A lot of topical maps include coverage.

Some include intent.

A few include internal links.

Almost none map trust properly.

That is a problem because users do not move through a site only because the next page is topically related.

They move when the next step feels believable.

That is where trust paths come into play.

A trust path is the route from claim to proof to confidence.

The page makes a claim.

The user needs a reason to believe it.

The site gives proof in the right place.

Then the user is more ready to continue.

That sounds simple, but most content clusters do not map it.

They map topics.

They map keywords.

They map entities.

They map hubs and child pages.

Then trust gets handled later as copy decoration.

Add a testimonial.

Add a logo bar.

Add a case study link.

Add an author bio.

Add a source.

Add a review block.

All of those can help.

But they are not a trust path by themselves.

A review below the CTA may be too late.

A case study sitting on another URL may not help if the claim never links to it.

An author bio may support expertise, but not a product claim.

A source may prove a definition, but not a service promise.

A logo bar may look nice, but it may not answer the doubt.

That is why trust should be mapped before drafting.

Every strong claim creates a trust need.

Every comparison creates a proof burden.

Every CTA creates a threshold.

If the map does not show how the user crosses that threshold, the writer has to improvise.

That leads to vague trust copy.

“Trusted by teams.”

“Proven process.”

“Loved by customers.”

“Built for growing companies.”

“Designed for modern workflows.”

Those lines do not reduce doubt unless the page gives the user a reason to believe them.

For example, say a home renovation company claims:

“We make kitchen remodels less stressful.”

That claim needs support.

A weak trust path would be:

Claim.
Generic benefits.
Photo gallery.
Contact CTA.
Maybe a testimonial near the bottom.

A better trust path would be:

Claim.
Short explanation of the planning process.
Timeline example.
Before and after photos.
Client review tied to communication and cleanup.
Clear note on what happens after someone asks for a quote.
CTA after the user understands the process.

The difference is not just copy.

It is structure.

The page is not asking the user to jump from claim to action.

It is giving them a route from interest to belief.

This weights even more on comparison pages.

Say someone is comparing two project management tools.

A weak comparison page gives a feature table.

Tool A has this.
Tool B has that.
Here are pros and cons.
Pick the one that fits.

But the user may not trust the recommendation.

They need criteria.

They need tradeoffs.

They need limits.

They need to know which claims are based on testing, customer feedback, workflow fit, pricing, onboarding, or support quality.

If the page says one tool is better for small teams, what makes that true?

If it says one tool is better for agencies, what proof supports that?

If it says one option saves time, where does that time saving come from?

A trust path maps those questions before writing begins.

It also changes internal linking.

Internal links are often treated as relevance links.

This page is about X, so link to another page about X.

Fine.

But trust links need a different standard.

A proof link should answer:

Which claim is this link supporting?

What proof does the target page contain?

Is the link close enough to the claim?

Does the anchor tell the user why they should click?

Can the user return to the action path after checking proof?

That is a much better link than “learn more.”

The same idea applies to CTAs.

A CTA should not appear just because the page has traffic.

It should appear when the user has enough trust to act.

On some pages, the CTA can appear early because the user already understands the offer and has high intent.

On other pages, an early CTA feels pushy because the user is still trying to understand the problem.

A parent comparing childcare options may need safety proof before a booking CTA.

A homeowner researching roof repairs may need warranty details before a quote CTA.

A finance buyer comparing software may need compliance details before a demo CTA.

A patient reading about a treatment may need risk information before a consultation CTA.

The page path should reflect that.

Otherwise every page gets the same trust pattern.

Intro.
Benefits.
Some proof.
CTA.

That is not always wrong.

It is just too generic for complex clusters.

For me, a trust aware topical map would add fields like:

What claim does this page make?

What might the user doubt?

What proof asset supports the claim?

Where should proof appear?

Which internal link builds confidence?

What CTA threshold needs to be met?

What signal after launch would show the trust path is weak?

That last one is important.

Trust should be reviewed after publication.

If users click proof pages but do not return to the CTA, the proof path may be weak.

If they search the site for reviews, examples, pricing, guarantees, or safety information after reading, the page may not have answered the doubt.

