http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-15 14:37:422026-09-15 14:37:42How Plastic Surgeons Can Get More Visibility in AI Search for Their City
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-15 14:28:592026-09-15 14:28:59How Can a LASIK Surgeon Become More Visible in AI Search and AI Recommendations?
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-15 14:21:062026-09-15 14:21:06How Orthopedic Surgeons Can Improve Their Local AI Visibility
Search isn’t shrinking, but its role in the purchase funnel is changing.
People are searching in fundamentally different ways, using cameras, conversations, and keywords to find what they want. At the same time, discovery is happening before many searches, as recommendation engines curate content and help people develop preferences before they ever type a query.
The searches marketers see may be less about discovering what to buy and more about confirming a decision that’s already taking shape. Those earlier stages are difficult to observe, so the activity that helped someone develop a preference can disappear from the purchase path.
A few weeks ago, I was on a walk in my neighborhood, enjoying a Pacific Northwest summer day. Along my usual route, I noticed a beautiful plant that I loved but didn’t recognize. Just a few years ago, I would have had to return home, type a clumsy description like “purple flower Northwest beautiful” into a search engine, and then scroll through dozens of pictures before finding the plant’s name.
This time, I simply pointed my phone camera at the plant, had Google Lens identify it for me, and then bought one at my local plant store the next day. No ad. No search bar. Just a camera and an answer.
Nothing about my experience is unusual anymore, and it should change how every digital marketer views search’s role in the purchase funnel.
The traditional funnel was often described as a straight line from awareness → interest → desire → action, with search activity doing the most work in the interest stage, when people were still figuring out what they wanted.
Now the funnel looks something like passive exposure → preference development → confirmation searches → purchase. Search is still a critical component of a buying decision, but it plays a smaller role in discovery.
That job has shifted to recommendation engines that can predict user intent and curate personalized content before the first query. By the time searches happen, they’re confirming a decision that’s already been made.
I see this play out in client accounts. The queries that are closest to purchase look more like caveman language, i.e., “brand name product model,” indicating that consumers know exactly what they’re looking for when it’s finally time to buy.
The catch here is that the first two stages of the new funnel are ones we can’t really observe. If someone watches a series of videos on social media, develops an interest in a product, and then conducts a Google search before purchase, that activity will appear to be a branded search conversion. The content that helped them form their decision never appears in the purchase path.
Consequently, most marketers still budget as if search does the discovery work because that’s what their data tells them.
Be the brand customers find first.
Track, grow, and measure your visibility across Google, AI search, social, local, and every channel that influences buying decisions.
Start your free trial
Examining YouTube’s unique position
As search plays a different role in the funnel, YouTube is taking on a larger role in discovery. Two things make it different: machine trust and human trust.
Machine trust: A study of over 100 million AI citations found that YouTube was the second most-cited social media platform, behind only Reddit. AI answer engines trust YouTube so much because of its massive library of content, including elements like video transcripts, which provide AI models with structured text they can display in answers.
Human trust: Consumers have more confidence in YouTube than other social platforms. Studies show that more than two-thirds of consumers find YouTube “somewhat or very trustworthy” when it comes to making purchase decisions.
Creators on YouTube also supply social proof, which now plays a critical role in purchasing decisions, particularly among younger buyers. Up to 70% of Gen Z follow an influencer, and 44% made a purchase based on an influencer’s recommendation.
YouTube enjoys the unique position of being highly trusted by both consumers and the machines making recommendations, and Google has taken steps to ensure the platform gets credit for its role in discovery. In January, it rolled out the Attributed Branded Search metric, which enables marketers to link YouTube ad exposure to a branded search within a specific timeframe.
ABS only captures exposure to YouTube advertising. The organic layer featuring review videos, product unboxings, and comparison videos remains invisible. If the measurable portion is driving tangible results, imagine what the rest of the channel is doing.
This huge invisible discovery layer extends well beyond YouTube. Much of that discovery happens outside the channels brands control.
AI tools don’t repeat your version of the brand. They repeat what the entire web says about you. Earned media drove 84% of AI citations, according to a Muck Rack review of 25 million ChatGPT, Claude, and Gemini responses across multiple industries.
Those third-party mentions, YouTube review videos, and Reddit threads form the raw material for the AI answers and feed recommendations that drive today’s purchasing decisions.
The practical outcome of this shift is that brand reputation management and search visibility have merged into a single role.
Brands still have an enormous role to play in driving discovery. However, those activities must extend beyond paid advertising. Here are three ways you can influence discovery:
Build a video library that answers questions about your product and your brand regardless of whether you’re running YouTube ads. These will become a database AI answers can draw from.
Data health has become a non-negotiable. The algorithm only knows what you tell it. Consequently, your product feeds should provide the algorithms with as much information as possible. It’s also critical to build brand consistency across the channels you control.
Show up in conversations you don’t own. Reddit threads involving your brand are feeding AI Overviews whether you participate or not.
Providing consistent and complete information across channels will give algorithms and AI tools something reliable to work with.
Don’t abandon search
A reshaped funnel shouldn’t be the reason you pull back from search. Google query volume reached an all-time high in early 2026 even as search’s position in the funnel changed.
The mistake won’t come from overinvesting in search. Instead, the mistake will come from expecting search to accomplish something it’s no longer suited for.
Rather than retreating from search, this is an opportunity to reimagine your marketing mix within a new framework. If search now occupies a different funnel position, how can you allocate your marketing resources to cover the upper-funnel work that still must be done?
The answer to this question will be different for every marketing team, but it’s a conversation that everyone should be having right now.
Search behavior won’t stand still
A few years ago, I never would have imagined that I could search for a plant’s name with a camera built into my phone, much less have a back-and-forth conversation with my computer. But here we are.
Marketers are working on the cutting edge, watching new technology change user behavior right before our eyes. Everything could shift again by this time next year. Nobody knows for sure. But the larger trend is clear: people are increasingly searching to confirm what they’ve already decided.
After working on my hreflang XML sitemap project with Gemini, my company decided to invest in paid Claude accounts. I took the opportunity to see what else Claude could do through its desktop app and found some great uses for it.
Here are five ways I’ve been using it in my everyday SEO work.
1. Implement a daily briefing
The first thing I set up was a daily intelligence brief. Using lessons learned from other AI tools, I crafted a detailed outline of the types of news I wanted to see, including industry updates, competitor news, mergers, and acquisitions.
The key to getting more useful information and less fluff is to be specific and use the output as a guide for improvement. Don’t accept schlock — you’re paying for the tool, so teach it to do what you need.
For instance, I have Gemini checking atmospheric conditions for radio wave propagation in my amateur radio hobby, and it took several adjustments to get useful output.
See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.
See your AI visibility
2. Connect to data sources for faster analysis
Yes, I can dig into Google Analytics and analyze the data, but how much faster would it be if I could just ask Claude to look at the data and answer questions for me? It turns out it’s much faster.
3. Create a better and faster hreflang XML sitemap
I decided to see how Claude would tackle the hreflang XML sitemap project. Like I did in my Gemini example, I told it what I wanted, fed it the latest versions of the sitemaps, and asked how it would approach the problem.
Claude surprised me by asking only for links to XML sitemaps as input. Using its own resources, Claude gathered all the pages and built the sitemap for me without any further intervention.
I double-checked the output and found it to be excellent on the first go, avoiding the need for the back and forth I experienced with Gemini — a huge time saver.
Our team supports three main businesses across several regions. This requires content updates to be replicated across websites, translated, and localized as appropriate. We need to track this work as it goes through the pipeline so nothing falls through the cracks.
