5 AI Search Shifts Marketers Can’t Afford to Miss Before Q3 via @sejournal, @hethr_campbell

Impressions are up. Clicks are down.

Rankings haven’t moved.

If that describes your last Search Console export, you don’t have a content problem or a technical problem. You have a SERP that no longer sends you the clicks your positions used to earn, and the playbook you’re running was built for one that did.

Five shifts are restructuring how search visibility works right now. Each one builds on the one before it, and each one comes with work you can start before Q3 planning is finalized.

1. Don’t Treat Organic CTR Decline as a Seasonal Dip

AI Overviews and zero-click results are absorbing clicks on queries where your positions are stable. If you’re reporting blended CTR, one metric is combining two different losses: clicks lost to AI Overviews on queries you still rank for, and clicks lost to competitors who now outrank you. Each requires a different fix.

How to Find Out If AI Overviews or Competitors Are Taking Your Clicks

The diagnosis starts in data you already have. Segmented correctly, your query data splits into two groups with very different jobs: one shows you where competitors are winning clicks you can fight to take back, and the other shows you where the SERP itself changed and your effort belongs somewhere new. Knowing which group is driving your decline determines what your team spends Q3 fixing.

In our upcoming SEJ Live, 6 experts will spend 3 hours helping you identify, repair, and expand your AI Search strategy for Q3.

2. Optimize for Being Surfaced, Not Just Ranked

Position one on SERPs no longer guarantees visibility.

LLMs and AI Overviews extract passages, synthesize across sources, and cite, which means your pages need to be extraction-ready, not just rank-ready.

A page AI systems can lift a clear answer from becomes a citation asset, and a page that ranks well but can’t be extracted stays invisible at the answer layer no matter its position.

Knowing which group your best pages fall into is the first thing to settle before rewriting anything, and it starts with understanding what “discoverable content” actually means when AI is doing the surfacing.

3. Build Presence on the Platforms Feeding AI Models

Reddit and LinkedIn have become citation sources, not just social channels.

In studies of AI outputs, Reddit threads keep showing up. So do LinkedIn posts from practitioners whose job titles, posting history, and engagement signal verifiable expertise.

Both now influence which brands get cited, and you don’t control either property.

Start by finding out whether the sources AI answers trust in your category mention your brand, your competitors, or neither.

4. Plan Paid & Organic in the Same Room

AI Mode ads are rolling out. ChatGPT ad testing is in flux. The same AI surfaces now blend organic citations and paid placements, and if you’re only watching the organic half, you’re missing where paid placements are taking visibility you thought your rankings secured.

5. Report Citations Alongside Clicks

Search Console clicks no longer measure your full search visibility. Being cited in AI answers is a second visibility metric that captures brand exposure clicks never record, and your leadership report almost certainly doesn’t include it yet.

Where to Take This Work Next

Shifts 3, 4, and 5 are exactly what SEJ Live covers on June 17: a free, virtual event with three sessions built to connect AI search, social, paid, and attribution into one strategy.

  • A fireside chat answering the AI visibility and traffic questions you submit
  • Reddit and LinkedIn experts on earning the citation signals AI models trust
  • A multi-channel session on building attribution you can actually act on

You shape these sessions: when you register, you can submit the question you most need answered, whether that’s AI citation tracking, AI Overview displacement, PPC overlap, or content extractability. The sharpest questions shape the conversation on stage, and yours could be one of them.

Register free for SEJ Live, June 17, 12–3 PM ET. Recordings included, so you can share every session with your team after the event.

Fix Your KPI Blind Spots: How To Finally Tie AI Search To Performance via @sejournal, @hethr_campbell

How do you prove AI search is driving revenue when your current measurement stack wasn’t built to track it?

Which metrics prove AI search value without click data?

What KPIs should you track to actually see SEO impact?

As zero-click journeys grow and AI influence moves off-site, traditional channel-level reporting leaves senior marketers without visibility into what’s actually driving performance.

👆 Your boss wants SEO revenue impact. Your dashboard shows clicks. Watch the full session right now.

Finally, The KPIs That Tie AI Citations Directly To Performance

In this on-demand session, DAC’s Felicia Delvecchio, VP of Media, Vincent DeLuca, Director of SEO, and Gavin Bowick, Lead Web Analytics, introduce a modern, launch-ready measurement approach that connects AI signals: citations, brand mentions, and recommendations, directly to media performance and revenue outcomes.

You’ll Learn:

  • A New AI Search Measurement Framework: Ways to track AI visibility, influence, and impact across the full funnel.
  • Connecting AI Visibility to Business Outcomes: How to tie AI signals to conversions using incrementality, MMM, and cross-channel insights.
  • The KPI Swap: Which metrics to replace click-based reporting with, and how to build enterprise-level reporting that reflects real performance.

