Document any changes to conversion tracking or values, and clearly communicate what’s being measured and why.

Market saturation is real

Doubling down on a single audience or geography can lead to diminishing returns.

If you increase budget without expanding reach, you may oversaturate the available audience. This can drive up costs without expanding opportunity. Effective scaling often requires:

Expanding into new markets or geographies.
Introducing new audience segments or personas.
Structuring additional campaigns instead of overloading a single one.

Define the goal: Efficiency or scale?

There’s a natural trade-off between efficiency and scale. At higher volume, it’s difficult to maintain peak return on ad spend. If stakeholders expect the same efficiency at significantly higher spend, misalignment is likely.

Be explicit about the objective:

Are you trying to maintain efficiency?
Are you trying to grow volume while staying within profitable limits?

Clarity here prevents frustration later.

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3 strategic questions to ask before increasing budget

1. Do you actually have impression share room to grow?

Impression share and share of voice are critical indicators of growth potential.

If you’re losing impression share due to budget, increasing spend can unlock gains.
If you’re losing impression share due to rank, increasing budget alone won’t solve the problem.

In those cases, you may be dealing with:

Bids that aren’t competitive relative to auction prices.
Campaign structure issues that limit performance.
Inefficient or irrelevant keyword coverage.

If impression share lost due to rank exceeds 50%, increasing budget is unlikely to drive incremental value because there’s either a structural issue or you’re underbidding. Raising the budget might solve the latter problem. However, you need to be prepared for higher CPCs.

Before increasing budget, audit the following:

Keyword duplication and overall coverage.
Bid levels relative to daily budgets and auction dynamics.
Search term quality and relevance.

Budget can’t compensate for structural inefficiencies.

2. Is there room for more demand, or are you just bidding higher?

Return on ad spend alone isn’t a sufficient signal for scaling.

Search campaigns primarily capture existing demand. They don’t lend themselves to creating it outside of AI surfaces.

If you increase budget without increasing demand, the system often responds by:

Bidding more aggressively on existing queries.
Increasing cost per click to win more auctions.
Recycling the same demand pool at a higher cost.

Sustainable growth requires expanding demand, not just competing harder for the same users.

This includes investing in:

Upper- and mid-funnel channels such as video and social formats.
Creative that communicates clear value propositions such as speed, reliability, or cost efficiency.
Messaging that influences how users think about your brand before they search.

AI-powered surfaces also play a role. Campaigns that use automation and broader matching approaches are more likely to capture incremental demand signals, especially when supported by strong visual and text creative.

3. Should this budget go into a new campaign instead?

Not all growth should happen within a single campaign.

If a campaign is already optimized and stable, allocating additional budget to it can introduce risk without creating new opportunities.

Consider alternatives such as:

Launching a new campaign targeting a distinct market or geography.
Creating new audience segments or product groupings.
Testing new campaign types or formats to expand reach.

This approach allows you to scale while protecting what’s already working, and it enables clearer measurement of incremental impact.

When increasing budget does make sense

You’re constrained by budget rather than rank

If impression share lost due to budget is high and conversion tracking is reliable, increasing budget can unlock incremental volume.

In this scenario, you’re not fully participating in available auctions, which creates room for additional spend to perform. This can mean more budget for high-performing keywords and more advertising hours.

The campaign is new and still learning

For newer campaigns, additional budget can accelerate the learning phase by providing more data.

If you’re already in a learning period and willing to accept short-term variability, increasing budget early can help the system stabilize and identify performance patterns more quickly.

You’re scaling demand alongside spend

Budget increases are most effective when paired with demand generation efforts.

This includes:

Expanding reach through new channels.
Increasing creative coverage.
Investing in AI-powered formats.

In this context, increasing budget becomes part of a broader growth strategy rather than a standalone tactic.

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What deliberate scaling looks like

A high-performing campaign with strong return on ad spend is a strong foundation, but it doesn’t guarantee that additional budget will drive additional value.

Before increasing spend:

Validate that performance reflects real business outcomes.
Confirm that there’s room to grow.
Align on efficiency versus scale.
Decide whether growth belongs in the current campaign or a new one.

Deliberate scaling protects existing performance while unlocking new opportunity.

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How ‘it’s just SEO’ took over the GEO conversation

June 2, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

Search has managed to do something impressive. At the precise moment it should be becoming more important and valuable to clients, large parts of the industry have chosen to argue themselves into irrelevance.

The real argument is about ownership.

Who gets to define what search becomes next?
Who gets the budget?
Who gets to explain what happens when search stops being a list of links and starts becoming a machine that recommends answers, brands, and actions?

“It’s just SEO” has done so much damage. It sounds calm and experienced, like the sort of thing a serious search veteran would say to quiet the room.

But it’s not strategy. It’s a meme constraining one of the biggest commercial opportunities the search industry has had in years.

Why memes matter to search

Memetics isn’t new. Richard Dawkins coined the term in “The Selfish Gene” in 1976, proposing that ideas, behaviors, and phrases spread through culture using the same logic as genes spread through populations. They replicate, mutate, and compete. The survivors aren’t necessarily the most accurate. They’re the easiest to copy.

Susan Blackmore took this further in “The Meme Machine,” arguing that humans are essentially meme machines: brains built to imitate, transmit, and store cultural information. The ideas that spread aren’t the truest ones. They’re the stickiest.

Consider “Happy Birthday to You.” The melody is simple enough to remember after one hearing. The words require no expertise to learn. The social context — a celebration, a cake, a room of people — gives everyone a reason to join in. Nobody decides to keep it alive. It keeps winning the competition for space in human memory and behavior.

“Jingle Bells” works the same way. It has no official guardian. It spreads because copying it costs nothing and signals belonging to a shared culture.

Slogans, rumors, political lines, and professional clichés travel the same way. They don’t survive because they’re correct. They survive because they’re easy to repeat, socially useful to the person repeating them, and emotionally charged enough to keep spreading. Accuracy isn’t part of the selection criteria.

SEO and GEO have a serious memetic issue.

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How ‘it’s just SEO’ became the dominant meme

When GEO entered the industry conversation, the reaction was immediate. Some people looked at generative search and saw a materially different interface. They saw AI systems summarizing, recommending, citing, and generating answers in ways that didn’t behave like classic search results. They saw a need for new tools, workflows, measurement, and thinking.

Others saw a threat. For much of the SEO influencer community, the response was containment. “It’s just SEO” became the line. Then the chant. Then the weapon.

The phrase worked because it was perfect meme material: short, repeatable, and certain without requiring much investigation. It also protected status.

If GEO is just SEO, the existing hierarchy stays intact. The same speakers keep the spotlight. The same consultants keep the authority. The same agencies keep the same budgets, or avoid having to rethink how the new landscape changes their work.

Then came the uglier meme: “GEO grifter.”

That one did even more damage. It didn’t just question the term. It framed anyone using it as suspect. It turned curiosity into suspicion and experimentation into opportunism. It encouraged dismissal instead of investigation.

This is how professional consensus often forms online. Visible people repeat a simple framing, algorithms reward it, and repetition starts to look like agreement.

And this is where the search industry started harming itself. As the framing spread, consultants repeating it gained visibility and social reinforcement, while clients and brands increasingly saw generative search differently.

Clients buy certainty, not acronym wars

Marketers outside the SEO echo chamber are already ahead of many search specialists. They can see the interface changing because they use generative systems every day.

I’ve seen it firsthand. At BrightonSEO and several recent conferences, I asked the room a simple question: Who here is using AI to make decisions, solve problems, or get work done?

The hands went up. Not a few hands. All of them.

Hundreds of people in different rooms gave the same answer without needing to be briefed, persuaded, or dragged through a 30-post LinkedIn argument about terminology.

When marketers and business people are already changing how they search, decide, and work, the industry doesn’t get to sit in the corner insisting nothing has changed.

Clients don’t buy theological disputes. They buy certainty.

SEO has never been an easy channel to sell. Many companies have been burned by vague retainers, vanity metrics, and content strategies that produced a library of articles nobody needed.

At the same time, good SEOs have built companies, saved jobs, and created revenue. Both things are true, which is why the current argument is so dangerous.

If the industry can’t explain what has changed, buyers will defer. They’ll move budget into paid search, paid social, or whatever advertising unit Google, OpenAI, or Meta sells them next.

Organic search won’t get the exploratory investment it needs because the people who should be leading the conversation are still arguing about whether the word GEO is allowed to exist.

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The B2B Institute already called this

LinkedIn’s B2B Institute and the Ehrenberg-Bass Institute make this clear in their report, “Easy to find: Being where B2B buying happens.” The argument isn’t built around acronym point-scoring. It’s built around mental and physical availability. B2B brands grow by being easy to think of, find, and buy.

