SMX Next returns online Nov. 18, and I’m excited to help shape a program focused on today’s complex search landscape and the tactics that will define success in 2027 and beyond.
Search marketing isn’t just changing. From my perspective, it has become an entirely new kind of challenge, and that is exactly why fresh voices and practical expertise matter so much right now.
In SEO, I’m seeing the field shift toward AI Overviews, search everywhere optimization, and the rise of autonomous AI agents that browse on behalf of users. Trustworthiness, digital authority, and precise alignment with user intent are no longer nice-to-have ideas. They are becoming essential.
On the PPC side, generative AI and deep automation are creating new levels of personalization. At the same time, they are raising urgent questions for marketers: How do we keep strategic control, protect data privacy, and avoid wasted spend?
If you’re an enthusiastic search marketer with a passion for sharing what you know, I hope you’ll consider submitting a session pitch for SMX Next. I’m looking for subject matter experts who can share insights, strategies, and tactics that help SEO and PPC marketers thrive in 2027.
Whether you’ve been speaking for years or you’re a practitioner ready to share something new you’ve developed, I want to hear from you. I’m especially interested in new speakers with diverse points of view and real-world experience.
The deadline for SMX Next pitches is Aug. 7.
When I review session proposals, I’m looking for ideas that feel original, specific, and useful. Advanced, forward-thinking topics or unique frameworks that aren’t already common at other search events will stand out.
I also want to see actionability. Be clear about what attendees will be able to do better, faster, or differently after your session.
Bring the data whenever you can. A case study, concrete example, or tested approach makes your pitch stronger, especially when you explain how the lesson can scale across different types of organizations.
Keep the scope focused. A 30-minute session works best when it goes deep on a narrow or specialized topic instead of trying to cover too much at once.
Most importantly, give attendees something tangible to take with them. I’m looking for sessions that leave people with a clear action plan, framework, or process they can put to work right away.
Visit this page for more details on how to submit a session idea, or go directly to this page to create your profile and submit your pitch.
If you have questions, feel free to contact me directly at kathy.bushman@semrush.com. I’m looking forward to reading your proposals!
My heart sank when I learned that Bruce Clay had passed away. I knew he had been in the hospital, but my mind went straight to the two long conversations we had last fall: one simply to catch up, and one for what would become a deeply meaningful podcast interview.
I first reached out to Bruce nearly 25 years ago. I had emailed him cold to ask whether I could republish some of his industry writing about ethics. He said yes. Somehow, the article I cited unintentionally ranked No. 2 on Google for “Bruce Clay” for years. I joked with him about that more than once, and he always seemed both amused and slightly annoyed, probably because I had done it with his own content and his own blessing.
A few years later, I worked with Bruce and many other search professionals on the board of the Search Engine Marketing Professionals Organization, better known as SEMPO. It was a business nonprofit built to support and legitimize the then-new search industry. We promoted best practices, helped make the business case for search, and later became involved in U.S. Internet policy work in the early 2010s.
SEMPO brought together board members from around the world, and in a very literal way, it took some of us around the world. That work is where I really got to know Bruce. Later, we would run into each other at conferences, sometimes even on the same panels. We were doing serious work, but we also had a great time doing it. The organization lasted about 15 years, and if I remember correctly, Bruce was one of its founding members around 2000 or 2001.
One memory of Bruce has stayed with me vividly. A group of us from the SEMPO board were walking back to our hotel on the east side of Midtown Manhattan after dinner. A snowstorm had just begun, one that would leave several feet of snow by the next day. The usual roar of traffic had been softened by the weather and the empty streets. It was eerie, but almost joyously quiet. The city that never sleeps seemed to be taking a nap under a blanket of snow.
Then something happened that I had never seen before, and have never seen since.
As snow poured silently into the streets, a massive lightning strike hit just a few blocks away, over Bruce’s shoulder. I do not know whether he saw it directly. It felt like an explosion. We stood there for several minutes trying to understand the contrast: a shattering bolt of lightning between skyscrapers, in the middle of a torrent of snowflakes, with not a drop of rain.
None of us knew what to call it. I believe Bruce called it “thunder snow,” and the name stuck. In that moment, his naming streak continued.
Bruce was, and remains, the real deal in search. His legacy was never only about coining a term. He pushed the field forward, taught others generously, and stayed deeply connected to the people he cared about. Like many of the earliest professionals in search, he helped shape practices that still feel foundational today. Through his writing, interviews, books, tools, and hundreds of industry events, he became one of the people the industry looked to for clarity. For many who remember the beginning, and for many who still followed him closely, Bruce was the GOAT.
I always felt that Bruce approached search intellectually. I do not think he saw it only as a job. It was exciting, unfinished, and new. Very few people get to help invent an entirely new discipline, and Bruce understood what that meant. He also recognized that AI is one of those moments now, and he approached it with the same curiosity, energy, and insight he brought to early search. Many people in the industry may only now be realizing that Bruce pioneered things they do every day. They feel obvious now, but they were not obvious then. Even the basics had to be debated and established.
He was not only passionate about search. He was passionate and generous toward the people in search. If you cared about the work, you were part of his tribe. That was true for thousands of people in the industry, myself included.
With Bruce, I could get deep into the weeds of the trade and still talk broadly about where everything was headed. He was an engineer with an MBA, and that combination came through in his leadership, expertise, and authority. He understood the work from top to bottom, and then back to the top again.
He was also genuinely kind. He had friends around the world. In our last conversations, I sensed that he was content with his life and accomplishments, and that he felt blessed by the path life had given him. He had nothing left to prove.
In the podcast interview, Bruce was as sharp and insightful as ever. He offered some of the most sensible thinking I have heard about where search is going in the world of LLMs. He was still innovating, just as he had been when search first began taking shape nearly 30 years ago.
Because search is so closely tied to language, I have been especially interested in how we think about, and what we call, this “new” thing. Bruce’s perspective helped crystallize my own research. Over the last year, I have watched much of the industry move toward the same conclusion he shared in our discussion.
If you are one of the many thousands of people who talked shop with Bruce over the years, I think you will recognize him in the ideas that follow. You may even relive some of your own conversations with him.
As I reviewed the podcast transcript, I realized we had recorded hours of conversation beyond search, including cars and all kinds of other subjects. At the end of our first conversation, he said goodbye with great love and care. That was Bruce. Those words land differently with me now, and they always will.
Rest in peace, Bruce. I miss you already.
What Bruce taught me in our final industry conversation
When I asked Bruce to talk about how he got started in the 1990s, he took us back to 1996. He had been working in corporate roles and wanted to become a consultant. His background was in math, programming, mainframes, PCs, networking, and optimization. When the Internet began moving into the mainstream, he saw something that matched both sides of his skill set: marketing and technical work.
He started studying search engines because that was where the opportunity was. He experimented with what they wanted, adjusted web pages, and watched rankings appear. Then people began calling him and paying him. What he thought might become a one-person consulting business grew quickly into something global, with offices and work across Japan, Australia, Asia, Europe, India, and beyond. Bruce told me he never would have predicted it would take off the way it did.
I reminded him how small the field was in those days. There were literally only tens of people doing this early on. Bruce was one of the first to build a legitimate service for businesses that needed to rank for their own brand names and for broader generic terms, while other corners of the field were still experimenting with black-hat tactics.
Bruce pointed out that this was three years before Google. Search was a wild west. There were more than 20 major search engines, and many of them were taking data from one another. At the first SEO conference he remembered attending, all of the leading people in the field sat together at one round table in a bar. He joked that if a natural disaster had happened there, the whole industry might have disappeared.
We talked about Danny Sullivan, Search Engine Watch, Search Engine Strategies, and the early vocabulary of the industry. Bruce had long been credited with helping coin the term “SEO,” though he was careful to say that no one can know who said something first. What he did know was that only a handful of people were in the room when the term started to take hold.