Walk away with a full-funnel measurement framework that aligns SEO, paid media, and AI visibility signals with revenue, built for enterprise teams that need to report up with confidence.

Unlock above to watch a data-backed, enterprise-tested measurement framework you can apply immediately to your AI search reporting.

AI Content Alone Won’t Fix Your SEO Rankings (Here’s What Will) via @sejournal, @hethr_campbell

Most in-house SEO teams and agencies have adopted AI for content briefs, drafts, on-page recommendations, and technical audits.

The output is up, but two things are happening at once, and SEO teams have to solve both:

Why More AI-Assisted Content Isn’t Moving Rankings

Search behavior has shifted. Long-tail queries (10+ words) have grown sharply, query complexity is up, and the queries that drive real intent now look more like natural speech than the keyword-stuffed phrases SEO was optimized for three years ago. AI trained on the open web is still writing for the older patterns. The result: a content library that goes to market faster than ever but matches fewer of the queries that actually convert.

The fix is in the training information. AI needs training material that already speaks in natural language, and the inputs SEO teams need are sitting in their own first-party data sources, organized into something the whole department can run.

What You Need For Better AI Outputs

Even when teams figure out the input problem, many times, the productivity gain doesn’t pass on to the rest of the department. AI lives inside one person’s saved prompts or one writer’s personal workflow. When that person is out for the week or moves teams, the output and workflow disappears with them.

In The 4-Layer AI Ops Playbook: From Better AI Output To Strong SEO Results, CallRail’s Darrell Tyler walks through the documented system his team uses across SMB and agency-side SEO to solve both halves. Four layers: Knowledge, Workflow, Governance, Application.

When the four layers are documented and shared, AI gets fed the natural-language inputs that match how people actually search now, and the team gets its hours back from the repetitive lifts (content optimization passes, rank reporting, technical audits at scale) for keyword strategy, content planning, and on-page and technical QA across products.

3 things SEO & content folks walk away with:

  1. A diagnostic for why faster AI output isn’t matching how people search today, and where the process gaps live in most teams’ workflows
  2. The full 4-layer AI Ops foundation, fueled by the natural-language data sources your team already owns
  3. A 90-day validation plan: which workflow to prove the method on first (briefs, audits, or rank reporting), what to put in place before expanding it across the team, and how to show rankings impact in the next two quarterly reviews

Who It’s Built For

In-house SEO leads, content marketing managers, and the agencies serving SMB clients. Anyone who has invested in AI tooling and is still trying to justify the spend to leadership. Anyone scaling content output without scaling the headcount that produces it.

Google SERP Layout Shift: Position 1 Now Appears Halfway Down The Page via @sejournal, @lorenbaker

Ranking #1 doesn’t mean what it used to.

In fact, 57% of organic position-one results sit above the fold on desktop, and only about 40% do on smartphone.

The takeaway from a guy who works at a rank-tracking company: rank alone is no longer enough.

Capper walked through your brand’s position from a pixel height lens, SERP result size, and SERP share of voice, and made the case that SEOs need to reframe their channel as brand impressions, not just clicks.

Watch the on-demand webinar right now & learn what mattered.

Position 1 Is Often Invisible. The Median Result Sits 635 Pixels Down.

Reaching position 10 takes about five full screens of scrolling.

On desktop, the median organic #1 result sits roughly 635 pixels from the top of the page, against a typical laptop viewport of about 800 pixels.

Position two is already, more often than not, below the fold.

The mobile picture is worse. “Nearly two thirds of the time or three fifths of the time, the number one organic result is not visible at all, not even the first row of text on a typical smartphone,” Capper said. “It’s pretty horrendous, right?”

Where Position 1 Went: AI Overviews & Paid Are Above the Fold

Once organic gets pushed down, what replaces it depends entirely on intent.

On informational SERPs, AI Overviews consume nearly a third of above-the-fold visual space on their own. Add Knowledge Graph and the figure climbs to roughly 41%, two fifths of what users see before scrolling.

Commercial SERPs are even more lopsided. Paid plus shopping units occupy more than 60% of above-the-fold space, with Popular Products pushing past two-thirds in some categories. Organic gets about 16%.

Vertical matters enormously. Watch on-demand to learn what to focus on, by vertical.

Optimize for Result Screen Size, Not Just Rank

Capper’s sharpest tactical reframe: stop prioritizing keywords by rank or volume alone, and start prioritizing by size.

A plain organic result is about 120 pixels tall. An organic listing with images, prices, and ratings (IPR) runs roughly 240 pixels, which is twice the visual footprint.

His Lord of the Rings analogy made it stick. When Gimli tells Legolas that taking down a tower-sized elephant “still only counts as one,” he’s obviously wrong.

Same in organic: a Brex Flowers listing with full rich results dwarfs a plain Trustpilot link beneath it. “Are you really going to say that this counts just as the same as the Trustpilot result? No, this is huge.”