This is where Claude has been a tremendous benefit. I took an example of content that had been generated, vetted, and approved by one of our in-house experts and decided to see if Claude could handle automating some of the international SEO process. The results were far beyond my expectations.
As usual, beginning with the end in mind, I uploaded examples of previous translation and localization work to show what I needed done. Next, I uploaded a new item and gave it specific instructions:
Look at the content and identify the page on the main .com website to understand the service and its context.
Check the regional websites and determine if that same service is offered.
If it is, create a translated/regionalized version using our standard template.
Create tasks in our workflow tracker (we use Asana) and space out due dates according to a standard formula.
Upload the content docs to the created task.
I was pleasantly surprised by how quickly Claude was able to do this. It accomplished everything I needed except uploading the document to the ticket. The translations were on par with what we were getting with Google Translate, as verified by our in-country experts.
While setting up a new WordPress website for one of our businesses, I found that one of the plugins we were using hadn’t been updated in several years and had known security issues. Thankfully, other security measures in place significantly mitigated the risk, but it’s always better to fix the problem than rely on other means.
I found a suitable substitute and thought about how long it would take my team and me to go through the 20+ websites we support and update the plugins. Even if it only took an hour per website, that’s more than 20 hours of dedicated, hands-on, fingers-to-keys work.
Before starting the process, I asked Claude if it could do it for me. Again, I was pleasantly surprised.
Using Claude’s Chrome extension, I logged into a staging environment for one of our smaller websites and told Claude what to do. It found all the places the plugin was being used, swapped it out with the new plugin, and did a quick visual check. There was one small discrepancy with the first attempt, which was quickly fixed when I pointed out the problem to Claude.
“Would you like me to check the other plugins for any security issues?” it asked when completed. Of course I would. It found an additional plugin that was unused and no longer supported. That was quickly deactivated and deleted.
My only time requirement was about five minutes per site of hands-on work as I:
Pulled a copy of each production website from staging.
Turned Claude loose.
Visually checked URLs that were updated.
Pushed the newly updated staging version to production.
Everything went on in the background as I worked on other tasks, and I didn’t have to get anyone else from my team involved.
I bet you’d be willing to trade getting the same result from five minutes of work vs. an hour any day of the week.
If AI can’t find you, customers won’t either.
Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.
See your AI visibility
How to work effectively with AI
AI tools can feel overwhelming, but they’re an incredibly useful asset when used properly. Follow these practical guidelines when implementing AI, and you’ll be blazing through those tedious SEO tasks in no time.
Be the strategist and let AI be the doer
Don’t just demand a final product. Discuss the architecture, the edge cases, and the logic first, and be specific. Treat the AI like an intern by showing examples of final products when possible.
Use the proper tool for the proper job
Not all AIs are alike, and some are better at some tasks than others. Read, learn, and experiment. There are a lot of us out there using AI tools, and people have found some amazing results using one platform that completely failed on another.
Check, double-check, and correct when needed
Much like embracing the iterative process I referred to in my last article about using AI tools, don’t be afraid to check thoroughly and correct when needed.
The reality that AI is really still in its infancy becomes readily apparent when it fails. You may have to spoon-feed information at first to get the result you want. Remember, you don’t have to be polite — be direct and clear for faster results.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/09/Claude-for-SEO-5-ways-to-automate-repetitive-work-IYTNbz.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-15 13:00:002026-09-15 13:00:00Claude for SEO: 5 ways to automate repetitive work
AI Overviews reshaped the search results page, yet most paid search playbooks still run as if nothing happened.
You still hear the same default rule: bid up where performance looks strong, bid down where it looks weak. That assumes every query is the same kind of opportunity, a small auction for the same kind of click. In B2B and industrial accounts, where sales cycles run for months, conversion data is thin, and much of the demand is informational, that assumption falls apart.
When Google answers a question directly on the results page, the value of a paid click varies across your keywords.
Some queries still lead to a real commercial decision, and winning the top spot above the answer is worth paying for.
Others get resolved inside the AI answer itself, so the goal shifts from buying a click to influencing the answer and building a brand strong enough to be included.
A third group has moved so far toward zero-click that paid search on its own can no longer rebuild the journey.
Those are three different problems, and a single bid lever can’t solve all three. Trying to is how accounts spend budget on the wrong surface. I separate them into three buckets: fight, influence, and generate demand.
The framework: Fight, influence, and generate demand
Each bucket describes a different mechanism on the results page, so each one calls for a different approach.
Bucket 1: Fight
Fight is for bottom-of-funnel queries where intent is clearly commercial, and a click still turns into direct leads and sales: product + modifier, supplier shortlists, “buy,” “quote,” “distributor,” and brand terms combined with buying intent. Here, the job hasn’t changed much. You want to win the auction that sits above the answer, and you want the ad itself to answer the buying question.
This is where paid competition concentrates, and the data show it. The likelihood of an ad appearing rose steadily with cost per click, according to a July SE Ranking study of commercial queries in Google’s U.S. AI Mode:
About 54% at $10 or more.
Around 24% for keywords under $2.
32% between $2 and $10.
AI Mode isn’t the same surface as AI Overviews, but what they share is the pattern: paid inventory follows commercial weight. Where the market already pays more for a click, AI-era ad surfaces appear more often. You rarely compete alone, since about seven in 10 answers with an ad in that study showed two advertisers in the block.
Fight is expensive for a reason, so treat it that way.
Reserve the premium for terms where winning absolute top still beats your incremental cost per acquisition. Use bid simulators to estimate the cost of that jump in position first.
Watch Absolute Top impression share and impression share lost to rank, because that’s how you see the AI block pushing you down the page.
What you shouldn’t do is pour Fight budget into informational queries the Overview has already answered.
See exactly how your competitors win.
Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.
Analyze your competitors
Bucket 2: Influence
Influence is for queries where the Overview, or a longer conversational answer, does the educating. Very often, the user won’t click.
The goal here isn’t to gain a cheap visit anymore. It’s to be part of how the answer gets built: cited as a source or presented as one of the brands named inside the answer.
As Shashi Thakur put it at Google Marketing Live EMEA 2026, the best ads must be answers. In an Influence query, your ad brief starts from the question the user is really asking, rather than from a keyword list, and it means accepting that SEO and PPC address the same challenge, not competing against each other.
In B2B and industrial accounts, the queries that trigger AI answer blocks are still mostly informational. In one of our accounts, the intent split of the terms surfacing these blocks looked like this:
Intent
Share
Informational
83.3%
Learn and solve
10.0%
Commercial
6.7%
For “learn and solve” and some “informational” terms, plan for share of the answer instead of maximum CPC.
There is a budget consequence worth stating plainly. On influence queries, we work very closely with SEO, and we’re usually not interested in paying to appear where organic is already winning. Being visible twice, in paid and in the answer, barely happens anyway.
When the data pushes us to move money into the Fight and Generate demand buckets, we deliberately avoid being aggressive on terms where SEO already earns the visibility. That keeps paid spend pointed at the gaps we’re interested in covering.
Bucket 3: Generate demand
Generate demand is for queries where zero-click has already won and paid search alone won’t rebuild the top of the funnel. Informational demand that used to create assisted journeys now often ends on Google, with no visit to anyone.
You have two options. You can:
Keep bidding while your CPCs climb and volume falls.
Fund the work that happens before the search: Demand Gen, YouTube, first-party content, and the review and community presence that shows up later when the same person searches with commercial intent.
This work is about recognizing that some keywords have stopped being capture channels and have become a signal of a demand gap. The capture still happens later, in Fight, once the person is ready to act.