At the time, other terms were in play, including “search engine positioning” and “ranking.” Bruce believed “optimization” won because it sounded technical, valuable, and precise. It was like fine-tuning a race engine. People could see themselves building a profession around it. Once the industry attached itself to that word, the term spread quickly around the world.
That led us into the newer terms now being proposed around AI, including AIO, GEO, and AEO. I have been writing about how many of these terms still depend on the word “optimization.” Bruce’s view was clear: search engine optimization was never limited to organic blue links. It was about optimizing for anything a search engine produces that can drive business and traffic.
In Bruce’s view, if AI appears inside search and influences discovery, citations, visibility, or traffic, then it belongs under SEO. GEO and AIO were not separate disciplines to him. They were extensions, just like link building or on-page optimization. He warned that many new terms are marketing labels more than practical new fields. If the work required to appear in AI results is still mentions, links, schema, authority, content structure, and rankings, then the work is still SEO.
That point stayed with me. Bruce said that if someone claims you no longer need SEO and only need AI optimization, you should watch closely, because either they are going to do SEO under a different name or they do not understand what they are doing. He believed ranking in AI was possible, but the method was deeper and more complex than traditional SEO. To him, it was still SEO, just several levels more advanced.
We also discussed whether AI feels like search did in the late 1990s. Bruce believed it does in important ways. AI depends heavily on search engines because search engines have spent decades fighting spam and building trust signals. AI systems do not yet have that same history, so they rely on what search engines have already learned to filter, evaluate, and rank.
Bruce also believed AI could still be gamed at the content level. If enough pages repeat a false idea, an AI system may begin to treat it as true. He had already seen examples of people trying to influence AI answers by placing their names into “best SEO” lists across enough sources. To him, this was a sign that AI would need its own version of the spam fight search engines have been having for decades.
One of the most important parts of our conversation was Bruce’s explanation of Google AI Mode and how it changes the way SEOs should think about structure. He described how a query can produce an overview, followed by sections and subsections that allow users to drill into narrower parts of a topic. When a user clicks into a section, the supporting sites can change to match that specific subtopic.
That means content cannot simply be built around one broad keyword anymore. Bruce believed pages need to be structured so each section can stand on its own as an expert answer. A page should support a topic, but every H2-level section may need its own clarity, completeness, and internal logic. In his view, this raises the importance of siloing across a site and within a page.
I framed this as a shift from keyword-led thinking to context-led thinking. Bruce agreed and connected it to entities, fan-outs, references, and cross-links. Keywords helped build the industry, but he believed the future depends on understanding entities in context. If content cannot answer the question clearly, it fails the core purpose of AI-assisted search.
Bruce described the long-term target as something like the Star Trek computer: no matter what question someone asks, the system provides the answer. We are not there yet, but that is the direction. For websites, he believed the future architecture is question-centered, highly usable, structured into sub-silos, and able to answer and refer within a page while also fanning out to supporting pages.
That naturally led us to content. Bruce said that for years SEO treated content like a stepchild, but now content is a peer. If SEO teams and content teams do not share the same goal, they will keep writing the way they did 20 years ago and fail in the AI search environment. He was already being hired to train content teams, even though he did not consider himself a “content guy” in the traditional sense.
He believed the industry still suffers because SEO and content do not cross-pollinate enough. Content marketers may not attend SEO conferences, and SEOs may not spend enough time learning how content teams actually work. That separation matters more now because the structure of a page, the expertise of each section, and the way a topic is divided all affect visibility in AI-driven search experiences.
Bruce’s advice was direct: stop spreading one keyword across a page and calling that optimization. Instead, build each section as if it were a standalone expert answer. If the sections belong to the same theme, they should support one another, but each needs to carry its own weight. In his words, the hierarchy is no longer only the page. The hierarchy is also the section of the page.
When I asked Bruce about AI-generated content, he made an important distinction. AI is a tool, not a solution. He did not believe businesses should simply generate content, read it once, and publish it. Detection tools are inconsistent, and search engines may not reliably identify every AI-generated page. But that does not make low-effort AI content a good strategy.
Bruce believed AI is strongest as a research assistant. His own Pre-Writer product was built around that idea: gather deep research and give a human writer a stronger starting point. The writer still finishes the work, adds style, voice, judgment, compliance, and business understanding. For Bruce, reducing a four- or five-hour writing project to two hours was a win. Replacing the writer entirely was not.
He was especially clear that writers are artists. AI does not know a business the way its people do, and it does not bring the same finesse or judgment. The future, in Bruce’s view, requires writers, SEOs, and AI workflows to be integrated around shared goals. Without that maturity, teams will keep producing pages that look like they were built for search 10 years ago, and those pages will be ignored.
We ended by talking about tools. Bruce reminded me that in the beginning, he wrote tools because none existed. He built one of the first page analyzers, including what he once called a keyword density analyzer. He later received a patent related to that kind of technology. His tools were never meant to replace large platforms like Semrush, Ahrefs, or Surfer. They were meant to extend them by analyzing things those platforms did not.
Bruce pointed people to seotools.com and described the tools as inexpensive power tools, not products designed for the masses. Some users did not understand them at first, but came back later when they saw the value. He was still building, still solving problems, and still thinking about what the industry needed next.
Near the end, Bruce mentioned a newer tool designed to show traffic loss through Search Console data over time, helping site owners see whether they had fallen off a cliff or declined gradually. It struck me as classic Bruce: while others complained that something should exist, he was building it.
I thanked him for the conversation, and he answered with warmth: he was glad I had him on, and he loved talking with me. I hear those words differently now. I am grateful we had that final conversation, and I am grateful for everything Bruce gave to search, to this industry, and to the people inside it.
I see TikTok becoming harder to ignore in SEO because discovery no longer happens in one clean path. Someone might find a restaurant on TikTok, verify it through Google Reviews, check Reddit for honest opinions, scan the menu on the business website, and then book a table. Someone else might take those same steps in a completely different order.
Nearly half of U.S. consumers used TikTok as a search engine in 2026, up from 41% in 2024, according to Adobe survey data. What stands out to me is why people search there: short-form video, storytelling, interactivity, tutorials, product reviews, personal stories, and influencer recommendations all make the platform feel more immediate than a traditional results page.
I also think TikTok recent updates show how seriously the platform wants to be part of the search journey. Many purchase decisions are visual, social, emotional, and trust-driven, which is exactly where TikTok has strength. With Local Feed, AI summaries, creator reviews, and shopping features, TikTok is trying to meet people at the moment they are exploring, comparing, and deciding.
So instead of asking whether TikTok is a traditional search engine, I ask a more useful question: how do I make sure people can find, understand, trust, and choose a brand wherever their search journey begins? More often than many marketers want to admit, that starting point may be TikTok.
TikTok SEO Is More Than Hashtags Now
I think of TikTok SEO much like traditional SEO: it is the work of making a business, place, product, service, or experience easier to discover. As TikTok has evolved, the discovery surfaces have expanded far beyond captions and hashtags.
In the past, I mostly associated TikTok optimization with captions, hashtags, trending sounds, posting times, and the hope that a video would land on the For You feed. Those pieces still matter, but they are no longer the full picture.
Today, I have to think about TikTok Search, recommendations, Local Feed, Places, reviews, comments, creator content, visual cues, product signals, and AI-assisted discovery. A stronger TikTok SEO strategy now includes search query relevance, spoken topic clarity, on-screen text, captions, hashtags, location context, creator reviews, comments, product visuals, and the searches people make after seeing a video.
TikTok documentation says search results can be shaped by how well content matches a query, along with hashtags, sounds, user interactions, language, and location. The For You feed also weighs user interactions, content information, user information, and watch behavior, which means usefulness and engagement both matter.
Local Feed Creates a New Discovery Surface
TikTok launched Local Feed in the U.S. on Feb. 11 as a home-screen tab for nearby content related to travel, events, restaurants, shopping, small businesses, and local creators. TikTok says posts can appear based on location, topic, and when the content was published.
