I’m tracking a growing Google Business Profile issue after several days of complaints from businesses that say reviews have disappeared from their local listings. Google has now confirmed that it is investigating the reports, and in some cases, review submissions on affected profiles appear to be paused.
What Google said. Google told us that when its systems detect suspicious review activity, it may take several actions, including removing reviews and temporarily pausing reviews on a profile to prevent further abuse. Google also said it is investigating the issue and will restore any reviews that were incorrectly removed.
What I’m seeing. As I documented on the Search Engine Roundtable, there are dozens of complaints in the Google Business Profile Forums from business owners and local SEOs who say their reviews have mysteriously vanished. In some cases, businesses are also unable to receive new reviews on their local listings.
From what I can tell, Google’s review spam detection systems may be identifying certain patterns and aggressively removing or blocking reviews on suspected Google Business Profiles. What remains unclear is whether this is tied to spammers abusing some profiles, a recent algorithmic adjustment, or Google’s systems becoming overly sensitive.
More details. Amy Toman, a volunteer Google Product Expert for Google Business Profiles, shared on LinkedIn that businesses or clients affected by this issue can post in the forum if they want to, but Google is already aware of the problem and working on it. She also noted that no timeline for a resolution has been provided yet.
She said she is seeing a new pattern where, after fake or spam reviews are reported, some Google listings receive a review block and all reviews are hidden. In at least one case, she said the rating was reduced to 0.
Why I care. If I noticed a sudden drop in reviews or stopped receiving new reviews this week, I would consider this issue a likely explanation. For local businesses, reviews can directly affect trust, visibility, and customer decisions, so even a temporary review disruption can be frustrating.
Google is investigating, and I’m watching to see whether missing reviews are restored and whether affected Google Business Profiles can begin receiving new reviews again.
I see vibe coding as one of the most accessible ways to create small pieces of software with AI tools like ChatGPT, Cursor, Replit, and Gemini. Instead of writing code line by line, I describe what I want in plain language, receive working code in return, paste it into an environment like Google Colab, run it, and test the result.
In this guide, I’ll explain how I approach vibe coding, where I think it works well, where it breaks down, and which SEO examples can inspire practical projects of your own.
Vibe coding variations
I use “vibe coding” as a broad term, but it helps to separate it from nearby approaches:
Type
Description
Tools
AI-assisted coding
AI helps write, refactor, explain, or debug code. I usually associate this with developers or engineers who already understand the systems they are building.
GitHub Copilot, Cursor, Claude, Google AI Studio
Vibe coding
AI handles most of the work after I provide the idea or prompt.
ChatGPT, Replit, Gemini, Google AI Studio
No-code platforms
Platforms handle what I ask for through visual interfaces, drag-and-drop workflows, or background automation. Many now use AI, but they existed before AI became mainstream.
Notion, Zapier, Wix
For this guide, I’m focusing only on vibe coding.
The barrier to entry is low. In most cases, I only need a ChatGPT account, free or paid, and access to a Google account. Depending on the project, I may also need API access or subscriptions to SEO tools such as Semrush or Screaming Frog.
I also like to set expectations early: by the end of this kind of workflow, I’m usually aiming to run a small program in the cloud. If I want to build a SaaS product or software I plan to sell, I treat AI-assisted coding as the more realistic path because it usually requires more technical knowledge, more testing, and more budget.
Vibe coding use cases
I find vibe coding most useful when I’m working with clear buckets of data and need a helpful outcome, not a perfect one. That might mean finding related internal links, adding pre-selected tags to articles, comparing groups of URLs, or building something playful where the output does not need to be exact.
For example, I built an app that creates a daily drawing for my daughter. I type a short phrase about something she told me, such as “I had carrot cake at daycare.” The app uses examples of drawing styles I like and a few pictures of her, then generates a drawing as the final output.
When I ask for precise changes, the tool often gets worse. I once asked it to remove a mustache, and instead it recolored the image. That is exactly the kind of limitation I expect with this approach.
