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.

How To Measure AI Search: Current KPIs You Need To Know [Webinar] via @sejournal, @hethr_campbell

If your organic traffic is down but your pipeline looks fine, you’re not imagining it. AI-generated answers are intercepting the journey earlier, meaning users are getting what they need from a citation or a recommendation before they ever hit your site. The click never happens. But the influence did.

That’s the measurement problem most marketing teams haven’t solved yet, and the KPIs they’re reporting on weren’t designed to catch it.

Your Brand Can Appear In 1,000 AI Responses & GA4 Shows Nothing

Citations, brand mentions, and AI recommendations don’t pass through your tag manager. They don’t fire an event in GA4 or register a session in your CRM. They happen in the interface of the AI tool, and by the time a user reaches your site, or doesn’t, the influence has already occurred.

Tracking these signals requires monitoring AI outputs directly: which queries surface your brand, in which tools, and with what frequency and context.

That’s a different data collection layer entirely from what most teams have in place.

Learn more in our upcoming SEO webinar.

Ways To Connect AI Signals To Business Outcomes Across Every Channel

Once you’re capturing AI visibility signals, the next problem is connecting them to outcomes.

Last-click and even multi-touch attribution models weren’t designed for journeys where the most influential touchpoint leaves no clickstream trace.

Learn: Incrementality testing, which lets you isolate the lift that AI visibility is actually driving by comparing performance across exposed and unexposed segments.

Learn: Media mix modeling, which takes a broader view, quantifying AI’s contribution alongside paid, organic, and direct channels in a single revenue model.

Used together, they give you a defensible number to bring into a budget conversation.

The Three-Layer Stack That Makes AI Search Defensible in a Budget Review

The stack works in sequence.

At the top, you’re monitoring AI visibility: citation rate, share of voice in AI responses, and brand mention frequency across tools like ChatGPT, Gemini, and Perplexity.

In the middle, incrementality and MMM translate that visibility into estimated conversion impact.

At the bottom, you’re tying those estimates to pipeline and revenue data so the whole chain holds up under scrutiny. The teams getting this right aren’t using one new metric. They’re connecting three existing disciplines, SEO, media measurement, and analytics, around a shared data model.

DAC’s Felicia Delvecchio, VP of Media, Vincent DeLuca, Director of SEO, and Gavin Bowick, Lead Web Analytics are running through exactly how that model is built in a free live session.

What This AI Search & Revenue Webinar Covers

  • How to track AI visibility signals: citations, mentions, and recommendations, across the full funnel
  • Which incrementality and cross-channel models connect AI influence to actual revenue outcomes
  • Which KPIs to retire in 2026 and which metrics reflect real performance across SEO, paid, and AI channels
  • How to build a reporting structure that aligns across SEO, media, and analytics teams, and holds up when you’re presenting to leadership

This one is worth showing up live for.

Data Shows AI Overviews Exposing Negative Reviews Without User Intent. What To Do Next via @sejournal, @EraseDotCom

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

Why does AI pull a 2023 Reddit thread into a 2026 comparison query?
What makes AI cite some complaints about my brand and skip others?
How do I get AI to stop citing old complaints in unrelated queries?

Four signals decide what AI exposes, and once you know them, you can work them.

Q1 2026 analysis surfaces four consistent patterns in what AI engines cite: recency plus volume, specificity that names features, platform authority (Reddit, major review sites), and recurrence across sources. The complaints that hit all four are the ones that show up unprompted in queries where users were looking for solutions, not problems. The fix isn’t a single takedown request; it’s a four-step audit-and-rebuild framework mapped to those same four signals.

When someone asks ChatGPT “which CRM should I choose,” these AI engines don’t just list features. They pull in user complaints, Reddit gripes, and years-old forum threads as part of their comparison. Your brand’s negative signal can appear in an answer about your competitor. Even more concerning, as Fast Company recently reported, there’s growing evidence of AI engines misquoting or misrepresenting brand statements, compounding the challenge of maintaining an accurate reputation in AI-generated summaries.

AI Comparison Queries Are Now Reputation Audits. Here’s What That Means.

Traditional reputation management focused on suppressing results when someone searched “[your brand] + reviews.” That’s still important, but it’s no longer sufficient.