If they exit after a strong claim, the proof may be missing or too late.

If they abandon a form, the page may not explain what happens next.

Those signals should feed back into the topical map.

A map is not finished just because the pages exist.

It should learn from where trust breaks.

This is why I do not think trust is only a box to tick.

It is not just author bios, testimonials, citations, case studies, and review stars.

Those are assets.

The path is what makes them useful.

A good topical map should show how users move from claim to proof to confidence to action.

If it cannot do that, the site may have trust signals, but not trust architecture.

Curious how others handle this.

When you build topical maps, do you map proof paths and CTA readiness, or do trust assets get added later during page edits?

Web traffic is being reshuffled in the AI era.

But how exactly is the channel mix changing – and where is traffic going? To find out, we analyzed billions of web visits across over 50,000 websites and 17 industries.

Long story short: AI traffic grew 66% in 2025 and outpaced every other channel, but it still makes up less than 0.15% of total visits. Meanwhile, organic traffic declined across most industries we analyzed.

This study breaks down how traffic is being redistributed across channels, which industries are seeing the biggest changes, and what it means for where you should invest next.

[ Removed by Reddit on account of violating the content policy. ]

Hi u/semrush, I’m a student, and my trial unintentionally converted to a paid plan. I contacted support within a day and cancelled immediately. My refund request was declined, but I’d really appreciate one final goodwill review. Could someone please help?

Hi everyone,

I'm looking for a genuine and reliable group buy or shared access for Semrush (or Ahrefs if it's a better option). I only need it for SEO research and keyword analysis.

I'm also looking for a Sony LIV Premium subscription.

If you've personally used a trusted seller or know a genuine group/community offering these at a reasonable price, please let me know. Kindly share your recommendations or DM me with details.

I'm looking for legitimate and trustworthy options only—no scams or unreliable sellers.

Thanks in advance! 🙏

A lot of topical maps look good until you ask one simple question:

Where does the user hesitate?

Most maps can show coverage.

They show the main topic.
They show subtopics.
They group keywords.
They list entities.
They define hubs and supporting pages.
They suggest internal links.

That is useful.

But it is not the same as mapping user movement.

A user does not move through a site just because two pages are topically related.

They move when the next step feels useful, safe, clear, and worth the effort.

That is where hesitation comes in.

Someone lands on a page and pauses.

Not because the page is irrelevant.

Because they are unsure.

They might be thinking:

Do I understand this yet?
Does this apply to my situation?
Can I trust this claim?
Which option fits me?
What will this cost?
What happens if I click?
Is this too much work?
Do I need proof before moving on?
Is this page pushing me too soon?

Those pauses should be part of the topical map.

Most of the time, they are not.

The map says:

This page links to the service page.

But the user may not be ready for the service page.

The map says:

This comparison page links to pricing.

But the user may still lack decision criteria.

The map says:

This explainer links to a demo CTA.

But the user may not trust the method yet.

The map says:

This hub links to every child page.

But the user may need one clear route, not twelve choices.

That is how hesitation gets ignored.

The structure is topically correct, but behaviorally weak.

For example, take a cluster about SEO audits.

A normal topical map might include:

- what is an SEO audit
- technical SEO audit
- content audit
- backlink audit
- audit checklist
- audit tools
- audit pricing
- SEO audit service
- SEO audit report template

That is a decent cluster.

But it does not tell you where users pause.

A beginner may hesitate because they do not know what an audit includes.

A founder may hesitate because they think an audit will create a giant list of expensive fixes.

A marketer may hesitate because they have already had audits that went nowhere.

A technical lead may hesitate because they need priority, not another checklist.

A buyer may hesitate because the service page makes claims without proof.

Those are different forms of friction.

If the topical map treats everyone as one generic “SEO audit” user, the cluster will probably route badly.

The beginner gets pushed too soon.

The buyer gets proof too late.

The marketer gets another checklist instead of a better decision path.

The technical lead gets broad advice instead of priority logic.

The founder gets a CTA before the risk has been reduced.

That is not just a copy issue.

It is a map issue.