Action item: Audit your top commercial keywords for IPR eligibility and prioritize schema work by pixel gain, not search volume.

Watch the on-demand webinar right now for access to the audit.

Brand Search Now Outranks Domain Authority as a Ranking Signal

Capper revived data from a presentation he gave nine years ago showing that branded search volume was a stronger predictor of organic rankings than Domain Authority. Run the same analysis today and the brand signal has only strengthened.

“Brand is getting stronger and stronger and stronger as a predictor of how you rank,” Capper said. “And again, how do you build your brand through SEO? You are visible.”

That creates a flywheel SEOs have under-pitched for years: visibility builds brand recognition, branded searches climb, rankings improve, visibility compounds. The point isn’t to abandon authority metrics, but to stop treating brand as a vague “awareness” outcome and start treating it as a measurable input to organic performance.

Learn what you should track right now, in the on-demand webinar.

Q&A: Most Helpful Questions from the Webinar

Q: Any suggestions on how to sell visibility and pixels to senior leadership who are entrenched in share of voice and ranking?

Tom answered: Pixels are the easier sell because you can show side-by-side SERPs where the traditional metric says you win but the visual reality says you don’t. He recommends blending pixels into how share of voice is calculated, since share of voice was always supposed to be a visibility analog, and pixels measure that more honestly than rank does. The harder pitch is repositioning SEO as a brand channel, but Tom’s shortcut works: “If you have other channels that drive impressions, bring up those impressions data, put them next to the impressions that you’re generating… SEO is an incredible impressions channel.”

Q: Should we skip all the SERP optimization and just go right to optimizing for agents?

Tom answered: Not yet. Agents still need to decide which businesses to surface, and they do that using grounded LLMs that rely on SERPs underneath. “One way or another, this is coming back to the SERPs.” On top of that, Google search still dwarfs LLM interfaces by traffic volume. He noted an unverified stat he saw minutes before the webinar that AI Mode had hit roughly one million users, which is “staggeringly low when you consider Google’s overall user base.” Agent-only futures may come, but the underlying answering APIs will still be SERPs in machine-readable form.

Q: What are accessible ways to measure AEO/GEO visibility, since there’s no equivalent of Search Console for LLMs?

Tom answered: Three things. First, track prompt-level brand visibility, but don’t fall into the “I track 10,000 keywords but only 50 prompts” trap. LLM response variety demands a real sample size. Second, think in terms of topic volume, not prompt volume, since most specific prompts have a volume of one. Third, focus on mentions and recommendations rather than citations: “This is not ranked tracking… you are trying to be the tool or the product or the brand that is mentioned and recommended within the response.” He also suggested server log analysis to see which pages LLM grounding bots are actually hitting.

Q: Is organic likely to keep getting worse, or will AI fatigue bring traditional search back?

Tom answered: It’s not getting better, but the pace may slow. He pointed to Google I/O as evidence: Google held back from rolling AI Mode out broadly, suggesting internal nervousness about user readiness. AI Mode handles informational queries reasonably but struggles with navigational searches and specific result types like weather widgets. Both ChatGPT and AI Mode have also been adding more links over time, because users still want to reach websites. His honest take: “I don’t think we’re going back to where we were. I’m afraid I think ultimately people quite like having the answer asked for them.”

Watch the Full Webinar

The full session, including Capper’s SERP comparisons, vertical-level breakdowns, and the AI tracking framework, is available on demand from Search Engine Journal. Watch it for the data, stay for the budget pitch.

Google’s Standards Haven’t Changed But AI Is Making That Harder To Ignore via @sejournal, @gregjarboe

Recently, Sam Sifton, who hosts The Morning newsletter for The New York Times, published a letter to his readers with an unusual subject line, “Who’s Writing This?

His prompt was a new book called “The Future of Truth,” written by Steven Rosenbaum with significant AI assistance. The Times reviewed the book and found more than half a dozen misattributed or entirely fabricated quotes conjured by the AI, including one attributed to tech journalist Kara Swisher. Swisher’s response said not only was the quote wrong, but “I also sound like I have a stick up my butt.”

Rosenbaum’s defense that the hallucinations “serve as a warning about the risks of AI-assisted research and verification” is the kind of sentence that would be more convincing if it appeared in a different book.

Sifton used the moment to tell his readers something he clearly felt they deserved to hear directly. The Morning is built by humans, for humans. His team may use AI to find information that gets verified elsewhere. They may use it for editorial logistics, buying time for more reporting, but the thought-making, the question-asking, the deep reading, and the writing that follows – those are tasks performed by journalists free of chips. “I write fueled by adrenaline and fear of errors,” he told his readers. “And I promise you that will never change.”