If my daughter were a client reviewing every detail, I would need someone with Photoshop or similar skills to make exact edits. For this use case, though, the result is good enough, and that is where vibe coding shines.
I would be cautious about building commercial applications solely through vibe coding. Some companies may even need vibe coding cleaners to clean up AI-generated work. But for demos, MVPs, internal tools, and quick experiments, I see vibe coding as a useful shortcut.
How I create SEO tools with vibe coding
When I create an SEO tool with vibe coding, I usually follow three steps:
I write a prompt describing the code I need.
I paste the code into a tool such as Google Colab.
I run the code and check whether the results match what I expected.
Here’s a real prompt example from a tool I built to map related links at scale. After crawling a website with Screaming Frog and extracting vector embeddings through the crawler’s OpenAI integration, I vibe coded a tool to compare topical distance between the vectors for each URL.
This is exactly what I wrote in ChatGPT:
I need a Google Colab code that will use OpenAI to:
Check the vector embeddings existing in column C. Use cosine similarity to match with two suggestions from each locale (locale identified in Column A).
The goal is to find which pages from each locale are the most similar to each other, so we can add hreflang between these pages.
I’ll upload a CSV with these columns and expect a CSV in return with the answers.
After ChatGPT generated the code, I pasted it into Google Colab, which is a free Jupyter Notebook environment for running Python in a browser. I then used “Run all” to test whether the program produced the output I wanted.
That is the clean version of the process. In practice, AI can make the workflow look perfect while still producing code that does not behave the way I need.
I expect issues along the way, and most of them are simple to troubleshoot if I keep the prompt and testing process clear.
First, I always state the platform I’m using. If I want code for Google Colab, I say that directly in the prompt.
Sometimes I still get code that depends on packages that are not installed. When that happens, I paste the error back into ChatGPT and ask it to fix the code or suggest an alternative. I do not need to fully understand the missing package to move forward. I can also ask Gemini inside Google Colab to identify the problem and update the code directly.
I also check outputs carefully because AI can sound confident while inventing data. One time, I forgot to specify that the source data would come from a CSV file, so the tool created fake URLs, traffic, and graphs. “It looks good” is not the same as “it is correct.”
If I connect to an API, especially a paid API from a provider such as Semrush, OpenAI, Google Cloud, or another platform, I need to request my own API key and keep usage costs in mind.
A Semrush subscription screen highlights 2 million Standard API units and a masked API key, underscoring the setup step needed for SEO automation and vibe-coded tools.
If I want an even lower execution barrier than Google Colab, I can use Replit.
With Replit, I can prompt what I want, and the platform can generate the code, design the interface, and let me test everything in one place. That reduces copy-and-paste work and gives me a shareable URL quickly. I still need to review poor outputs and keep iterating until the app behaves properly.
The tradeoff is cost. Google Colab is free unless I use paid API keys, while Replit charges a monthly subscription and usage-based API fees. The more the app runs, the more expensive it can become.
SEO vibe-coded tools that inspire me
Google Colab is the easiest place for me to start, but SEOs are taking vibe coding much further. I’ve seen people create Chrome extensions, Google Sheets automations, and even browser games.
I’m sharing these examples because they show what is possible when useful SEO ideas meet practical AI tooling. If I see a tool and wish it had a different feature, that is often a sign that I could try building a version for myself.
A Replit vibe-coding session turns a simple prompt for toddler-friendly daily jokes into a published web app, illustrating how AI tools can quickly prototype playful ideas.
GBP Reviews Sentiment Analyzer by Celeste Gonzalez
After vibe coding SEO tools in Google Colab, Celeste Gonzalez, Director of SEO Testing at RicketyRoo Inc, pushed the idea further by creating a Chrome extension. “I realized that I don’t need to build something big, just something useful,” she explained.