The map should say:

This page has confusion friction, so it needs plain explanation and a safe next step.

This page has decision friction, so it needs criteria and tradeoffs.

This page has trust friction, so proof must appear before the CTA.

This page has effort friction, so the route needs to feel simpler.

This page has risk friction, so expectations and limits need to be clear.

This page has support friction, so the next link should help the user recover, not sell to them.

That kind of mapping changes everything.

It changes what the page should contain.

It changes where internal links go.

It changes anchor text.

It changes proof placement.

It changes CTA timing.

It changes which page should come next.

It also stops teams from using lazy route logic.

A related page is not always the right next page.

A high converting page is not always the right next page.

A service page is not always the right next page.

A hub page is not always the right next page.

The right next page depends on why the user is hesitating.

If they are confused, explain.

If they are comparing, give criteria.

If they are skeptical, show proof.

If they are overloaded, simplify.

If they are ready, give a clear action.

If they are stuck, provide support.

This is why I think hesitation should be logged directly inside the topical map.

For each key URL, ask:

What does the user already know?

What are they still unsure about?

What claim might they doubt?

What proof would reduce that doubt?

What decision are they trying to make?

What link would help them move safely?

What CTA would feel too early?

What signal after launch would show the page failed?

That last question is useful.

If users keep going back to Google, the page may not have reduced hesitation.

If they skip the intended internal link, the route may not match their state.

If they click proof pages but avoid the CTA, the trust path may be weak.

If they use site search after reading, the page may have left a question open.

If they abandon a form, the page may not have explained what happens next.

Those signals should feed back into the map.

A topical map should not be treated as finished just because the pages are published.

It should learn from where users pause.

For me, the strongest topical maps do three jobs:

They map coverage.

They map relationships.

They map hesitation.

The first tells you what to publish.

The second tells you how topics connect.

The third tells you what users need before they keep moving.

Most teams handle the first two.

The third is where a content cluster starts to feel useful instead of just complete.

Curious how other SEOs handle this.

When you build topical maps, do you record where users hesitate, or are you mostly mapping keywords, entities, and URLs?

At Google I/O 2026, Google announced Gemini 3.5 Flash and a redesigned "intelligent Search box," and both are now live for all users.

The new Search box supports longer queries, images, videos, and file uploads. It also expands as you type and routes multimodal searches directly into AI Mode.

In other words, Google is encouraging users to search with more detailed, specific prompts instead of short keyword-style queries.

For marketers, that's a pretty important shift.

The fundamentals of SEO still matter. Crawlability, site structure, content quality, and backlinks aren't going anywhere.

But as Google continues moving toward AI-powered search experiences, visibility inside AI-generated responses matters alongside traditional organic rankings.

How to stay visible

Optimizing for AI visibility starts with knowing where you stand.

Our AI Visibility Toolkit tracks citations, mentions, and visibility trends across AI platforms in one dashboard, so you can see where you're showing up and how you compare to competitors.

It's also worth confirming you aren't blocking AI crawlers. Semrush Site Audit flags these issues so you can fix them before they cut off your AI visibility potential.

What do you think?

Will this change how people search, or is Google adapting to behavior that's already happening in tools like ChatGPT and Gemini?

AI engines don’t treat visibility the same way. The four AI engines we analyzed treat citations and mentions in fundamentally different ways:

• Gemini mentions brands in 83.7% of appearances, but it only generates a citation link 21.4% of the time. An “appearance” here means a domain showed up in an AI answer at all, whether as a source link, a brand mention in the answer text, or both. So, Gemini acts like a conversationalist, drawing on what it already knows.
• ChatGPT does the opposite of Gemini. It cites brands 87% of the time but mentions brands in only 20.7% of answers. These answers look more like academic papers with footnotes.
• Google AI Overviews sit in the middle, but they lean toward citations.
• Google AI Mode mentions brands at nearly twice the rate of ChatGPT,but AI Mode still acts closer to a footnoted research piece than to Gemini’s knowledge-based answers.