Physical availability covers three dimensions: presence, prominence, and portfolio. In a digital world, that means being discoverable across every environment where buying actually happens, not just the ones that existed five years ago.

The report explicitly describes GEO as “the new wave of SEO” and states that generative engine optimization rewards foundational brand-building: authority, relevance, thought leadership, authentic reviews, and earned mentions. It also notes that generative search and LLM-powered discovery are reshaping how information is surfaced, with relevance determined by content authority and context, not keywords.

The marketing scientists aren’t saying “write more keyword articles and relax.” They’re saying discoverability is changing, but the underlying fundamentals remain.

Be easy to think of and easy to find.
Build distinctive assets, create authority, and show up where buyers are looking.

This isn’t a choice between SEO and GEO. It’s a physical availability problem in a new search environment.

The 9 a.m. to 5 p.m. test

“It’s just SEO” collapses too much into one bucket. SEO already means different things to different people. To one person, it means technical hygiene. To another, content production. To another, digital PR. To another, ecommerce feeds, internal linking, and category pages. To another, local search or revenue-focused organic growth.

So when someone says GEO is “just SEO,” the obvious question is: Which SEO, exactly?

“Just SEO” sounds simple until you ask what it means between 9 a.m. and 5 p.m.

What are you doing today to increase the likelihood that a generative system recommends your brand in a buying situation?
What are you measuring?
What sources are you influencing?
What third-party evidence are you earning?
What brand associations are you strengthening?
What prompts, citations, and recommendation contexts are you monitoring?

If the answer is “helpful content,” we’re in trouble.

Helpful content isn’t a strategy. It’s a phrase so vague it means everything and nothing.

Brands need extractable, repeated, credible information about the problems they solve and the situations in which they should be chosen.

That’s why GEO is closer to digital PR, brand strategy, and content marketing than many people want to admit.

No name, no budget

Markets don’t fund things they can’t name.

A name isn’t decoration. It’s a buying mechanism. It’s how a nervous CMO turns a vague threat into a line item. It’s how procurement understands why last year’s SEO retainer isn’t automatically the answer to this year’s generative search problem.

If GEO is “just SEO,” it gets dragged into the existing SEO budget. And most SEO budgets are already fighting for oxygen. So the industry’s grand commercial plan is this: take a new interface, a new buyer behavior, a new measurement problem, and a new competitive surface, then hide it inside the same budget clients were already reluctant to increase.

That’s commercial self-sabotage.

Call it GEO, AI search visibility, or SEO evolved. The exact label matters less than creating a commercially legible category. Once a category has a name, it can have a brief. Once it has a brief, it can have a budget, a team, a process, a dashboard, and a target.

Kill the name, and you don’t protect SEO. You shrink the market it should’ve owned.

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A better way to frame the shift

There’s a simple way out of this mess.

Call GEO “SEO evolved” if that helps. Call it “SEO rebranded for generative search” if that allows people to cross the bridge without losing face. But stop pretending nothing has changed.

Search is becoming generative, and brands need to become easier for AI systems to retrieve, understand, and recommend.

The goal is no longer just to rank. It’s to be recommended. To be:

Present in the answer.
Visible in the journey.
Credible sources.
Easy to choose when a buyer moves from curiosity to consideration.

That requires SEO skills. It also requires digital PR, brand strategy, technical understanding, measurement, and serious marketing thinking. GEO is SEO growing into the rest of marketing.

The brands that adapt to that shift will earn visibility as search changes. The ones still treating it as a naming debate risk missing the commercial opportunity entirely.

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Google expands Data Manager API with GMP event ingestion

June 1, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

Google is consolidating measurement and audience activation workflows across its advertising platforms. New Data Manager API capabilities allow advertisers and partners to send offline conversion data to multiple Google Marketing Platform destinations and improve Customer Match performance with IP-based matching.

What’s happening. The Data Manager API now supports offline conversion event uploads to Campaign Manager 360, Search Ads 360 and Display & Video 360. The update expands the API’s role as a centralized data ingestion layer across Google’s advertising ecosystem.

Advertisers can now use a single schema to send conversion data across multiple Google products, replacing fragmented workflows that previously required separate integrations. The API also supports encrypted user identifiers, including email addresses and phone numbers, and allows events to be routed to multiple destinations in a single request.

Between the lines: Google is encouraging advertisers that still rely on the Campaign Manager 360 API for conversion uploads to migrate to Data Manager API. The company says the newer framework simplifies implementation while providing greater flexibility for measurement and attribution use cases.

The update also introduces IP ingestion support for Google Ads Customer Match through a new CompositeData field. Advertisers can now upload IP addresses alongside traditional identifiers such as email addresses, phone numbers and postal addresses.

Beginning in Q3 2026, Google says including IP addresses with corresponding observation timestamps will help improve Customer Match rates, potentially increasing audience reach and match accuracy.

Why we care. The changes make it easier to unify conversion measurement across Google’s advertising products while improving audience matching capabilities. For advertisers managing large-scale first-party data programs, higher match rates and a streamlined data ingestion workflow could translate into better attribution and audience activation.

The bottom line. Google is positioning the Data Manager API as the central hub for conversion and audience data, giving advertisers a more unified way to manage measurement and Customer Match across its ad platforms.

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How a ‘client brain’ gives AI the context SEO work needs

June 1, 2026/0 Comments/in Search Engine Land, Web Design News/by Dubado Solutions

Every SEO agency has a hidden context tax. It shows up when a strategist, content lead, or analyst opens Claude and starts rebuilding all the dos and don’ts for that particular account from memory: the brand voice, the keyword cluster killed last quarter, the CMS limitation, the founder’s rejected angle, the competitor the client doesn’t want mentioned.

That’s the part of AI adoption we’re still underestimating. LLMs can help with specific SEO tasks, but the problem with unleashing AI on more complex work is providing enough account context to make it useful without creating more review work.

One solution is a per-client memory system called a “client brain.” It gives account context a place to live, allowing AI to support the work without treating every task like it’s the first day on the account.

Context is the problem

Context is essential for any worker. A senior SEO account lead onboards human teammates onto client accounts by sharing the strategy, history, politics, preferences, constraints, client language, technical limitations, and all the “don’t do that again” lessons that never quite make it into the brief.

LLMs have inherited that same agency problem. The difference is that AI hits it every time it’s asked to support the work without knowing the account.

A lot of the AI conversation in SEO right now is about connecting data sources. Load GSC, GA4, Ads, crawl data, rank tracking, and maybe CRM data into one place, so that we can finally “chat” with the data.

That’s genuinely useful, especially with live alerts. But for agencies, analysis is just one part of the job. AI also needs account context to summarize a technical audit without recommending a fix the dev team already rejected or to write a brief that sounds like the client and fits the strategy.

That kind of work depends on institutional memory: the account knowledge that builds up after months of working with a client and its stakeholders.

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A client brain is the solution

A client brain gives that institutional memory a shared home. The team updates it as decisions are made, feedback comes in, and the account evolves. This isn’t a replacement for human judgment. It’s infrastructure that helps that judgment travel across workflows.

In an agency world, SEO work rarely belongs to one person. The strategist sets direction, the content lead builds the brief, the writer drafts, the analyst checks performance, and the technical SEO reviews implementation.

When context stays in people’s heads, every handoff creates drift. When it’s shared, the work stays aligned. A strategist ramps faster, a writer misses fewer client preferences, and the team spends less time re-explaining the account.

What a client brain is

A client brain is a structured, per-client knowledge base that AI reads before it starts the work. Think of it as the institutional memory of an SEO account, written in a way the machine can use.

Not all client knowledge behaves the same way. Some knowledge is stable: the brand, audience, positioning, voice, product, category, and lines the client doesn’t want to cross. Some knowledge is active: decisions, experiments, objections, failed angles, technical blockers, and lessons from client feedback.

Those two types of knowledge need different homes. A client brain splits them into two layers: the soul and the memory.

The soul is static, identity-level knowledge: Who the brand is, how they speak, who they serve, what they sell, and what “good” sounds like for them
The memory is dynamic, experience-level knowledge: What the team tried, what worked, what failed, what the client rejected, and what changed during the campaign

This split keeps the brain usable. If everything goes into one big file, brand principles get buried under meeting notes, and old keyword decisions start looking like the current strategy.

The technical anatomy of a brain

A client brain doesn’t need to be a complicated system. It is built as a simple folder of plain-text Markdown files. You don’t need special software, a database, or a custom interface.