What Google’s Guidance Actually Says

In February 2023, Danny Sullivan and Chris Nelson published Google’s guidance on AI-generated content. The position, which has not meaningfully changed since and was reinforced again recently in Matt Southern’s reporting on Google’s new AI search guide, is this: Google’s ranking systems aim to reward original, high-quality content that demonstrates E-E-A-T (expertise, experience, authoritativeness, and trustworthiness). The focus is on the quality of content, not how it is produced.

That sounds, on a quick reading, like a green light for AI content. It isn’t, or at least it isn’t a green light without conditions that matter enormously.

Google’s guidance specifically says that using automation to generate content with the primary purpose of manipulating search rankings violates its spam policies. And it draws an analogy that SEO professionals should analyze and evaluate: about a decade before the 2023 guidance was written, there were understandable concerns about content farms, which mass-produced large volumes of human-generated content. No one thought it reasonable to ban all human-generated content. Instead, Google improved its systems to reward quality. The helpful content system, the E-E-A-T framework, the information gain patent, the ongoing Quality Rater Guidelines updates through 2025 – all of it is the same enforcement mechanism, applied again, at greater sophistication.

Rosenbaum’s book is exactly the kind of content that Google’s systems are designed to identify and discount. Not because it used AI, but because it used AI carelessly, without the verification, the original reporting, and the editorial accountability that Google’s quality signals are trained to detect.

Sifton’s newsletter is exactly the kind of content those same systems are designed to reward. Not because it is human-generated, but because it is produced by people with genuine expertise, direct experience, and accountability to a specific audience. It is built by humans, for humans, in precisely the sense Google’s helpful content guidance has always intended.

Will Sifton’s Letter Change Anything?

The question at the center of this commentary is whether Sifton’s look at AI’s expanding role will change what Google is doing, change how practitioners write for AI, or change how they win in AI visibility.

The honest answer is no, not directly, and that’s the point.

Google’s guidance has been consistent since February 2023. It was consistent before that in spirit, through Panda in 2011, through E-A-T, through the Helpful Content Update in 2022, through the transition to E-E-A-T later that year. What changes is only the acuity with which people spot it on the horizon.

What Sifton’s letter does, that Google’s technical documentation cannot, is make the human cost of the alternative legible. Rosenbaum’s Kara Swisher hallucination is not an edge case or a technical failure. It is what happens when the thought-making is outsourced entirely, when the question-asking stops, when no one is writing fueled by adrenaline and fear of errors. It is a book about the future of truth that cannot be trusted.

For SEO professionals, the practical implication has not changed since Amit Singhal’s 23 Panda questions in 2011. Does the article provide original content or information, original reporting, original research, or original analysis? Does it have the kind of quality you’d expect to see referenced by a magazine, encyclopedia, or book? Would you be comfortable giving this to your editor and putting your name on it?

Sifton’s promise to his readers is that he would. That accountability is not a stylistic choice. It is the entire mechanism by which trust is built with an audience, and by which Google’s systems learn to surface content worth surfacing.

The Real Lesson

AI is not indifferent. It is responsive, adaptive, and improving faster than any previous technology transition in the industry’s history. That’s exactly what makes it useful and exactly what makes the question of how you use it so consequential.

But the standards that determine whether content earns trust, from readers and from Google’s ranking systems alike, do not move on AI’s schedule. They have been moving in the same direction for as long as Google has existed. Every approach that has assumed those standards would yield to scale, to automation, and to the next optimization trick has found the same thing.

They don’t yield. They move right along as though nothing happened.

More Resources:


Featured Image: Roman Samborskyi/Shutterstock

Why LLMs Cite Reddit Instead Of Your Brand: A Practical AI Visibility Audit [Webinar] via @sejournal, @lorenbaker

When a customer asks ChatGPT, Gemini, or Google AI Overviews about a brand near them, the answer increasingly comes from a Reddit thread.

For many brands, their owned content is not showing up.

Across most industries, Reddit is now the single most-cited source in AI search. For multi-location brands, that creates a problem most haven’t solved: showing up consistently inside answers across every market, neighborhood, and language they operate in.

Why Reddit Sits Behind So Many AI Answers

AI search engines weight community signals heavily because they read as authentic, peer-validated, and ongoing.

Reddit’s threaded conversations, upvote patterns, and topic communities give models exactly the kind of context their retrieval systems prioritize. The brands earning AI citations are the ones whose community presence and whose location data give models something credible to surface.

What You’ll Learn In This AI Search Webinar

  • The community signals Claude, Gemini, and Google AI Overviews actually weigh, plus which subreddit and content patterns get cited most often.
  • How trusted, structured location data works in tandem with community signals to land multi-location brands inside AI answers.
  • The 5 specific plays multi-location brands across retail, QSR, healthcare, financial services, automotive, and hospitality are running right now.
  • How to scale AI search across dozens (or hundreds) of locations without losing the local voice that makes communities trust you.