It’s time for a reputation audit.

AI Overviews and LLM-powered search engines treat every product comparison as an opportunity to synthesize user sentiment. When evaluating options, these tools actively scan for negative reviews on complaint sites, Reddit discussions, forum threads, gripe site entries, and customer support complaints that made it into public view.

The critical difference: users aren’t asking about problems. They’re asking about solutions. But AI engines interpret “helping” as including negative signals from your brand footprint.

Why Some Complaints Show Up in AI Answers & Others Don’t

Not every negative mention gets pulled into AI-generated answers, but certain patterns increase surfacing likelihood:

  • Recency + volume: Fresh complaints with multiple corroborating sources rank high.
  • Specificity: Vague posts get filtered out. Detailed complaints that include product names and outcomes are weighted as valuable context.
  • Platform authority: Reddit, Trustpilot, G2, and industry forums get treated as trusted sources.
  • Recurrence across sources: If the same issue appears in multiple places, AI engines treat it as a verified pattern.

The 4-Step Framework: How to Audit, Remove, Rebuild, and Suppress Your Brand’s AI Reputation Signals

Understanding what’s in your negative signal footprint, prioritizing what can and should be addressed, and building a positive content layer that represents your brand accurately when AI tools pull information is the key to success.

Map what AI engines can access about your brand across platforms where complaints surface.

  1. Open ChatGPT or Perplexity and type: “What are the pros and cons of [your brand] vs [top competitor]?” Take a screenshot of the response and note any negative claims.
  2. On Google, search site:[key platform].com “[your brand name]” + “scam” OR “complaint”. This forces the search engine to show you only the filtered conversations AI models are currently scraping.
  3. Search for your brand on Google and check the featured snippets for anything negative, other SERP features like People also ask for negative or adversarial searches.

Key platforms to check:

  • Review platforms (Trustpilot, G2, Capterra, Yelp, Google Business Profile).
  • Reddit (search your brand name + product category + complaint terms).
  • Industry forums (Stack Overflow for tech, niche communities for specialized services).
  • Facebook groups and community pages (particularly industry-specific or local groups where your customers congregate).
  • Social media (Twitter/X, LinkedIn discussions, TikTok comments).
  • Legacy gripe sites (RipoffReport, Complaintsboard); while largely deindexed, content may still be cited by AI engines.

Document these details:

  • Content type and platform.
  • Date posted.
  • Specific claims made.
  • Factual accuracy.
  • Current visibility in Google and AI summaries.

Focus on detailed complaints with enough context that AI engines might treat them as credible sources.

Step 2: Prioritize Based on Surfacing Likelihood

Focus on:

  • High priority: Recent complaints with specific details, issues mentioned across multiple platforms, content on high-authority platforms (Reddit, major review sites), complaints naming features or pricing specifically.
  • Medium priority: Older complaints (1-2 years) still in search results, isolated reviews without corroboration.
  • Low priority: Very old content (3+ years) with low engagement, complaints about discontinued products.

How To Create A Priority Matrix

Create a simple scoring matrix to decide what to tackle first:

  • High Priority: Content that appears in AI summaries AND has high organic visibility (check Semrush or Ahrefs for estimated monthly visits to that specific URL) or compare them against queries for those keywords that you have available in search console – if it’s a branded search, you should have full visibility on this from search console.
  • Verified Impact: For platform-specific reviews (G2, Trustpilot, Google Business), use your internal analytics to track how many users are clicking “Helpful” on negative reviews. A review with 50+ “Helpful” votes is a massive signal that AI engines will not ignore.

Step 3: Remove or Respond Where Possible

Some negative content can be removed outright. Some deserve a response, and some require both.

How to Get Negative Content Taken Down

If the content violates platform policies (false information, impersonation, harassment), request removal through the platform’s reporting process.

For legacy complaint sites and gripe sites, professional content removal services can often negotiate takedowns based on inaccuracies or policy violations, though as reputation defense strategies evolve for AI, the focus has shifted from simply removing content to building stronger positive signals.

For content that mentions you but doesn’t necessarily focus on your brand (like a Reddit thread comparing five tools where yours gets one negative mention), removal usually isn’t an option, but you can dilute its impact by ensuring positive mentions appear more frequently in similar discussions.

