Tag: Semrush

My AI Content Gap Workflow for Smarter SEO Priorities
I can publish consistently, follow SEO best practices, and still watch competitors outrank me. When that happens, I usually find that the issue is not content quality alone. It is content coverage. Competitors are answering questions my audience is already asking, while my site is not fully part of that conversation yet.

That is where I use content gap analysis. It helps me identify the topics competitors rank for that I do not, then decide which opportunities are actually worth pursuing.

Finding gaps is rarely the hard part. SEO tools make that fairly easy. The real challenge is making sense of thousands of keywords across several reports and deciding what deserves attention first.

My workflow combines competitor data, first-party search data, and AI so I can prioritize content opportunities around business impact instead of search volume alone.

I bring my SEO data together before analyzing it

In this workflow, I use Semrush to identify competitive opportunities, Google Search Console to validate where my site already shows signs of authority, and Google Analytics to add business context. Then I use Claude to bring those datasets together, group related opportunities, identify patterns, and help me decide what belongs on the content roadmap.

I follow this process in one of two ways.
- I export reports directly from the platforms and upload them to Claude.
- If I have connected those platforms through MCP (Model Context Protocol, a standard that allows AI models to connect securely to data sources), I let Claude pull the data directly without manual exports. The workflow changes, but the analysis does not.
Here is the process I use to turn a pile of SEO data into a prioritized content plan.

Step 1: I choose the right competitors

A content gap analysis is only as useful as the competitors I compare myself against. That sounds obvious, but it is one of the easiest places to go wrong.

If I compare my site to Amazon, Reddit, or Wikipedia, I will end up with thousands of keyword “opportunities” that were never realistic in the first place. My goal is not to find every site ranking for my target keywords. My goal is to find businesses competing for the same audience.

I usually start with Semrush’s Organic Competitors report. Instead of relying only on a list of known competitors, I use this report to find domains that compete across many of the same keywords. From there, I narrow the list to three to five sites that closely match the business and target audience I am analyzing.

I do not worry if a few familiar names do not make the cut. Business competitors and organic search competitors are not always the same.

I also filter out sites that can distort the analysis, including large marketplaces like Amazon, community-driven sites like Reddit or Quora, reference sites like Wikipedia, local directories, review sites, and publishers that do not directly compete with the business.

There are exceptions. If I am analyzing a publisher, comparing against other editorial sites makes sense. The key is choosing competitors that create the type of content I am realistically trying to outperform.

A Semrush competitor analysis view turns organic search data into a clear map of rival domains, traffic potential, keyword overlap, and content gap opportunities.

Before I move forward, I sanity-check the competitor list with stakeholders. Sales or product teams may know about newer competitors or strategically important niches that do not yet show up clearly in Semrush.

Once I have settled on the right competitors, I am ready to find the gaps that matter most.

Step 2: I gather and prepare the data

With the competitor list finalized, I collect the data Claude will analyze. Whether I upload exports or connect through MCP, the goal is the same: bring together competitive rankings, my site’s search performance, and engagement data so I can separate meaningful opportunities from noisy keyword lists.

I like to pull data from three core sources.

Semrush: I find the gaps

I start with Semrush’s Keyword Gap tool using the competitors selected in Step 1.

From there, I pay close attention to three buckets: keywords competitors rank for and I do not, keywords where I rank but competitors rank higher, and keywords where I rank but competitors do not.

The first bucket often points to missing topics or content hubs. The second bucket can reveal quicker wins, especially when my site already appears on Page 1 or Page 2. The third bucket shows existing strengths that I should protect and continue building around.

Google Search Console: I validate the opportunity

Next, I check Google Search Console before assuming every missing keyword deserves a new page.

For example, Semrush may show that I do not rank for a keyword, but GSC might reveal that I already receive impressions for closely related queries. That tells me Google has started associating my site with the topic, even if rankings have not caught up yet.

Those “almost there” topics often deserve a higher priority than topics where I would be starting from scratch.

In GSC, I look for queries with high impressions and average positions between 8 and 20, existing pages ranking for related terms, and long-tail queries that reveal additional search intent.

Google Analytics: I add business context

Search volume is only part of the story. Engagement metrics help me answer a more important question: if I improve visibility for this topic, is it likely to support business goals?

A Semrush content gap analysis view reveals where a competitor ranks and the analyzed site does not, turning keyword overlap data into a practical roadmap for SEO content opportunities.

I review metrics such as organic sessions, engagement rate, average engagement time, key events or conversions, and landing page performance.

If a related content hub already drives engaged visitors or conversions, expanding that topic may be a smarter investment than chasing a completely new keyword with higher search volume.

I clean the data before handing it to Claude

If I am manually downloading the data and uploading it to Claude, I clean it first. Claude is excellent at finding patterns, but it can only work with the data I provide. Cleaner data leads to cleaner topic clusters and better recommendations.

I remove duplicate keywords, competitor-branded terms, careers queries, login queries, support queries, locations or product lines outside the business, keywords with clearly different search intent, and high-intent commercial keywords that are too broad to compete for.

For a manual workflow, I export Keyword Gap data from Semrush, query data from Google Search Console, and landing page performance data from Google Analytics, then upload the files to Claude. For a connected MCP workflow, I ask Claude to retrieve the Keyword Gap report, GSC query data, and GA4 landing page metrics directly from connected accounts.