Building core logic of the soul

To get started, go into your existing client project folder and create a sub-folder named brain, then create one more folder inside that named soul. This folder path (brain/soul/) is where the core logic of the system lives. It consists of five files, each doing one specific job:

brain/soul/
├── company-profile.md
├── style-guide.md
├── audience.md
├── keyword-map.md
└── never-do.md

company-profile.md

This is the operating version of the client, not the polished marketing version. Who is this client? What do they really sell? Who do they compete with? Where do they win? Where are they not trying to play?

Six honest sentences usually beat a six-page deck because the AI doesn’t need the full brand story. It needs enough context to avoid bad adjacent decisions.

A real example, anonymized:

“[Client] is a DTC Japanese-style kitchen knife brand selling chef knives, paring knives, and care accessories. They serve home cooks who value craftsmanship over price, with an average order value around $180. Their differentiator is free in-house sharpening for life. They compete with Made In and Misen on the tier just below Shun and Global. They don’t sell to commercial kitchens or restaurant supply, those have separate procurement cycles. Their highest-converting traffic comes from long-form reviews and YouTube cooking channels, not paid social.”

That’s enough information for AI to make better SEO choices. It knows not to chase restaurant-supply keywords, not to position the brand as the cheap alternative to Shun, and to weight content toward reviews, comparisons, and care guides.

The point isn’t to sound impressive. The point is to be true.

style-guide.md

This file is where most teams accidentally write something useless. “Warm but professional” doesn’t help AI much. Neither does “expert but accessible.” What works is concrete instruction: one paragraph on tone, a few examples that pass, and a few that fail.

audience.md

The audience file is where the team stops writing for demographics and starts writing for people. “Small business owners aged 35 to 55” is a targeting box, not an audience. Useful audience context captures worries, objections, misconceptions, language, and what earns trust.

keyword-map.md

You do not need to create a 500-row export from your keyword tool. Instead, capture how the brand thinks about the category: primary terms we own, secondary terms we want, competitor-owned terms we approach carefully, and terms we don’t want to touch.

never-do.md

This is the file I wish I’d had years ago. It’s the list of things AI should never propose, never write, and never recommend.

Some are brand-level: “Never describe the client as an industry leader.”
Some are operational: “Don’t suggest content that requires legal approval unless the account lead confirms it first.”
Some are strategic: “Don’t recommend State X landing pages. The client doesn’t serve that state yet.”

Every “we already discussed this and decided no” should eventually end up here. AI is very good at confidently resurfacing dead ideas. This file stops the team from having the same conversation every month.

Memory captures decisions, patterns, and logs

Memory lives in brain/memory/. It’s organized differently from the soul because it comes from doing the work.

brain/memory/
├── decisions/    — choices made and why
├── patterns/     — things that worked or didn’t, by task type
└── log/          — chronological notes by date

The decisions/ folder stores choices made and why. A memory entry looks like this:

# 2026-04-21 — Content brief for Q2 implant campaign

Decided NOT to target "dental implants near me" as the primary keyword.
Reason: Client doesn't accept Medicaid; the highest-volume "near me" searches in our markets skew Medicaid.
Pivot to "premium implants [city]" framing.
Source: Client strategy call notes, 2026-04-21.
Tags: client:[name], task:content_brief, type:decision

The reason matters more than the decision. If AI only knows “don’t target dental implants near me,” it may avoid that keyword forever, even when the context changes. If it knows why, it can make better adjacent decisions later.

The patterns/ folder

This stores what the team learns across repeatable work. After enough AI visibility audits, for example, our system started building a pattern file around where those audits tend to break: changing DOM selectors, fabricated review counts, Cloudflare blocking direct fetches, and tools returning partial data without making the failure obvious.

The log/ folder

Here is where you keep the running journal: meeting summaries (AI transcripts are great here), daily notes, client comments, and small updates that don’t yet deserve to become formal decisions. Most of it won’t be read again. But when something breaks two months later, the answer is often in the log.

One warning: A brain should capture operating knowledge, not raw sensitive data. Don’t turn it into a warehouse for exports, transcripts, credentials, private client documents, or anything the team wouldn’t want surfaced in the wrong context.

Store the lesson, not the raw data.

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Building the brain step-by-step

Step 1: Pick the right starting client

Don’t start with every client. Pick the account where context loss is already costing you time.

Usually, that means a long-running client with a strong brand voice, a history of rejected ideas, and multiple people touching the work each week.

Step 2: Block 90 minutes and write the soul together

Get the account lead and strategist in the same room or on the same call. Open the five soul files and write in plain sentences. Use real examples. Don’t try to make it perfect.

The goal isn’t to create a brand book. It’s to write down the context your best account person already carries around in their head.

Step 3: Decide where the brain lives

If you’re solo, a local folder may be enough. If you have a team, you need one shared source of truth.

Technical teams can use git: track the Markdown files, not raw client data. Non-technical teams can use Google Drive, Notion, or another shared workspace. The tool matters less than the rule: one client, one brain, one place everyone trusts.

Step 4: Set ownership rules

Soul changes need friction. That’s intentional. If every passing comment gets added to the soul, the brand layer gets polluted. The account lead should own it, review changes, and decide what becomes stable client truth.

Memory should be easier to update. Anyone working on the account should be able to add a sourced entry when a client rejects an angle, a tactic fails, a blocker appears, or the team learns something that shouldn’t be lost.

Step 5: Schedule maintenance

Memory gets messy if nobody owns it. Every couple of weeks, someone should clean the brain: consolidate duplicates, remove stale notes, surface conflicts, and check whether old decisions are still true.

Then schedule a quarterly soul review and ask one question: “Is anything here no longer true?” A stale brain is worse than no brain because the AI will sound confident while working from old context.

How AI agents read the brain

Once a brain exists, the question becomes operational: Which files should the AI agent read whenit starts a brief, audit, competitor analysis, or reporting summary?

This is where the brain proves its day-to-day value. A strategist, content lead, and analyst may all touch the same client in the same week. Without shared context, the brief drifts from the strategy, the content drifts from the brief, and the audit repeats what the team already knows.

The brain keeps that work aligned without turning every task into another meeting, Slack thread, re-explanation, or rewrite. There are three ways to handle this.

Version A: Load everything

The simplest version is to have the AI read every file in the brain folder before it starts: all soul files and the full memory folder.

For a new client, that might only be a few thousand tokens. For a client active for six months, it can become 30K to 50K tokens per session. That’s a real cost, but often still cheaper than the human time lost re-explaining the account every week.

Start here if you’re testing the idea. Run the same task twice: once with the brain loaded, once without it. Use something real, like a content brief, metadata rewrite, technical summary, or internal linking recommendation. If the brain-loaded version is more accurate, more on-brand, or avoids a mistake the team would normally catch manually, you’ve got your signal.

Version B: Route by task type

The next version is selective loading. Instead of asking AI to read everything, you give it a router file that tells it which parts of the brain to load based on the task.

For example:

# claude.md

At the start of every task, ALWAYS read:
- brain/soul/company-profile.md
- brain/soul/never-do.md

IF the task involves writing copy, ALSO read:
- brain/soul/style-guide.md
- brain/soul/audience.md

IF the task involves SEO content briefs, ALSO read:
- brain/soul/keyword-map.md
- brain/memory/decisions/ latest 5 entries
- brain/memory/patterns/content_briefs.md

IF the task involves debugging a tool failure, ALSO read:
- brain/memory/patterns/tool_failures.md

AI reads the instructions, decides which rules apply, and loads only the relevant files. Token cost drops. Context gets cleaner. This is where most agencies should stop for a while.

It’s still just Markdown. No database. No new platform. No complicated setup. The discipline is in writing useful files, keeping them current, and making sure AI reads them before doing the work.

Version C: Vector retrieval

The more advanced version is vector retrieval. If you’re managing 20 or more active clients, each with deep memory, you can tag entries with metadata, embed them into a vector store, and retrieve only the most relevant items at the start of each task.

AI can also write back to memory, but this needs guardrails. Don’t ask it to summarize every session and dump the result into the brain. That creates noise fast. Write to memory only when something specific happens: a task fails, and the team finds a workaround, a client rejects an angle, the account lead corrects the AI on something client-specific, or a decision gets made that should affect future work.

Event-triggered writes are useful. Session-end summaries usually aren’t. And every write needs a source.

Using the brain across Claude Code, Chat, and Cowork

The surface matters less than the pattern. Whether the team is using Claude Code, Claude Chat, Cowork, or another AI workflow, the rule is the same: AI should read the client’s soul before doing anything important.