Her extension, the GBP Reviews Sentiment Analyzer, summarizes sentiment analysis from reviews over the last 30 days and shows review velocity. It also exports the information to CSV and works on Google Maps and Google Business Profile pages.
Instead of relying only on ChatGPT, Celeste used Claude to create stronger prompts and Cursor to turn those prompts into code.
AI tools used: Claude (Sunner 4.5 model) and Cursor
APIs used: Google Business Profile API (free)
A vibe-coded GBP Sentiment Analyzer turns review data into a quick snapshot, showing negative sentiment trends, key topics, and an export option for SEO workflows.
Platform hosting: Chrome Extension
Knowledge Panel Tracker by Gus Pelogia
I became obsessed with the Knowledge Graph in 2022, when I learned how to create and manage my own knowledge panel. Later, I discovered that Google’s Knowledge Graph Search API lets me check the confidence score for any entity.
That led me to build a vibe-coded tracker that checks entity scores daily, or at any frequency I choose, and returns the results in a Google Sheet. I can track multiple entities at once and add new ones whenever I need to.
The Knowledge Panel Tracker runs entirely in Google Sheets, and the Knowledge Graph Search API is free to use. This guide explains how to create and run it in your own Google account, or you can see the spreadsheet here and update the API key under Extensions > App Scripts.
AI models used: ChatGPT 5.1
A spreadsheet-based Knowledge Panel Tracker turns entity searches into structured SEO data, comparing names, entity types, descriptions, and confidence scores at a glance.
APIs used: Google Knowledge Graph API (free)
Platform hosting: Google Sheets
Inbox Hero Game by Vince Nero
I also like the idea of vibe coding a link building asset. That is what Vince Nero from BuzzStream did with the Inbox Hero Game. The game asks players to use the keyboard to accept or reject a pitch within seconds, and it ends if they accept too many bad pitches.
Inbox Hero Game is more complex than running a small script in Google Colab, and it took Vince about 20 hours to build from scratch. “I learned you have to build things in pieces. Design the guy first, then the backgrounds, then one aspect of the game mechanics, etc.,” he said.
The game was built with HTML, CSS, and JavaScript. “I uploaded the files to GitHub to make it work. ChatGPT walked me through everything,” Vince explained.
A retro arcade-style Inbox Hero screen turns PR pitch triage into a fast keyboard game, challenging players to accept or reject emails before time runs out.
He also found that longer prompt threads became less useful over time, “to the point where [he’d] have to restart in a new chat.”
That became one of the hardest parts of the project. Vince would add a feature, such as a score, and ChatGPT would “guarantee” it had found the error, update the file, and still return the same problem.
In the end, Inbox Hero Game shows that it is possible to create a simple game without coding knowledge. It also shows where a developer becomes valuable when the goal shifts from “working prototype” to polished product.
AI models used: ChatGPT
APIs used: None
Platform hosting: Webpage
How I think about vibe coding with intent
I do not expect vibe coding to replace developers, and I do not think it should. What I do see is a practical way for SEOs to prototype ideas, automate repetitive tasks, and explore creative experiments without a heavy technical lift.
The key is realism. I use vibe coding where precision is not mission-critical, I validate outputs carefully, and I stay alert for the moment when a project grows beyond “good enough” and needs stronger technical support.
When I approach vibe coding thoughtfully, it becomes less about shipping perfect software and more about expanding what I can test. For internal tools, proofs of concept, and SEO side projects, the best results come from pairing curiosity with restraint.
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.
AI Overviews and Google AI Mode are increasingly shaping the discussions within the SEO community. In this evolving landscape, search is transitioning from a mere information retrieval tool to a powerful recommendation engine.
As a travel brand, this shifts the dynamics of online discovery. It’s no longer just about making your website understandable to search engines; it’s about ensuring AI systems recognize when to recommend your business.