About the Speakers

Amanda Kusner, Sr. Solutions Consultant at Uberall, works directly with multi-location enterprises on location data strategy and AI search visibility across retail, QSR, financial services, automotive, healthcare, and hospitality. Peter Wischmann, Senior Sales & GTM Leader at Reddit, brings the platform-side view on how community signals get surfaced in AI search and what brands can actually do about it.

Register Today

If your brand operates across multiple locations and you’re trying to figure out how to land inside AI answers in every market you serve, this session is built for you.

When Marketing Leaders Can’t Explain Search Performance via @sejournal, @coreydmorris

Search marketing produces an enormous amount of performance data, but many marketing leaders still struggle to explain what those results mean for the business.

SEO and paid search reports often focus on visibility, clicks, and conversion metrics. And, in many cases, data sources for that information don’t match up, as I explored in my previous column.

While valuable, these metrics don’t always translate clearly into business impact and leave chief marketing officers and marketing leaders with reports they can present, but not always confidently explain. And, in some cases, get caught looking defensive and reactionary versus confident and proactive in presenting performance data and having the pulse of what is happening.

I learned this the hard way early in my career as an SEO. A few months into SEO work with an attorney, I had rankings, traffic, and tracked conversions that all looked great. My stomach dropped when I was told, “that’s great Corey, but I didn’t get a single new case from any of this.” It wasn’t comfortable for me back then to go beyond the marketing key performance indicators. As I grew in my career, though, I never forgot about that day.

Today’s attribution world is a mess, and it is more complex when we add in AI visibility, sources, and even Google’s own SERP changes with AI integration to our SEO and SEM data.

This article explores the gap that exists between search marketing performance metrics and executive-level business outcomes, and offers guidance for how marketing leaders can translate search results into meaningful business narratives.

1. Start With The Business Outcome, Not The Metric

The deepest business metric (not marketing) that you have access to is where I recommend starting. There’s not a universal truth on what the ultimate business metric is due to the complexity of different organizations and how much access there is to data, so this is possibly wildly different for everyone.

The goal is to try to remove a CEO vs. CMO disconnect. Or, one between marketing leadership and other leadership functions in a business. It is clear that when marketing leadership is engaged at a business leadership level (not just marketing channels), companies grow faster.

Whether it is actual revenue, customer lifetime value, or something further upstream like qualified leads (and however complex that scoring might be), going beyond the basic web conversion data point can be incredibly helpful in marketing leadership.

When you have the ability to map out business outcome metrics working backward to search metrics, you can demonstrate the impact of the work in a way that shows value versus showing activity.

As a marketing leader, this might seem overly personal, but you can often look at what metrics your role’s performance is accountable for and start there to ensure you have a clear view of search’s impact on those KPIs.

Whether you’re new to your marketing leadership role, or just need to level-set with peers or other executives you report to, a structured goal-setting process can be powerful. You can do so by gaining one-on-one perspectives on what metrics matter to each person. However, it can be really powerful to do this in a workshop format. One where the pressure of past results is off. Most importantly, fostering conversation and Q&A where every person in the room answers questions around what metrics matter to them personally, their functional area, and to the company. This process can help everyone recognize how aligned they are or how far off, which helps you to set a baseline for things that might not be in the purview of marketing, but still have a profound impact on performance measurement.

2. Focus On Fewer, More Meaningful Metrics

When we have too many metrics in our performance data, we can dilute the message we’re trying to convey in reporting. I have sat through presentations that include slide after slide of numbers to only see an executive from another function of the business derail the presentation with impatience, wanting to know what the key takeaway is.

“Is this working?”

“What is the ROI?”

Or, “why are we not showing up for [insert keyword]?” when there’s numbers and KPIs overload.

Not every metric needs to be included in performance reporting, and when you can prioritize what leadership peers or higher-ups actually care about, then you can zero in on it.

These can be uncomfortable questions, challenges, or confrontations in reporting meetings. It can be incredibly helpful for CMOs/marketing leaders to partner with CFOs/financial counterparts to create a shared measurement framework to reduce guessing and to define a shorter, more meaningful set of metrics.

If there isn’t already some type of executive scorecard or overall business reporting format that you contribute to, you might find success in taking a first step in proposing the creation of one. Working one-on-one with a finance counterpart is a great start–as noted. Often, there isn’t an owner or accountable party for unifying all of the metrics. There is likely a source of truth, like a customer relationship management or enterprise resource planning, that matches up down the road with financial reports. Getting buy-in and help on a personal level from other leaders can help make subjective sets of data that different people look at come together in common metrics and shared success language.

3. Explain What Changed And Why

I personally don’t like to use the term “reporting” when looking at performance. I’ve written before about the START Planning Process, where the “R” in that process is intentionally for “review” and not “reporting.”