I see Local Feed as another organic discovery touchpoint, especially for local businesses. A restaurant can appear while someone is deciding where to eat nearby. A wellness club can show up when someone is looking for weekend plans. A venue can answer practical before-you-go questions before a guest ever reaches the box office.
There are limits I would keep in mind. TikTok precise location setting is optional, off by default, available only for users 18 and older, and still rolling out across the U.S. TikTok also says private accounts, accounts for users under 18, and posts limited to Friends or Only You will not appear in Local Feed.
Local Explorer Shows TikTok Is Investing in Places
TikTok Local Explorer Program is one of the clearest signs I have seen that the platform wants to build stronger place-based discovery. The program encourages people to submit location-based reviews and rewards participation with experience points, levels, badges, community access, and other perks.
I would not assume every market has the same access or level of activity, because availability has been limited and uneven by region. Still, the direction matters: TikTok is building more ways for users to evaluate places inside the app.
I have also seen TikTok incentivize reviews for places that do not already have TikTok reviews. In one example, a coffee shop had no TikTok reviews, and I was offered a $1 Promote coupon to leave one.
When a place does not have native TikTok reviews, I have seen TikTok pull reviews from TripAdvisor and, in some cases, Google. That makes the Places tab a useful comparison surface where people can evaluate reviews, videos, and comments before deciding whether to visit a local business.
Visual Search Links Matter More Than Exact Keywords
TikTok increasingly adds automated search links and related query prompts beneath videos. I pay attention to these because they show how TikTok can connect a video to a broader topic, place, or product discovery path.
For example, a video about a place like Glen Ivy may show a search bar at the bottom that lets users explore more related content. Those search bars can appear even when a creator has not overloaded the description with exact-match keywords, which tells me TikTok is reading more than just captions.
TikTok Shop Turns Discovery Into Buying
With TikTok Shop, someone can see a product in a video, search for it, compare it through comments and creator content, and buy it without leaving the app. That makes TikTok more than a discovery channel for ecommerce brands; it can become part of the full purchase path.
I would optimize TikTok Shop content around the information TikTok needs to understand a product. Search relies heavily on how well a shopper query matches product information such as titles, categories, attributes, and content context.
TikTok Shop has also released Shoppable Photos in beta for select sellers. Eligible sellers can create image-based posts, include multiple photos, and tag products directly in the post. These posts may appear in the For You feed, Search, and the Shop tab, giving sellers a simpler way to showcase inventory without producing a full video.
AI Is Becoming Part of TikTok Discovery
I am also watching TikTok AI-assisted discovery features closely, even though availability varies by market, account, and test. Features such as Tako, AI Overviews, Quick Highlights, AI summaries, and Content Studio all point in the same direction: TikTok wants to help users search, summarize, and create faster.
Tako is TikTok chatbot, and it lets users search in a way that feels similar to using the app search bar. It can surface relevant TikTok videos and external sources, including articles.
TikTok also now offers AI Overviews for some searches. When users search a topic, they may see an AI-generated summary of the results. If they click a visual search bar, they may also see Quick Highlights that summarize that search experience.
The Places tab includes AI summaries too, and users can see how many posts were used to generate a place summary. For local businesses, that makes the quality and clarity of creator posts, customer videos, and reviews even more important.
On the creator and seller side, TikTok AI tools can help generate captions, hashtags, and even videos. I would treat these tools as helpful support, not a substitute for real strategy, because features like Content Studio are still not available to everyone and remain in testing.
How I Would Improve Visibility on TikTok
On TikTok, visibility comes from what people search for, what TikTok can understand, and what the camera actually shows. That means I would focus less on cleverness and more on showing people what they need to see before they choose a business, product, or place.
For restaurants, I would show menu items, exterior signage, the dining room, takeout packaging, seasonal dishes, and neighborhood cues. Those visuals help both users and TikTok understand what the place offers and where it fits.
For retail, I would show product displays, packaging, try-ons, shelf layout, gift ideas, and the storefront. The more clearly a video communicates what is available, who it is for, and where someone can get it, the stronger the discovery signal becomes.
I would also build simple habits into every TikTok content workflow: use location context naturally, show products clearly, show the storefront or interior when relevant, mention the city or neighborhood when it helps, create timely content around local moments, tag the physical location when appropriate, and work with creators who already understand discovery-driven content.
Keyword Research
I would start TikTok keyword research inside the app because that is where the search behavior is happening. Seed topics might include best brunch, World Cup outfits, things to do in [location], wedding inspiration, or gluten-free bakery.
From there, I would search each phrase on TikTok, document autocomplete suggestions, review suggested filters, look for Others searched for prompts, study top videos, and pay close attention to comment themes. I would also test city and neighborhood modifiers, then compare TikTok findings with Google Search Console, Google autocomplete, Reddit, YouTube, and site search data.
TikTok Creator Search Insights can add another useful layer by showing personalized information about search topics, content gaps, and how content tied to searched topics is performing.
Keyword Placement
I would place the core topic where TikTok and viewers can recognize it quickly: in the first few seconds of the video, the first text overlay, the opening of the caption, relevant hashtags, location tags, pinned comments, reply videos, the profile bio, playlist names, and creator briefs.
Comments and Reviews
I would treat comments and reviews as visibility assets, not afterthoughts. That means pinning genuinely helpful comments, replying to repeated questions with videos, correcting misinformation when trust is at stake, watching for recurring objections, and turning repeated questions into FAQs, landing page content, Google Business Profile posts, and future videos.
A creator saying that a bakery is the best gluten-free option in Portland because it takes cross-contamination seriously may be more useful than a generic five-star review. That kind of specific language can shape website copy, FAQ strategy, and customer messaging.
Referral Traffic and Branded Search
I would track TikTok referral traffic and monitor branded searches over time. When a TikTok post performs well, I would annotate it and compare branded search trends against a baseline.
I would look for directional movement in branded clicks, branded impressions, TikTok referral traffic, Google Business Profile actions, and engagement on related pages. At the same time, I would avoid giving TikTok credit for every increase without considering PR, paid campaigns, email, promotions, seasonality, and other marketing activity.
Attribution may never be perfect, but imperfect measurement does not make TikTok influence meaningless. I would rather measure directional impact than ignore a channel that is clearly shaping discovery behavior.
I Would Explore TikTok Instead of Ignoring It
Someone may find a business on TikTok before they ever search for its name on Google or ChatGPT. Someone else may turn to TikTok midway through the journey to decide whether the business is worth the trip, the purchase, or the recommendation.
Either way, I believe TikTok has earned a meaningful role in modern SEO strategy. Between Local Feed, Places, Tako, AI summaries, creator reviews, and TikTok Shop, the platform keeps adding new ways for businesses to be discovered, and many of those opportunities are still underused.
After nearly 30 years at Microsoft, I am seeing one of Bing’s most influential search leaders close a remarkable chapter. Fabrice Canel announced that he is retiring from Microsoft, writing on LinkedIn, “I am retiring from Microsoft, effective today July 1st.” He also reflected, “Today marks nearly 30 years with Microsoft. Thirty years…”
When I think about Fabrice Canel’s impact, I think first about the foundation of Microsoft Bing Search. He was responsible for indexing at Bing, including crawling, URL discovery, content selection, and content processing. Those areas are core to how search engines understand the web, and Fabrice helped shape them at massive scale.
He was also the person behind the IndexNow initiative, and he played a major role in creating and powering Bing Webmaster Tools. For anyone working in SEO, publishing, or technical search, those contributions matter because they helped make discovery, indexing, and webmaster communication faster and more practical.
I have watched Fabrice contribute far beyond product work. He has spoken at countless industry events, including SMX, and has written extensively about how search works, how sites can perform better in Bing, and how search is evolving with generative AI. He helped run one of the world’s most important search engines, while also giving the SEO community tools, education, and direct insight.
In his retirement message, Fabrice addressed fellow Microsoftees, engineers, attorneys, marketers, webmasters, publishers, SEO champions, product leaders, journalists, people across search and AI, and even friends at Google. His note was warm, personal, and full of gratitude for the people who shaped his Microsoft journey.