How AI is Revolutionizing Travel Planning
Interacting with large language models (LLMs) has become a routine for many of us. We use them to structure conversations by project, creating folders for our upcoming trips and building on previous chats to refine our preferences and travel profiles.
This is a major shift from the conventional searching methods. Traditionally, we would start our travel plans with Google searches for terms like:
“Hotels in Porto”
“Things to do in Rome”
“Best restaurants in Barcelona”
Today, the process is much more conversational. Instead of a series of disjointed searches, I might open a new folder labeled “Summer 2026” in ChatGPT and begin with a broad question, gradually sculpting it into a complete itinerary.
“Where should I stay in Porto for a quiet weekend within walking distance of the historic center?”
“Which area of Rome is best for families with young children?”
These discussions naturally expand to include restaurant recommendations, tourist attractions, accommodation options, transportation tips, and more detailed daily plans.
When I ask my AI assistant these questions, I’m not looking for a list of websites. What I truly want is an insightful recommendation.
Impact of AI Overviews on Travel Search
AI Overviews gather data from multiple points to deliver highly curated recommendations instead of just a list of links. For this reason, trust, consistency, and context have become vital factors for online visibility.
A traveler might decide to book my hotel based on an AI-generated suggestion without even visiting the website. Instead, their next steps could include a branded search or a visit to a review platform where they might finalize their booking through an OTA.
To win over AI model recommendations, I need to precisely define my brand. It’s crucial for AI to be certain of who I am, what I offer, whom I serve, and the contexts in which my brand is relevant.
Selecting a primary category and maintaining a clear brand position are imperative. Investing in digital PR and securing mentions beyond my own website can help too. Being featured in travel articles on relevant topics can significantly boost visibility.
Moreover, ensuring that my business information is consistent, accurate, and easy to find across my website, Google Business Profile, TripAdvisor, OTA listings, and social media is essential.
Understanding the Role of Zero Click Visibility
The methods for evaluating search performance are evolving. While traditional SEO metrics will remain relevant, it’s important for travel marketers like myself to broaden how visibility is measured.
One critical error is viewing fewer clicks as a decrease in visibility.
A traveler might learn about my property through an AI response and then decide to search for it later or visit a review profile on a platform like TripAdvisor.
That’s why seeing growth in branded searches is a promising sign of AI visibility. Monitoring AI mentions, citations, and assisted conversions is also worthwhile.
Assisted conversions highlight the channels and touchpoints that lead to bookings, even if they aren’t the final source of conversion. I can track these in Google Analytics 4 by navigating to Advertising > Attribution > Conversion Paths and Attribution Reports.
Leveraging TripAdvisor and OTA Listings
Platforms like TripAdvisor have grown beyond being review sites, and OTAs offer more than just booking services.
When someone requests AI recommendations, the system doesn’t rely on a single data point but synthesizes information from multiple avenues.
My website forms a part of this ecosystem.
AI builds confidence in its guidance by cross-referencing data across different platforms. What others say about my brand through reviews, travel guides, media references, OTA listings, or local mentions is increasingly significant. It’s large-scale reputation management.
This additional context helps AI identify when my property is relevant to specific traveler needs, like:
Family-friendly environments.
Ideal for business travelers.
Located in walk-friendly areas.
Renowned for exquisite dining.
Suitable for luxury or budget travel.
Distinguishing My Travel Brand
For example, if I manage a family-friendly hotel, it’s important to highlight features like family suites, kids’ activities, and family-oriented reviews. Alternatively, a romantic destination should emphasize aspects like cozy atmospheres, spa facilities, and exclusive packages.
Similarly, a hotel catering to business travelers should spotlight meeting rooms, workspaces, high-speed internet, and its proximity to business hubs. On the other hand, a restaurant known for its culinary excellence should consistently be mentioned in reviews, receive media attention, and third-party accolades focusing on its food quality, head chef, or dining experience.
While some businesses naturally fit various categories, having a clear primary positioning helps generative search engines easily identify when my brand is appropriate for a recommendation.