Generate demand fills the pool that Fight then fishes from. Measured only on last-click Search ROAS, this work always looks weak because its payoff appears as brand searches and better-qualified pipeline weeks later.
Bucket
The job
The paid response
Fight
Clear purchase intent, where the click still feeds the sales pipeline directly
Pay for absolute top when your brand isn’t already winning the answer box
Influence
Top and mid-funnel, where the answer shapes consideration
Win share of the answer through citation and SEO coordination, not raw CPC
Generate demand
Zero-click is winning and Search alone can’t rebuild the funnel
Build demand before the search, through Demand Gen, YouTube, and awareness
AI Overviews sit at the top of the page, sharing space that used to belong to ads and organic results. The answer often satisfies the user before they scroll, changing the economics of paid search in three ways.
1. Click-through falls on informational and mid-funnel queries
When users get a usable answer without leaving Google, both organic and paid lose clicks. The effect is stronger when your brand isn’t mentioned in the AI Overview.
The clicks that remain tend to be better qualified, but the drop in volume usually exceeds the lift in quality, so total conversions decline.
2. Cost per click goes up
There are fewer premium positions at the top. More advertisers are shifting their budgets to paid search to offset lost visibility.
The pressure is strongest in B2B and services, and on comparative queries such as “best X” or “X reviews.” Pure transactional queries hold up better because users still need to click through to buy or request a quote.
3. Position gets harder to read
There are often only a few useful slots above the AI Overview or AI Mode answer, and falling below the Overview can sharply reduce click-through, especially on mobile. Absolute Top impression share and impression share lost to rank become especially useful for understanding that pressure.
Underneath these changes is a shift in behavior. People can research and decide within a single answer experience, shortening the window to capture and persuade them. Intent still lives in your account, but the economics attached to each type of intent have changed. That’s why you should classify a query before you touch the bid.
The framework only pays off if your account can distinguish between valuable and low-value traffic, so two foundations come first.
One is semantic coherence: keyword, ad, and landing page as a single unit of meaning, with each ad group built around one clear concept. Mixing concepts teaches the system the wrong matches, and that gets worse the moment you hand control to broad match, Performance Max, or AI Max.
The other is conversion quality: primary conversions that reflect real business value, lead scoring, and offline or CRM signals wherever you can pass them back. Smart Bidding and the AI surfaces amplify whatever you give them, so thin data makes every bucket noisier.
With those in place, classification becomes a routine you can repeat:
Pull your search terms and map intent, separating clear commercial intent from research and from purely informational queries.
Use SEO data, Search Console, AI Overview Reports, plus manual SERP checks as a proxy for where Overviews appear and whether your brand is cited.
Sort each term into Fight, Influence, or Generate demand, and revisit the sorting regularly because the SERP keeps changing.
Step
Action
Data Sources
Output
1. Map intent
Pull search terms and classify by intent type.
Google Ads keywords and search terms (+ Search Console).
Check where AI Overviews appear and whether your brand is cited.
AI Overview reports, manual SERP checks.
Map of AI Overview presence by term.
3. Sort into buckets
Assign each term to a strategic bucket.
Intent mapping + AI presence data.
Fight, Influence, or Generate demand list.
4. Revisit Regularly
Re-sort terms as SERP landscape changes.
Ongoing SERP monitoring, AI Overview reports.
Update bucket assignments and review budget assigned to each.
On Fight terms, accept a position premium only where absolute top still wins your incremental cost per acquisition.
On Influence terms, align ad copy with the question the Overview answers, coordinate with SEO on content and citation, and pull back paid pressure where organic already wins.
On Generate demand terms, move budget into YouTube, Demand Gen or content when the Search clicks are gone but the query still matters and measure through brand search and assisted pipeline.
AI Max and broad match sit on top of this classification. They don’t replace it. Google needs broad match or keywordless campaigns for your ads to appear around AI answer surfaces, which expands reach while reducing control over what you show.
Use them where you already know the bucket and your tracking is clean, and introduce them through small, gradual tests.
See where competitors are investing, which keywords drive their results, and how to capture more of the market.
See who’s stealing your traffic
Put the budget where the opportunity is
Accounts that apply one rule to every query will keep funding the wrong mechanism, seeing it as rising costs they can’t explain. Accounts that re-segment and move money between buckets and into channels outside the auction spend less time arguing with a results page that has already answered the user.
In B2B and industrial search especially, shifting from optimizing bids to reallocating budget across the three buckets is the difference between defending last year’s setup and building for how people search now.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/09/AI-Overviews-and-paid-search-A-three-bucket-framework-qvUwZt.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-15 12:00:002026-09-15 12:00:00AI Overviews and paid search: When to fight, influence, or generate demand
It’s been a pretty crazy few weeks in AI safety. We’ve watched agents go off the rails and do things nobody expected them to do, in production, on other people’s systems. So it’s a reasonable moment to ask how you use AI safely when the thing it’s touching is a live ad account with real money moving through it every hour.
Almost every conversation I have about agents in ad accounts opens the same way.
Do you trust the AI?
While it’s a good question, it’s often asked to shirk responsibility and conclude that AI shouldn’t be used. A better question is: How can we trust AI?
Then the conversation turns to what we can build around the AI model to make it safe for our business to use.
To explain, it’s always helpful to frame it the way you’re already evaluating collaborators. Nobody asks whether they trust a new PPC agency in the abstract. They ask what the team has access to, what they’re allowed to change without checking first, and who reviews the work. Three different questions with three different answers, and you’d never accept “I simply trust them” as a substitute.
Same three questions for an agent:
What can it see? An agent working off a thin data layer will give you a confident answer built on a third of your account. It won’t tell you it’s guessing when it doesn’t know it’s guessing.
What is it allowed to do? Not what you told it to do. What it is structurally prevented from doing, no matter what anyone tells it.
Who signs off? Not “we review the change history afterward.” Who has to say yes before?
Most agentic PPC setups I look at have a decent answer to the first question, but there’s a lot of benefit in becoming more stringent with the answers for the other two.
We’ve been thinking about safe AI for PPC a lot at Optmyzr, so let’s take a look at what we’ve learned so you can get yourself to safer AI faster.
Each layer pays for itself on its own
The reason I’d rather write this as three techniques than as one system: you don’t have to do all three to get value. Each one closes a different failure mode, and each one is useful the day you turn it on.
Layer 1: Better grounding means fewer confidently wrong answers. That’s worth having whether you ever let an agent write anything. Most people should start here.
Layer 2: A policy layer means the changes that do get made stay inside limits you set. That holds whether the change came from an agent, from a script, or from a person having a bad Tuesday.
Layer 3: A review step means nothing reaches the account without someone seeing it first. And as a nice side benefit, you end up with a record of why. That’s worth having even if your data layer is thin and you have no policies at all.
Then these layers compound, which is the part I find genuinely satisfying. Grounding makes the agent’s proposals worth reviewing, so the review step feels like leverage instead of homework. Policies filter the obvious non-starters before a human ever sees them, so the queue stays short enough that people keep opening it. Each layer makes the next one work better than it would on its own.
So, if you’re starting from zero, start with grounding. It has the fastest payoff and the least process to stand up. If you’re already letting an agent make changes, add policies this week. If you have both, the review queue is what turns it from something you use into something your team uses.
Order matters less than accumulation. Any one of these leaves you better off than you were yesterday, and each one you add compounds with the last.
Layer 1: Ground it
A blind agent is a dangerous agent.
Ask an agent connected to a thin data layer why your CPA went up last month. It will answer you. Fluently. Immediately. Based on whatever slice of your account it could actually reach.