You can argue with me that this is just semantics (and, since I’m an SEO at heart, I’ll accept a healthy debate), but I feel it is important for there to be balance in any level of performance analysis. Reporting, in my mind, is looking at what happened and into the past. Review has some “reporting” but also covers the here and now and looks forward. It is confident and in control.

We’re not just sharing what happened, but we’re owning and answering for what changes happened, what caused them, and using real campaign examples, competition, algorithm/platform changes, and talking about it connected with broader business implications and not just deep in search silos.

Leaving data up to interpretation can lead to a wide range of assumptions, and the data should not be left to speak for itself regarding what happened and why.

You don’t have to abandon slides or totally change your reporting format. However, you can change the order and only show the slides and metrics that tell a meaningful story and push deeper drill-down metrics that might be a distraction to hidden slides, linked reports, and things that you can have handy if needed, but that don’t create default distraction opportunities.

4. Connect Performance To Strategy

Performance data, no matter how real-time the dashboard is, how confident and positive the presentation or conversation might be, is typically delivered at a moment in time.

We all have short memories. If we’re in marketing leadership and close to the work within the team, we’re focused on the details of what we’re doing in the moment. For broader leadership outside of marketing, they are buried in their own day-to-day, and all of us likely don’t have our marketing strategy memorized.

Analytics should serve decision-making, and not simply be for a presentation. Framing data points in a decision-driven approach will shift the conversation and empower you in where you’re going with the digital strategy.

Having a documented, detailed, accountable, and actionable strategy and plan is critical. The next most critical thing is being able to connect what happened, where we are now, and where we’re going directly back to that strategy that was agreed upon in the past, as it is the objective source of truth to keep from chasing distractions or having debates about details that aren’t fully connected to the business outcomes we’re working toward with intention.

Marketing strategies and plans are often dozens of pages long in document format or sets of slides. They are rarely pulled up and walked through after the initial sign-off on the strategy. Bringing the strategy deck to every performance review meeting isn’t advised. However, having parts of it handy is important. It is easy to get lost on tangents about “what ifs” and disconnected tactics. With performance anchored to specific strategic initiatives, you can keep the strategy in front of stakeholders in context with performance data, so there are clear and objective details to stop tangents before they happen. Practically, this can be a strategic aspect of the plan right next to the KPI in a slide or in a dashboard to reduce the risk of data points being taken out of context.

5. Provide A Clear Point Of View

I would love to live in a world where search marketing and business numbers speak for themselves, and I could simply stand behind them. That world doesn’t exist, though, (I’ve learned the hard way), and if we don’t have a point of view to share on performance, rooted in the truths of our strategies and tactics, then we’re creating a vacuum for someone else to apply their own interpretations.

For a number of reasons, CMOs can “suffer from a crisis of confidence” and not fully own areas where they have a unique impact in the C-suite and beyond.

When it comes to digital marketing, we have to be confident about what is working, what isn’t working, and what needs to change. This is where we show up as leaders to own the subject matter. While we might need the approval of others, need their cooperation, or need to reach certain milestones, we want to avoid situations that put us in reporting mode, getting defensive, losing the message, and taking away from where we’re going.

Search marketing changes fast. I don’t have to tell you that. If you’re struggling with potentially coming across as defensive or if legit changes in the search industry risk sounding like excuses, I recommend developing your own POV on search. My team’s is 11 pages and is updated quarterly. It helps provide philosophical information about what we do in search, why, and references the third-party sources that go beyond our own experience to justify our strategies and tactics. This type of documentation can be helpful to offer to stakeholders for reading outside of performance reviews and to help extract things from inside your team’s heads out into the open that can be stood behind, challenged, or referenced to make things more objective and less personal when questions come.

6. Define What Happens Next

Whether in an informal conversation, a formal presentation deck, or providing context to a dashboard, you could have already addressed how the strategy is woven into the performance metrics and where we’re going next.

Being consistent in keeping everyone focused on the forward momentum (or corrections) of a plan incrementally in reporting or in conclusion, you don’t want to understate what is happening next.

Outlining next steps, priorities, adjustments, resources needed, and any strategic adjustments puts the focus on where you’re going, what you need to accomplish, and what to expect in the next review setting, especially if you tend to have your review (or reporting) derailed consistently by other stakeholders. This isn’t about closing the loop on what happened, but about closing the next loop and setting up the next review for meaningful impact, as MIT Sloan notes regarding how analytics success isn’t found in just data collection, but in proactive data management and insight.

Paid search benefits from being able to make quicker updates that effect change. But, both paid search and SEO can benefit from tangible action plans. While they are ongoing disciplines, treating short-term tasks like smaller projects or agile sprints can help connect activity to results. Crafting a brief, project plan, or documented sprint can go a long way in helping demonstrate the short-term activities that are planned, so there’s no wondering about what mystic or magical tactics are going to happen to ensure the conversation and review have progressed at the next interval for those who aren’t in the details with you.