This principle holds for travel destinations too. AI-driven engines depend on signals from reviews, travel guides, local listings, and related content when suggesting where tourists should stay, visit, or explore.
Strengthening Entity Signals Across Platforms
As AI systems place more focus on entities instead of individual web pages, I must create a robust and consistent digital presence.
1. Clarifying Attributes with Structured Data
Structured data aids search engines and AI in interpreting key business details. For travel entities like mine, this includes lodging types, amenities, locations, and more.
Emphasize the attributes that truly set my property apart. This can span from family-friendly amenities to wellness-centered experiences, renowned dining options, pet-friendliness, or proximity to major landmarks.
The clearer and more structured my information is, the better the chances AI-powered experiences will spotlight my business in relevant recommendations.
2. Resolving Entity Ambiguities
It’s crucial to review third-party portrayals of my brand. Inconsistencies can diminish the trust AI systems have in my brand information, as AI pulls data from various sources.
Think of a hotel with differing phone numbers, outdated details, varying categories, or conflicting amenity information across platforms—these inconsistencies confuse AI systems.
Ensuring my business data is consistent across my website, Google Business Profile, TripAdvisor listings, and OTA profiles will reduce ambiguity and strengthen AI’s confidence.
3. Prioritizing Operational Information
Start by evaluating existing customer reviews.
What did they enjoy most during their visit?
What made their stay memorable?
What areas need improvement?
Such feedback provides insight into what genuinely differentiates my brand. Details about amenities, accessibility features, business hours, parking, and pet policies help AI address specific travel-related queries with confidence.
Google Business Profile is another vital source for operational data. The categories, attributes, amenities, and working hours mentioned on the profile enhance AI’s ability to answer travel queries accurately and helpfully.
To provide further context, I can also use Google Business Profile to publish posts that link back to my site’s content. Consistently posting on Google Business Profile can boost engagement, increase profile visits, and encourage customer interaction, ensuring my listing remains updated with fresh content about my offerings.
Cultivating AI-Trusted Signals
Generative search levels the playing field more than traditional search. AI favors recommending businesses, not just their websites. Visibility isn’t solely determined by what transpires on my site; it encompasses the comprehensive digital footprint that my brand projects.
For travel brands, this means I must think broader than just rankings and clicks. Reviews, OTA listings, travel guides, media mentions, and business profiles all contribute to how AI recognizes and recommends my brand.
It’s time to get creative, try new approaches, and collaborate with complementary businesses. Most crucially, it’s time to build the trust signals that AI systems rely on.
I’ve noticed a fascinating shift in Google’s Ask Maps function—it’s transitioning from simple listings to offering more personalized recommendations. This change is not just about showcasing local businesses anymore; it’s about truly understanding user needs and suggesting the best options.
The other day, I dug into some local service queries—think plumbers, electricians, HVAC services—and was amazed to find how Ask Maps narrows down options by user intent. It’s evaluating businesses based on factors like responsiveness and specialization, which feels fresh and user-focused.
What’s even more exciting is how Ask Maps frames these businesses. It’s not just a list; there’s guidance involved, which is a leap beyond traditional local retrieval methods. So, I decided to explore this by testing across five levels of local intent, ranging from simple searches to detailed conversational prompts.
As the complexity of queries increased, I saw a clear pattern: Ask Maps shifted from merely listing businesses to interpreting which ones truly fit the ask—and why. This is huge.
This exploration pulled insights from specific locality tests, so while it’s directional, it’s not exhaustive across all markets or queries.
The five-level intent model I developed was based on what I’ve learned about how people search for local services. I structured these not by traditional keyword categories but from simple inquiries to complex, conversational decision-making.
At the basic level, requests start simple, like “I’m looking for an HVAC company nearby.”
Then, I experimented with queries involving more service specifics, like “I need an electrician to upgrade my panel in an older home.” This was fascinating as it introduced nuances into what I look for in search results.