What it couldn’t see might have been the entire answer. It’ll make do with what it has.
Realize that grounding is a safety feature, not a convenience feature. Every gap in what an agent can see is a place where it will make something up, and it will do it in exactly the same confident tone as everything else it says. There is no tonal tell.
So here’s what I think you should expect from an MCP, or any data layer, that you’re going to let an agent reason over for PPC.
The full query layer for Google Ads. Real GAQL. Any resource, field, segment or metric the API exposes, including the ones no packaged report covers. Not a curated summary of what a product manager thought was interesting. The moment your data layer is a curated subset, you’ve limited which questions the agent can answer well, and you haven’t told anyone which ones those are.
GA4 sitting alongside the ads data. So “what happened after the click” is part of the same question, instead of a second tool and a manual join. A lot of the diagnosis questions people actually ask are cross-boundary questions. If the boundary is still there, the agent guesses across it.
Complete change history, every actor. UI edits, scripts, Optmyzr, other tools. So “who made the change that moved our ROAS in March” is a question with an answer, rather than a group chat.
Negative keywords consolidated across all four levels. Account level, shared lists, campaign, ad group, plus a deterministic check of whether a given query is already blocked and by which negative, and a list of campaigns sitting with no negative protection at all. Negatives are a common place to watch an agent reason confidently and wrongly, because the true state is scattered across four places and nobody assembles it.
Auction Insights, with a drill-down into one competitor domain, shows your own performance on every keyword you share with them. Competitive questions are the ones where a hallucination is hardest to catch, because you have no independent read on the answer.
Vertical benchmarks. Your CTR, CPC, conversion rate, and impression share as a percentile against other accounts in your industry, rather than against a blog post average from three years ago that everyone quotes and nobody sources.
Multiple ad platforms. Google, Microsoft, Meta, Amazon, LinkedIn, OpenAI, TikTok, Yahoo. Budget questions are rarely single-platform questions, even when the person asking works mostly in one.
A stored profile of each account. Business model, economic posture, bid strategy mix, structure, budget behavior, what’s already been tried and what happened when you tried it. The agent reads this before it opens its mouth.
Optmyzr’s MCP has all of the above today, and it’s a one-click install from the Claude directory rather than an API console, a developer token, or an engineer on speed dial.
Grounding raises the quality of everything the agent says. The next layer decides what it’s allowed to do about it.
Layer 2: Gate it
How do you stop an AI from blowing through a month of ad budget by mistake?
Not by asking it nicely. Not in the prompt.
You use a policy layer that controls what an AI can and cannot do, and you separate it from the AI itself, so the AI can never change the rules.
Set the rules once. Enforce them everywhere, always.
This is automation layering, which I’ve been writing about across my books for years, applied to a new first layer. The original idea is simple: one system does the work, like Google’s own bidding and budget automations, and a second system, your own automations, scripts or rule engine strategies, validates that what the first system did actually makes sense for your business before it sticks.
AI just took over the first job. It’s now the thing making the recommendation. The second layer didn’t become less necessary; it became more necessary because the first layer is more creative and sometimes unpredictable.
Account policies are how you write down the “never do this.” Rules you set once, on the account itself, about what is allowed to happen there. No bid increase above 10% in a single move. No budget change beyond a set threshold. These campaigns don’t get touched. No competitor brand terms added. Whatever your version of “absolutely not” happens to be.
And the policy doesn’t care who’s asking.
An agent proposing a 20% bid increase gets blocked. A hallucination gets blocked. An instruction hidden in a document gets blocked. A junior with a misplaced decimal gets blocked. You, at 11pm on a Friday, in a hurry, on your phone, get blocked.
Same rule, same verdict, no exemption for good intentions or seniority.
Want it through anyway? Override it deliberately. It goes on the record with your name attached. An override you can perform without noticing is not a guardrail but a speed bump made of paint.
This structural detail matters: this isn’t a setting for the AI. It’s a setting on the account, and the AI is one more thing subject to it. Same as a script. Same as a person.
What PPC account policies look like. Screenshot from Optmyzr.com, September 2026.
A rule that lives in the prompt is a rule the model can be talked out of by a clever user, by an injected instruction sitting in a document it was asked to read, or by its own drift over a long session. A rule that lives on the account holds under every path into the account.
Layer 3: Keep a human in the loop
Everyone says they’re in the loop.
Few people can tell you which screen, which person, or which queue enforces being in the loop. Ask, and you usually get “we check the change history afterward,” which means nobody’s checking.
And good luck getting a quick answer when someone asks you to produce the reasoning behind an AI-assisted decision from three months ago.
So when we built the write path into our MCP, we didn’t leave the loop to good intentions. There is no route from the agent to your ad account that doesn’t first stop at a human.
We took a pattern from engineering, where it’s been settled practice for decades: nobody pushes code to production without a change request that another person reviews. Why the account where you spend six figures a month deserves less process than a CSS tweak is a question our industry has never really answered.
Here’s the actual sequence.
The agent proposes. Every write, whether a bid, a budget, a paused campaign, or a new negative, becomes a draft change request. Nothing reaches the ad platform yet. The agent’s job ends at “here’s what I think you should do, and why.”
Policies evaluate it. Each row is checked against your account policies and carries its verdict with it, so a blocked row shows up as blocked, with the reason attached rather than in a log somewhere.
A human opens the review. The rows, the stated reasoning for each one, the policy warnings, a timeline of what happened when, and the list of people eligible to approve it.
You preview exactly what would go live. The exact deterministic changes, like your target ROAS for “Brand Campaign” will go from 200% to 220%, rather than a natural-language paraphrase of what the agent believes it’s about to do.
You confirm. Only then does anything change in the ads account.
What change requests look like in a PPC account. Screenshot from Optmyzr.com, September 2026.
Whether it’s an AI or a colleague suggesting the change, everything goes through the same pipe. In the case of a colleague, the change request is a second pair of eyes. In the case of an AI, it’s the first pair.
An audit trail for AI in PPC
The change request queue turned out to be more valuable than just the safety it was built for.
Because it’s not just a list you go to in order to be the human in the loop. It’s a complete record of intent.
When a client asks in November why their target CPA was moved in March, you have the proposal, the rationale, the policy verdicts, who approved it, and when. Was it a human? Was it an AI? What was the data behind the recommendation? Who made the final call?
Try assembling that from a chat transcript six months later. Try assembling it from change history, which tells you what changed but never why.
We built this for safety, and it turned into the best account documentation we’ve ever had. If you’re an agency, that’s a credibility argument as much as a safety one.
What good looks like
Put the three layers together, and you get something I’d describe, approvingly, as boring.
Not underpowered. Boring.
As in: you know what it can see, you know what it structurally cannot do, and you know nothing reaches the account without you. The excitement belongs in the findings, not in wondering what it got up to while you were at lunch.
That’s the bar I’d hold any agentic PPC setup to, ours included, and it’s a bar you climb one rung at a time:
It sees the whole account because gaps in what an agent can see are where it starts inventing, confidently.
It’s bounded by rules you wrote, which live on the account rather than in the prompt, and which apply to everyone identically.
It can’t act alone, because every write becomes a change request with policy verdicts attached, reviewers named, and a confirmation step no agent can fabricate.
You don’t have to arrive there in one move. Pick the layer that closes your biggest gap, ship it, then add the next one.
We’ve been building this against real accounts and genuinely stupid edge cases since well before MCP was a term marketers used. It’s a one-click install from the Claude directory now.