In Claude Code, place the brain folder at the root of your project and add a claude.md instruction telling it to read /brain/soul/ at the start of every task. Treat never-do.md as a hard constraint, not a suggestion.
In Claude Chat, create one project per client and upload the contents of brain/soul/ into Project Knowledge. Don’t share one project across clients. That’s how one client’s tone, rules, or constraints start bleeding into another.
In Claude Cowork, use a task template that attaches the brain folder at the start. For repeatable tasks like content briefs, SERP reviews, metadata refreshes, or AI visibility audits, build the brain attachment into the workflow.

You’re not just making AI faster. You’re making the starting context consistent.

Where this breaks (and how to fix it)

Once the brain starts shaping real work, a few failure modes show up quickly. Most aren’t technical problems. They’re maintenance problems, which means they’re fixable if someone owns the review process.

Drift: AI produces work that’s almost right, but slightly off. Usually, the style guide is too abstract. The fix isn’t more adjectives. It’s better examples: pass/fail pairs, before-and-after intros, weak and strong meta descriptions, or a sentence the client rewrote with a note explaining why.
Stale soul: The client repositions, changes their offer, shifts into a new market, drops a service, or changes how they want to talk about themselves. Nobody updates the soul, so AI keeps producing work from the old reality. The fix is a quarterly soul review. Ask: “Is anything here no longer true?”
Memory rot: Some memory entries were true when written, but stop being true later. A client rejected comparison content six months ago, then decided to test it. The fix is to date entries clearly, include the reason behind each decision, and remove or update entries when the account changes.
Fabrication: This is the failure mode to take seriously. AI can write false memory, not maliciously, but because it’s trying to be helpful. When a task fails or a source is incomplete, the model may still produce a clean-looking note that sounds plausible.

We’ve seen AI fabricate ChatGPT search queries, report review counts that weren’t tied to reality, and create explanations for tool failures that sounded reasonable but weren’t supported by the output. Memory compounds. One false entry can influence future briefs, audits, recommendations, and client-facing work.

The fix is provenance. Every factual memory entry needs a source: a meeting note, client quote, tool output, strategist correction, or linked deliverable. No source, no entry.

A brain is only useful if the team trusts it. Trust doesn’t come from the folder structure. It comes from knowing where the knowledge came from.

How to get started this week

You don’t need the full system to start. Start with one client, one 90-minute session, and one before-and-after test.

Pick one client. Choose the account where re-explaining context costs the most time.
Block 90 minutes this week. Write the five soul files with the account lead and strategist. Use plain sentences, real examples, and concrete corrections. Don’t let adjectives do all the work.
Add a router file. Keep it simple at first. At the project root, add one instruction: “At the start of every task, read everything in brain/soul/.”
Run a real SEO task twice. Use a content brief, keyword cluster, meta description rewrite, SERP analysis, internal linking recommendation, or audit summary. Run it once with the soul loaded and once without it. Compare the outputs honestly.
Start writing memory from the next session. When AI recommends a ruled-out keyword angle, a client pushes back on tone, or a technical recommendation gets blocked by the CMS, capture the lesson and the reason.

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AI works better when account knowledge survives

Most teams don’t have an AI intelligence problem. They have a context problem. They haven’t written down what their best account people already know, or separated stable client knowledge from working history. That’s what the client brain fixes.

The agencies that get the most from AI won’t just be the ones with better prompts, models, or automations. They’ll be the ones that preserve the context behind the work: the client history, rejected angles, technical constraints, tone corrections, and small decisions that make an account make sense.

Because speed without memory creates more review, more correction, and more “we already talked about this” moments.

The real opportunity isn’t using AI to push more SEO work through the system. It’s using AI to carry forward the context that makes the work better.

Read more at Read More

Build a blog that drives real results

May 28, 2026/0 Comments/in Web Design News, Yoast SEO/by Dubado Solutions

A blog can grow your audience and build trust, but only if you do it right. AI search now answers questions before users click, so your posts need to stand out, not just rank. Where do you start? What should you write? How do you keep readers coming back? This guide covers everything from finding inspiration and writing great posts to optimizing for search, building an audience, and even making money.

Key takeaways

Blogging boosts SEO and serves as a powerful marketing tool, enhancing brand visibility and reader engagement.
Regular content creation helps improve Google rankings and allows targeting of new keywords.
Effective blogging requires careful planning, keyword research, and understanding search intent to draw an audience.
Original and readable posts attract readers; tools like Yoast SEO help optimize content for search engines.
Engagement through comments and social media is crucial for maintaining a blog’s visibility and attracting traffic.

Why blog?

If you have a website of any kind, you must blog occasionally. It doesn’t matter whether you have an online shop, a personal website, or a portfolio. Besides being great fun, blogging is one of the best things you can do for SEO. Not only that, thanks to a high-quality, unique blog, you can turn your site into a powerful marketing tool.

Google’s AI Overviews/AI Mode and other AI search platforms favor blogs that answer questions clearly and thoroughly. If you’re not blogging, you’re missing a great way to get seen in search and connect with your audience.

Blogging for SEO

Adding content regularly should be a part of every sustainable SEO strategy. If you write regularly, Google will see your site as active, alive, and relevant. These signals help your pages rank better in both traditional and AI-powered search. This also gives you more chances to appear in AI-generated snapshots, where Google summarizes answers for users.

In addition, blogging allows you to rank for new keywords and to keep ranking for those you’re already being found for. Since AI search favors fresh, well-structured content, regular updates ensure your site stays competitive. Your blog also gives you another way to target search intent, whether users are looking for answers, comparisons, or solutions. We’ll discuss that in more detail later on.

Blogging as a marketing tool

A blog is one of the best marketing tools for any website. It helps readers get to know your brand and products beyond just sales pitches. People remember stories, not ads, so share behind-the-scenes details or real customer experiences to build trust. Not everyone visiting your website is already committed to you or your products. A quality site will work in your favor in those cases: if you can offer people useful information in a post, they’re more likely to remember and convert in the future. Today, this kind of authentic engagement matters more than ever, as AI search prioritizes brands that users already know and trust.

Read on: To blog or not to blog »

A blog isn’t valuable just because it exists. It becomes valuable when it helps your audience solve problems, understand something better, or see your expertise in action. In today’s search landscape, the goal isn’t simply to publish, or even to publish more. It’s to create content worth being found, cited, and remembered.

Carolyn Shelby – Principal SEO at Yoast

Setting up a new blog

If you’re starting a new blog, preparing beforehand is important. A little planning now prevents headaches later, especially with AI search favoring well-organized, intent-driven content. Take some time to think about your niche and do proper keyword research. Remember, don’t just chase search volume. Focus on topics your audience actually cares about, like questions they’re asking or problems they need solved.

Please don’t forget to set up a clear and manageable structure for your blog. A logical layout helps both readers and search engines navigate your content, which improves engagement and rankings. If you give some thought to how you want to set up your blog before you start writing, it will save you a lot of work later. These include tasks such as mapping categories, setting up cornerstone topics, and developing an internal linking strategy. A strong foundation makes it easier to adapt as AI search evolves.

Keep on reading: How to start a blog »

What should you blog about?

You can only blog with ideas, so you’ll need many to keep a successful blog going. Whether blogging is your site’s main purpose or you use your blog as a marketing tool, you must consider which topics you want to cover. Don’t forget to think about what your audience needs to read. Where do you look for inspiration?

Keyword research

You’ll have to decide which terms you want to be found for before you start writing your content. To decide that, you need to get inside people’s heads and find out which words they use while searching for your type of business. Think beyond single keywords. Consider phrases, questions, and even conversational queries people might ask AI search tools. When you write, use these exact terms in your content to signal relevance to both search engines and AI-powered results. Keyword research is the first step in SEO copywriting and an essential part of any successful SEO strategy, even as search itself evolves.

Targeting the right search intent with your blog

As you’re doing keyword research, it’s important to know not only which keywords your audience uses but also what they’re looking for. People use search engines with a specific goal, so they have a particular intent for each query. The results pages provide some insight into a query’s intent. AI search tools like Google’s AI Overviews and AI Mode now prioritize content that directly matches what users are looking for, whether they want to learn, compare, or buy.

In many cases, people are looking for information, so search engines favor informational pages. This is where your blog shines. For example, if you run an online shop, your product pages target commercial or transactional intent, but informational blog posts can attract a much larger audience. Write relevant, helpful articles to pull people into your site early in their research phase.

Which intent to target depends on your niche and goals. Are you trying to educate, entertain, or convert? Either way, aligning your content with intent is non-negotiable today.

Where do you get inspiration for your posts?

If you’ve done your keyword research properly, you’ll end up with a long list of keywords and keyphrases to write content about, and you know which intent you want to target. A keyphrase is not yet a topic, though. You’ll need an angle or a specific story around a keyword to write a decent blog post, as well as a keyword.