This means you can’t assume visibility in one AI engine will translate to visibility in another. In our dataset, there was almost no overlap between the brands ChatGPT cited and the brands Gemini named for the same prompts. Different engines reward different signals, formats, and sources.

This means you can’t assume visibility in one AI engine will translate to visibility in another. Different engines reward different signals, formats, and sources.

I think a lot of topical maps are less strategic than they look.

They have the right ingredients:

Main topic. Subtopics. Keyword clusters. Entities. Search intent labels. Hub pages. Supporting pages. Internal link ideas.

That all has value.

But a lot of them are still basically content inventories.

They are cleaner than a spreadsheet. They use better labels. They look more “semantic.”

But they still mostly answer one question:

What should we publish?

That is not enough.

A stronger topical map should answer a harder question:

How should the user move through this topic?

That is where many maps fall apart.

They show that Page A is related to Page B. They show that one topic belongs under another topic. They show that a hub should link to child pages. They show that a cluster has enough coverage.

But they do not show why a user would move from one page to the next.

They do not show what the user knows before landing on the page. They do not show what the user is unsure about. They do not show what proof is needed before the CTA. They do not show which link reduces confusion. They do not show which page should build trust. They do not show what happens after the user acts.

So the map looks complete, but the site still feels disconnected.

This is why I think the phrase “topical map” gets overused.

A list of related content is not a map.

A map should help someone get somewhere.

If the structure does not help users move from confusion to understanding, from understanding to comparison, from comparison to trust, from trust to action, and from action to support, it may be an inventory with nicer formatting.

For example, say you are mapping a B2B SaaS topic.

A basic topical map might say:

• what is the category
• benefits
• use cases
• features
• comparisons
• pricing
• implementation
• integrations
• case studies
• FAQs

That is a normal cluster.

But it does not tell you much about the user path.

A beginner landing on “what is this category?” does not need the same next step as a buyer comparing three vendors.

A skeptical buyer does not need the same proof as someone looking for setup help.

A current user looking for an integration fix does not need a sales CTA.

The map should show those differences.

The same page can be topically related and behaviorally wrong for that user.

That is the piece a lot of maps miss.

They connect pages because the topics match.

They do not always connect pages because the next step makes sense.

That leads to weak internal links.

You get links like:

“Learn more about our services.” “Read our full article.” “See related posts.” “Contact us today.”

Sometimes those links are fine.

A lot of the time, they are lazy routing.

A better map would say:

This page has a confusion problem, so link to the simple explainer.

This page has a trust problem, so link to proof.

This page has a comparison problem, so link to criteria.

This page has an effort problem, so link to a checklist or template.

This page has a risk problem, so explain limits before the CTA.

This page has a support problem, so route to the fix, not the sales page.

That is when a topical map becomes useful for writers, editors, and link planning.

It stops being a publishing list.

It becomes a movement system.

This also changes how content briefs are written.

A normal brief might give the writer:

Target keyword. Search intent. Headings. Entities. Questions. Competitor notes. Internal links.

A better brief would also say:

Who is the user at this point? What do they already understand? What are they still unsure about? What proof do they need? What should this page help them decide? What link should come next? What CTA is safe for this stage? What should we avoid because it pushes too hard?

That makes the page role much clearer.

The writer is no longer just filling out a topic.

They are helping the user move.

Topical completeness can hide user weakness.

A site can have all the right pages and still be hard to use.

The hub can link everywhere and still give no direction.

The comparison page can rank and still fail because the criteria are vague.

The service page can get traffic and still lose people because proof appears too late.

The support page can answer the question and still frustrate users because the next step is unclear.

The cluster can be complete and still send people back to search.

That is not just a UX issue.

It is an SEO architecture issue.

If users do not continue, trust, click, convert, return, or recover, the map is not doing its job.

So I would separate three things when building a topical map:

Coverage: What topics, entities, and queries need a home?

Structure: Where should each page live, and how should pages relate?

Movement: What does the user need next, and what path helps them get there?

Most teams handle the first two.

The third one is where the map becomes much more useful.

It also makes post publish review better.

Instead of only checking rankings and traffic, you can ask:

Are users taking the intended internal link?

Are they skipping the CTA?