Step 3: I ask Claude to find the story in the data

At this point, I should have a clean dataset that combines competitive keyword gaps, Search Console performance, and Google Analytics data.

This is where the workflow becomes much more useful. Instead of scrolling through thousands of rows looking for patterns, I ask Claude to organize the data into something I can actually build a strategy around.

The mistake I see most often is asking AI to “cluster these keywords.” That usually produces clusters based on keyword similarity alone. That can be useful, but it does not tell me what to do next.

Instead, I ask Claude to think like an SEO strategist. I give it context about the business, including products or services, target audience, primary business goals, content priorities or constraints, and the exported or connected data from Semrush, GSC, and Google Analytics.

Then I ask Claude to organize opportunities by search intent, funnel stage, business relevance, existing authority signals from GSC, user engagement from GA4, recommended content format, and internal linking opportunities.

Rather than returning a spreadsheet of grouped keywords, I want Claude to produce topic clusters with a clear recommendation for each one.

For example, one cluster might be labeled Technical SEO Audits and include supporting keywords, estimated opportunity, existing pages that could be updated, whether a new page is needed, internal linking recommendations, a priority score, and the reasoning behind the recommendation.

A content gap workflow turns scattered SEO signals into topical clusters, showing where AI search visibility, privacy-first analytics, and technical SEO need deeper coverage.

Another cluster might reveal that several competitor keywords can be addressed by expanding an existing guide instead of publishing three separate articles. That is the kind of insight that is hard to spot manually but much easier for AI to surface.

I separate quick wins from long-term investments

Not every opportunity belongs on the same roadmap. As part of my prompt, I ask Claude to classify each cluster into quick wins, new content opportunities, and authority plays.

Quick wins are existing pages that can be refreshed, expanded, or better optimized. New content opportunities are topics that deserve dedicated content because the site has little or no visibility. Authority plays are larger subject areas that may require multiple pieces of content and ongoing investment to compete effectively.

This simple step helps me move from an overwhelming keyword list to a roadmap with both short-term wins and long-term initiatives.

I do not skip the human review

Claude can organize information remarkably well, but it does not know the business the way I do.

Before moving on, I ask whether the topic supports business goals, whether multiple search intents are being combined into one cluster, whether existing content could already satisfy the need, whether the opportunity is realistic given authority and resources, and whether I would actually assign the topic to a writer.

If the answer is no, I refine the cluster or remove it.

The goal is not to accept every AI recommendation. The goal is to spend less time organizing data and more time making strategic decisions.

The biggest prompt lesson is simple: I do not ask Claude to organize keywords. I ask it to recommend what my content strategy should be based on the data I have provided.

Step 4: I score and prioritize the opportunities

Once Claude has grouped the keywords into topic clusters, the next step is deciding what deserves attention first.

This is where many content gap analyses fall apart. Teams naturally gravitate toward the biggest search volumes, but volume is only one piece of the puzzle. A topic that attracts qualified visitors and supports business goals is often a better investment than a high-volume keyword that is difficult to rank for or unlikely to convert.

I score each opportunity across several criteria before I build a roadmap.

A prioritized content gap roadmap turns scattered SEO data into clear next moves, ranking quick wins by impact, effort and AI visibility.

Business relevance

I start with a simple question: if this content performs well, does it help the business?

Topics aligned with products, services, or the customer journey should carry more weight than informational topics with little commercial value.

Existing authority

Next, I look at signals from Google Search Console. If my site already earns impressions or ranks on the second page for related queries, Google has likely established some level of topical authority.

In those cases, improving an existing page or expanding a content hub may produce results much faster than starting from scratch.

Search demand

Search volume matters, but I do not let it dominate the scoring model.

A collection of related long-tail queries with moderate demand can sometimes generate more qualified traffic than one broad keyword.

Ranking difficulty

I review the current search results before committing to a topic. I look at whether authoritative brands dominate the first page, whether the intent is informational, commercial, or transactional, what types of content are ranking, and whether I can realistically create something more useful or complete.

This quick reality check keeps me from chasing opportunities that are not practical.

Estimated effort

Finally, I consider the work involved. Some opportunities require a light refresh of an existing article. Others call for a new content hub supported by multiple pages.

Both can be worthwhile, but they should not carry the same priority when resources are limited.

I let Claude apply the framework

Once I define the scoring criteria, Claude can evaluate every topic cluster consistently.

For example, I may ask Claude to score each opportunity on a five-point scale for business relevance, existing authority, search demand, ranking difficulty, and content effort. Then I ask it to calculate an overall priority score and explain why each recommendation received that score.

A tactical SEO refresh brief turns AI-assisted content gap analysis into page-level priorities, surfacing validation lessons, effort estimates, and the biggest opportunities.

The explanation is just as valuable as the number. If I disagree with a recommendation, I can adjust the weighting, add more business context, and ask Claude to score the opportunities again.

By the end of this step, I have more than a list of content ideas. I have a prioritized content strategy that shows what to tackle next, what can wait, and what is not worth pursuing.

Step 5: I turn priorities into page-level recommendations

Once I have prioritized the opportunities, the next step is figuring out exactly what to change.

Rather than handing a team a ranked list of topics, I ask Claude to generate page-level recommendations for the highest-priority opportunities. This is where connected data becomes especially valuable.

Because Claude has access to Semrush research, Google Search Console performance, Google Analytics metrics, and my prioritization framework, it can evaluate each page in context instead of treating every recommendation the same.