He described his three decades at Microsoft as a wonderful adventure, from solving real business problems with IndexNow to helping webmasters and publishers thrive in the constantly changing world of SEO and AI. He thanked colleagues, partners, publishers, and the people he trained and mentored, saying they are ready to carry the mission forward.
Fabrice also shared that, after many conversations with family and friends, he decided to take advantage of Microsoft’s Voluntary Retirement Program. His message ended with the same sense of warmth and storybook style that many in the industry have come to associate with him: gratitude for Microsoft, confidence in the Bing team’s future, and a final wish that everyone stay curious, keep innovating, and make content easier to find.
Why do I care so much about this? Because Fabrice has been a true friend to the search industry. His work will live on through the products, systems, and initiatives he helped create, and his willingness to share knowledge has made a lasting difference for SEOs, publishers, developers, and search professionals.
I know Fabrice has trained a team to continue the work, and I believe Bing remains in good hands. Still, I would be lying if I said I am not sad to see him retire. It has been an honor to work with him and learn from him over the years, and his legacy at Microsoft Bing will be felt for a long time.
I know competitive brand bidding is now a common PPC tactic, but that does not mean I treat it as harmless background noise. When competitors, affiliates, coupon sites, or misleading advertisers show up on branded searches, they can inflate CPCs, divert high-intent traffic, and confuse people who were already looking for my brand.
I have seen how much difference visibility can make. Industry examples show that brands often uncover meaningful CPC inflation once they start tracking competitor bidding, affiliate activity, and trademark misuse. In documented cases, brands reduced branded CPCs by 25% to 75% after identifying infringing advertisers and enforcing their policies.
In this guide, I walk through how I monitor branded keywords, identify who is advertising on them, and decide what actions may be available based on the evidence I find.
Choosing Keywords So I Do Not Miss Hidden Activity
When I want to find out who is using my brand in search ads, I start by deciding which keywords I need to monitor.
The biggest mistake I try to avoid is watching only my exact brand name. That is a useful starting point, but it rarely shows the full picture. Some advertisers deliberately target brand-related coupon, discount, review, or alternative queries because those searches often come from high-intent users and attract less scrutiny.
For example, someone searching for “Brand coupon” or “Brand discount code” may be much closer to buying than someone searching for the brand alone. Those queries often attract coupon affiliates, loyalty sites, and unauthorized advertisers trying to intercept branded traffic.
I also pay attention to searches that include terms like “reviews” or “alternatives,” because those queries can bring in competitors and comparison sites that position themselves directly against my brand.
Misspellings matter too. Some advertisers target spelling variations because they are less likely to be monitored and may face less competition.
For a solid monitoring setup, I include my core brand name, “official page” and “login” variations, coupon and promo-code searches, review and alternative searches, commercial terms such as “buy,” “order,” and “sign up,” common misspellings, and localized versions of my brand name.
If I am using Bluepear, its built-in AI assistant can generate keyword suggestions from this kind of list and help me expand coverage faster.
The number of terms I monitor depends on the size of the brand portfolio, including trademarks, local branches, and product names. For many small to medium-sized brands, I would start with about 20 keywords and then expand as new risks, markets, and opportunities appear.
Choosing Locations and Monitoring Frequency
I do not rely on a single search from my office, on my device, at one moment in time. Search results are too dynamic for that. Two people searching the same branded keyword can see completely different ads and organic listings depending on their location, device, timing, and other variables.
I also assume that some advertisers may be trying to hide their activity. A fraudster or an affiliate violating my PPC policy might run ads outside normal business hours to reduce the chance of being caught. If I only check manually during the workday, I may never see those ads.
When I monitor branded search results, I look across the countries and markets where my brand operates, regional differences within those markets, mobile and desktop results, different times of day, and weekday versus weekend activity.
Frequency matters just as much as coverage. Some violations appear briefly and then disappear. Running checks multiple times throughout the day gives me a better chance of capturing activity that would otherwise go unnoticed.
Tracking all of these variables manually can become tedious, especially when a brand operates across multiple markets. Bluepear accounts for locations, devices, time zones, and redirects that can obscure the true destination of traffic. I can set the parameters once and gain continuous visibility without turning monitoring into a weekly time sink.
Reviewing Search Results and Recording Evidence
I do not assume every advertiser bidding on my branded keywords is breaking a rule. Competitors may be allowed to bid on branded keywords if they do not use my trademark in their ad copy. Affiliates may also be authorized to promote my brand under specific program conditions.
Still, I need to know when an advertiser’s behavior crosses the line from legitimate brand bidding into trademark misuse, policy violations, or customer deception.
The first signal I investigate is trademark use in ad copy. If the ad mentions my brand name in the headline or description, and my trademark rules or affiliate policies restrict that use, I treat it as a possible compliance issue.
I also look for misleading claims. Phrases that imply the advertiser is “official,” references to exclusive offers, or language that suggests authorization when none exists can confuse users and deserve review.
Coupon and discount promotions need special attention. I verify whether the advertised discount, promo code, or offer is legitimate, because some affiliates use expired, misleading, or fabricated offers to win clicks.
I also watch for impersonation signals. Some ads and landing pages are designed to resemble a brand’s official website. Even if the advertiser does not directly claim to be my company, that kind of presentation can still confuse users and divert branded traffic.
Because advertisers can change ad copy, pause campaigns, or remove landing pages at any time, I collect evidence quickly. I record the ad copy, SERP position, triggering keyword, location, URLs, redirects, landing page content, and timestamps.
Bluepear can handle this automatically by compiling a report with the relevant details, which makes follow-up easier when I need to contact an affiliate, review a competitor’s behavior, or escalate a trademark issue.
Identifying Who Is Behind the Activity
Sometimes I cannot immediately tell whether an advertiser is a competitor, an affiliate, a coupon site, or something riskier. Branded search results often include multiple participants with different motivations, so I need to understand who I am dealing with before I decide what to do next.
I look for patterns. A direct competitor domain usually points to competitor bidding. A coupon or cashback page may indicate an affiliate, coupon site, or loyalty site. Affiliate network tracking links often suggest affiliate activity, although they can also appear in more questionable setups. Product comparison pages often point to competitors or comparison publishers.
Other signals raise the risk level. If an ad uses my trademark, claims to be “official,” sends users through multiple redirects, promotes coupon codes I cannot verify, or lands on a page that imitates my brand’s design or messaging, I investigate more carefully.
No single signal gives me a definitive answer. I combine multiple pieces of evidence before drawing conclusions. Once I know who is advertising on my brand terms, I can move beyond detection and decide whether their activity aligns with my policies and business goals.
What I Do Next
After I identify who is advertising on my brand terms and review their ads, the next step is choosing the right response.
Competitor Brand Bidding
Not every competitor bidding on my branded keywords requires immediate intervention. Before acting, I ask how often the competitor appears, which keywords they are targeting, whether they are using trademarked terms in ad copy, and whether they are sending users to comparison content or direct offers.
In many cases, I monitor the activity and evaluate its business impact over time. Documenting patterns helps me establish a baseline, which can support future compliance reviews or legal conversations if escalation becomes necessary.
Affiliate Violations
If an affiliate is bidding on restricted branded keywords or violating program rules, I gather evidence and contact the affiliate or network. My workflow is straightforward: document the violation, verify the affiliate ID, share the evidence, request removal or corrective action, and apply program enforcement measures if needed.
Screenshots, timestamps, and redirect data make those conversations much easier because I can show exactly what happened, where it happened, and when it was detected.
Trademark Misuse
Trademark-related issues require careful review. I look for unauthorized trademark use in ad copy, ads that create confusion about brand affiliation, impersonation attempts, and misleading claims that the advertiser is an official brand representative, partner, or reseller.
The right response depends on the circumstances, internal policies, and applicable laws. In many jurisdictions, competitors are generally allowed to bid on trademarked keywords. However, ads that confuse users about the advertiser’s relationship with my brand may raise trademark or unfair competition concerns, depending on the facts and local law.