When Responding Publicly Actually Helps You

Legitimate complaints about real issues, misunderstandings you can clarify with facts, or service failures where an explanation adds credibility. Keep responses factual, non-defensive, and focused on resolution. AI engines can pull your response into summaries, giving you a chance to reframe the narrative.

When Engaging Makes Things Worse — Skip It

Fake reviews, emotional rants without substance, old complaints about discontinued products, or situations where engagement will amplify visibility.

Step 4: Build a Positive Content Layer That AI Engines Prefer

This is where ongoing reputation management becomes critical. You need owned and earned content that AI engines will preferentially cite when answering comparison queries.

What Goes Into A Positive Content Layer

  • Structured FAQ content: Create pages answering common objections and questions with clear headers and schema markup.
  • Case studies: Detailed examples with metrics, timelines, and direct customer quotes give AI engines concrete data to cite.
  • Community presence: Contribute to Reddit and forums where your audience asks questions. Build credibility through value, not promotion.
  • Third-party validation: Get featured in roundups and comparison articles on authoritative sites.
  • Regular content updates: AI models prioritize recent content. Keep your owned content fresh.
  • How this plays into broader online reputation management: What you’re building isn’t just an AI strategy—it’s a defensible reputation infrastructure. Comprehensive, recent, authoritative content across multiple touchpoints creates a buffer that makes it harder for isolated negative signals to dominate.

How To Build A Positive Content Layer 

  1. Turn your FAQ into a knowledge base that addresses common objections (e.g., “Is [your brand] worth the price?”). Depending on how much reach and authority your brand has, it can be worthwhile to publish these as their own pages with a clear H1 question as the headline and breadcrumb the Q and As in a format like /faq/[service area]/[objection] to create more internal linking opportunities and depth rather than just having everything on a massive FAQ page.
  2. Reach out to some of your satisfied customers and ask for a 2–3 sentence quote about a specific outcome they achieved. Publish these as a case study snippet on your site. Specificity (metrics, timeframes) helps to ensure LLMs treat content as credible evidence rather than marketing copy. Link to their LinkedIn or business website, if possible, to help reinforce that it is a real review for a real customer.
  3. Identify high-authority “Best of” lists or industry roundups where your brand is missing and email the editors to provide a unique expert insight or updated product data for inclusion. These seed high-trust citations that AI engines prioritize when synthesizing brand comparisons and reputation summaries. The higher they rank on Google, the better.

Monitoring becomes essential at this stage. Track which keywords trigger AI Overviews that mention your brand, watch for new complaints surfacing in high-authority platforms, and measure whether your positive content is getting cited in AI-generated comparisons. This isn’t a one-time project; it’s an ongoing program.

Start Here: Your Easy Steps to Managing Your AI Reputation

If you’re dealing with high-stakes reputation issues where missteps could amplify problems, specialized online reputation management services and experts like our team at erase.com can help you move faster and avoid pitfalls. The goal isn’t just reacting to what’s already out there; it’s building a system where positive signals consistently outweigh isolated negatives when AI engines scan for information.

The shift is already here. The question is whether you’re managing it proactively or discovering it reactively when a prospect mentions “something they saw in ChatGPT.”


Image Credits

Featured Image: Image by Erase.com. Used with permission.

How To Measure SERP Visibility When Rankings Aren’t Enough [Webinar] via @sejournal, @lorenbaker

Rank #1 and still invisible?

It happens more than you’d think.

That’s why this SEO webinar is key.

Organic Visibility Isn’t What It Used to Be

SERP features, local packs, knowledge panels, featured snippets, shopping ads, now dominate significant portions of the page.

For certain intents and verticals, even the top organic result sits below the fold for most users. That means your rank doesn’t tell you whether searchers are actually seeing your brand.

STAT’s Sr. Search Scientist Tom Capper has been working through something genuinely different: pixel height data from a large-scale analysis of search results. Instead of asking “where do you rank,” he’s asking “how many pixels from the top of the SERP does your result appear — and what’s already taken up all the space above it?”

Join This SEO Webinar & Learn

About the Speaker

Tom Capper is Sr. Search Scientist at STAT, where he leads large-scale research into search result behavior and organic performance. His work is grounded in data analysis at a scale most SEO teams don’t have access to, and this session is a direct look at his findings.