If you tried agentic PPC once, got a confidently wrong answer, and quietly shelved it, that’s the failure I’d most like you to come back and retest.
Having safe AI for our ad accounts can’t be something we expect to get just from picking the right model. It’s something we achieve by layering in processes and technologies we control.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/09/Optmyzr-20260915-feature-elqMEv.jpg?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-15 11:00:002026-09-15 11:00:003 ways to make AI safer in a live ad account by Optmyzr
A high-converting landing page drives one specific action. Every element on the page should serve that single goal.
The median landing page converts at 6.6 percent across all industries. Your target should reflect your vertical, since averages range from under 2 percent in ecommerce to over 10 percent in other industries.
Tools like Unbounce’s Smart Copy speed up copy iteration, and its Smart Traffic feature dynamically routes visitors to the variant most likely to convert. Human review is still what catches poor-quality output, however.
Cart abandonment averages over 70 percent industry-wide. Landing page sign-up and checkout flows suffer from similar friction.
High-converting landing pages follow six core formats: text-only, lead generation, click-through, long-form, video, and SEO. Each format serves different goals, so choose yours based on the action you want visitors to take.
Ever wondered why your landing page pulls in traffic but barely converts anyone?
You’re not alone.
The median landing page converts about 6.6 percent of visitors, and plenty of pages fall well below that.
The problem is rarely traffic or budget. It’s the page.
Effective landing pages follow a proven structure, with every element earning its place through testing. I’ll walk you through how that process works so you can stop guessing and start optimizing with a clear plan.
What Is a High-Converting Landing Page?
A high-converting landing page is a webpage that excels at getting visitors to take one specific action. That could be signing up for a newsletter, registering for a webinar, claiming a discount, making a purchase, or downloading your app.
Here’s an example from Revolut that directs visitors to download the digital banking company’s app:
A strong landing page turns visitors into new subscribers and fresh leads, which you can nurture through your sales funnel until they’re ready to buy.
Core elements include a compelling call to action (CTA), a sign-up form, an attention-grabbing headline, and subheadings. It also contains easy-to-read content, images, and perhaps a video.
High-priority elements like your sign-up form should go above the fold (the visible part of the webpage when you first see it) for maximum visibility. I’ll cover placement strategy in more detail later.
Types of High-Converting Landing Pages
High-converting landing pages come in several formats. Here are the ones worth knowing about.
Text-Only Landing Pages
These skip the videos and heavy graphics, relying on copy and a few supporting images to keep the focus on the message.
Copyblogger is a strong example that pairs minimal visuals with a clear CTA button.
Lead Generation Landing Pages
Lead generation landing pages collect details such as a name, email, and phone number. The idea is to turn prospects into leads by offering a free lead magnet, such as templates, a short course, eBooks, or access to a webinar.
Although the form is relatively extensive (with several additional fields out of view), it makes segmenting and targeting easier. Its basic but striking design keeps it clutter-free while relaying the essential information.
Click-Through Landing Pages
Click-through landing page conversions aim to get people to take an action, such as signing up for a freebie, requesting a product demo, registering for an event, or buying a product. Typical features include a clear CTA, an engaging headline, and magnetic content designed to persuade the reader.
Shopify uses an enticing discount offer for new users and features a single-field sign-up form and a simple yet tempting CTA button.
Long-Form Landing Pages
A long-copy landing page is common in the digital marketing industry. It’s a go-to format for showcasing the benefits of your products or services to close a sale.
These longer pages use strong headlines and subheadings, features and benefits, images, social proof (e.g., reviews), and CTAs throughout. They might also include case studies, trust badges, and a video chat option, as SuperOffice does.
Video Landing Pages
Video landing pages feature video as the primary content element.
This format works well for products or services that require setup or configuration. When you visit Slack, for example, you’ll find a video that walks you through what the messaging software does and how to use it.
Video also brings reach. Digital video has a worldwide audience of 3.88 billion, just over 63 percent of whom watch on smartphones.
Demand keeps growing, too.
A Wyzowl survey finds that 84 percent of consumers want more videos, and 96 percent have watched an explainer video to learn about a product or service.
Campaign vs. SEO Landing Pages
You can build landing pages for two broad purposes: long-term organic visibility or short-term campaign performance.
An SEO landing page targets organic traffic by answering specific search queries. These pages need ongoing optimization to maintain rankings.
Zendesk, for example, uses a clean, structured features page with short text paragraphs and updates it regularly as new product features roll out.
A campaign landing page supports a specific product launch or marketing push. It relies on paid advertising to drive targeted traffic and uses frequent A/B testing to find the best-performing version.
Here’s an example from Canva, which uses a campaign landing page to promote a free graphic design course and drive sign-ups through its Design School.
How to Create a High-Converting Landing Page
Here’s my five-step process to build a strong landing page.
1. Choose Your Tools
Several tools can help you start building landing pages:
Unbounce: A drag-and-drop landing page builder with no-code A/B testing, AI-powered copywriting, and real-time analytics. Free 14-day trial.
OptimizePress: A WordPress-based builder for conversion-optimized landing pages, funnels, and membership sites. 14-day money-back guarantee.
Instapage: A landing page platform for building, optimizing, and scaling personalized pages. Free 14-day trial on Create and Optimize plans.
Leadpages: A builder with optimized templates, lead magnet delivery, and built-in analytics. Free seven-day trial.
GetResponse: A marketing platform with a drag-and-drop landing page creator and AI content generator. Free 14-day trial.
PopUp Domination: A pop-up tool that adds email opt-in forms to your landing pages to capture more subscribers. Works with every web host and email provider. Free 14-day trial.
OptinMonster: A lead capture tool that adds pop-ups, slide-ins, and other opt-in forms to your pages to grow your email list. 14-day money-back guarantee.
2. Conduct Market Research
Every good landing page starts with a thorough understanding of your target market and customers, so you can create value and provide a desirable customer experience.
Research your target market to understand what your customers need, the language they use, and what might prevent them from converting. That might be price concerns or unclear value, for example.
From there, dig into keyword research to find the search terms your audience uses, including long-tail keywords that signal high intent. Run a competitive analysis to see what’s already ranking and where the content gaps are.
My free tool, Ubersuggest, can help with all of this. Here, it shows keyword ideas for “Starbucks” broken into suggestions, questions, and prepositions, each with search volume data.
These categories help you understand how your audience searches for a topic so you can match your landing page copy to real user intent.
3. Design With Conversions in Mind
A high-converting landing page brings together several design elements that work toward a single goal. Here’s what the anatomy looks like:
Some elements deserve extra attention:
Headline: It’s the first thing people read, so make it bold, clear, and benefit-driven. It also needs to be creative and to the point while creating a sense of urgency. Alternatively, use it to target specific customers and offer a solution to their problems.
Subheadlines: Take the same approach with subheadings and keep them short and direct. Ultimately, subheadings should reinforce the headline’s message and keep readers moving down the page.
Call-to-action button: Your CTA button needs to stand out visually. Use a contrasting color and keep the text simple, with wording like “Download now” or “Buy now.”
Videos and images: Showcase your product and feature testimonials with videos and imagery. Consider including a hero image (a large, eye-catching image that visitors will see first). Headshots also work well.
Trust badges: Awards, trade badges, company logos, and testimonials are trust indicators that help reduce hesitation for visitors who are close to converting.
Testing page variations is how you go from guessing to knowing what works. It’s how you know which version converts best so you can use the results to inform future landing page designs. My A/B testing introduction covers the fundamentals.