The advertising platform’s policies matter too. Google allows advertisers to bid on trademarked keywords, but it may restrict trademark use in ad text when a valid trademark complaint is submitted. Google also prohibits ads that use trademarks in a confusing, deceptive, or misleading way.
Before I take action, I collect as much evidence as possible, including screenshots, detection timestamps, URLs, redirects, and landing page content. Once the facts are documented, I may contact the advertiser directly, submit a trademark complaint to the advertising platform, send a cease and desist letter, or escalate through legal channels if necessary.
Why I Keep Monitoring Brand Search
The main lesson is that branded search protection is not a one-time audit. Affiliates can activate and pause campaigns throughout the month. Some violations appear only on weekends, outside business hours, or in specific markets. An advertiser that disappears today may return next week with new ad copy, a new domain, or a different affiliate account.
That is why I treat brand protection as an ongoing process. Occasional searches are not enough. I need consistent monitoring and a repeatable investigation workflow that shows who is appearing on my brand terms, how they operate, and whether action is warranted.
If I want easier visibility into my branded search landscape, Bluepear helps identify issues earlier, respond faster, and make more informed decisions about protecting traffic and advertising investments.
I see Google Ask Maps changing local visibility in a meaningful way. Instead of showing people a long list of nearby businesses and leaving them to sort through everything, Ask Maps narrows the options, interprets the searcher’s intent, and explains why certain businesses look like a strong fit.
That changes how I think about local SEO. Visibility is no longer only about ranking somewhere near the top of a long results list. It is increasingly about whether Google understands a business well enough to recommend it with confidence.
I would not treat Ask Maps as a separate optimization channel or a brand-new tactic to chase. I would focus on making the business easier for Google to understand, easier to match to real customer situations, and easier to trust. The foundations of local SEO still matter, but the way those signals work together matters even more.
Visibility in Ask Maps starts with filtering
One of the first things I notice about Ask Maps is how small the result set can be. In testing, it often showed around three to eight businesses, depending on the query. That feels very different from traditional Google Maps, where people can scroll through dozens of options and compare them on their own.
With Ask Maps, much of that comparison happens earlier. Google filters the market first, interprets what the person is really asking for, and then presents a smaller group of businesses with an explanation of why each one fits.
That means I have to think beyond the question of whether a business ranks. I also have to ask whether Google has enough confidence to include that business in a short recommendation set and explain why it belongs there.
I think of this as a two-step problem. First, Google decides which businesses are eligible for the query. Then, it decides which eligible businesses it can confidently recommend.
Ask Maps needs enough detail to explain the business
Ask Maps does more than list businesses. It interprets and describes them. Even for simple searches, I often see businesses framed around qualities such as responsiveness, experience, specialization, professionalism, or the kinds of situations they seem best suited for.
That creates a different optimization challenge. It is not enough for Google to know that a business exists or that it offers a basic service. Google needs enough information to answer a more practical question: when should this business be recommended?
To support that, I want Google to understand the types of jobs the business handles, the situations it commonly deals with, the concerns customers usually have, and how the business approaches those situations.
If that information is vague, scattered, or inconsistent, Ask Maps has less to work with. When Google cannot clearly explain why a business fits a specific situation, I would expect that business to be less likely to appear as a recommendation.
Google Business Profile becomes the identity layer
For me, the Google Business Profile sits at the foundation of this whole process. In earlier-stage queries, Ask Maps appears to rely heavily on profile data, including business descriptions, services, reviews, ratings, hours, and operational details.
Many businesses still treat their profile like a basic listing to fill out and keep current. That is necessary, but I do not think it is enough for an environment where Google is trying to describe and recommend businesses. The profile needs to communicate a clear, specific identity.
A generic profile might say that a business offers plumbing, HVAC, electrical work, or another broad service. A stronger profile clarifies the kinds of problems it handles, the situations it is built for, and the details that make it useful to specific customers.
For example, I would use the profile to reinforce details such as emergency availability, response times, specific repair or installation types, experience with older homes, complex systems, or common customer problems the business solves.
That level of specificity gives Google more direct evidence. Instead of forcing the system to infer what the business is known for, I want the profile to make that identity clear.
Reviews shape positioning, not just credibility
Reviews have always mattered in local search, but I see them playing a more structured role in Ask Maps. Review language can show up in the way Google describes a business, especially around themes like responsiveness, honesty, communication, professionalism, and quality of work.
That tells me reviews are doing more than supporting credibility. They are helping define how the business is positioned.
I would still pay attention to rating, volume, and recency. But I would also look closely at what customers actually say. The language inside reviews can give Google useful context about what the business does, how it works, and what customers value about the experience.
A vague review such as “great service” signals satisfaction, but it does not explain much. A detailed review that mentions a same-day response, a drain backup, clear communication about options, and a repair-focused solution gives Google several stronger signals about the business.
Over time, those patterns accumulate. In that sense, I view reviews as one of the main ways Google learns what a local business is known for.
Website content matters more when decisions get harder
I also see website content becoming more important as queries become more complex. For basic service searches, the Google Business Profile and reviews may carry a lot of the weight. But when the search involves higher cost, uncertainty, or trust, Google appears to look for deeper supporting evidence.
That is where the website can help. Many service pages explain what a business offers and why it is qualified. That still matters, but it does not always match how people search when they are trying to make a difficult decision.
In more situational searches, people are not just looking for a service. They are trying to understand a problem, compare options, reduce risk, and decide what to do next.
That is why I would build content around the customer’s situation, not just around the service name. Stronger pages explain what leads to the problem, how to recognize it, what options are available, how to think through the decision, and what outcomes to expect.
For example, a furnace repair page can go beyond a basic list of services. It can cover common symptoms, when repair makes sense, when replacement might be worth considering, and how a homeowner can evaluate the decision. That kind of content lines up more closely with the prompts Ask Maps is trying to interpret.
I also see a strong fit for jobs-to-be-done pages. Instead of organizing every page around a service category, I would create pages around the situation the customer is trying to solve and the decision they are working through.
Trust signals matter more as risk increases
As searches move from simple service needs into decision-making, trust becomes more important. When people mention cost, honesty, uncertainty, or fear of making the wrong choice, Ask Maps tends to highlight qualities such as transparency, fairness, careful workmanship, and clear communication.
That makes sense to me because it reflects how people actually think in those moments. When someone faces an expensive repair or an unexpected issue, they are not only asking who can do the work. They are asking who they can trust to handle it correctly.
I would support that trust with evidence across the business’s online presence. Reviews can show that customers felt respected and informed. Website content can explain the process. Examples of completed work can show experience. Clear “what to expect” sections can reduce uncertainty.
The higher the perceived risk, the more supporting evidence matters. I want Google to see a consistent pattern that the business explains options clearly, avoids unnecessary pressure, handles similar situations, and leaves customers confident in the outcome.
Detailed customer reviews do more than boost ratings. They give Google Ask Maps the context it needs to understand, position and confidently recommend a local business.
External signals should reinforce the same story
For more complex or trust-heavy queries, Ask Maps may look beyond the Google Business Profile, reviews, and website. Third-party platforms, directories, and other public sources can help reinforce how Google understands a business.
I do not take that to mean every external mention is equally important. I take it to mean consistency matters. If a business is described one way on its website, another way in reviews, and differently across directories or social platforms, the overall picture becomes harder to interpret.
When those signals align, they strengthen each other. Business descriptions, services, customer experiences, types of work handled, and overall positioning should tell the same story wherever they appear.
From a practical standpoint, I would not try to appear on every possible platform. I would make sure the important sources are accurate, credible, and consistent.
I would optimize for evidence, not just keywords
As local search decisions become more specific and higher risk, Google needs deeper signals from business profiles, reviews, and website content to recommend the right provider.
Taken together, these patterns push me to think differently about optimization. Traditional local SEO often starts with keywords and rankings. Those still matter, but they do not fully explain what Ask Maps is doing.