The most interesting insights emerged from situational queries and those involving trust or decision-making, revealing how Ask Maps balances offering a realistic number of options with the depth of interpretation. The shifts were consistent: as we went from simple prompts to narratives, Ask Maps fine-tuned business selection and added layers of explanation.
From this testing, I realized the intricate way Ask Maps processes information—using Google Business Profiles, reviews, and even external sources. While reviews dominated initial impressions, Ask Maps dives deeper on complex queries, pulling from business websites and informative content to guide users through decisions.
Overall, the direction Ask Maps is heading could redefine our local search approach. If it continues evolving, it might influence how visibility is determined—not just by listing presence but by the ability to comprehensively understand and meet the user’s needs.
Your local business rankings might be suffering, and surprisingly, it could all be due to your map pin. Google’s placement of your business on their map significantly impacts your visibility, and addressing hidden addresses and setup issues is crucial.
I’ve often found myself engrossed in the ongoing debate within the local SEO community about the ‘hide address’ toggle for service area businesses (SABs). Many business owners consider this option a mere privacy setting, but it’s much more—a decision that affects how Google’s algorithm perceives your physical relevance.
Here are some questions to consider:
Does your defined service area affect your ranking?
Does hiding your street address impact your visibility in the local pack?
Is Google erasing that data, or does your map pin become an invisible anchor?
These are foundational questions in understanding how proximity works when you choose to ‘hide’ on the map.
How Google Determines Your Map Pin
It’s essential to know that your address and map pin are not the same. Entering an address into your Google Business Profile doesn’t just place a pin; it’s processed through Google’s geocoding engine, comparing it against their database.
Understanding Google’s data models is key to understanding why your pin might be misplaced:
When Google finds a reliable match, they place your pin accurately at your building’s rooftop. Understanding how these data models work can help explain why SABs sometimes rank differently in local searches.
Is Your Map Pin Placement Accidental?
Don’t be mistaken, it’s not a bug but a failure in converting text to precise map coordinates. When this fails, your business may end up with a map pin that’s misplaced, affecting your local ranking authority.
When unable to secure a high-confidence match from your building, Google defaults to using the city’s center as your pin’s fallback location, often causing your business to rank from a less relevant area.
Suite Number Issues
I’ve warned clients countless times about the pitfalls of including suite numbers in Address line 1. These numbers aren’t street-level data; embedding them can lead to geocoding conflicts, making your map pin default to a broader location like a city center.
Properly Anchoring Your Map Pin
For accurate map pin placement, ensure your address in Google’s system is geocoding-friendly. Keep unnecessary details out of the first address line and verify how Google reads your address using their developer tools.
When addressing geocoding problems, prepare for possible re-verification requests. Stay consistent in your corrections until Google verifies your business’s precise location.
When I first heard the term “contact page,” my mind immediately envisioned a simple space filled with contact info and a form. However, it turns out that this is a major oversight from a local SEO standpoint. Let me guide you on crafting a contact page that not only elevates your Google prominence but also converts more leads.
Google pays special attention to your contact page
Joel Headley, the former head of Google Business Profile Support, once shared with me that Google actively crawls and interprets your contact page to extract details about your business. This revelation illuminated the common inadequacy of contact pages that simply display a business’s name, address, and phone number (NAP), coupled with a basic contact form.
Google is essentially requesting, “Provide me with your business data,” while you might be responding, “No data for you.” Instead, I encourage you to treat your contact page with the same importance as a multi-location landing page. Here’s what your contact page needs to transform visitors into paying clients:
Business identity.
Contact information.
Trust factors and social proof.
Location-specific content.
Amenities.
Call to action.
1. Business identity
Your contact page should be a reflection of your brand, just like every other page on your site. Here’s what to include:
Your business logo, matching all marketing materials and signage.
Your slogan, with potential keywords for SEO enhancement.
A concise introduction detailing your business’s function, location, and unique value proposition (UVP).