Start by testing one variable at a time:
Button colors or page backgrounds
Long versus short sign-up forms
Content order, images, or copy
Run each test for at least two weeks, then compare conversion and engagement rates to make data-driven decisions. Optimizely, Crazy Egg, and Hotjar all offer A/B testing tools. Check how your page renders across devices with MobileTest.me.
Here are some best practices for landing page optimization:
Use a clear user interface: For your landing page to work, ensure the user interface uses simple navigation and other elements, nudging users toward one clear action. The focus is on your visitor, not you. Align every element on your landing page to appeal to the end user. David Risley’s landing page for Blog Marketing Academy is a great example.
Use SEO: Optimize your landing page with keywords by performing keyword research and understanding search intent.
Keep a narrow focus as you optimize. The clearer and simpler your page, the more likely you’ll get someone to take the action you want.
Ryan McHugh, the director of CRO at NP Digital, puts it this way:
“One of the biggest mistakes site owners make when building landing pages is not having a clear, single focus for the page. Landing pages should have one primary objective, whether it’s capturing email addresses, getting sign-ups, or making a sale. When a page has too many distractions, including links, navigation options, or too much information, it can confuse visitors and decrease conversions. Keep the content blocks concise and relevant. Focus on a clear value proposition and be sure to place a consistent and action-oriented call to action prominently on your pages.”
A few ways to put that into practice:
Move non-essential information to the “About” section.
Keep forms short.
Make the CTA the focal point and place secondary links below the fold.
Design Best Practices
Your color palette, typography, and visual hierarchy all affect whether visitors convert or bounce. Here are the design details to get right.
Think about color psychology: Colors shape brand perception and influence buying decisions. If you’re using a solid background color, make sure the text contrasts enough to stay readable. A dark background needs light text, and vice versa.
Use clean and legible fonts: The most popular fonts for a landing page are PP Neue Montreal, GT Super, Aeonik, and Inter, all chosen for readability and impact.
Be selective with links: Every link on your landing page is a potential exit. If your goal is to capture leads, minimize outbound links and keep the focus on your CTA.
One more thing: Keep your visuals tidy and polished.
If you’ve spent more than 30 minutes on the internet, chances are you’ve seen one of those ads with an annoying GIF or clickbait headline like “Lose 50 lbs with this one weird trick.” Avoid distracting elements on your website that may sidetrack visitors from the main message.
Wix does this by using a limited but eye-catching color palette, a contrasting CTA button with an enticing offer, and inspirational language.
Finally, be mindful of mobile users. I can’t tell you how discouraging it is to visit an online store on my tablet only to find that the CTA button overlaps with the pricing.
Copywriting Best Practices
Good design gets visitors to your landing page. Good copy gets them to convert. Here are the copywriting best practices for high-converting landing pages. My copywriting guide goes deeper on these concepts, but these fundamentals will get you started.
Include Very Important Attributes (VIAs):
Showcase the VIAs that appeal to visitors. Identify two to five key features, benefits, or pain points, and highlight them on the front page rather than overwhelming visitors with a long list.
Use the AIDA Model
AIDA stands for attention, interest, desire, and action. Apply this practice to content creation by using captivating graphics to drive attention, hold interest with valuable context, stimulate desire by connecting it to product benefits, and lead to action with a CTA.
Additionally:
Format your copy so it’s easy to read: Use an organized structure full of headings, bullets, and quotes, and don’t be afraid of white space. This makes your site look less cluttered.
Cut your fluff: Are you familiar with the phrase “kill your darlings”? You’re about to do that here by cutting anything that doesn’t add value. Use simple language and keep paragraphs short.
Social proof: You can add things like media mentions, accreditations and awards, and usage stats.
Don’t forget large language models (LLMs): You have to consider AI platforms and the LLMs that power them. LLMs tend to surface content that answers questions directly and quickly. Structure your copy around longer-form, question-based queries, and lead each section with a clear answer to improve your chances of appearing in AI-generated results.
Landing Page SEO Best Practices
SEO for landing pages comes down to a few essentials:
Headings: Use H1, H2, H3, and H4 tags to give your page a clear structure for both readers and search engines. Include your main keyword in the title and H1, and work secondary keywords into your other headings.
Meta tags: Your title tag and meta description tell search engines what your page covers and shape how it appears in search results. Keep them concise and keyword-relevant.
Alt text: Alt text describes your images for search engines and screen readers. It supports both SEO and accessibility, so treat it as a requirement.
Local SEO: If your landing page targets local customers, develop localized content that drives traffic to your door.
Search intent ties all this together. Every element on your page should reflect why someone is searching, not just what they’re searching for.
LLMs and AI search features reinforce this principle. Follow SEO fundamentals and focus on answering question-based queries directly to keep your landing page visible in AI results alongside traditional search.
How AI Is Changing Landing Page Optimization
While AI is rooted in traditional SEO concepts, it’s significantly changing the creative side of landing page workflows.
Copy that once took a week to draft can now be generated and tested in an afternoon. A/B testing now happens dynamically, with visitor traffic routed to the best-performing test variant in real time.
Additionally, search itself has shifted, with AI Overviews providing zero-click information that used to require a click-through.
Each change reshapes what you should build.
AI Tools for Copy and Page Creation
AI writing tools have dramatically sped up the timeline for drafting landing page copy. What used to take a week of copywriter iteration can now be a starting draft in minutes, with multiple variants ready to test by the end of the day.
Unbounce’s Conversion Benchmark Report shows that the correlation between difficult words on a page (three-plus syllables) and its conversion rate is 62 percent stronger than it was in 2020, and pages written at a fifth- to seventh-grade reading level convert more than two times higher than professional-level writing.
Raw AI output tends to be wordier and more formal than what converts. It’s great for accelerating the creation of variants, but think twice before skipping a human read that catches corporate voice creeping back in.
AI-Powered Testing and Traffic Optimization
Traditional A/B testing splits traffic 50/50 and waits for statistical significance before declaring a winner, which usually takes weeks and burns through budget on the losing variant the whole time.
Thankfully, we can avoid some of that loss now with AI-powered traffic optimization. Tools like Unbounce’s Smart Traffic analyze each visitor’s attributes in real time (device, referrer, campaign source, and browsing behavior) and route them to the variant most likely to convert for that specific profile.
That means you can run multiple variants without paying the full traffic cost of a losing test, allowing you to optimize continuously rather than in discrete two-week cycles.
<h3> AI Search and AEO Implications for SEO Landing Pages </h3>
Traditional SEO best practices still apply in AI search, but AI Overviews and answer engines create some nuance.
According to HubSpot, nearly 30 percent of marketers report decreased search traffic as consumers turn to AI tools, and over 92 percent are already optimizing for both traditional and AI-powered search.
Pages that answer specific questions directly and carry visible experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) signals are what AI systems draw on when generating answers. The signals are concrete: author bylines, credentials, client results, and publish dates.
Clean heading hierarchy and structured data make it easier for AI to parse your content and pull from it accurately. Pages built on keyword density without substance get passed over.
What’s a Good Landing Page Conversion Rate?
A “high-converting landing page” only means something once you have a benchmark to measure against. That benchmark varies by industry and traffic sources, and it shifts depending on where the page sits in your marketing funnel.
Three data points can help you set realistic targets.
Average Conversion Rate by Industry and Channel
Unbounce research puts the median at 6.6 percent across all industries.
Email is the highest-converting traffic source at 19.3 percent on average, well ahead of paid social (12 percent) and paid search (10.9 percent). Google leads among paid search channels.
The device people use to find you plays a role, too. Unbounce found 83 percent of landing page visits happen on mobile, yet desktop still converts about 8 percent better on average.