For each priority page, I ask Claude to produce a recommendation that explains why the page was selected, the primary keyword cluster, current rankings and impression data, supporting evidence from GSC and competitor research, recommended updates, estimated effort, expected impact, and priority level.

One of the biggest advantages of this approach is validation.

Before recommending a refresh, Claude can compare URL-level Search Console data against the original analysis. Sometimes what looks like a strong opportunity turns out to be misleading. A keyword may have inflated impression counts, a URL could have been mislabeled in an export, or the page may not be as close to ranking as it first appeared.

Catching those issues before assigning work can save hours of unnecessary effort.

The recommendations also make stakeholder conversations easier. Instead of saying, “I think we should update this page,” I can point to the supporting data, explain why it is a priority, estimate the effort involved, and tie the recommendation back to the larger content strategy.

I treat these recommendations as implementation plans rather than full content briefs. They help SEO and content teams understand what should change, why it matters, and where to focus first. Writers can then use those recommendations to create or update content with confidence.

Step 6: I measure whether the gap is closing

Publishing the content is not the finish line. It is the start of the next round of analysis.

Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

I begin with Google Search Console, tracking whether target queries are gaining impressions, improving in average position, and generating more clicks. When I refresh an existing page, I compare performance before and after the update to see whether the changes actually moved the needle.

Next, I look at Google Analytics. Better rankings do not always translate into better business outcomes, so I review organic traffic alongside engagement and conversion metrics. If an updated page attracts more visitors but fails to keep them engaged or contribute to conversions, I know it is time for another round of optimization.

If I am using Claude through MCP, I can also ask it to compare performance over time and summarize what changed. I might ask which refreshed pages improved the most, which content clusters gained the most visibility, which recommendations drove the strongest business results, and which opportunities still need attention.

Instead of comparing reports month after month, Claude can quickly surface significant changes and point me toward the pages that deserve attention.

I do not treat content gap analysis as a one-time exercise. Competitors publish new content, search behavior shifts, and my own site authority evolves. I like to repeat this workflow every quarter, or more often in fast-moving industries, so I can keep finding new opportunities and stay ahead of competitors.

The tools will continue to improve, but the repeatable workflow is what creates the advantage.

I build a repeatable content gap analysis process

A content gap analysis helps me prioritize opportunities worth pursuing instead of chasing every possible keyword.

Semrush helps me uncover competitive gaps. Google Search Console shows where I already have momentum. Google Analytics adds the business context that rankings alone cannot provide. Claude brings those datasets together, helping me identify patterns, prioritize opportunities, and create actionable recommendations in a fraction of the time it would take manually.

Whether I upload reports or connect my tools through MCP, the workflow stays the same. I gather the right data, validate the opportunities, let AI organize the information, and apply my own expertise to decide what comes next. That is the part AI cannot replace.

The biggest advantage is not simply having better prompts or faster analysis. It is having a repeatable process that helps a team make smarter content decisions every quarter.

Prompt template: My prioritized content gap roadmap

Here is the prompt I use after I have gathered the data, whether I have uploaded exports from Semrush, Google Search Console, and Google Analytics or connected those tools to Claude through MCP.

“You are an experienced SEO strategist helping me perform a content gap analysis.
I’ll either provide exported reports from Semrush, Google Search Console, and Google Analytics, or you’ll access those tools through connected MCP integrations.
My goal is to identify the highest-impact content opportunities based on competitor visibility, existing authority, business value, and implementation effort.
Here’s my business context:
– Company:
– Industry:
– Products/services:
– Target audience:
– Primary business goals:
– Geographic focus:
– Any strategic priorities or constraints:
– Tone of voice: [Insert brand voice adjectives here (e.g., authoritative, conversational, technical)].
Using the available data, complete the following tasks.
1. Identify content gaps
Organize keywords into these categories:
– Competitors rank and we don’t.
– We rank below competitors.
– We rank and competitors don’t.
Highlight any content gaps, opportunities to consolidate pages, or keyword cannibalization issues.
2. Validate the opportunities
Use Google Search Console data to determine:
– Which topics already receive impressions.
– Which pages rank between positions 8 and 20.
– Which existing URLs have the strongest chance of improving with optimization.
Use Google Analytics data to determine:
– Which pages drive meaningful engagement.
– Which pages contribute to conversions.
– Which content hubs are worth expanding.
3. Create strategic topic clusters
Group related opportunities by:
– Search intent
– Business relevance
– Funnel stage
– Recommended content type
– Internal linking opportunities
Don’t cluster based only on keyword similarity. Focus on topics that should become part of the same content strategy.
4. Prioritize every opportunity
Score each topic cluster using:
– Business relevance
– Existing authority
– Search demand
– Ranking difficulty
– Estimated effort
Assign each opportunity a priority (High, Medium, Low) and explain why.
Separate recommendations into:
– Quick wins
– New content opportunities
– Long-term authority investments
5. Recommend next steps
For every high-priority opportunity, recommend whether we should:
– Refresh an existing page
– Consolidate multiple pages
– Create a new page
– Build a pillar page with supporting content
Include supporting evidence for every recommendation.
6. Deliver the results
Create:
– An executive summary
– Prioritized topic clusters
– A scored opportunity table
– Page-level recommendations for the highest-priority URLs
– A phased implementation roadmap (30, 60, and 90+ days)
If you find conflicting data between Semrush, Google Search Console, and Google Analytics, explain the discrepancy and recommend which source should guide the decision. The output should both be HTML and a Google Sheet.
Before presenting your final recommendations, validate your own analysis. If reviewing Search Console or Analytics data changes your original recommendation, explain why and update your prioritization accordingly.”