Final Thought

In marketing leadership, it is on us to own our metrics and performance. That means going beyond the basics of simply reporting on the search marketing KPIs to stakeholders. It means demonstrating leadership in connecting the dots between search performance and business performance outcomes.

This is territory that, early in my career, I struggled with. How could I answer for things that happen beyond the conversation? Well, I had to learn through both wins and losses to understand it and grow comfortable with it.

Owning and leading in marketing, for search performance, means having the POV, connecting back to objective strategy anchors, and having a “review” mindset balanced with what happened, where we are, and where we’re going, and not getting stuck reporting the past or letting others control the narrative or come up with separate opinions that differ from the truth.

More Resources:


Featured Image: fotogestoeber/Shutterstock

The New Rules of Search: Key AEO & Content Marketing Trends for 2026 via @sejournal, @hethr_campbell

Are you optimizing and aligning your AEO strategy for your top-performing LLM?

Does your current SEO strategy put your brand at risk for losing visibility?

How do you measure search success when AI answers replace the click?

Which AEO tactics actually drive visibility across answer engines right now?

👆 Get a concrete framework for investing in visibility across AI search. Register above to watch the full session, right now.

The AEO Trends To Help You Gain More AI Citations & Execute A Budget-Smart Strategy

Shannon Vize, Sr. Content Marketing Manager at Conductor, and Pat Reinhart, VP of Services & Thought Leadership at Conductor, shared field-tested strategies to help you operationalize AEO and build brand authority across fragmented AI search experiences.

You’ll Learn:

  • Which AEO Trends & Content Types Generate The Highest Chance Of AI Citations: A prioritized breakdown of the content marketing and AEO trends that will drive search visibility and performance.
  • How to Measure Search Success Across Different AI Channels: Practical ways to reframe your KPIs and content investment for a world where specific AI platforms capture intent before the visit happens.
  • Agentic Workflows That Scale AI Visibility: Specific tactics for using agentic tools to produce authority-building content formats at scale.

Walk away with a prioritized framework for shifting your content investment toward visibility-first tactics, covering agentic workflows, authority-building formats, and the metrics that actually reflect performance in AI search.

Whether you’re leading digital strategy or driving day-to-day execution, this on-demand session will give you the clarity and direction needed to evolve your approach for an AI-first search landscape.

Register above to watch the full recording and get the actionable AEO framework and trend analysis your team needs to drive visibility and performance in AI-first search.

90% Of Brands Have Zero AI Search Mentions, New Study Finds 4 Key SEO Insights

This post was sponsored by Victorious. The opinions expressed in this article are the sponsor’s own.

A year into the shift toward AI search, the marketing industry is full of confident takes about the factors that impact AI visibility. But we’ve seen very little data to support commonly held assumptions.

We wanted to see what correlations we could find between traditional search performance and AI mentions and citations. So we built a study to see if we could uncover evidence-based recommendations from the data.

The Study Methodology: Comparing Traditional Search vs. AI Search Performance

To compare how brands perform in traditional search versus AI search, we needed a dataset that captured both signals for the same companies during the same period of time.

We built it out in four phases.

Step 1: Determine The Brand Set.

We selected a representative cross-section of 177 brands across five verticals: healthcare, SaaS, financial services, ecommerce/retail, and legal services.

Step 2: Capture The AI Visibility Signal.

For each brand, we tested vertical-specific prompts across eight AI platforms: ChatGPT, Perplexity, Gemini, Google AI Overview, Google AI Mode, Microsoft Copilot, Claude, and Meta AI. That gave us 107,011 AI responses to analyze.

For every response, we recorded two things: whether the platform named the brand (mention), and whether it linked to the brand’s domain as a source (citation).

Step 3: Pull The Organic Performance Data.

For the same 177 brands, we tracked domain-level organic performance in Semrush during the first quarter of 2026, including traffic trends and Authority Scores.

Step 4: Cross-Reference The Two Datasets.

We joined the AI visibility data with the organic data so every brand had three comparable measures: mention rate, citation rate, and Authority Score. That structure let us look at the relationship between traditional ranking signals and AI visibility, and whether those factors were more or less related across the different verticals.

Why We Tracked Mention Rate & Citations Separately

One metric doesn’t capture AI visibility, so we tracked both mention rate and citation rate as separate signals. For example, a brand can be mentioned often and cited rarely, or cited often and rarely mentioned. Tracking both separately, rather than collapsing them into a single “AI visibility” score, ended up being central to the nuances we could pull from the different verticals.

Finding 1: Most Brands Have No AI Mentions At All

Of the 177 brands in our dataset, only 18 had any AI mention rate above zero in Q1 2026. That means 89.8 percent of the brands we tested were largely absent from AI search across the eight platforms we measured. They weren’t mentioned. The brands weren’t surfaced in relation to answers, as sources, or examples.