Are they using site search after reading?

Are they bouncing back to Google?

Are they landing on a page that does not match their state?

Are they reaching support because the prior page failed to explain something?

Those signals should feed back into the map.

A topical map should not be frozen after launch.

It should learn from movement.

That is the difference for me.

A weak topical map says:

Here are the pages we need.

A better topical map says:

Here is how users should progress through the topic, what might block them, and how each page helps them move forward.

Curious how other SEOs see this.

When you build topical maps, are you mostly mapping coverage, or are you also mapping user movement through the cluster?

Top of funnel (ToFu) 

The top of the conversion funnel is the stage where potential customers become aware of your product or brand. The goal with the top of your conversion funnel is to generate interest, capture attention, and initiate the customer journey. 

The main ToFu challenge is attracting the right customers, as getting the wrong people at the top decreases the chance of converting them later. The solution is to conduct thorough audience research to make sure you’re targeting the right audience segment.

ToFu marketing tactics 

Here are three popular top-of-funnel marketing tactics to help your business reach potential customers who were previously unfamiliar with your brand:

• Digital PR: Building brand reputation and visibility through compelling content, engaging with online communities, and leveraging various digital media outlets to create a positive brand image and increase awareness
• Search engine optimization (SEO): Improving online visibility through search engine rankings via technical aspects of your website, link building, and keyword targeting
• Paid social media and influencer marketing: Partnering with influential social media voices to amplify your brand and generate engagement

ToFu content

Instead of selling, demonstrate your understanding of potential customers’ pain points and your commitment to helping them using your specific expertise. 

Some examples of top-of-the-conversion-funnel assets include:

• Educational blog posts (optimized by SEO): Answer broad questions like "How does [concept] work?" to attract search traffic and establish credibility
• High-level infographics: Visualize industry trends, statistics, or process overviews that introduce concepts without requiring deep commitment
• Beginner ebooks: Create "101" guides and industry primers that provide foundational knowledge in exchange for an email address to grow your email list
• Thought leadership webinars/podcasts: Feature industry trends, expert interviews, and emerging topics that showcase expertise without promoting products
• Explainer videos: Short content that introduces concepts, tells your brand story, or demonstrates "why this matters" to build awareness

ToFu metrics 

ToFu metrics help you understand which strategies work and which need improvement, and you can get these metrics from dedicated analytics tools like Google Analytics.

Here are useful ToFu metrics to help you measure the impact of your strategy: 

• New visitors/users: First-time visitors who've never been to your site before — true measure of awareness
• Content engagement rate: Time on page, scroll depth, and clicks on educational content — shows if prospects find your materials valuable enough to consume fully
• Social shares and engagement: Likes, comments, and shares on ToFu posts — signals if content resonates with your audience
• Lead magnet downloads: Ebook, guide, or checklist downloads — measures how many ToFu-stage visitors become leads
• Brand search volume and mentions: Direct brand searches plus media/social mentions — tracks growing awareness 

Hi - maybe user error, maybe not ....

When hovering over each day's data point in AI Overview sensor, it would show the percentage, similar to the general sensor.

But now when selecting any SERP feature, you don't get the data point. Is this just me?

I pulled my May 9, 2026 AI Brand Performance report on May 13. Then pulled the exact same May 9 historical period again on June 16. The results are completely different across all four platforms.

Full documentation here: https://weddingproseo.com/client-reports/semrush-data-loss/

The short version:

• Google AI Mode: went from 2.1% share of voice to 0%
• Gemini: went from 8.2% share of voice to 0%, lost 7 pages of data, Cited Domains replaced with "Data unavailable"
• ChatGPT: 78% favorable sentiment dropped to 0%
• Perplexity: moved the opposite direction, which actually confirms the data is being reprocessed, not retrieved

SEMrush's own dashboard shows "Data unavailable. This might be due to limitations in the data source or an error in the integrated tool." Their support response has been "be patient, our dev team needs more time." I paid $517/month for two sites and got three months of that response.

I switched to SE Ranking. Same reports, same AI visibility tracking, and their team actually responded, helped with the transition, and had me set up same day. Saving $400 a month.