The 90-Day AI Search Sprint: How To Rebuild Your Marketing For 2026 Visibility via @sejournal, @hethr_campbell

AI visibility is a learnable, measurable skill: The 90-day playbook to build it is here.

When a potential customer asks Gemini or Claude to recommend a solution like yours, does your brand get mentioned?

Do you have a plan to restructure your growth engine around AI visibility, or are you still waiting to see how it plays out?

👆 Get the 90-day AI visibility playbook. Unlock the recording, above .

Learn:

How To Transform Your SEO Strategy In 90 Days, Like Google, & Headspace

AI Overviews, ChatGPT search, and Perplexity are where your buyers are going now. This on-demand session gives you the signals that drive AI discoverability, a phased 90-day framework, and a look at how funded teams are restructuring to stay ahead.

Jason Shafton, Founder & CEO of Winston Francois, shared battle-tested strategies from scaling growth at Google, Headspace, Kajabi, and 10+ funded startups. to help you restructure your marketing for AI-era discovery and stay visible where your buyers are actually searching.

You’ll Learn:

  • Current AI Search Signals: Which factors drive citation and discoverability in AI search.
  • The 90-Day Visibility Framework: A phased plan to audit your baseline, run AI-native experiments, and scale what’s working.
  • How Growth Teams Are Restructuring: What funded startups are cutting, doubling down on, and handing to AI, so you can benchmark your own approach.

Register above to get actionable, practitioner-tested frameworks for winning AI visibility, built from real experience.

🎬 Unlock the on-demand replay above to watch the full recording on your schedule.

The Tech SEO Audit for the AI Search Era: How to Maximize Your AI Visibility via @sejournal, @JetOctopus

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

How do I optimize my site for ChatGPT and Perplexity, not just Google?

How do I know if AI bots are actually crawling my site?

How should my technical SEO strategy change for AI Search?

A significant portion of your site’s search impressions in 2026 are generated by machines researching on behalf of humans.

Those machines don’t care about your keyword rankings. They care whether your:

  • HTML loads cleanly in under 200 milliseconds
  • Product detail page is reachable in fewer than four clicks
  • Content answers a specific, nine-word question that has never appeared in any keyword research tool in your career.

This isn’t speculation. It’s what our server log data across hundreds of enterprise websites is showing us, consistently, since mid-2025.

What’s Actually Happening On Your Site

My colleague, Stan, flagged a pattern in a Slack message: query lengths were growing at rates that didn’t correlate with human behavior.

A 161% growth rate in 10-word queries year-over-year is not driven by users who suddenly got more verbose. It’s driven by AI agents decomposing a single user prompt into dozens of parallel sub-queries, a process researchers now call “fan-out.”

Query Length Growth in 2025

Image created by JetOctopus, Aggregated GSC data across hundreds of enterprise properties, 2025

The gradient is the tell. Human search behavior doesn’t scale this cleanly by word count. Machines do. By October 2025, 7-plus-word queries reached nearly 1% of total query volume, roughly triple their historical share.

More revealing than the volume is the CTR. While impression counts for 10-word queries spiked 161%, click-through rate collapsed to 2.26%, down from 8–11% in 2023.

The AI reads your page, extracts the answer, synthesizes it for the user. Your site never gets the visit.

We call these “phantom impressions.” They’re real signals that your content is being evaluated inside AI reasoning chains. If you’re filtering them out of your reporting because they don’t drive traffic, you are flying blind.

The Three Bots Visiting Your Site & Their Impact On SERP Visibility

Not all AI crawlers are equal, and treating them as a single category is the first mistake most technical SEOs make.

Training bots crawl broadly and ignore click depth. A training visit means the AI knows your content exists, not that users will ever see it.

AI search bots drop off quickly beyond two or three clicks from the homepage and typically visit each page only once a month.

AI user bots are initiated when a real person asks a question in ChatGPT, Perplexity, or Claude, and the AI researches the answer on their behalf. These are the only visits that translate to actual AI visibility.