This prompt is only a starting point. I add business context, editorial guidelines, and scoring criteria that are unique to the organization I am analyzing. The more context I give Claude, the more useful and actionable its recommendations become.

Inspired by this post on Search Engine Land.
July 8, 2026
Goodie vs. Semrush: A Smarter AEO Platform Comparison

When I compare Goodie and Semrush for AI search visibility, I’m looking beyond traditional SEO dashboards. I want to understand how each platform supports answer engine optimization, from monitoring AI visibility to improving the signals that influence AI-generated answers.

A modern AEO performance dashboard brings AI search visibility, brand mentions, traffic attribution, and revenue signals into one measurement view.

For me, the key difference comes down to focus. Goodie is built around AEO monitoring, optimization, agentic commerce, and revenue attribution, while Semrush brings the depth of a broader SEO and competitive research platform.

A Semrush project dashboard brings SEO health into one view, from keyword rankings and site audit trends to optimization ideas and backlink toxicity signals.

In this comparison, I look at how both platforms help brands get discovered, cited, and recommended across AI search experiences, and how each one connects visibility to measurable business impact.

Inspired by this post on HiGoodie Blog.

July 6, 2026
How AI Is Reshaping Search Demand Across 1M Keywords

I do not see search demand disappearing. I see it moving. In this analysis, 29% of high-volume search demand is declining, while nearly the same amount is growing somewhere else. Overall demand is essentially flat because people are redistributing how and where they search instead of abandoning search altogether.

That changes how I think about SEO strategy. I would not start by panicking over shrinking keywords. I would start by identifying which queries are losing volume, which ones are gaining momentum, and where a brand can build enough authority to appear in both traditional search results and AI-generated answers.

This study looks at where search demand is shifting, which industries are seeing the sharpest changes, and what those patterns mean for SEO teams trying to adapt to AI-driven discovery.

How I studied AI’s impact on search

In 2024, Gartner predicted that traditional search engine volume would fall 25% by 2026 as consumers shifted to AI chatbots and virtual agents. Fractl and Search Engine Land set out to test that prediction. (Disclosure: I’m the co-founder of Fractl.)

I worked from Semrush data covering 1,010,848 high-volume keywords, each with at least 10,000 monthly searches, across 379 brands in eight verticals. I also looked at survey responses from 1,004 U.S. consumers to better understand how AI is changing the way people search.

The analysis measured which keywords gained or lost search volume over the past year, how those shifts differed by industry, and how consumer behavior is evolving as AI tools become part of everyday discovery.

The 29% search decline is real, but it depends on the vertical

Across more than 1 million high-volume keywords, I found that 29% of search volume is in measurable decline. That is 4 percentage points above Gartner’s forecast. In a dataset representing 35.4 billion monthly searches, that difference represents a meaningful amount of search activity.

The impact is not evenly distributed. FinTech showed the largest decline at -37.7%, while Lifestyle saw the smallest decline at -15.2%. Only three of the eight verticals, Insurance, SaaS, and Lifestyle, came in below Gartner’s 25% threshold. FinTech, HealthTech, and Wellness were well above it.

The pattern makes sense when I look at how information-heavy each category is. When a chatbot can answer the question completely, such as summarizing drug interactions, explaining deductibles, or giving a quick overview of a fund, search volume is more likely to fall. When people need to compare prices, complete a transaction, or navigate to a specific site, search demand tends to hold up better.

That is why transactional verticals such as SaaS, Lifestyle, Insurance, and Travel are growing or staying close to flat. Information-heavy verticals such as HealthTech, FinTech, and Wellness are seeing the largest declines.

Before reacting to broad claims about AI-driven search decline, I would benchmark these findings against the specific vertical in question. HealthTech and FinTech teams should expect more exposure than the overall 29% decline suggests. SaaS and Lifestyle teams have more reason to challenge the idea that search demand is simply collapsing.

Search demand is being redistributed

The headline number gets attention, but the offset is just as important. Demand did not vanish. It moved to a different set of words, and those are the terms I would want to understand first.

Among the high-volume keywords tracked, 40.7% are in measurable decline, meaning they lost more than 15% of their volume over the past year. Within that group, the average decline is -41%, and 112,378 keywords lost more than 40% of their volume. For brands that depend on those terms, the impact is significant.

At the same time, 20.1% of keywords are growing by more than 15%. When I add up the volume on both sides, the decline and growth almost cancel each other out.

The 285,489 declining keywords represent roughly 10.29 billion monthly searches. The 140,835 growing keywords represent roughly 10.31 billion monthly searches. Across the entire dataset, the net change is +16.8 million searches per month.

Fewer keywords are growing than declining, but the growing keywords carry more volume each. That is why the totals balance out. In practical terms, I see demand relocating more than shrinking.

The vertical-level growth-to-decline ratios show where that new demand is landing. Lifestyle leads at 2.6x, with 40% of keywords growing versus 15% declining. SaaS follows closely at 2.5x, with 48% growing versus 19% declining. HealthTech sits at the other end with an inverted ratio of 0.4x, making it the most disrupted vertical in the set.

The first audit I would run is simple: pull the tracked keyword set, filter it by year-over-year volume change, and see which side of the ledger the portfolio sits on.