Current events, your own work, and comments from your readers are just some things that could inspire new posts. For example, if customers keep asking the same question, that’s a sign you should write about it. Reading a lot is also a good way to find inspiration for your articles. Read magazines, newspapers, and other posts.

Of course, AI platforms and LLMs like OpenAI’s ChatGPT, Google’s Gemini, Perplexity, or Anthropic’s Claude can help, while Yoast AI Brand Insights can help you find out how you appear in chatbots.

Looking at your site’s stats or browsing the internet can also lead to inspiration. Which posts get the most traffic? Which ones keep readers on the page longest? Double down on what works. Pay attention to trending topics in your industry, but don’t just copy what’s already out there. Always ask yourself, how can you make this better or more engaging?

Be sure to keep a list of ideas for new posts on your mobile phone. Inspiration strikes when you least expect it.

Keep reading: How to get blog post ideas: 11 tips to find inspiration »

Beat writer’s block with Yoast AI Content Planner

Yoast AI Content Planner, available to Yoast SEO Premium users, helps you overcome frustrations about what to write next. It scans your existing content, identifies gaps, and generates five tailored post ideas. Each proposed post comes with a ready-to-use draft framework. Just pick an idea, and Yoast SEO provides a title, outline, focus keyphrase, meta description, and section notes to jumpstart your writing. If the first set of ideas doesn’t fit, refresh for new options. It’s all built into the WordPress editor, so you can go from blank page to first draft quickly.

Yoast AI Content planner feature example, showing possible article ideas for a travel site — An example of content suggested by the Yoast AI Content Planner

How to write a high-quality blog post

Writing requires some skills, and it’s more difficult for some people than for others. We’ll give you some tips to make writing easier for you later on, but first, let’s discuss two important aspects of high-quality posts: originality and readability.

Original content

Your posts should always be fresh, new, and original. Each one should stand out from other articles on the same topic. Today, this matters more than ever. Google’s AI search tools now filter out generic, repetitive content, so your posts need to offer something unique. Focus on what makes you different, even in a crowded niche. Your content should also be something people want to read. With competition fiercer than ever, good isn’t enough, so you need to go further.

Avoid commodity content. These kinds of posts rehash what’s already out there without adding value. Google recently warned that AI-generated summaries and search results prioritize content that stands out, not just repeats the same ideas. If your post doesn’t offer a new perspective or a fresh take, it risks being ignored. Don’t forget to ask yourself if this post teaches or solves a problem in a way others don’t.

With AI-generated content flooding search results, Google prioritizes human expertise and unique insights. These are the things AI can’t fake. Your blog can provide those, if you do it well.

Read on: The importance of original content for SEO »

Readable content

After writing a post with original content, you should ensure your article is easy to read. Readability is vital for your audience. If your text is well structured and clearly written, people will understand your message. Readability also impacts SEO, as Google’s AI tools favor content that’s simple to scan and digest. If your post is easy to read, with a clear structure with subheadings and logical paragraphs, chances are it’ll rank higher in the search engines, too.

Keep on reading: Does readability rank? »

Practical tips on how to write high-quality blog posts

Plan before you write

Before you start, take a little time to think about what you want to write. Who is your audience, and what do you want to tell them? What should they know, understand, or do after reading your post? Which topics will you cover, and in what order? Answering these questions upfront saves time and keeps your writing focused.

Write clear paragraphs

Start each paragraph with the most important sentence, then explain or expand on it. This way, readers and AI systems can grasp your main points just by skimming the first sentences. Keep paragraphs short; seven or eight sentences is plenty. Think about the order of your paragraphs and ensure they flow logically. Avoid complex words when simpler ones work. Your goal is to be clear, not to confuse readers with jargon.

Keep reading: Practical tips to set up a clear text structure »

Get help and ask for feedback

Our Yoast SEO plugin helps you write readable posts. For example, the readability analysis checks for long sentences and suggests transition words. This is especially useful today, as AI search tools prioritize well-structured, easy-to-read content. If you use Yoast SEO Premium, you’ll also get AI features like Yoast AI Optimize to refine your writing.

However, tools aren’t everything. Always have someone proofread your post. A fresh pair of eyes catches typos and ensures your message is clear. If your proofreader struggles to understand your post, your audience will too.

Need more guidance? Here’s a step-by-step guide to crafting the perfect blog post!

Read on: 5 tips to write readable blogposts »

Optimize posts for search engines

After you’ve written a blog post that’s both original and readable, you should make sure your content is optimized for search engines. You should maximize the likelihood that Google will pick up your content. Don’t try to game the system, but make sure your article is genuinely good for search engines and readers alike. You must take this final step after you’ve written your post, though. SEO should never compromise your idea’s originality or the readability of your text.

the yoast seo premium analyse for a post about site structure, which has two red traffic lights, one for keyphrase in subheading use and one for competing links — Yoast SEO helps you optimize your blog post

How Yoast SEO helps

Yoast SEO gives you the tools to fine-tune your post without guesswork. We call this process “Yoast your post.” It’s about making small, smart adjustments to improve visibility.

The red and orange traffic lights highlight areas that need attention, like keyword placement or readability.
The plugin might suggest adding your focus keyword in the first paragraph or a heading to signal relevance.
It also helps you craft a compelling Google preview, which includes the titles and descriptions users see in search results.

Don’t just set it and forget it. Use Yoast SEO to spot opportunities, make improvements, and give your post its best shot at being discovered.

Keep on reading: Use Yoast SEO to make your content findable »

Blog engagement

Blog engagement is an important SEO factor. If your audience leaves comments on your posts and you respond, Google will see that your blog is very much alive and active. If people share your post on social media or talk about it online, it will definitely drive more traffic. Engagement goes beyond just comments and shares. Citations, when others reference your content, and mentions, even without links, also signal authority and trust.

Replying to comments is important for building engagement, but it takes effort. Answering questions and joining discussions shows your audience you value them, which encourages more interaction. Positive feedback is easy to handle, but negative comments require care. Please just stay professional and keep the conversation constructive.

For more tips, check out our guide on handling comments.

Marketing your blog

If you’re writing posts, you need an audience. Nobody wants to perform in an empty room! Ranking well in search engines through flawless SEO will, of course, help. But there is always more you can do.

Social media and newsletters

Social media is a powerful way to connect with your audience and drive traffic. Start with a Facebook page and an X or Reddit account, but don’t stop there. If your audience is younger, Instagram and TikTok are essential for engagement. Short-form video content, such as Instagram Reels or TikTok videos, can help your posts reach a wider audience.

A newsletter is another great way to keep readers coming back. Collect email subscribers and send regular updates with your latest posts, exclusive insights, special discounts or gifts, or behind-the-scenes content. This builds a direct line to your audience, independent of algorithm changes.

Keep reading: Does social media influence SEO? »

Monetizing your blog

Growing your audience doesn’t automatically mean growing your income. Many bloggers focus on goals beyond money, like building a community or sharing expertise. But if you do want to monetize, here are the most effective strategies:

Advertising: Display ads like Google AdSense can generate revenue, but they work best with high traffic.
Affiliate marketing: Promote products you trust and earn commissions on sales made through your links.
Sponsored posts: Brands may pay you to write about their products or services.
Sell your own products or services: Use your blog to drive traffic to your online shop, courses, or consulting services

If you have an online shop, your blog can boost its rankings by attracting organic traffic and linking to your products.

Read on: Monetizing your blog »

Maintaining a blog

Starting a blog is easier than maintaining one. Writing blog posts regularly can be a lot of work. You don’t need to blog daily, but you should decide on a frequency and stick to it so your audience will know what to expect. Consistency builds trust, and trust keeps readers coming back. Blogging does require some discipline.

As your blog grows, you’ll probably face new SEO problems. How do you keep coming up with new content and keep your old content up to date? How do you manage different authors? What do you do when traffic to your blog is decreasing? And how will you keep your blog’s structure in shape?

Some challenges and how to solve them

As your blog grows, new problems pop up. Here’s how to tackle them:

Running out of ideas. Repurpose old content, and update outdated posts with new data or insights. Use the Yoast AI Content Planner to generate fresh topic ideas from your existing content. Don’t forget to listen to your audience, as their comments, emails, and social media threads can contain questions to answer.
Keeping old content fresh. Please audit your blog every six months, fix broken links, and refresh outdated advice. It might make sense to add “Last Updated” dates to show readers and Google that your content is up to date. AI search tools prioritize fresh content, which can revive traffic for old posts. If you have a lot of similar content, you can merge posts and combine thin or overlapping articles into a single comprehensive guide.
Declining traffic. Please check Google Search Console regularly to see which posts have lost rankings and why. Then, you can improve this underperforming content by adding depth, updating keywords, or merging with stronger posts. Promote strategically, and share old but valuable posts on social media or in newsletters.