The same report links simpler, shorter copy to higher conversion rates, which is why readable pages consistently outperform corporate-voice ones.
Conversion Rate by Industry: Ecommerce, SaaS, and More
Industry averages swing hard from the overall median. Statista puts the average conversion rate across all ecommerce sites at under 2 percent, a fraction of the 6.6 percent Unbounce reports across landing pages more broadly.
In ecommerce, skincare pages convert the highest at 2.4 percent (above average), while luxury apparel pages bottom out at just 0.3 percent.
The takeaway here is that chasing a single universal benchmark is a mistake. Compare your ecommerce conversion optimization against the tightest peer set you can find, then work on the specific friction points holding them back.
Cart Abandonment and Checkout-Stage Conversion Loss
According to the Baymard Institute, the average cart abandonment rate is 70.2 percent, based on 50 different studies.
Excluding pure window-shopping, the top reasons shoppers bail are:
Extra costs like shipping and fees (40 percent)
Slow delivery (20 percent)
Distrust of payment security (19 percent)
Forced account creation (18 percent)
Overly long checkout flows (17 percent)
Baymard estimates that a better checkout design alone could recover $260 billion in lost ecommerce orders in the U.S. and the European Union.
The same friction points hit landing page sign-up and checkout flows, too. Anywhere you ask a visitor to take an action, reasons like those above are what keep them from following through.
Audit your page against that list.
Want to See These Best Practices in Action?
Reading about what works only gets you so far. To see how real brands apply these principles, check out my breakdown of over 30 landing page examples that convert, along with the specific design choices that make them work.
FAQs
What are high-converting landing pages?
High-converting landing pages are web pages designed to encourage visitors to take a specific action, such as signing up for a newsletter or making a purchase. These pages are optimized for conversion rates, designed to maximize the number of visitors who take the desired action.
What are the key components of a landing page?
Every landing page needs a clear headline, benefit-focused copy, a compelling CTA, and social proof. Trust signals and mobile-friendly design round out the essentials.
How do you create a high-converting landing page?
Start with a clear, compelling headline that highlights your product’s main benefit, and make sure your copy focuses on your target audience’s needs and pain points.
It’s also important to include high-quality images and videos that showcase your product in action and give visitors a sense of its capabilities, include a CTA, and keep text brief. Always A/B test content to see which option works best.
Finally, be sure to include testimonials, simplify navigation, and clearly state your value proposition.
What is a good landing page conversion rate?
Unbounce’s Conversion Benchmark Report puts the average conversion rate at 6.6 percent across all industries, but you shouldn’t be chasing a generic number. Your target should reflect your industry, since individual averages range from under 2 percent in ecommerce to over 10 percent in other verticals.
How can you improve your landing page conversion rate?
Start with a message match between your ads and page copy, since a mismatch kills conversions faster than any design flaw. Simplify your forms, then A/B test your headline and other elements.
Do landing pages help SEO?
Yes, when built for it. Landing pages that match specific search intent and load quickly can rank organically and drive long-term traffic beyond your paid campaigns.
Can you have too many landing pages?
The consensus is no. Digital marketing experts agree that it makes sense to have multiple landing pages. For instance, you might want to target different demographics.
Conclusion
There’s no single formula for a high-converting landing page. Don’t be afraid to test different approaches and see what works for your company and your offer.
Start by getting to know your buyers and what appeals to them. Get a sense for their pain points, and then build pages that solve them. Pair that with a CTA that compels them to take action and watch your conversions grow.
Apply the optimization elements in this guide, then let A/B testing strategies show you what converts best.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-14 16:00:002026-09-14 16:00:00How to Create A Landing Page That Actually Converts
You’re looking at a page that should be performing better, and you want answers fast.
Maybe it’s a product page that slipped in rankings, a competitor’s post outranking yours, or a client page that isn’t converting.
You don’t need a full site crawl to start diagnosing.
SEOquake’s free Chrome extension audits any page and tells you what’s working (and what isn’t), all without leaving the page.
In this tutorial, I’ll walk you through an SEOquake workflow that takes you from triage to diagnosis in just three minutes:
Triage: Open SEOquake’s Quick View pop-up for an instant SEO snapshot (1 minute)
Diagnose & Decide: Switch to the report for deeper analysis and exports. Then, decide whether it’s a page edit or a wider issue that needs a full site audit (2 minutes).
What Is SEOquake?
SEOquake is Semrush’s free SEO Chrome extension for auditing any webpage’s technical signals, on-page SEO, linking strategy, and core Semrush metrics.
Install the extension, open any page, and click the SEOquake icon in your browser for faster, easier SEO research and analysis.
The SEOquake extension provides two ways to audit:
The Quick View pop-up provides both page- and domain-level metrics for a fast snapshot of performance and on-page and technical issues
The Full Report goes deeper with 28 on-page SEO audit checks and guidance on what to do for each one
Results are color-coded throughout every report, so you can quickly diagnose pages and prioritize fixes.
Here’s what each color tells you:
Green is a pass
Orange is a warning — worth a look, but not an emergency
Red is a failure — fix these issues first
Blue provides useful context for checks that don’t have a pass/fail answer
We’ll use these color cues to stay focused throughout the audit.
Note: To show you how SEOquake’s three-minute page audit works, I’ll be analyzing a real product page from UnderFit, a men’s undershirt brand. Want to audit along with me? Install SEOquake for free.
Triage Any Page’s SEO Issues with the Quick View Pop-Up (Minute One)
Triage is the first minute of SEOquake’s three-minute page audit.
In 60 seconds or fewer, you’ll answer this question: Is anything obviously wrong with this page?
The Quick View pop-up has five tabs:
Page Info: Title tag, meta description, canonical, and robots.txt status
Content: Heading structure, keyword density, and image alt text
Audit: Technical and page-level checks color-coded by severity
Schema: Detected structured data types and social preview information
Semrush: Domain-level organic search traffic, traffic cost, and backlinks, plus page-level URL backlinks
I’ll focus on three tabs: Page Info, Audit, and Content (about 20 seconds per tab) to get a fast read on UnderFit’s product page.
Start with Page Info for Core SEO Elements
SEOquake’s Page Info tab is a great starting point because it provides a quick overview of key SEO elements.
I open the pop-up on UnderFit’s page and assess the results.
This is a quick scan, so I don’t overthink it.
The five things I prioritize:
Title tag and meta description: Are both present? Are the character counts within range?
Canonical: Is it pointing to the URL I want indexed?
Robots.txt: Does the file allow crawlers to access the page?
Schema.org markup: Is structured data detected and valid?
Link, image, and heading counts: Do the numbers fit the page type?
Then, I quickly scan for anything marked red — or anything that doesn’t line up with on-page SEO best practices.
On UnderFit’s page, SEOquake flagged the Schema.org markup in red.
The reason points to JSON-LD errors, which means search engines may not parse the structured data correctly.
For a product page, that’s a real concern.
Invalid product schema can make the page ineligible for product rich results, including price, availability, and ratings in search.
So, that’s going straight onto my list to check in the Full Report.
The rest is mostly clean aside from two elements:
Title tag (65 chars): Present but flagged orange — slightly outside the ideal range
Meta description (182 chars): Present but flagged orange — outside the ideal range
These aren’t blockers, but I note them down.
Scan the Audit Tab for Technical Problems
Some issues don’t surface in Page Info, so I go to the Audit tab next.
SEOquake’s Audit checks for technical SEO essentials, including:
Crawling & Indexation
Security & Protocol
Page Configuration
On the UnderFit page, everything is green or orange.