Non-branded queries are the most vulnerable

I see non-branded queries as the easiest ones for AI chatbots to replace. When a search term does not include a brand name, the user is not necessarily trying to reach a specific site or source. The full exchange can happen inside the chat window.

Across the dataset, 90% of all tracked search volume is non-branded. HealthTech, at 99.6%, and Wellness, at 98.5%, are the most exposed. Insurance, at 73.8%, and SaaS, at 82.0%, are less exposed, and both are growing overall. SaaS volume is up 48% year over year, while Lifestyle is up 40%.

If I wanted to identify the content most at risk, I would start with keyword patterns. They offer one of the clearest signals in the study.

The reason SaaS and Lifestyle can be heavily touched by AI and still grow comes down to what happens after the AI answer. If AI recommends a project management platform or a couch, many people still search for the specific brand, retailer, review, or product page before buying. The AI answer creates a downstream search.

HealthTech and FinTech often behave differently. A drug-interaction question or a “what is a deductible” query can be answered completely inside the chat window. There may be no next step that sends the user back to Google.

If a category produces complete AI answers with no natural next click, I would treat AI visibility as a core strategy, not an SEO side project. In those cases, showing up in the answer may be the entire opportunity.

70% of consumers use AI more, but only 17% use search less

The keyword data shows what is happening in the index. The survey data shows what is happening in the minds of the people doing the searching.

Search behavior is spreading across more platforms. Many people are adding AI to their routines without giving up Google.

Social platforms are also acting like search engines in a way they did not a few years ago. YouTube leads at 68%, followed by Reddit at 57%, Instagram at 42%, Facebook at 40%, and TikTok at 33%.

If I had not already prioritized YouTube and Reddit, I would move them up the list. Both rank ahead of TikTok, Instagram, and Facebook as search destinations, and both can also surface in Google results, which gives visibility there a compounding effect.

What has actually moved from Google to AI

More than a third of respondents, 35%, say they have not replaced traditional search with AI for anything yet. Among those who have, how-to guides and tutorials have taken the biggest hit.

For purchase research, 47% of consumers start with a traditional search engine, tied with online retailers at 47%. Only 13% start with an AI chatbot, and shoppers check an average of three online sources before making a purchase.

The number I would bring to a strategy meeting is this: nearly one in five consumers, 18%, have bought something based on an AI recommendation without checking it against a separate search.

That creates a different kind of buyer journey. In that path, the brand may never receive a search-driven touchpoint. To be considered, the brand has to be one of the names the chatbot returns.

Gen Z and millennials are 2.5x more likely than baby boomers to buy based on an unverified AI recommendation, at 20% versus 7%. Across all consumers, 59% say they are likely to visit a brand’s website after an AI chatbot mentions or recommends it.

That is the emerging conversion funnel I am watching closely. Brand mentions in AI answers are starting to function like rankings. Visits to a brand’s website after an AI mention are starting to look like the new click-throughs.

Trust is still mixed. In the survey, 33% of consumers trust AI and traditional search equally, 46% trust search more, and 20% trust AI more.

More than half of consumers, 56%, are at least somewhat skeptical of AI product recommendations. I read that as a sign that people are willing to let AI filter and shortlist options, but many still want to verify before they buy.

The 5-year outlook: Google is not going away, but the shift matters

When asked whether Google will still be their primary search tool in five years, 52% of consumers say yes, including 17% who say definitely and 35% who say probably. Another 27% are unsure, while 20% say probably or definitely not.

The top reasons people prefer AI over traditional search are better summaries across sources, at 21%; faster and more direct answers, at 20%; and the ability to ask conversational follow-up questions, at 19%. More personalized results and avoiding website click-throughs were much lower, at 6% and 4%.

When asked what would bring them back to traditional search, the top answer was AI giving unreliable answers, at 35%. That means much of this shift depends on whether AI maintains trust as adoption scales. More accurate search results followed at 29%, then a preference for multiple source links at 22%, and privacy concerns at 20%.

The 20% who expect to leave Google are not the majority, but I would not dismiss them. A strategy does not need to be rebuilt entirely around them today, but brands do need to appear where those users are already moving.

What this means for content and SEO strategy

I see Gartner’s 25% prediction as a useful directional warning. The real shift may be steeper, but calling it only a decline misses the more important story. Total search volume is basically flat. What has changed is which searches carry the demand.

AI visibility is not just a threat to manage. I see it as a distribution channel. With 59% of consumers saying they are likely to visit a brand’s website after an AI mention, GEO has become a meaningful part of brand discovery.

Earned media, credible third-party coverage, and strong entity signals all help brands appear in chatbot answers. That is why digital PR and GEO are becoming more closely connected.

Search is moving, not disappearing.

The brands that lose will be the ones still optimizing mainly for queries that AI now answers better. The brands that win will be the ones building enough authority to become the answer, whether that answer appears in Google or inside a chatbot.

Methodology

This study combined two data sources to test Gartner’s 2024 prediction that traditional search engine volume would fall 25% by 2026.

Fractl and Search Engine Land analyzed Semrush search volume data for 1,010,848 high-volume keywords with 10,000 or more monthly searches each, covering 379 brands across eight verticals: FinTech, HealthTech, Wellness, Travel, Education, Insurance, SaaS, and Lifestyle. The dataset represented 35.4 billion in aggregate monthly search volume. Keyword-level year-over-year volume change was measured as of April 2026 and classified as declining, meaning more than 15% loss; stable, meaning within 15%; or growing, meaning more than 15% gain. Query pattern groupings, including “What is X,” “Best X for Y,” “X vs. Y,” and “How to X,” were applied at the keyword level.