Your contact page isn’t just about providing contact avenues; it should convince visitors of their decision’s wisdom before they reach out.
Clear expectations
Clearly communicate what a customer can expect post-contact to solidify their choice to connect with you:
Expected response times.
Upcoming steps and confirmations from your team.
Additional useful information about your team, location, or differentiators.
Experience and credentials
Boost trust and conversion rates by displaying involvement in:
Industry associations, locally and nationally.
Chamber of commerce groups.
Professional organizations.
Meetup and neighborhood associations.
Better Business Bureau ratings.
Tip: Link association names to your business listing on their sites.
Awards and accomplishments
Include any awards and press mentions, with links to the relevant articles or sites. If there are many, consider a dedicated media section.
Reviews and testimonials
Embed external reviews and include testimonials to enhance trust. Enhance authenticity by showing reviewer photos, names, cities, and profiles.
Your review section is also an excellent place to request additional Google reviews, especially from repeat customers, using a link and call to action.
Review your Google Business Profile’s attributes and list those on your contact page, along with other unique attributes. This specificity aids traditional and AI searches in understanding if you meet distinct needs.
6. A clear CTA button
With a well-structured contact page, a compelling call to action (CTA) is essential. Use vibrant, eye-catching CTAs throughout the page to encourage engagements.
Treat your contact page like a local SEO asset
Your contact page should be seen as a local SEO asset. By investing effort similar to creating a multi-location landing page, you elevate your engagement and conversion rates, surpassing most competitors. Keep this list handy to ensure all necessary sections are covered.
I’ve noticed something puzzling in my local business performance lately. Despite high rankings, the number of calls and website visits from Google Business Profiles seems to be dropping at an alarming rate.
This disconnect is becoming increasingly common in local search. Rankings are stable, but visibility and customer engagement are not keeping pace.
The alligator of local SEO, if you will, has made its presence known.
The visibility crisis behind stable rankings
I’ve observed that across various U.S. industries, the familiar local 3-packs are often getting replaced or supplemented by AI-run local packs. These new formats differ significantly from the traditional map results many of us are used to optimizing.
According to Sterling Sky’s analysis of Google Business Profiles, a startling pattern emerges. Clicks-to-call are taking a nosedive, particularly for law firms managed by Jepto.
When AI-powered packs take over, the landscape changes notably in four key areas:
Shrinking real estate: AI packs frequently display only two businesses instead of the usual three.
Missing call buttons: The summaries generated by AI often omit the instant click-to-call functionality, complicating the customer’s journey.
Different businesses appear: Companies featured in AI packs do not necessarily align with those in the traditional 3-pack.
Accelerated monetization of local search: The presence of paid ads increasingly results in the loss of direct call and website buttons in traditional 3-packs, thereby reducing opportunities for organic conversion.
There’s an additional challenge compounding this issue:
Measurement blind spots: Most rank trackers have yet to account for AI local packs. A business may hold a top spot in a traditional 3-pack that users rarely encounter.
In 2026, AI local packs surfaced only 32% as many unique businesses as traditional map packs, according to Sterling Sky. Astonishingly, in 88% of the 322 markets examined, the total number of visible businesses plummeted.
Meanwhile, paid ads are steadily claiming the space that once belonged to organic results, marking a clear transition toward a pay-to-play environment in local search.
What Google Business Profile data shows
This trend is echoed in the U.S., where Google is proactively testing new local formats, as indicated by data from GMBapi.com. Increased impressions from traditional 3-packs are being nudged out by:
AI-powered local packs.
Paid placements inside traditional map packs: Sponsored listings now appear adjacent to or within the map pack, relegating organic results and removing essential call and website buttons. This interrupts organic customer interactions.
Expanded Google Ads units: Even Local Services Ads are consuming space that once granted organic visibility.