I find it more useful to think in terms of evidence. For a business to be recommended, Google needs enough information to understand what it does, what types of jobs it handles, what situations it fits, how customers experience it, and whether it can be trusted in higher-stakes decisions.
Each source contributes something different. The Google Business Profile establishes the baseline identity. Reviews add real-world context. Website content provides depth and explanation. External sources help confirm the same picture.
Individually, none of those elements tells the whole story. Together, they create a clearer and more consistent understanding of the business. That is where the shift from ranking to recommendation becomes most obvious: keywords can support relevance, but evidence supports recommendation.
My practical framework for Ask Maps visibility
When I evaluate a business for Ask Maps visibility, I would look at five areas: identity, relevance, trust, context, and consistency.
Google Ask Maps rewards more than keyword relevance. This visual shows why reviews, service details, trust signals, and real proof help local businesses get recommended.
Identity asks whether Google can clearly understand what the business does and where it operates. Relevance asks whether the business can be matched to specific services and situations. Trust asks whether there is enough proof that customers feel confident choosing it.
Context asks whether the content reflects the decisions customers are actually trying to make. Consistency asks whether different sources reinforce the same understanding of the business.
I do not see this as a checklist to complete once. I see it as a practical way to evaluate how clearly and consistently a business is represented across the sources Ask Maps appears to use.
What I would avoid
With any new search feature, it is easy to overcorrect. I would avoid treating Ask Maps as an isolated channel that needs thin content, unnatural profile language, generic service-page duplication, or review language that feels forced.
Those tactics may create more content, but they do not necessarily create more useful evidence. The better approach is to align more closely with how customers actually search, evaluate options, and make decisions.
A practical local SEO framework shows how businesses can earn visibility in Google Ask Maps by clarifying identity, proving relevance, building trust, adding context, and staying consistent online.
When the business presence reflects real customer needs clearly and consistently, it naturally creates the kinds of signals Ask Maps seems to rely on.
What I still do not know about Ask Maps
I would treat all of this as directional, not definitive. Ask Maps is still being tested and refined, and the system is not fully documented.
The result structure can vary by query and test environment. The feature’s usability is also still changing. In many cases, users may still need to click into a Google Business Profile to call, book, or engage, rather than acting directly from the Ask Maps response.
Measurement is another open issue. Right now, I do not see a clean way to isolate Ask Maps visibility or performance inside standard reporting tools. That makes it difficult to attribute calls, traffic, or conversions directly to this experience.
I also would not assume the same signal weighting applies to every query. Google Business Profile data, reviews, website content, and external sources may all matter, but their relative importance likely changes based on the search intent and the complexity of the decision.
The real shift is from ranking to recommendation
I see Ask Maps as a version of local search where retrieval, evaluation, and decision support are moving closer together. Instead of making users search, compare, research, and decide across several steps, Google is trying to guide more of that process inside one experience.
That changes the meaning of visibility. In Ask Maps, it is not enough for a business to simply appear. The business needs to be understood well enough for Google to explain why it fits the situation and trusted enough to be recommended.
For businesses and SEOs, I would not respond by chasing a narrow trick. I would build a clearer, more complete, and more consistent representation of the business across the sources that shape Google’s understanding.
The businesses most likely to benefit are the ones that are easiest to interpret, easiest to trust, and easiest to match to real-world customer needs.
I’m deeply saddened to share that Bruce Clay, widely known as the Father of SEO, passed away in late May. Bruce was one of the true founding figures of the SEO industry, having launched a professional SEO agency back in 1996, long before search marketing became the discipline we know today.
For me, Bruce’s impact is hard to overstate. He was the first sponsor of the first-ever SEO conference, and he gave an extraordinary amount of his time, resources and money to help build the search community. Few people have supported this industry for as long, or with as much generosity, as Bruce did.
Tribute video. The Bruce Clay, Inc. team prepared a tribute video honoring Bruce’s life and legacy. It describes him as a pioneer who devoted much of his life to helping the SEO industry grow. During his three decades as CEO of Bruce Clay Inc., he wrote three books, built tools, spoke at conferences, hosted training events and helped the company expand internationally.
Because of Bruce’s founding principles, hundreds of employees around the world have contributed to SEO, and thousands of students have benefited from his experience and teaching.
The Bruce Clay team told me, “We are absolutely heartbroken, but we find strength in the vibrant community and lasting values that Bruce built. Our teams in the U.S. and around the world remain dedicated to carrying forward the mission Bruce loved so dearly.”
Kyle Pouliot, Sr. Video Production Manager at Third Door Media, also shared a personal reflection with me.
“I’ve gotten to know Bruce on a more personal level over these past few years and interacted with him frequently for our online conferences. What I’ve learned about Bruce in that time is that he was genuinely thoughtful and caring about the search community. Never short of an honest opinion, Bruce shared some really practical ideas for Search Engine Land and SMX. He loved sharing his deep experienced knowledge to everyone, it didn’t matter if you were a beginner or 20+ year industry veteran, he treated everyone the same. We talked about the hundreds of golf balls that would find their way into his property every day, food, raising kids and how incredible the weather was in Simi Valley. He will be greatly missed.”
On a personal note, I’ve known Bruce Clay since I entered the SEO industry more than 20 years ago. He was a role model to me, often a mentor, and always someone who was approachable, professional and deeply caring. In many rooms, he was likely the most generous and thoughtful person there.
I loved his SEO talks. I loved meeting him at industry events. And I especially valued the personal emails he sent about shaping the future of our industry. Those moments showed me how much Bruce cared, not just about search, but about the people building it.
I see existing content as a goldmine, but only when I have a practical way to improve it. The hard part is usually finding the time, and that is where Claude has made a large, messy job feel much more manageable for me.
I do not start by building a giant content audit system. I start with one article, run one focused audit, refine the output, and then turn the prompt into a reusable Claude skill. Over time, those one-off audits become a working library I can improve every time I use it.
I use Claude to uncover topical gaps, flag outdated information, check brand voice, and evaluate whether a page is easy for AI systems to retrieve and cite. The real value comes from iteration: each time I improve a skill, the next audit becomes faster and more useful.
Here are six content audit workflows I would build in Claude. The first four work at the page level, so I can start with a single article before moving into larger library-wide analysis.
Page-level audits
When I am not ready to build a full workflow, I start with page-level audits. These audits only require one article, which means I do not need a content inventory, a data export, or a complicated setup. After each session, I ask Claude to turn the process into a reusable skill for future page-level reviews.
1. Brand voice consistency
I use a brand voice consistency audit when a content library has drifted over time. Voice can shift because of new writers, changing services, product updates, or evolving positioning. This audit helps me spot where a page no longer sounds aligned with the brand.
If I do not have detailed brand guidelines with strong examples, I let Claude extract the voice guide from high-quality content. That usually works better than relying on vague phrases like “conversational but authoritative” or “educational, not too formal.”
I pick three to five articles that represent the brand at its best. If possible, I download them as markdown files and ask Claude to describe how the voice works in concrete terms.
How the articles usually open, such as whether they begin with a direct claim, a counterintuitive statement, or a specific scenario.
How sentences and paragraphs are built, including average length, range, rhythm, and how paragraphs tend to close.
Three to five personality dimensions framed as “We say X, but not Y,” with do and don’t examples.
Words and phrases the brand tends to use, and words or phrases it should avoid.
Specific constructions, phrases, and conventions the brand never uses.
Instead of accepting a vague voice description, I want Claude to return concrete observations. For example, it might say that articles open with a direct claim rather than a scene-setting paragraph, sentences average 15 to 20 words and rarely exceed 30, and transitions are functional, such as “here’s why that matters,” rather than formulaic, such as “furthermore.”
I also want example pairs, such as: “We’d say ‘the data shows three things,’ not ‘there are multiple factors to consider.’” The goal is not to create a voice guide for writers. The goal is to create one an LLM can understand and apply consistently.
Once I like the output, I ask Claude to save it as a skill and evaluate an article against it. If Claude flags issues I disagree with, I update the skill until the feedback becomes useful and repeatable.