Bot Type What Triggers It Crawl Depth Impact on AI Visibility
Training bots Model education cycles Deep — ignores click distance None directly. Awareness only.
AI search bots New URL discovery & fresh content Shallow — ~1 visit/month beyond 2–3 clicks Critical gatekeeper. If it misses a page, user bots won’t find it either.
AI user bots Real user query in ChatGPT / Claude / Perplexity Selective — driven by speed and structure High. Closest proxy to an AI impression.

Your site can receive heavy crawling from training and search bots and still be completely absent from AI-generated answers. If you’re not segmenting AI bot traffic by type in your log analysis, you have no idea which third of the iceberg you’re measuring.

Which SEO Signals Do LLMs Respect?

Robots.txt is your primary lever.

Most major AI platforms (ChatGPT, Claude, Gemini) follow robots.txt directives. Perplexity is a partial exception: PerplexityBot respects robots.txt, but Perplexity-User, the user-triggered bot, does not. Cloudflare confirmed this in an investigation. Most sites haven’t audited their robots.txt with AI access in mind. Do it.

Sitemaps are broadly supported.

ChatGPT, Claude, and PerplexityBot all use XML sitemaps for URL discovery. Keep them accurate.

Signals Best Saved For SEO & Ranking Efforts

These signals below don’t appear to impact AI visibility, but are still key for ranking for queries that still trigger traditional SERPs.

Canonical tags and noindex directives do nothing for AI bots.

AI crawlers don’t build a search index, so they have no use for these meta-signals. Content hidden from Google using noindex is fully visible to ChatGPT’s crawler.

LLM.txt does nothing.

Our log data shows major AI bots don’t read this file. Don’t invest time here.

JavaScript rendering is a critical blind spot.

Most AI crawlers (ChatGPT, Claude, Perplexity) don’t render JavaScript. If your product pages load key content client-side, those agents read an empty shell. Server-side rendering is the only architecture that works universally. The exception is Google Gemini, which uses the same Web Rendering Service as Googlebot.

How To Make Sure ChatGPT, Perplexity & LLMs Can Reach Your Content

AI search bots visit deep pages roughly once a month and drop off sharply beyond three clicks from the homepage. The pages with the most specific, answerable information are often the hardest for agents to reach.

The fix: Elevate your most valuable deep pages through internal linking, ensuring they’re reachable within four clicks.

Pages crawled by training bots but never reached by user bots are your highest-priority targets. Pages AI user bots visit frequently are telling you what to scale: more content covering the same topic cluster and depth.

Optimize Content For Longer, Fan-Out Queries

95% of the queries driving AI citations have zero monthly search volume. They’re synthetic sub-queries generated by AI models. But they show up in GSC: impressions, no clicks, query lengths you’d never target voluntarily.

How To Find Fan Out Query Opportunities

To surface fan out queries that are worth chasing, connect your GSC API to JetOctopus (to bypass the 1,000-row UI limit) and filter for: query length greater than 7 words, impressions under 50, clicks at 0, over the last 3 months. That’s your Fan-Out Opportunity Matrix, the exact questions AI agents are asking about your content.

Prompt Types That Fan Out Most

Image created by JetOctopus, 2025

If your content isn’t structured to answer list and comparison queries, with explicit rankings, pros/cons, and side-by-side specs, you’re leaving the highest fan-out surface area unoptimized.

“Product review” intent queries surged from 239 in June 2025 to over 40,000 by September 2025. That 16,000% increase was AI agents systematically harvesting structured opinion data. If your product pages lack this depth, you’re invisible to that harvest.

The Technical Audit: Where to Start

Step 1: Identify AI User Bot Traffic In Logs

Pull raw server logs (Apache/Nginx) and export all lines containing these user agents: OAI-SearchBot and ChatGPT-User, PerplexityBot and Perplexity-User, Claude-SearchBot and Claude-User. Then manually group hits by user-agent patterns and endpoints in a spreadsheet. To distinguish training bots from user bots, you’ll need to maintain your own classification list — one that changes often and isn’t standardized.

In JetOctopus Log Analyzer, this segmentation is built in: filter by bot type (training, search, and user) in a few clicks and immediately see which pages AI user bots visit (your AI-visible content, ready to scale) versus pages training bots hit but user bots never reach (your highest-priority fix targets).