Impression trends continue to vary due to seasonal factors, market disparities, and occasional API glitches. Nevertheless, a clearer picture emerges by focusing on GBP actions rather than mere impressions.
Mentions within AI-generated results still count as impressions, even if they no longer convert into calls, clicks, or visits.
External factors, such as known Google API issues in June, also contribute to these fluctuations. Additionally, the spike in Google Ads investment by significant advertisers towards year-end heavily affects Mobile Maps impressions.
Currently, there’s no method to differentiate these impressions by Google Ads, organic results, or AI Mode.
Despite these challenges, user behavior is undeniably shifting. Interaction rates are dwindling, with fewer direct actions taken from local listings.
Year-on-year data from the U.S. indicates that while impression losses remain moderate and somewhat seasonal, GBP actions are disproportionately affected.
In contrast, data from the Dutch market, where SERP experiments are limited, shows far more stable action trends.
The evidence is clear. AI-driven SERP alterations, increasing Google Ads, and the removal of call and website buttons from the Map Pack are eroding organic real estate. Despite appearances, businesses have fewer opportunities to convert visibility into actual user actions.
Local SEO is becoming an eligibility problem
Traditionally, local optimization focused on key ranking factors like proximity, relevance, prominence, reviews, citations, and engagement.
There’s now an additional layer to consider: eligibility.
Some businesses find themselves absent in AI-powered local results not due to a lack of authority, but because Google’s systems deem them inadequate for the specific query context. Research from Yext and experiences shared by experts like Claudia Tomina emphasize the importance of aligning three core signals:
Business name
Primary category
Real-world services and positioning
Misalignment in these areas can prevent businesses from appearing in certain result types, regardless of how well their Google Business Profile is optimized.
How to future-proof local visibility
Navigating today’s zero-click reality involves moving beyond reliance solely on a well-optimized Google Business Profile. Here’s a new playbook for local SEO.
The eligibility gatekeeper
Inclusion in local packs is now influenced more by perceived relevance and classification than by links or review quantity.
Hyper-local entity authority
AI systems rely on platforms like Reddit, social media, forums, and local directories to evaluate if a business is legitimate and active. Inconsistencies across these platforms can erode visibility without any obvious signs.
Visual trust signals
High-quality and frequently updated photos, along with video, are critical. Google’s AI evaluates visual content to gauge services, intent, and categorization.
Embrace the pay-to-play reality
The hard truth is that Google Ads, particularly Local Services Ads, is now essential to retaining prominent call buttons that organic listings are steadily losing. Adopting a hybrid strategy that merges local SEO with paid search is no longer optional but necessary.
What this means for local search now
Local SEO has evolved beyond a simple directory exercise. Google Business Profiles remain central to local discoverability but now exist within a broader ecosystem informed by AI validation, constant SERP changes, and Google’s pursuit of local search monetization.
Visibility no longer depends solely on where your GBP ranks against local rivals. Search engines, including AI-infused SERP features and advanced models like ChatGPT and Gemini, are increasingly focused on understanding a business’s genuine purpose, not merely its listing position.
Success lies in being widely verified, consistently active, and contextually relevant within the AI-visible ecosystem.
Our findings reveal that there is little correlation between businesses ranking well in traditional Map Packs and those prioritized in Google’s AI-generated local answers. This discrepancy offers a real opportunity for businesses willing to adapt.
In essence, this entails blending local input with central management.
Authentic engagement across multiple channels, locally tailored content, and actual community signals are necessary alongside brand governance, data consistency, and operational scale. Businesses deeply ingrained in their community, discussed, recommended, and referenced, both online and offline, find themselves halfway there.
For agencies and brands with multiple locations, the challenge is balancing control with local nuances and ensuring trusted signals extend beyond Google, encompassing Apple Maps, Tripadvisor, Yelp, Reddit, and other pertinent review ecosystems. Producing locally relevant content and citations at scale without losing authenticity is the real test.
Even if rankings appear stable, true performance is occurring elsewhere.