Fractl and Search Engine Land also surveyed 1,004 U.S. consumers about their search habits, AI tool adoption, and purchase research behavior. The sample was 52% women, 46% men, and 1% nonbinary, with 49% millennials, 26% Gen X, 16% Gen Z, and 9% boomers. The median respondent age was 41, with a range of 18 to 82.

Inspired by this post on Search Engine Land.

July 3, 2026
ChatGPT Thinking Mode Is Reshaping Brand Citations

I see ChatGPT’s high-reasoning mode acting like a very different search surface for brand visibility. In a Semrush analysis with Kevin Indig, ChatGPT cited different domains than it did in minimal reasoning mode and ran nearly five times as many web searches before answering.

By the numbers, the shift is hard to ignore. Only 25.6% of cited domains overlapped between minimal and high reasoning for the same prompts. That means nearly three in four sources changed when ChatGPT moved from Instant-style answers to Thinking-style answers.

I also noticed that Thinking mode used more sources overall. Citation rates rose from 50% in minimal reasoning to 68% in high reasoning. When ChatGPT did cite sources, it used more of them too, increasing from 2.6 to 4.5 citations per response. Across the test set, high reasoning ran 1,130 web searches, compared with 245 for minimal reasoning.

Reddit lost ground in high-reasoning answers. Reddit’s citation share dropped from 15% to 7% when high reasoning was turned on. User-generated content and review sites also declined, falling from 14.3% to 6%.

At the same time, I saw more weight shift toward institutional and official sources. Government and academic sources rose from 1.9% to 8.8%, while official documentation and support pages grew from 12.4% to 17.5%.

Comparison prompts drove the most search activity. At the comparison stage, high reasoning averaged 24 sub-queries per prompt, compared with 5.5 for minimal reasoning. Average citations also peaked there, reaching 9.8 per high-reasoning response versus 5.8 for minimal reasoning.

For example, I would expect a CRM comparison to trigger separate searches for pricing, integrations, security, support pages, and documentation before ChatGPT forms its final answer.

Early citations also appeared to last longer. High reasoning was more likely to carry a brand from early research into later buying questions. In four of the 20 journeys tested, a brand cited at the problem stage still appeared at the selection stage. Minimal reasoning showed no full-journey persistence, meaning no brand cited at the Problem stage survived through to the Selection stage of the same journey.

I also found the domain reuse pattern important. High reasoning reused the same domains more often within a single answer, with the same domain appearing multiple times in 51 of 100 high-reasoning responses. Minimal reasoning did this in 26 of 100 responses.

Finance saw the biggest citation jump. The lift varied by category, but finance had the largest increase, with citation rates rising 28 percentage points in high reasoning. Health and lifestyle rose 24 points, while B2B SaaS gained 16 points.

Consumer tech barely moved, rising only 4 points. Even though high reasoning ran more sub-queries for consumer tech prompts than for any other category, it often landed on the same brands and sources as minimal reasoning.

Why I care about this: content can appear in fast ChatGPT answers but disappear when users ask more complex questions. Visibility depends on whether my pages, documentation, and third-party references can surface across the smaller searches ChatGPT runs before it answers.

About the data: Semrush and Indig tested 100 prompts across 20 buyer journeys in B2B SaaS, finance, consumer tech, and health and lifestyle. Each prompt ran once in minimal reasoning and once in high reasoning. The analysis tracked citation rate, cited sources, and fan-out queries.

The report: Only 25% of cited sources overlap between ChatGPT’s different reasoning modes [Study]

Inspired by this post on Search Engine Land.

July 3, 2026
Unlocking AI Search Success with Adobe’s New Tool

I’m excited to share how Adobe’s latest tool is changing the game for businesses eager to boost their brand visibility in AI-driven searches.

With the backing of 300 million AI prompts and the comprehensive data of Semrush, this platform is adept at tracking mentions, gauging share of voice, and identifying content gaps across prominent AI platforms.

Adobe introduced a pioneering solution for brands aiming to bolster their visibility and trustworthiness across AI interfaces. As part of the Adobe CX Enterprise, this tool offers an agentic AI system to streamline customer lifecycle management, covering everything from initial acquisition to fostering long-term loyalty.

AI traffic is skyrocketing. The way LLMs are utilized for product and service research represents a major pivot for both marketers and consumers. Recently, Adobe revealed data underlining this massive surge in AI traffic to U.S. retail sites—up by an impressive 1,324% from October 2024 to May 2026. The travel industry saw an even greater increase of 2,215% in the same timeframe.

As Vice President of strategy and product, Loni Stark, remarked to MarTech, “We used to get back the same thing—a SERP page with links. Now results seem random, but aren’t when scaled, and companies lack tools for this.”

Understanding brand visibility in AI search. Adobe Brand Visibility marks Adobe’s first venture into generative engine optimization (GEO), following its acquisition of Semrush. By integrating Adobe LLM Optimizer with Semrush’s AI Optimization tool, it provides unmatched insights.

Drawing from a staggering database of 300 million real-world AI search prompts, Adobe Brand Visibility helps teams pinpoint which prompts lead to brand exposure or loss.