Site structure

As your blog grows, it’s important to regularly analyze its structure. Organize your categories, subcategories, and tags well. As your blog grows, its structure will change and evolve. To keep your site structure clean, you can organize by topic clusters. Group related posts under pillar pages, like “SEO basics” linking to “Keyword research,” “On-page SEO,” et cetera. Don’t forget to update internal links. When you publish new posts, link to 2-3 relevant older ones. If your site becomes unwieldy, prune low-value content. Delete or redirect posts that no longer serve your audience. As long as you stay on top of that, your structure will remain SEO-friendly!

Keep on reading: Why you should add links to a new post as soon as possible »

Content planning

As your blog grows, writing shifts from spontaneous posts to strategic planning. Without a system, teams risk duplicate topics, inconsistent tones, or missed opportunities. A clear plan keeps your content organized and aligned with your goals, whether that’s driving traffic or conversions. Use tools such as editorial calendars, topic clusters, and the Yoast AI Content Planner to streamline the process. Assign roles and document guidelines for voice, style, and formatting to maintain consistency.

Planning saves time and reduces last-minute stress. An editorial calendar maps out topics, deadlines, and authors in advance, while topic clusters group related posts to boost SEO and reader navigation. Regular audits help you spot gaps and adapt to trends, keeping your blog relevant and valuable.

Avoiding content cannibalization

If you’ve been blogging in a certain niche for a long time, you’re bound to address the same topic more than once in your blog posts. That’s not necessarily a problem, but do make sure you’re not eating into your own ranking chances. Keyword cannibalization occurs when you have several different articles that could rank for the same and similar keyphrases. When a search engine can’t tell which article should rank highest for a certain query, it’s likely both will rank lower. The solution: stay on top of this by regularly doing an SEO audit of your blog posts to find and fix keyword cannibalization.

Conclusion

Blogging is great. It’s one of the most powerful tools for growing your website, whether it’s an online shop or personal blog. It boosts your search visibility and turns visitors into followers. But to get the best results, you’ll need more than just good writing.

Start with a good keyword strategy to target what your audience is searching for. Keep your content original and structured for AI search. Google’s algorithms, and your readers, reward clarity and depth. As your blog grows, stay organized with planning tools and engage with your audience to stay in the flow. Use our tips to build a blog that ranks and delivers real value. Now, go write something great!

Keep reading: WordPress SEO: The definitive guide to higher rankings for WordPress sites »

The post Build a blog that drives real results appeared first on Yoast.

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PR and SEO: How to Build More Authority Together (5 Steps)

May 26, 2026/0 Comments/in Web Design News/by Dubado Solutions

PR and SEO used to be separate disciplines.

Now you can’t afford to keep them siloed.

Google and LLMs both rely on third-party signals — backlinks, brand mentions, expert commentary, and coverage in trusted publications — to decide which brands deserve visibility.

PR and SEO both generate those signals, but most teams still operate independently.

When they do collaborate, it’s usually to treat PR as a link-building opportunity rather than a real partnership.

This leaves authority on the table.

But the real gains happen when these teams operate as one.

In this article, you’ll learn a five-step playbook for turning PR and SEO into an always-on authority engine.

I also spoke with two digital PR experts about how they’re partnering with SEO to build more authority across search, media, and LLMs.

Free resource: Download our PR + SEO Outreach Planner to align pitching, prioritize outlets, and track results. It includes a pitch ownership guide for deciding who pitches what and when.

Step 1: Align PR and SEO Research

An always-on PR and SEO partnership starts with shared intelligence.

Without it, you get predictable gaps:

Content that ranks but doesn’t earn media mentions or AI citations
Coverage that builds awareness but doesn’t improve search visibility
AI citations and media coverage that go to competitors because they published first

Use PR Insights to Identify Emerging Content Opportunities

The biggest authority wins don’t come from PR and SEO staying in their own lanes.

They come from each team sharing insights that shape angles, assets, and placements.

For PR, this could be:

A sudden spike in journalist inquiries or media coverage around a topic

A new phrase or framing gaining traction among industry voices

Recurring themes across newsletters, conferences, or trade publications

Britt Klontz, digital PR consultant and founder of Vada Communications, says the strongest results come when PR and SEO combine their strengths at the ideation stage:

The best collaborations with SEO happen when PR is brought in early, before an asset or campaign is completed. We used to ask, ‘Can PR promote this?’ Now we ask, ‘How do we build something together that will help with search, media, and brand visibility from the start?’

To facilitate this partnership, build a regular channel for PR to flag insights to SEO.

This could be a shared Slack channel, spreadsheet, or standing agenda item.

For example, when I was the editor of the Hootsuite Blog, our PR team notified us that LinkedIn was shutting down its “Elevate” feature and suggested we should write a blog post about it.

No search volume existed yet, but we created the content anyway.

Hootsuite – LinkedIn Elevate shutting down

The post started gaining backlinks and driving a surprising amount of demo requests almost immediately.

Months later, the search volume appeared. And our post ranked #1.

LinkedIn Elevate shutting down post – Organic rankings

Today, it still ranks near the top of the SERPs for terms like “LinkedIn elevate alternatives.”

Google SERP – LinkedIn Elevate alternative

AI tools like Claude also use the blog post as a top source for relevant prompts:

That’s the power of PR and SEO sharing information and acting on it quickly.

Rankings, backlinks, and AI citations that would have gone to a competitor built lasting authority for Hootsuite instead.

Use SEO Insights to Inform Content Topics

SEO has signals PR can act on too, including which topics are heating up and editorial gaps.

When conducting keyword research for PR, SEO should flag two things:

Informational gaps: Questions audiences are actively searching for, but no one is answering well yet
Trending terms in your niche: Journalists are likely already interested, which gives PR a clear opening

That’s why Rola Tfaili, communications manager for North America at Xero, brings SEO into her process from the start:

I want SEO insights — like emerging search trends, keyword gaps, and audience intent — to directly shape our PR narratives and campaign angles from the outset, before content is developed.

Here’s how you can do the same.

Not all keyword tools show you trends over time, so I’ll use Semrush for this step.

Note: If you don’t have a subscription, sign up for a free trial of Semrush One, which includes Semrush Pro and the AI Visibility Toolkit.

Search any term in the Keyword Magic Tool and look at the “SERP Features” column.

Keyword Magic Tool – Email marketing – SERP Features

Two features in particular signal strong PR potential:

News and Top Stories: Google surfaces these for time-sensitive or trending queries — sometimes within 24–72 hours of a news event. If your topic triggers these features, journalists are actively covering it, and PR has an immediate opening.
Discussions and Forums: This signals that audiences are seeking advice or firsthand experience on the topic, which is often a sign of unmet demand and/or increasing interest

Next, use the Keyword Overview tool’s 12-month trend graph to confirm whether a topic is gaining momentum, seasonal, or fading.

A consistently rising trend is your strongest signal — media interest is likely to be building as well.

Keyword Overview – Is email marketing dead – Trend

Pro tip: Don’t overlook existing topics. A trending term you already own is a valuable opportunity. PR can pitch it to journalists as a timely angle, repurpose it into new formats, or use it as a hook for a broader campaign.

For LLMs, you need a tool like Semrush’s AI Visibility Toolkit that shows actual prompt data, not just search queries.

This gives you insight into the exact prompts your competitors are earning AI visibility for, but you aren’t.

Those gaps are worth flagging to PR, especially if competitors are being cited as authorities on topics your brand should own.

Visibility Overview – Klaviyo – Topic Opportunities

Use your shared doc or Slack channel to provide real-time insights, so neither team works from stale data.

Topics that show up in both PR’s emerging trends and SEO’s keyword data are your highest-priority opportunities.

Step 2: Collaborate on AI-Ready Assets

An AI-ready asset is built to be found, cited, and trusted by search engines and AI models (while being valuable to humans).

This is also called answer engine optimization (AEO), which is the process of creating and structuring content for AI systems.

It can include optimizations like:

Headings that mirror how people search
Front-loaded key stats, details, and definitions
Sections that focus on one core idea
Bullet lists and tables that make key information more extractable

When you combine PR’s distribution power with SEO’s technical expertise, you get assets that earn visibility across search, media, and LLMs.

Original Research and Reports

Original data has long helped brands earn backlinks — now, it helps you build AI visibility too.

A collaborative workflow for this asset would look something like this:

SEO identifies the topic based on search demand and content gaps, and PR validates whether the angle is pitchable and shapes the findings into quotable hooks.