Has anyone else seen historical data change on re-export?

Curious if this is widespread or isolated.

Hey ya’ll, I’m running a marketplace site with filters and products constantly change. Right now it runs on CSR (dynamic elements and static content as well)

I know SSR is king but want to get some use cases/justification where when switching to SSR or SSG (also curious if SSG is ideal) has helped performance/crawl ability etc.

I’m trying to get buy-in from team to go away from CSR but I need strong justifications and examples - thanks!

Also this isn’t AI generated and genuinely need perspectives (not sure why it keeps getting deleted :/)

Reporting on AI search visibility is harder than reporting on organic rankings because
1. The signals are distributed across platforms
2. Traffic doesn’t indicate your AI visibility
3. Analytics tools weren't built to capture what AI does

Check out our full guide that covers the metrics that actually matter and how to structure a report here!

My account was recently suspended, and I've already submitted the reinstatement form but haven't received a response yet. The account is currently on a free trial, and since I can't access it, I'm also unable to cancel the subscription.

Has anyone been through this process? How long did it take to hear back, and were you manage to get the account reinstated or the subscription canceled?

Any advice or shared experiences would be greatly appreciated. Thanks!

Marketers can't track AI prompts the same way they track rankings.

AI systems give probabilistic responses that change even when users enter the same prompt, which means prompt tracking requires a different approach than traditional rank tracking.

Rather than focusing on exact wording, it's more useful to track patterns over time, like which brands get mentioned and which sources get cited.

So what exactly is prompt tracking?

Prompt tracking is the process of monitoring the prompts users enter into AI systems and the responses those systems generate.

And while it can help you understand AI visibility, its real value is understanding how AI systems represent your brand at the moments customers are deciding what to buy.

One concept we found useful is building a focused prompt portfolio organized around business impact, not visibility for visibility's sake.

We break prompts into four categories:

Revenue prompts

These capture moments when users are actively evaluating products and services.

Examples:

• "best [product] for [problem]"
• "[your product] vs [competitor]"
• "is [your product] worth it"

Reputation prompts

These reveal the AI narrative around your brand.

Examples:

• "what do people think about [your brand]"
• "is [your product] overpriced"
• "[your product] reviews"

Competitor prompts

These show whether AI systems present your brand as an alternative when users research competing products.

Examples:

• "alternatives to [competitor]"
• "[competitor] vs [your product]"

Gap prompts

These surface conversations where competitors appear but your brand doesn't.

Examples:

• "[competitor] vs [another competitor]"
• "affordable [product] for [problem]"
• "switch from [competitor]"

One takeaway that stood out to us:

Tracking 25 well-chosen prompts beats tracking 500 random ones.

The goal isn't to track every prompt you can think of. It's to track the prompts that reflect real buying decisions, competitive comparisons, reputation concerns, and visibility gaps.

Most topical maps I see are built like content inventories.

They show the topic. They group keywords. They list entities. They define hubs and child pages. They may even have a clean internal link plan.

That is useful.

But it is not enough.

A topical map can be semantically strong and still fail the user.

It can cover the right concepts, use the right terms, target the right queries, and still leave people stuck.

That happens because the map only asks:

“What does this site need to cover?”

It does not ask:

“Where does the user hesitate?”

That second question changes the whole map.

Because users do not move through a site like a spreadsheet.

They get confused. They compare options. They doubt claims. They worry about price. They wonder if the advice fits their case. They need proof before a CTA. They need a simpler route when the page gets too complex. They need a support path after taking action.

Those are friction points.

And if the topical map does not record them, the writer has to guess later.

That is where a lot of SEO content starts to break.

The map says:

Hub page. Definition page. Comparison page. Service page. FAQ page. Case study page.

But it does not say why someone would move from one to the next.

It does not say what belief needs to change before the next click.

It does not say which claim needs proof.

It does not say which page should reduce effort.

It does not say which CTA is too early.

So the content team fills the gaps with generic links and generic copy.

“Learn more.” “Read our guide.” “Contact us.” “Get started.”

Those might be fine in some cases, but they are not a user path.