Additionally, utilizing Adobe’s first-party data from owned channels, marketers gain a holistic view of how their brands appear on platforms like ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. Metrics encompass mention frequency, reach, competitive share of voice, and content gaps, allowing AI agents to offer prioritized recommendations that teams can rapidly implement and evaluate results.

Competitive intelligence unleashed. Adobe Brand Visibility offers tools for competitive brand analysis, comparison, and trend tracking, enabling marketers to effectively benchmark against competitors.

Featuring advanced SEO intelligence driven by Semrush’s extensive data of 28.5 billion keywords and 43 trillion backlinks, this platform underscores the continued importance of SEO fundamentals for AI search visibility. It shows the potential for existing search authority to yield AI citations and identifies opportunities for content investments across channels.

While there’s still much to learn about leveraging LLMs for brand visibility, Stark is confident in Adobe’s leadership position in this emerging space.

As Stark stated, “Adobe had proprietary data while Semrush offered data and trends. Though we may not have all answers, we possess unrivaled data.”

Inspired by this post on Search Engine Land.

June 18, 2026
Unveiling ChatGPT’s Traffic Path: Google’s Surprising Role

I recently came across an intriguing Semrush study that revealed some fascinating insights into ChatGPT’s traffic patterns. Despite a whopping 206% increase in referrals, surprisingly few sites actually see significant traffic. This is largely because many queries are backed by pre-trained knowledge rather than live web searches.

According to the study, over 30% of outbound clicks go to just 10 domains. Google alone claims more than 20% of these clicks. It’s intriguing to see how much weight the tech giant holds in this landscape.

ChatGPT is gradually leaning less towards live web searches. It only triggers search functions in 34.5% of queries now, a decline from 46% in late 2024. This shift indicates a change in how the platform’s role is evolving in navigating the web.

Let me break it down further. Although ChatGPT’s referral traffic saw a significant rise, the traffic mainly flows towards a limited number of sites. In fact, about 21.6% of this traffic heads straight to Google, followed by nine other domains that make up a total of just over 30%.

Many other websites are left with a small fraction of residual traffic. The number of domains receiving any referrals peaked at around 260,000 in 2025 but has since settled near 170,000.

Why is this important for us? The visibility on ChatGPT doesn’t always translate directly into traffic. Often, the impact of referrals may seem marginal. Plus, the decline in search-triggered queries makes securing citations and traffic even more challenging.

While ChatGPT defaults to pre-trained knowledge, it resorts to web searches in certain scenarios, like when users request sources, inquire about current events, or when the model shows uncertainty.

I’ve noticed a shift in user behavior—most ChatGPT prompts don’t mirror typical search queries. Instead, between 65% and 85% reflect complex, conversational inputs, indicating a transformation in engagement. Interestingly, the number of queries per session jumped 50% in late 2025.

Looking into the data, Semrush analyzed over a billion lines of U.S. clickstream data between October 2024 and February 2026. This analysis tracked prompts, referral destinations, and patterns in search usage.

For those interested, more detailed insights can be found in the ChatGPT traffic analysis. The study, titled “ChatGPT traffic analysis: Insights from 17 months of clickstream data,” is an enlightening read.

Inspired by this post on Search Engine Land.

April 7, 2026
Why Human-Written Content Outperforms AI on Google

I’ve come across some fascinating findings that demonstrate the prowess of human-written content on Google. According to data from Semrush, it turns out that content crafted by us, humans, stands a significant chance of claiming the top spot in Google’s search results, unlike its AI-generated counterpart.
The Semrush study, analyzing 42,000 blog posts, revealed that human-written content dominates the No. 1 position on Google 80% of the time. In comparison, purely AI-generated pages manage to capture this coveted spot only 9% of the time.
The details. Semrush conducted an analysis of 20,000 keywords and their top 10 results, utilizing an AI detector to classify the content.
Human-authored pages outshined both AI-generated and mixed content across all top 10 positions.
The gap was most pronounced at Position 1, where human content had an 8x higher likelihood of ranking.
Meanwhile, I noticed that AI-generated content tended to appear more frequently in the lower spots on Page 1, with a nearly double increase from Positions 1 to 4.
Yes, but. AI detection tools, as widely acknowledged, can be inconsistent. This inconsistency often leads to misclassifications between human and AI-generated content, introducing a degree of “fuzziness” in these classifications.
Why we care. While AI-generated content can occasionally perform well, the data suggests that the insights and intuition of human writers still drive superior results. For competitive queries, originality, expertise, and sound editorial judgment remain valuable advantages.
Perception vs. data. It’s intriguing that 72% of SEO professionals regard AI content as performing as well as or even better than human content. Yet, the actual ranking data clearly indicates a strong advantage for human-written content at the top.
How teams use AI. It doesn’t surprise me to find that AI is widely adopted, especially in creating a hybrid workflow:
A substantial 87% of teams retain significant human involvement during content creation.
64% employ a human-led, AI-assisted approach.
AI proves most beneficial in research, drafting, and optimization stages.
However, AI usage noticeably declines for multimedia, localization, and tasks requiring heightened judgment.
What’s driving adoption. While AI speeds up output, it doesn’t consistently enhance content quality.
73% of respondents highlighted faster production as AI’s primary benefit.
Yet, only 19% asserted that it improves content quality.
About the data: The analysis’s foundation lies on 42,000 blog pages from 200,000 URLs associated with 20,000 keywords. GPTZero was used to classify content for this study, which also includes insights from a survey of 224 SEO professionals involved in content and search.
The study. Does AI content rank well in search? [Survey + Data study]

Inspired by this post on Search Engine Land.