I can then use that skill to find voice inconsistencies in older content, check new drafts for alignment, and even generate more on-brand first drafts. I still edit the output, but the starting point is much stronger.
When I need to improve content performance, I use a coverage comparison to find topical gaps. This helps me understand what competing pages cover that my article misses.
I use the Claude in Chrome extension to have Claude review the top three to five ranking pages for my target keyword. Then I ask Claude to compare those pages against my content and highlight the most important gaps.
What competitors are doing well.
What my article already does well.
Where I can improve the piece without bloating it.
If I want the output in a table, I ask Claude to format it that way. If I want a downloadable DOCX for review or handoff, I ask for that instead.
When Claude recommends additions I would never publish, I make a note of those exclusions before packaging the workflow into a skill. That way, the skill gets closer to my editorial standards each time I refine it.
3. Freshness audit
Old content adds up quickly, and it is hard to prioritize refreshes while I am also producing new material. A freshness audit skill helps me identify what needs attention without rereading every older article from scratch.
I give Claude an older article and ask it to flag anything time-sensitive: statistics tied to a specific year, named tools or platforms, references to “current” or “recent” trends, and claims that depend on a market, regulatory, or product context that may have changed. I am not asking Claude to rewrite the article yet. I am asking it to build an issue list I can act on.
If my company has launched new products, removed old services, changed positioning, or updated terminology, I include that context in the input. That helps Claude flag what should be added, removed, or revised.
I use an AEO and AI retrievability audit to understand whether a page is likely to be surfaced in AI-generated answers. Tools such as ChatGPT, Perplexity, and Google AI Overviews tend to favor content that answers questions directly. If an article buries the answer under too much preamble, or structures key information in a way that is hard to extract, it becomes less useful for those systems.
I give Claude the article and the target query, then ask it to evaluate several retrieval signals.
Whether the article answers the main question directly and early.
Whether key statements are specific enough for an LLM to quote or cite.
Where an FAQ-style section would improve clarity.
Whether the page includes authority signals, such as primary research, first-person experience, outbound citations, or specific examples.
Once I save this as a skill, it becomes an extra editor focused specifically on AI visibility and answer retrieval.
Library-level audits
Once I am ready to move beyond individual pages, I use library-level audits. These require performance data, a content inventory, a connector, or a manual export.
5. Performance triage
When I think about a traditional content audit, performance triage is usually what comes to mind. It helps me analyze a content library and identify the pages that deserve attention first.
Before I begin, I make sure Claude has access to the right data through a connector such as BigQuery or the Semrush API. If that is not available, I export the data I normally use for large-scale audits, such as traffic, clicks, engagement metrics, conversions, rankings, and related performance signals.
I ask Claude to prioritize pages that have suffered meaningful performance drops in the past six to 12 months, pages with high impressions but consistently low click-through rates, and pages that have been live long enough to rank but never gained traction.
I also define what a meaningful performance drop looks like for the site I am analyzing, because traffic patterns vary by industry, audience, and page type. Then I ask Claude for a prioritized list of what is worth investigating and why. From there, I use the page-level audits above to diagnose the problem.
If I have run this analysis before, I give Claude the previous output. That helps the skill learn the kind of prioritization and reasoning I expect.
I treat entities as a major part of AEO and semantic search. A topical gap analysis helps me see whether my content library has enough coverage to build authority around the entities tied to my brand.
The core question I ask is simple: what is my content library not covering that it should?
To start, I create a list of target entities. For example, at my agency, I want to be known for SEO and AEO. If I have a clear list of services or products, I can use that instead of a formal entity list.
Using Cowork or Code, I ask Claude to analyze my sitemap and compare it to those target entities. If I have a Screaming Frog export with URLs, page titles, and meta descriptions, I use that as input for a more accurate analysis.
Then I ask Claude to identify topic clusters that are missing or underrepresented based on the target entities, services, or products. If I want prioritization, I can use the Semrush MCP so Claude can check search volume for potential keywords.
Not every gap is worth filling. I filter the results against audience needs, business relevance, and editorial standards. Then I feed those decisions back into Claude so the skill produces better recommendations next time. The final list can go directly into my content creation workflow or be handed off to a content team.
I do not try to audit everything at once
I have seen content audits stall because the scope feels too large, not because the team lacks data. My preferred approach is to pick one audit and one article, run the workflow, save the skill, and use it again on the next piece.
For me, iteration is part of the value. I enjoy taking one Claude skill, improving it, and then chaining it with other skills to uncover more content opportunities. Starting small is what makes the system easier to keep using.
I think one of the biggest mistakes in AI marketing is positioning a product as a replacement for people. That message can win attention in the short term, but I believe it quietly drains trust over time.
This is a little different from what I usually write about, but it matters. The way we talk about AI shapes how customers, employees, executives, and markets respond to it.
In this memo, I want to focus on three things: why “substitution positioning” feels powerful at first but weakens a brand later, what the data says about whether AI is actually replacing people, and how I think companies should position AI instead.
The cardinal sin of positioning in the AI era is replacement. I call it substitution positioning. It is tempting because it sounds bold, efficient, and disruptive. But over time, it creates anxiety, skepticism, and credibility problems.
We have seen this pattern already. Anthropic CEO Dario Amodei predicted that software engineering jobs could disappear within 6 to 12 months as models began doing most or all of what software engineers do end to end. Yet demand for software engineers has continued to look strong.
OpenAI CEO Sam Altman also predicted that many customer support jobs would go away because AI could handle that work better. Soon after, customer service hiring began outpacing the broader job market.
I understand why fear works as a marketing tool. The fear of being replaced gets attention fast. It got me, too. When powerful AI models gained traction, I worried about my own future. But when I still see AI companies hiring copywriters, SEOs, engineers, and support teams, I sleep better.
Fear sells because it taps into fight-or-flight. Layoffs make that story even louder. They let companies frame cost-cutting as innovation and make the replacement narrative feel more real than it may actually be.
But I do not think the facts support the clean replacement story. In New York, companies can indicate when mass layoffs are caused by technological innovation or automation. In one reported period, more than 160 companies filed mass layoffs affecting roughly 28,300 workers, and not one chose AI as the reason. That list included companies such as Amazon and Goldman Sachs.
Researchers at Yale also studied employment data from the Current Population Survey over 33 months and found no evidence of job displacement from AI. To me, the pattern looks less like instant replacement and more like the earlier waves of computers and the internet changing how work gets done.
That is why I keep coming back to this point: stop trying to make replacement happen. It is not happening in the simple, dramatic way many AI narratives suggest.
AI is powerful, but it is also inconsistent. In its current form, it can do some tasks better than humans and fail badly at others. That paradox is often called the Jagged Frontier.
The Jagged Frontier idea matters because it explains why some people see AI as transformative while others remain lukewarm. A BCG and Harvard study of 758 knowledge workers found that people get the most value from AI when they understand what it is good at and where it breaks down.
Microsoft reached a similar conclusion in its 2026 Work Trend Index Annual Report. The company found that a small group of advanced AI users, described as Frontier Professionals, were not simply using AI more often. They also knew which mode of AI use fit each task.
That distinction is important. The best AI users are not handing everything over blindly. They are applying judgment. They know when to use AI as a helper, when to use it as a collaborator, when to use agents for multi-step workflows, and when to keep a human firmly in control.
I still do not trust most AI workflows enough to leave them running with no maintenance, review, or quality assurance. The question I ask is simple: would I bet my brand, customer experience, or revenue on a fully automated workflow with no human oversight?
Klarna is a useful warning here. The company publicly promoted the idea that AI was doing the work of hundreds of agents and helping reduce headcount. Later, it reversed course and rehired humans after leadership acknowledged that aggressive cost-cutting had lowered quality and that customers still wanted a human option.
That is the tradeoff I see with substitution positioning. It creates immediate attention, but it can damage long-term credibility. The words often do not match the operational reality.