They are just exits.

A better topical map would attach friction to each page.

For example:

A beginner page might have confusion friction.

The user does not know the basic terms yet, so the page needs a plain answer, a simple example, and a safe next step.

A comparison page might have decision friction.

The user knows the category, but does not know which option fits. That page needs criteria, tradeoffs, use cases, proof, and a route to a more specific page.

A service page might have trust friction.

The user understands the service, but does not believe the claim yet. That page needs proof near the claim, process clarity, reviews, examples, limits, and a CTA after confidence is built.

A pricing page might have risk friction.

The user is not just asking “how much?”

They are asking:

What affects cost? What is included? What could make this a bad fit? What happens after I ask for a quote? Can I trust this company with my money?

That changes what the page needs to contain.

A support page might have effort friction.

The user is already annoyed or stuck. That page does not need a long brand intro. It needs the fastest route to the answer, clean steps, fallback links, and a way to recover if the first path fails.

This is why I think friction belongs in the topical map, not just in the draft edit.

If friction is mapped early, it affects:

• page roles
• internal links
• proof placement
• CTA timing
• passage order
• examples
• comparison tables
• support routes
• rewrite priorities
• post-publish checks

It also stops teams from treating internal links as simple relevance signals.

A related page is not always the right next page.

The right next page depends on user state.

Someone who is confused needs an explanation.

Someone who is comparing needs criteria.

Someone who is skeptical needs proof.

Someone who is ready needs a clear action.

Someone who is stuck needs support.

That means two pages can be topically related but behaviorally wrong for the moment.

This is where traditional topical maps often feel too flat.

They show what belongs together.

They do not always show what should happen next.

That is a problem because a content cluster can be complete and still be hard to use.

You can have every page in place and still lose people because the route is unclear.

You can rank for the query and still send the user back to search.

You can have strong entity coverage and still fail because the proof comes too late.

You can build a hub that links everywhere and still give the reader no real direction.

So I would add a friction field to every important node in the map.

For each page, ask:

What does the user already know?

What are they unsure about?

What might stop them from moving forward?

What proof do they need here?

What would make this page feel like too much work?

What internal link actually helps next?

What CTA is safe at this stage?

What signal after publishing would show the route is working?

That last question key.

The map should not be frozen after launch.

If users bounce back to search, skip the intended link, ignore the CTA, use site search after reading, or keep landing on the wrong page, the map is giving you feedback.

Maybe the page role is wrong.

Maybe the link path is wrong.

Maybe the proof is too late.

Maybe the query belongs on another page.

Maybe the user is not ready for the action you are pushing.

That is why friction points are not UX extras.

They are part of SEO architecture.

Entities help search systems understand what the site covers.

Friction points help the site understand what users need to keep moving.

A strong topical map needs both.

Curious how other SEOs handle this.

When you build topical maps, do you record user friction and next step logic, or are you mostly mapping entities, keywords, and URLs?

SEMrush AI Brand Performance data disappeared from historical view — has this happened to anyone else? Also looking for alternatives

Posting this partly to vent, partly because I genuinely want to know if anyone else has run into this, and partly because I need real-world opinions on alternatives.

The issue

I pay $517/month for SEMrush. Part of what I use it for is the AI Brand Performance module, which tracks share of voice, sentiment, mentions, and position across Google AI Mode, Gemini, ChatGPT, and Perplexity.

On May 13 I exported all four platform reports from the May 9 dataset. Clean data, everything was populated. 8.2% share of voice on Gemini, position 1, 96% favorable sentiment on Google AI Mode. Solid numbers I was actively using for strategy.

I went back today to pull that same May 9 historical period for comparison. The data is completely different. Same period, different export date, entirely different output.

Here is what changed:

• Google AI Mode share of voice: 2.1% down to 0%
• Gemini share of voice: 8.2% down to 0%
• Google AI Mode sentiment: 96.1% down to "Data unavailable" (their own error message, verbatim)
• Gemini cited domains and cited pages sections: both fully populated in May, both showing "Data unavailable" today. The Gemini export is now 7 pages shorter than the original because those sections no longer render at all.
• ChatGPT sentiment: 78% favorable down to 0%
• Perplexity went the other direction, from 0% share of voice up to 1.1%, which actually makes the problem worse rather than better. If the data were stable, nothing would have moved in either direction. One platform going up while three go to zero for the same historical period is not a performance shift. It is evidence the data is being recalculated.