Together, they design the study so it’s structured for citations, with a clear methodology, front-loaded stats, and branded visuals that are easy to share.

Semrush blog – LinkedIn AI visibility study

SEO content teams might be tempted to create this type of asset on their own, then ask PR to pitch it.

But Britt says if PR is involved earlier, they can help answer questions like:

Is this a real story?
Is there a sharper edge here?
Do we need more reliable data?
Is there a better hook that fits the time?
Would it be more interesting if an expert gave their opinion?

That kind of information can make an asset more useful and impactful.

Pro tip: Give your asset a unique, branded name — like ‘The State of X Report’ or ‘The X Index.’ If journalists mention it without linking, people can still search for it and find you.

Don’t limit original data to a blog post.

High-value assets should have their own crawlable landing page — no gates, no PDF-only content.

Use the same URL each year for recurring assets to build authority. Then, link these pages to related content on your site (and vice versa).

This way, search engines and AI see your topical coverage as connected, not random.

Free Tools

Free tools that solve a specific pain point earn AI visibility, backlinks, and return visits long after launch.

This includes calculators, templates, checklists, and interactive assets.

Backlinko tools – Reddit SEO opportunity

The gap here is usually distribution.

SEO can build and optimize tools, but without PR’s contacts and timing, even the best ones can be limited by organic performance.

A strong hook helps, too.

Britt says an asset is easier to promote when it “blends search insights with something more personal, like proprietary data, a strong point of view, or a story angle that is relevant right now.”

The payoff is an asset that is reported on and shared widely across channels.

NerdWallet’s tariff calculator is a good example of this in action.

It launched as tariffs dominated headlines — and earned media coverage because of it.

Spectrum News – NerdWallet tariff impact calculator

Podcasts

A branded podcast can generate tons of coverage, review articles, and inclusion in “best podcasts on X topic” listicles.

Getting your experts on other podcasts is also valuable for building authority and visibility.

Third-party mentions get your brand and subject matter experts into the conversation, both in search engines and LLMs.

Google AI Mode – Best content marketing podcasts

PR typically drives guest placements, but SEO can identify which shows already rank or get cited by AI for your target topics, so you’re pitching the ones that build the most authority.

Press Releases

When published on your site and optimized properly, press releases can become standalone, crawlable assets that increase your AI mentions.

In fact, press release citations in LLMs grew 5x between July and December 2025, according to Muck Rack.

To get the most out of press releases, both teams need to contribute.

Rola has seen the benefit of this collaboration firsthand:

For key assets like press releases, we integrate SEO insights early — before content is developed — and include SEO in the review process to ensure we’re maximizing visibility.

PR shapes the story and the hook. SEO makes sure the on-site version is crawlable, optimized, backed by citable data, and linked to related assets.

So the press release doesn’t just generate buzz, it feeds your broader authority.

Explainer Content

Explainers are easy-to-digest resources (usually articles or videos) that simplify complex topics or highlight key info about your brand.

They help journalists and LLMs write accurately and consistently about you — especially if your category is niche or complex.

SEO can use keyword and prompt data to identify the questions your explainers should answer and structure them so AI can parse and cite individual sections.

PR knows which questions journalists and analysts ask most often — and where the current gaps are in how your brand gets described.

The format can vary:

One-page proof point packet with key stats and third-party validation that PR sends alongside pitches
YouTube video with citable brand facts or product details
Dedicated pressroom that organizes assets by category with founder bios and press releases

(Bonus points for all three.)

Step 3: Co-Build Your Third-Party Presence

Brands are 6.5x more likely to appear in AI answers through third-party signals than their own content, according to AirOps.

This means PR and SEO have a real opportunity to work together to build more visibility across search and LLMs.

Rola sees this as an important shift for PR teams:

When we align closely with SEO to ensure our key messages land in credible, third-party outlets, we’re not just generating press; we’re helping position the brand to appear in AI search platforms. That intersection between PR, SEO, and now AEO is where I think we’ll see the most measurable impact moving forward.

Expert Commentary

When your experts are quoted consistently — on your own site, social media, and in trusted publications — Google and LLMs begin to associate them (and your brand) with that topic.

The biggest coordination gap is knowing where to focus.

SEO has the data on which topics have the most search and AI demand — and which publications are already earning citations for them. PR knows which journalists and outlets are most receptive and what angles resonate.

Together, they can pinpoint the exact publications and topics where a placement will improve results for both teams.

Then shape the commentary accordingly.

Concrete, data-backed quotes with a specific stat or firsthand insight are far more citable than generic thought leadership — especially for AI, which favors specificity it can extract and serve directly in an answer.

Getting your experts quoted online is a strong start — but it works best when paired with the other authority-building sources below.

Further reading: 5 Best HARO Alternatives (Expert Review)

Review Sites and Forums

Review sites like G2, Yelp, Google Reviews, and Trustpilot are trusted by AI for the same reason they’re trusted by humans.

They aggregate specific, unbiased information about products from verified users.

And AI frequently cites them for product recommendations:

Reviews across multiple sites also strengthen your brand’s authority signals.

It gives AI detailed evidence of what category you belong in, your core features and pricing, and why you should be trusted.

Forums work similarly — AI pulls from Reddit threads and Quora answers when users ask for honest recommendations or firsthand experience.

Brands that show up authentically and positively in these conversations earn another layer of trust signals.

You can’t control these mentions, but consistently showing up as a helpful, knowledgeable voice in your category’s communities builds the kind of organic mentions AI models trust.

PR and SEO should jointly identify which review sites and forums matter most in your industry.

Keep review profiles current and monitor relevant forum conversations for opportunities to contribute genuinely.

Further reading: How to Build a Brand Subreddit: Full Setup Guide (+ Examples)

Wikipedia

A Wikipedia page gives Google and AI a neutral, third-party source of facts about your brand.

It also helps establish your brand as a recognized entity in Google’s Knowledge Graph.

It’s a common source for Google’s Knowledge Graph, and it’s baked into LLM training data.

But to qualify for a page, you need to meet Wikipedia’s Notability Criteria.

This includes having significant coverage in reliable, independent sources that address your brand directly and in detail.

PR can help you earn this kind of coverage by pitching stories about your company to journalists in reputable publications.

Once you have a page, you won’t be allowed to edit it directly, as Wikipedia’s rules prevent self-promotion.

But SEO can monitor the page for inaccuracies and flag corrections, and PR can handle reputation monitoring to keep the narrative positive.

Pro tip: Use the same brand name, category language, and positioning everywhere: across your website, social profiles, press releases, and review site listings. The more consistent your language, the more confidently AI and Google can categorize and recommend your brand.

Step 4: Unify Your Outreach Strategy

If PR and SEO know what — and to whom — each team is pitching, you avoid mixed messages and misaligned timing.

And your odds of a yes go up.

It doesn’t take much to fix. Just a shared source list, a strategy to split pitching, and a regular check-in to stay aligned.

Pro tip: Download our PR + SEO Outreach Planner to put the tips in this section into action.

Build a Shared Target Source List

SEO has a list of high-authority domains that show up in organic rankings and AI citations. PR has a list of journalists, analysts, creators, and publications that influence their category.

Merging these gives you a single view of every third-party source worth going after.

Build it as a shared spreadsheet with three columns:

PR Sources
SEO Sources
AI Citation Sources

Then prioritize.

Any source that appears on more than one list goes to the top. It has double (or triple) the potential to impact your authority and visibility.

Pro tip: Update your list quarterly as sources can shift fast — especially in LLMs.

Create a shared pitch doc to go with your source list. Use PR’s standard pitch brief, or if one doesn’t exist, create one. Include headline stats, agreed-upon positioning language, and target URLs.

Whoever sends the final pitch customizes it to their contact. But using the shared pitch doc as a starting point ensures your basic story stays consistent.
Split Pitching by Strengths
Many high-priority pitches will need both PR and SEO to weigh in. But not all.

Divide the work of pitching based on what each team does best.

Generally, that means structured, technical placements for SEO and editorial, relationship-based placements for PR.

Your company may want to organize these tasks differently depending on industry or org structure, but here’s what I suggest:


SEO	PR
Pitch for inclusion in industry listicles	Pitch journalists and editors on newsworthy content
Fix unlinked brand mentions	Offer expert commentary to reporters
Reach out to sites with broken or outdated links	Submit to industry awards
Identify warm contacts from referring domains	Brief analysts at firms like Gartner and Forrester
Monitor AI citations for new outreach targets	Explore sponsored placements in newsletters, podcasts, and trade publications

Plan Pitching in Advance

Meet quarterly or monthly — whatever works for your schedules — to decide who is going to pitch what, to which outlets, and when.