Step 2: Audit Technical Accessibility Of Deep Pages

Pick a sample of deep URLs and check HTML payload size, confirm key content isn’t injected via JavaScript by viewing raw HTML, simulate crawl depth by counting clicks from the homepage, and test load time in Chrome DevTools or Lighthouse. Also check whether important content sits behind accordions or “View More” elements — these require JavaScript execution that AI bots skip entirely. For large sites with thousands of deep pages, this sampling approach misses a lot. AI agents don’t click. If information only appears after user interaction, it doesn’t exist for these crawlers.

Step 3: Clean Up Your Robots.txt

Open your robots.txt and review all Disallow and Allow directives for every user-agent line by line. AI bots follow Disallow rules, so make sure you’re not accidentally blocking important URLs. Manually test key URLs to confirm they aren’t blocked. A 30-minute audit here can prevent you from blocking crawlers you want in, or exposing content you’d rather keep out.

Step 4: Map Your Phantom Impressions

Export data from GSC Performance reports filtered by impressions with zero clicks. Because of the 1,000-row UI limit, you’ll need to use the GSC API or export in chunks by date and query, then merge datasets in spreadsheets or BigQuery. Also factor in query frequency: long queries appearing daily are likely not fan-outs.

Connect your GSC API to JetOctopus to bypass the row limit and build your Fan-Out Opportunity Matrix automatically — the exact questions AI agents are asking about your content, ready to act on.

Step 5: Monitor The Changes

Set up a recurring export process — pull GSC data monthly and compare impressions over time, re-run log analysis scripts and diff bot activity, track Core Web Vitals separately in PageSpeed Insights or CrUX. You’ll end up stitching together multiple data sources with no unified alerting, making it hard to catch regressions early.

JetOctopus Alerts covers exactly this: unified notifications for changes in AI bot activity alongside Googlebot behavior, Core Web Vitals, on-page SEO issues, and SERP efficiency drops, so you catch regressions before they compound.

The New KPI: Technical Accessibility

SEO in 2026 is restructuring around one constraint: can an AI agent crawl, reach, and extract a fact from your 50,000th product page in under 200 milliseconds?

If the answer is no, your rankings, backlinks, and content quality become irrelevant for a growing share of search interactions. The machines are searching. The question is how quickly you can see what’s actually happening.

Start with your logs. Everything else follows from there.

Want to see exactly how AI bots are interacting with your site: which pages they reach, which they skip, and where your fan-out opportunities are hiding? Book a live walkthrough of the JetOctopus platform. We’ll pull your actual log data and show you what your GSC reports aren’t telling you.

Image Credits

Featured Image: Image by JetOctopus. Used with permission.

From Reddit to Revenue: Building Real Community That Drives Sales and AI Visibility via @sejournal, @hethr_campbell

AI search doesn’t pick brands at random. They cite brands with trust signals across multiple channels.

Are you building brand proof in the places AI actually looks to validate mentions?

Do you know which community conversations are shaping what AI says about your category?

Reddit is one of the most powerful and least utilized of those channels.

👆 Register above to watch the exact framework that generated a 2,000% AI visibility boost in just 90 days.

Add 1 Marketing Channel. Earn 2,000% More AI Visibility & Real Revenue.

In this on-demand SEO webinar, Bartosz Goralewicz, CEO of OGS Media, and Brent Csutoras, Reddit Official Advisor and Owner of Search Engine Journal, shared proven strategies for building Reddit community that earns buyer trust, drives revenue, and strengthens your brand’s multi-channel AI visibility.

You’ll Learn:

  • Data on how Reddit fits the AI visibility picture — Why community-driven trust signals are part of how AI tools evaluate brands, and how Reddit presence impacts AI’s required proof-of-trust.
  • How to avoid 7 trust-breaking mistakes — Identify the specific behaviors that destroy Reddit credibility, and what authentic community engagement looks like when it’s working across both sales and AI visibility.
  • The 5-stage Reddit & AI Search framework — Gain access to the exact strategy that drove 2,000% AI visibility growth and six-figure enterprise deals in 90 days.

Register above to watch the 5-stage Reddit & AI search playbook that builds the kind of authentic brand proof AI uses to decide which brands to mention, cite, and recommend.