April 6, 2026
Effortlessly Integrate Semrush SEO Data with Profound Agents

I’ve always sought ways to streamline my workflow, especially when it comes to managing multiple SEO tools. That’s why I’m thrilled to bring Semrush’s comprehensive SEO data directly into the heart of Profound Agents. This integration allows me to seamlessly access domain metrics, analyze backlink profiles, conduct thorough keyword research, and dive into organic search data—all within the same environment where I monitor AI visibility, generate content, and perform competitive analysis.

Imagine the efficiency of handling everything without the need for exports, constant tab switching, or manual data collation. This integration is more than just a feature; it’s a game-changer for anyone who’s passionate about optimizing their SEO strategy while saving time.

Inspired by this post on Try Profound Blog.

March 31, 2026
59% of SEO Roles Now Senior-Level: The AI-Powered Shift

I’ve noticed a significant shift in the SEO industry toward senior, strategy-focused roles. As AI increasingly handles execution tasks, the demand for seasoned strategists has grown, along with an increase in salaries and responsibilities that span multiple channels.
The change in hiring trends is evident when looking at a recent Semrush analysis of 3,900 job listings. It appears companies are now prioritizing leadership skills, innovative experimentation, and cross-channel visibility over purely technical execution.
Why it matters to me. The landscape for SEO careers and skillsets is evolving. Entry-level positions are mostly focused on execution, while leadership roles require a firm grasp of strategy across various domains such as search, AI assistants, and paid channels, ensuring they drive significant revenue.
What’s changing now. Senior roles account for 59% of job listings, clearly dominating the landscape. In contrast, mid-level positions like specialists and managers are less prevalent, with only 15% and 10%, respectively.
Companies are redirecting their budgets towards strategic roles as AI tools begin to absorb more of the technical workload.
The shift in skills. The skills in demand now extend beyond traditional SEO to include coordination, experimentation, and decision-making capabilities:
Project management is mentioned in over 30% of the listings, highlighting its importance.
Communication is highlighted in 39.4% of non-senior roles, indicating its fundamental role in the industry.
Experimentation is noted in 23.9% of senior roles, compared to just 14% of other roles.
Technical SEO appears in approximately 6% of postings, showing its niche but crucial role.
Tools and channels. The modern SEO toolkit now includes analytics, paid media, and comprehensive data tools.
Google Analytics is cited in up to 47.7% of job listings, underlining its importance.
Google Ads features in 29% of the listings, showcasing its growing relevance.
Demand for SQL skills is rising, especially at the senior level.
AI tools, such as ChatGPT, are increasingly mentioned, reflecting their future role in SEO.
AI expectations. AI literacy is shifting from being a nice-to-have to an essential skill:
31% of senior roles now reference AI capabilities.
Nearly 10% of listings highlight familiarity with LLMs.
Concepts such as AI search and AEO are increasingly common in job descriptions.
Pay and positioning. SEO is being increasingly recognized as a vital business function:
The median salary for senior roles has reached $130,000, markedly higher than the $71,630 for other roles, with some positions offering even more.
Preferred degrees are leaning towards business and marketing, reflecting the strategic emphasis.
Remote work prevalence. Remote options are available in over 40% of job listings, indicating a shift towards flexible work environments across all levels.
About the data. This analysis by Semrush covers 3,900 SEO job listings in the U.S., gathered from Indeed as of November 25. The roles were deduplicated and segmented by seniority before a semantic keyword extraction analysis was applied.
Discover more about the study. For a deeper dive into these findings, check out Semrush’s detailed study titled What 3,900 SEO Job Listings Reveal for 2026: Experiments, AI, and Six-Figure Salaries.

Inspired by this post on Search Engine Land.

March 31, 2026
Adobe Phases Out Marketo SEO: Export Your Data by March 31

As a regular user of Adobe’s Marketo Engage, I recently learned that Adobe plans to discontinue the SEO feature by the end of March 2026. This information was detailed in Adobe’s February 2026 release notes, and I wanted to share my thoughts and remind you to take action if you use this tool.

I know how crucial it is to export existing SEO data before the tool’s deprecation on March 31, 2026. You can find export instructions on this page. It’s essential to act promptly, as the SEO tile will be removed from the platform starting April 1.

So, what’s the story here? Adobe’s Keith Gluck mentioned that retiring underutilized features will allow the Marketo Engage team to refocus resources elsewhere. If you’re seeking alternative SEO tools, Adobe’s acquisition of Semrush in 2025 offers a robust solution, with Semrush being a comprehensive SEO and visibility tool. (Remember, Semrush now owns Third Door Media, the publisher of Search Engine Land.)

I wasn’t taken aback by this news, as it’s been common knowledge among those who keep up with Marketo updates. Reports have indicated that not many users fully tapped into the SEO capabilities. Additionally, the Marketo Engage team hasn’t prioritized these features recently.

With shifts in the search landscape, driven by rapid advancements like LLMs, saying goodbye seemed timely. Semrush’s entry into the Adobe ecosystem feels like a fitting evolution of their service offerings.

If you’re interested, check out a related update on LinkedIn:

Inspired by this post on Search Engine Land.

March 20, 2026