This runs counter to a lot of the current industry chatter, which treats AI visibility as a race that’s already well underway. Our data shows a very different picture. For an overwhelming number of brands, the race hasn’t yet begun.

The fact that only 18 of the 177 brands in our research registered any AI mentions at all indicates that brands willing to take AI visibility seriously now will be competing against a small number of incumbents in their vertical, not against the entire category.

Finding 2: AI Visibility Patterns Vary By Vertical

Once we broke the data down by vertical, three distinct patterns emerged.

Mentioned & Cited: Healthcare, SaaS & Financial Services Brands

“Q1 2026 Quarterly Search Report: Mention rate vs. citation rate, by vertical: Healthcare, SaaS, and Financial Services” created by Victorious. May 2026.

Brands within these three verticals were consistently mentioned and cited, but for different reasons. Healthcare brands benefit from clear entity identifiers such as names, locations, specialties, and network affiliations, which reinforce the signals that AI platforms use to evaluate expertise and authority. SaaS brands are commonly featured on third-party platforms such as G2, Reddit, and LinkedIn, where products are discussed by users and reviewers. Financial Services benefits from strong editorial media presence on platforms like MarketWatch, Bankrate, and NerdWallet, which are common sources AI platforms turn to for financial questions.

Financial Services was also the only vertical where citation slightly exceeded mention, which suggests AI platforms trust the content slightly more than it trusts specific brands yet.

In each case, the brands that show up have something AI platforms can attach the brand identity to: structured data, third-party validation, or editorial coverage. The brands that don’t show up usually lack one or more of those.

Mentioned More Than Cited: Ecommerce & Retail Brands

“Q1 2026 Quarterly Search Report: Mention rate vs. citation rate for Ecommerce/Retail” created by Victorious. May 2026.

Ecommerce posted the widest gap in our dataset. AI platforms recognize these brands but pull their source material from somewhere else, usually marketplaces, aggregators, and review sites rather than the brands’ own domains.

For these brands, recognition comes from marketplace presence and consumer familiarity. The bigger challenge for ecommerce brands is giving AI platforms content worth citing on their own domain, instead of leaving the field to Amazon, Reddit, and review aggregators.

Cited But Rarely Mentioned: Legal Services

“Q1 2026 Quarterly Search Report: Mention rate vs. citation rate for Legal Services” created by Victorious. May 2026.

Legal services posted the inverse pattern as ecommerce brands. AI platforms regularly source content from legal sites, but they rarely credit the firm behind the article.

Closing that gap means building the entity signals that connect a piece of content back to a recognizable firm.

Findings 3 – 4

Each AI platform draws from a different set of sources.

ChatGPT, Perplexity, Gemini, and Copilot show preferences for specific types of content. The full report breaks down mention rates by platform and vertical, so you can focus on the AI platforms your buyers actually use.

Personalization may be compounding early AI visibility.

Google’s Personal Intelligence update pulls signals from a user’s Gmail and Photos into AI Mode responses, biasing results toward brands the user has already encountered. If that effect holds, brands that win a user’s first AI interaction on a topic could compound their visibility faster than later entrants. The full report walks through what we’re watching in Q2 to test this.

Key Takeaway

If you take away nothing else from this data, remember that you haven’t lost first-mover advantage. With only 18 of the 177 brands we measured earning mentions AI search, there’s still white space in your vertical waiting to be claimed.

You can read the full Q1 2026 Quarterly Search Report on our site.


Image Credits

Featured Image: Image by Victorious. Used with permission.

In-Post Images: Images by victorious. Used with permission.

Inside AI Citation: Proven Strategies To Get Your Brand Cited via @sejournal, @lorenbaker

When customers ask AI a question, only a handful of sources get cited in the answer.

Which content signals does AI evaluate when selecting sources to cite?

Is your brand’s content structured to be one of them?

This is no longer a technology question; it is a brand and content strategy question. Find out exactly what earns AI citations.

Register above to watch the full on-demand session.

Learn How AI-powered Search Generates Answers

In this SEO webinar, Wayne Cichanski, VP of Search & Site Experience at iQuanti, unpacked how AI systems generate answers and what determines whether your brand’s content earns a place in them.

You’ll Learn:

  • How AI retrieval works: Understand the mechanics behind how AI-powered search selects and cites content, so you know exactly what you’re optimizing for.
  • AI citation signals: Identify the topical authority and brand trust signals that determine whether your content earns a place in AI-generated answers.
  • Practical content strategies that drive citation: Walk away with specific, practical tactics for creating and restructuring content that increases your brand’s AI visibility.

From topical authority to content structure and brand trust signals, you’ll learn the mechanics of AI retrieval into clear implications for performance marketers and digital leaders.

Register above to get actionable, practitioner-level strategies for building the topical authority and content structure that AI systems reward with citations.

👆 Register above to watch the recording on your schedule.