Replacement positioning could work if customers truly wanted full replacement and if the technology were consistently ready for it. I do not think either condition is true.
Cost reduction is a strong AI argument because it shows up quickly on the P&L. Productivity gains usually take longer. They build inside companies over time and often take even longer to appear across the broader economy.
But when replacement positioning goes beyond cost-cutting and becomes people-cutting, I believe it starts to antagonize the very people companies need to win over.
We have already seen backlash. Duolingo’s AI-first memo drew heavy criticism before the company reframed AI as a tool to accelerate work rather than replace contractors. Surveys have found that some workers refuse to use AI tools because they fear job loss. Pew has reported that many U.S. adults are more concerned than excited about AI in daily life. Reuters/Ipsos polling has shown widespread fear that AI will permanently displace workers.
There is also a quality problem. When employees believe the purpose of AI is to replace them, they may disengage or produce lower-quality work. In my view, that is not just an adoption issue. It is a positioning failure.
Executives often feel more excited about AI than the employees asked to use it every day. That gap matters. If leadership talks about AI as a replacement engine, employees hear a threat. If leadership talks about AI as leverage, employees have a reason to learn.
Token economics also complicate the replacement story. Some companies have bragged about massive AI usage, but token costs are still a real business variable. As those costs normalize, the math may make junior employees look interesting again, especially when human judgment, context, and accountability are part of the output.
So what should replace replacement? I think the answer is enhancement. Instead of positioning AI as a way to remove people, I would position it as a way to make capable people more effective.
AI can be used in two broad ways. A company can try to reduce the number of people, or it can grow output with the same number of people. The data I have seen suggests that productivity gains often create the stronger return.
A National Bureau of Economic Research paper surveyed 750 executives about AI’s impact on productivity and labor markets. Larger firms showed more interest in replacing labor costs, but the highest ROI came from productivity growth.
That is the lesson I take from the research: doing more with the talent you already have is often stronger than trying to remove the talent that knows what good work looks like.
Building products has become easier, but distribution has not. When supply explodes, the scarce thing is not output. The scarce thing is being the product, brand, or service that actually gets chosen.
That is why positioning matters more than ever. Product quality still matters, but the way I frame AI use can determine whether people see it as empowering or threatening.
My takeaway is simple: I would stop selling AI as a people replacement. I would sell it as judgment leverage, workflow acceleration, and creative expansion. Fear can get attention, but empowerment is a better long-term strategy.
This post first appeared on the author’s website and is republished here with permission.
Every so often, I see a product launch turn into a marketing lesson bigger than the product itself. Selena Gomez’s Rare Beauty did that with a new fragrance, but it was not only the scent that drew attention. The bottle became the story. Its accessible, easy-to-use packaging sparked conversation, earned praise from accessibility advocates, and reminded me how powerful inclusive design can be when it is built into the product from the start.
For me, the lesson is clear: accessibility is not a side note. It can become the campaign. One thoughtful design choice created cultural impact that would be hard to buy with media spend alone. It also showed why accessibility can build loyalty, strengthen brand reputation, support compliance, and drive measurable growth.
Accessibility as a campaign strategy
I do not see Rare Beauty’s accessibility work as a one-off moment. From packaging to pricing to its ongoing mental health advocacy, the brand has consistently made inclusivity part of its identity. That matters because consumers can usually tell when a brand is chasing attention versus when it is acting from a real strategy. They reward brands that lead with values and follow through.
Rare Beauty is not alone. I see leading brands across industries using accessibility as a differentiator, not a footnote. Apple often frames accessibility features as part of product innovation. Microsoft has brought inclusive design into mainstream campaigns, including adaptive gaming products that positioned accessibility as a source of creativity and connection. In fashion and retail, brands like Tommy Hilfiger and Unilever have put adaptive design into product launches and brand identity instead of treating it as a niche offering.
Studies from Edelman and McKinsey show why this shift matters. According to those studies, 73% of Gen Z choose to buy from brands they believe in, and 70% say they try to purchase products from companies they consider ethical. I do not see those as fringe preferences. I see them as mainstream expectations that should change how marketers build trust and growth.
The $18 trillion market marketers overlook
More than 1.3 billion people globally live with a disability. Together with their friends and family, they control more than $18 trillion in spending power, according to the Return on Disability Group. I believe marketers should view this as more than a compliance issue. It is a growth opportunity, a reputation opportunity, and a trust-building opportunity with one of the world’s largest and most passionate consumer groups.
That passion often turns into advocacy. In discussions with AudioEye’s A11iance Team, a group of individuals with disabilities who regularly share feedback on real-world accessibility experiences, one member said, “If I find a website that works and works very well for me, I will always recommend it to friends and family because I want people to have the same experience that I have.”
Another A11iance Team member, Maxwell Ivey, put it this way: “The cheapest form of advertising is word of mouth, and people with disabilities can have some of the loudest voices when we find people willing to make the effort. Because it’s that sincere effort over time that really counts with us.”
When accessibility becomes part of the customer experience, I see it create something media budgets cannot easily buy: trust and loyalty that scale through advocacy. But the reverse is also true. In a survey of assistive technology users, 54% said they do not feel eCommerce companies care about earning their business.
That should get every marketer’s attention. Too many brands are still fighting for the same crowded audience segments while overlooking a major opportunity in plain sight. When they do, they leave loyalty, advocacy, and revenue on the table.
Here is where I see many brands stumble: accessibility often stops at the shelf. Marketers invest heavily in packaging, store displays, and product design, while digital experiences lag behind. Yet those digital experiences are often the first and most important touchpoints customers have with a brand.
As accessibility-led design earns more attention, loyalty, and earned media, the gap between physical product innovation and digital experience becomes harder to ignore.
AudioEye’s 2025 Digital Accessibility Index found an average of 297 accessibility issues per web page detectable by automation alone. Each issue can create friction in the customer journey, cost a conversion, or introduce compliance risk under frameworks such as the Americans with Disabilities Act (ADA) and the European Accessibility Act (EAA).
I would not launch a campaign without a brand review or a legal check. In the same way, I do not think any digital touchpoint should go live without an accessibility review.
Four moves marketing leaders can make
Too often, I see accessibility treated as a risk to manage instead of an advantage to use. The marketers who gain ground will be the ones who change that mindset. I would start with four practical moves.
1. Make accessibility your campaign hook
I would not hide accessibility in the fine print. I would lead with it. Brands like Rare Beauty have shown that inclusive design is the story. Build campaigns where accessibility is not an afterthought, but the differentiator that earns attention and loyalty.
2. Bake it into your brand system
Accessibility should not sit off to the side. I would make Web Content Accessibility Guidelines (WCAG) alignment part of the brand system, right alongside typography, logos, and tone of voice. When accessibility is documented and expected, it becomes easier to apply across every campaign.
3. Use data as your proof point
Marketers are storytellers, but numbers strengthen the story. I would track accessibility improvements such as fewer user-reported barriers, higher accessibility scores, stronger alt text, better color contrast, and more usable forms. Then I would connect those metrics to business outcomes like conversion, reach, and sentiment to show how accessibility drives ROI, not just compliance.
4. Protect accessibility like brand safety
I would treat accessibility with the same seriousness as brand safety. Every update, seasonal campaign, and product drop should be monitored for accessibility. Trust and reputation are too valuable to leave exposed.
The competitive advantage
Rare Beauty’s fragrance launch proved something important to me: when a brand leads with accessibility, the story can write itself. Loyalty builds more authentically, and momentum feels more natural because the value is real.
The larger opportunity is that many brands still do not see it. They continue to treat accessibility as a compliance checkbox when it can be a growth strategy.
For marketers, that is the wake-up call. Accessibility builds loyalty. It strengthens brand reputation. It supports compliance. And it can drive measurable growth across marketing efforts.
Rare Beauty showed how accessibility can capture attention at the shelf. Now I see the next opportunity clearly: making sure that same accessibility carries through online. When every touchpoint welcomes everyone, every campaign has a better chance to deliver its full impact.