That usually means there’s no obvious technical emergency. But orange warnings still require a judgment call.
I’ll evaluate anything in orange against what I know about the site and the SEO risk.
For this page:
XML sitemap: Flagged as missing, so I’ll verify the sitemap exists and includes this URL
Meta robots: Flagged orange for review. A conflicting directive can block indexation, so it’s worth a closer look.
Hreflang: Shows zero tags, but I wouldn’t worry about that for a U.S.-only store
With technical issues noted, I move on to the Content tab.
Review the Content Tab for Structure and Keywords
Next, I open the Content tab to check whether the page is structured clearly.
Content structure helps users navigate the page and can make it easier for search engines and AI systems to interpret it, so this is worth double-checking.
In the Content tab, I check:
Title tag and meta description: Are both present, and are the character counts within range?
Heading and word counts: Do the H1/H2/H3 distribution and total word count fit what I’d expect for a product page?
Heading Structure: Is there one clear H1, and do the headings follow a logical hierarchy?
Keyword Density: Do the top terms match the page topic, and do important terms appear in the title, meta description, and H1?
Images: How many images are missing alt text or flagged as oversized?
Seeing UnderFit’s heading structure laid out makes me pause.
The H1 appears way down the page, and the hierarchy jumps levels throughout — going from H1 straight to H4, for example.
That’s worth reviewing for accessibility, reader navigation, and overall content clarity.
The image summary also flags that 36 images are missing alt text.
Since images do a lot of selling on a product page, I’ll look into this further.
Missing alt text gives search engines less image information and makes the page less accessible to shoppers.
Neither is a blocker, but I’ll check both in the next step of the audit: Diagnose.
Diagnose Issues with the SEOquake Full Report (Minutes 2–3)
In the first minute of the audit, I found a few issues on UnderFit’s product page that I want to investigate in SEOquake’s Full Report.
In the next two minutes, I’ll diagnose the cause of each flag. Then I’ll decide whether I can fix the issues at the page level or need to run a site-wide audit.
I click “View Full Report” at the top of SEOquake’s Quick View pop-up to open the panels.
The Full Report has six panels:
Page Info: Basic page signals, technical snapshot, and public Semrush metrics
Audit: Technical SEO checks with fix guidance
Keyword Density: Key terms by phrase, length, density, and prominence
That confirms what triage suggested, so it goes onto my fix list, too.
After that, I work through the remaining flags the same way — including the heading hierarchy issues and missing alt text from triage — adding actionable items to my list.
Pro tip: Once you’ve worked through the flags, export the full audit as a PDF to share with a developer or client, or to keep a record before you make changes.
Spot Topic Coverage Gaps in the Keyword Density Panel
If a page isn’t performing the way you’d expect, Keyword Density is worth a quick look.
It tells you which terms appear most frequently in the page’s visible content and whether the topic coverage looks right for the page type.
For UnderFit, I start with the Keyword Cloud to get a quick read on what the page is about.
The core product terms are the most prominent: undershirt, fit, fabric, v-neck, cotton. That’s exactly what I’d expect for this page type.
Side note: If you’re auditing a competitor, SEOquake’s Keyword Density report is a fast way to see which topics they’re covering that you aren’t.
Then I dig into the keyword tables for more detail.
The “Found in” column confirms those terms are showing up in the right places — “undershirt” appears in the meta description and H1, while “fit” and “underfit” both appear in the title tag.
Next, I scan the multi-word phrase tables for any missing or off-topic phrases.
Most reinforce the page’s topic: “the invisible undershirt,” “v-neck undershirt,” and “built to last.”
Some odd phrases, like “zoom zoom,” come from interactive elements and image labels in the HTML rather than the main product copy.
That’s normal — tools like SEOquake can pick up text from buttons, image labels, and other page elements, so widget text sometimes appears in the data.
Check Link Health in the Links Panel
Broken, vague, or unhelpful links can make it harder for search engines and users to navigate pages effectively.
The Links panel shows the status of all internal and external links on the page, including dofollow/nofollow, anchor text, and HTTP response codes.
On UnderFit’s page, the report shows one broken internal link and 37 broken external links.
So I go to the External Links table to dig deeper — they’re mostly JavaScript links.
I’ll add those to the fix list to confirm whether they’re functional or actually problematic.
Pro tip: If you’re sharing findings with a developer or content team, export the table as a CSV. It provides them with the affected URLs, anchor text, follow status, and response codes in a single file.
Decide on a Page Fix or Site Audit Using the Semrush Panel
I’ve now worked through the triage flags and have a list of issues to address on UnderFit’s product page.
But before I fix anything, I need to decide whether each issue is specific to this page or points to a broader site problem. The Semrush panel gives me the additional context I need to make that call.
By default, it shows domain- and page-level metrics, including:
Semrush Rank: The domain’s organic visibility ranking
SE Traffic: Estimated monthly organic search traffic for the domain
Root Domain Backlinks: The domain’s total backlink profile
URL backlinks: Total backlinks to the page
Optionally, I can connect SEOquake to my Semrush account to unlock deeper data.
This includes Traffic Analytics data (engagement metrics such as bounce rate, visit duration, and channel-level traffic) and detailed backlink reports.
Pro tip: To connect your Semrush account to SEOquake, click the gear icon in the top-right corner of the Quick View pop-up, then click “Connect to Semrush” or access the Integrations tab in Settings.
A weak page on a strong domain — like UnderFit, where the domain has solid traffic and a reasonable backlink profile — usually points to a content or page-level problem.
But some flags aren’t cleanly at the page level.
The XML sitemap warning, for example, is almost always site-wide, and JSON-LD errors might repeat across all product templates.
Those aren’t problems I can confirm or fix with SEOquake alone, so I’ll move to a full site crawl with a tool like Semrush’s Site Audit.
Diagnose Any Page in 3 Minutes with SEOquake
As you’ve seen, the SEOquake Chrome extension lets you audit any page in three minutes and walk away knowing exactly what’s working, what’s broken, and what to do next.
Install SEOquake from the Chrome Web Store and run your first audit today:
Triage using the Quick View pop-up to catch obvious problems
Diagnose the flags in the Full Report to understand what’s wrong
Decide whether it’s a page fix or a site-wide issue
Escalate to Semrush Site Audit when the problem is bigger than the page
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-09 21:24:182026-09-09 21:24:18How to Audit Any Page in Under 3 Minutes with SEOquake
Your schema tells search engines what your brand is. But that doesn’t mean Google’s systems understand it the same way.
That gap can reveal where your content strategy is falling short.
Our next SMX Now on Sept. 16 at 1 p.m. ET will feature Ray Martinez, VP of SEO at Archer Education. He’ll show you how to measure the difference between the entities you define and the ones Google’s natural language processing (NLP) actually recognizes.
You’ll learn how to build an entity audit using schema.org markup, the Google Cloud Natural Language API, and an agentic coding tool such as Antigravity, Claude Code, or Codex. The process turns your existing schema into a queryable knowledge graph, then compares it with competitor content to uncover topics they cover, gaps they miss, and entities Google doesn’t yet connect with your brand.
Martinez will also show you how to turn those findings into action. You’ll learn how to create content around under-recognized entities, strengthen those connections with structured data and internal data, and make your pages more retrievable and citable across search and AI engines.
You’ll leave with a repeatable way to measure discoverability, find content opportunities, and track whether Google and AI systems are getting better at understanding what your brand actually does.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/09/smx-now-schema-google-nlp-content-strategy-OHIQTq.jpg?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-08 18:02:192026-09-08 18:02:19SMX Now: Find the entity gaps holding back your content strategy