This will help prioritize high-impact efforts and reduce accidental duplication of work.

Map outlets to objectives and target KPIs to determine ownership.

Every time you meet, review results from the last period. Prioritize more of what’s working and cut what isn’t.

Further reading: Journalist Outreach: 9 Steps to Earn High-Authority Links

Step 5: Report on PR and SEO Performance Together

PR and SEO usually track different metrics, like mentions and outlet quality vs. rankings and organic traffic.

The fix isn’t merging into a single dashboard.

It’s building a shared lens for evaluating what each asset actually did, no matter which team owns it.

Britt recommends that both teams agree on a shared set of questions to evaluate each asset:

Did it get any attention?
Did it get picked up by reliable sources?
Did it help with search goals?
Did it contribute to conversions?
Did it have results that lasted longer than a short-term spike?

As Britt puts it:

The best shared work usually helps with more than one thing at a time, like visibility, authority, discoverability, and brand credibility.

Visibility: Did We Show Up in the Right Places?

Getting in front of your audience more often — and in the places they care about — is one of the main advantages of having PR and SEO collaborate.

Track these metrics to see if it’s working:

Quality mentions in relevant outlets: Not raw mention count. A placement in a niche newsletter your buyers trust outweighs 10 mentions on unrelated blogs. PR likely already has a media monitoring tool for this.
Recurring format mentions: Listicles, comparison posts, and “best of” roundups will continue to earn backlinks and AI citations over time. They also show how your brand is positioned relative to competitors. Track these separately in your media monitoring tool or a shared spreadsheet.
Share of voice in category coverage: Report on the percentage of category coverage that mentions your brand vs. competitors. Free tools like Google Alerts and Mention’s share of voice calculator give you a general sense of how you’re doing. But paid media monitoring tools let you dig into specific platforms, outlet types, and topics.

For AI specifically, track how often your brand appears in AI answers for queries you care about.

You can manually check your top questions and prompts in LLMs to see if your brand is mentioned, but this gets tedious at scale.

The AI Visibility Toolkit is helpful here. It automates tracking so you’re not manually checking every LLM for every query.

You get an overall AI Visibility score for your brand, which measures how often you’re mentioned in AI systems compared to other brands.

The Competitor Research tool shows how your AI visibility stacks up against competitors, which is one of the clearest ways to show leadership whether you’re gaining or losing ground.

It also tracks your Share of Voice across AI platforms, a single metric that reflects the combined impact of your PR and SEO efforts.

Authority: Did We Become More Credible?

This is where you show if your brand is becoming a trusted source online.

Start by tracking new referring domains.

New backlinks matter too, but new domains are more meaningful because they represent more unique sources vouching for your brand.

Reporting on your website authority is also helpful. This is a third-party estimate of the level of trust search engines are likely to assign to your domain, based on your backlink profile and other signals.

Different SEO tools calculate it differently (and call it different things).

So, focus less on the score and more on the direction it moves over time.

Backlink Analytics – Backlinko – Authority Score graph

Note: Meaningful changes to your Authority Score can take 3-6 months to appear.

The AI Visibility Toolkit tracks your mentions, citations, and cited pages over time, and tells you percentage increases and decreases.

When your authority score and AI mentions are both climbing, you’ll know your PR and SEO work is paying off.

Visibility Overview – Sprout Social – Main Metrics

Expert commentary placements, direct requests from journalists, and new journalist relationships are also worth tracking.

Increases in any of those areas are a strong signal that you’re gaining trust.

Google Alerts can catch mentions to help you track expert commentary placements, but a tool like Semrush’s Brand Monitoring gives you a more comprehensive picture.

It lets you track any query (SME names or other keywords) and provides:

Total mentions
Estimated reach
Traffic
Mentions with backlinks
Sources (Social media, news, and blogs)

Demand: Did It Help People Take the Next Step?

Did improving visibility and authority have any impact on your business goals and revenue?

PR and SEO sometimes sit at the top of the funnel, so this can be tricky to answer.

Start with these metrics to prove demand:

Referral traffic
Assisted conversions
Branded search lift

Track your referral traffic to show the number of visitors who visit your site directly from media coverage.

Even if numbers are low, they’ll tell you which topics make your audience want to know more about you. Then you can publish more on those in the future.

GA – Traffic Acquisition – Session source / medium

Tracking assisted conversions shows you conversions where organic search or referral traffic appeared somewhere in the buyer’s journey, but not necessarily as the last click.

PR and SEO content may not convert on the first visit, but it still influences the buyer’s journey.

This metric captures that concept.

Find this in GA4 under Advertising > Key event attribution paths, and switch to “Source/Medium” to see which specific outlets have the most impact.

GA – Advertising – Key event attribution paths

As AI search has decreased click-through rates, branded search queries have become one of the clearest signals that your PR and SEO efforts are building real awareness.

It’s a metric Britt prioritizes for exactly this reason:

I track branded search lift because it’s a sign that coverage or visibility made someone curious enough to go look up the company by name. That matters to me because not every asset will result in direct clicks.

The metric is also important to Rola:

Branded search lift connects awareness and intent, showing how media exposure actually drives people to seek out your brand.

Google Search Console tells you how often people search for your brand by name and how many of those searches result in a click to your site.

Look for spikes around major coverage dates to directly tie increases to your PR and SEO efforts.

Turn PR and SEO Into an Always-On Authority Engine

The brands earning the most trust right now aren’t doing it with PR or SEO in siloes.

They’re showing up consistently across media, blogs, review sites, search engines, and AI because all of those channels feed the same authority signals.

That takes more than a “quick sync” before campaigns. It takes an always-on partnership.

You don’t need to overhaul everything at once.

Start small:

Co-create one high-impact asset (and keep AEO best practices in mind)
Merge your source lists
Plan 3 pitches using our PR and SEO Joint Outreach Strategy Template

When you’re ready to go deeper on how to optimize your brand’s presence in AI, check out our complete guide to AI optimization.

The post PR and SEO: How to Build More Authority Together (5 Steps) appeared first on Backlinko.

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Posts

The keyword is dying because users stopped using it

AI Brief is a technical replacement for keywords

How to try AI brief now

The new funnel, and why short keywords still have a job

What AI Brief does to the four human PPC roles

The teacher

The doctor

The pilot

The restaurateur

Why AI Brief feels different

The lower-funnel case is easy to make

The reseller trap: When your lower funnel depends on someone else’s brand

Upper funnel is inventory management

Why a fixed split is the wrong answer

Building a dynamic split logic

The conversation nobody wants to have

Key Takeaways

Why This Is Happening

Why the Old Playbook Falls Short

What to Do Now

Thinking About The Bigger Picture

FAQs

Is search traffic gone for good?

What types of content still drive clicks from AI-influenced searches?

How do I know if AI is affecting my traffic?

Should I be investing in Answer Engine Optimization (AEO)?

Conclusion

5 stages that turn customer success into SEO signals

OPID is the business, not a content opportunity

Your customer is now two customers, and only one of them can watch you work

The agent checks your story against the open web

Onboarded: Getting the customer from sale to first success

Performed: Delivering a measurable outcome against a baseline

Integrated: When the customer makes you a repeatable use case

Devoted: When the customer sells you to the next customer

The proof AI needs already exists

Codifying is your job, and every channel depends on it

What to evaluate before increasing budget

Learning periods matter

Validate that performance is real

Market saturation is real

Define the goal: Efficiency or scale?

3 strategic questions to ask before increasing budget

1. Do you actually have impression share room to grow?

2. Is there room for more demand, or are you just bidding higher?

3. Should this budget go into a new campaign instead?

When increasing budget does make sense

You’re constrained by budget rather than rank

The campaign is new and still learning

You’re scaling demand alongside spend

What deliberate scaling looks like

Why memes matter to search

How ‘it’s just SEO’ became the dominant meme

Clients buy certainty, not acronym wars

The B2B Institute already called this

The 9 a.m. to 5 p.m. test

No name, no budget

A better way to frame the shift

Context is the problem

A client brain is the solution

What a client brain is

The technical anatomy of a brain

Building core logic of the soul

Memory captures decisions, patterns, and logs

Building the brain step-by-step

Step 1: Pick the right starting client

Step 2: Block 90 minutes and write the soul together

Step 3: Decide where the brain lives

Step 4: Set ownership rules

Step 5: Schedule maintenance

How AI agents read the brain

Version A: Load everything

Version B: Route by task type

Version C: Vector retrieval

Using the brain across Claude Code, Chat, and Cowork

Where this breaks (and how to fix it)

How to get started this week

AI works better when account knowledge survives

Table of contents