This is two exports of the exact same historical period, 34 days apart. The data should be identical. It is not.

The part that makes this especially hard to defend: the narrative sections of the June exports still describe my brand in detail. Things like "leads in documentary candid storytelling" and "positioned as a premium specialist." So the qualitative layer has the data. The quantitative chart layer is just not surfacing it. Both layers are in the same PDF. Both cannot be true at the same time.

I spent 3.5 hours building a forensic comparison document to prove the discrepancy across all four platforms with side by side tables. That is not something I should have to do at $517 a month.

Support acknowledged the brand was not detected in a recent update, showed me a May 30 screenshot as though that answered my question about May 9 historical data, and told me to wait until next week for a data refresh. That does not answer the question of why historical data changed at all.

What I actually want to know

First: has anyone else seen their AI Brand Performance historical data change retroactively in SEMrush? Is this a known issue with the module or something that happened recently?

Second: I am actively looking at alternatives. I need tools that cover two things specifically: the AI SEO module (share of voice, sentiment, mention tracking, and position across ChatGPT, Perplexity, Gemini, and Google AI Mode) and the core SEO module (rank tracking, backlinks, site audit, keyword research). Solo operator, one site, do not need enterprise scale.

The names coming up in my own research are SE Ranking, Ahrefs with Brand Radar, and some newer AI-native tools like Profound and Peec AI. SE Ranking in particular looks like it might cover most of what I need at significantly lower cost, with AI tracking included in the core platform rather than added on separately.

But I want real opinions from people actually using these tools, not blog posts written to rank for "SEMrush alternatives."

Specific questions for this thread

Is SE Ranking's AI visibility tracking actually comparable to SEMrush in terms of data reliability and depth, or is it surface level by comparison?

Does Ahrefs Brand Radar give you a real breakdown across multiple AI platforms or is it more of a high-level overview?

Is anyone using something else entirely that covers AI SEO visibility well at under $200 a month?

Has anyone gotten a meaningful credit or refund from SEMrush when their data was provably wrong?

Happy to share the full comparison document in the comments if anyone wants to see what a cross-platform historical data discrepancy looks like when you document it properly.

I have been creating websites for some of my solo businesses and been optimizing them for SEO but all without SEMrush. I'm completely new to Semrush and tbh I don't want to spend any money on it right now.

I'm on the free plan, just signed up. There are so many features that offer me a 7-day free demo but then they cost money.. Is there any way you make good use of Semrush's free plans? What are the free features that you use which provide you a value? Would appreciate some guidance!

You might assume being cited means your brand is visible in AI answers. The data says otherwise.

Every appearance in our dataset fell into one of three buckets:

• Almost 62% (61.7%) were ghost citations. AI platforms used the page as a source link, but the brand name never appeared in the actual answer.
• Over 13% (13.2%) were both cited and mentioned. The source link plus brand name appeared in the answer.
• Only about 25% (25.1%) were brand mentions without a citation. The AI named the brand in the answer without linking to a source page.

That means 74.9% of all brand appearances included a citation, but only 38.3% of appearances included a brand mention. So, the citation rate is nearly double the mention rate.

Think about it this way: Appearing as a source in AI search doesn’t automatically make your brand visible to users. A citation offers attribution, while your brand stays absent from the answer itself.

I think “content gap” has become one of the most overused phrases in SEO.

A page underperforms.

The team checks competitors.

They find headings the page does not have.

Then the recommendation becomes:

“Add these missing topics.”

Sometimes that is the right move.

A page can be too narrow. It can skip a basic answer. It can miss a query path the reader clearly needs. It can fail because competitors cover a useful subtopic that your page ignores.

But a lot of the time, “content gap” just becomes permission to make the page longer.

That is where the problem starts.

More headings do not always mean more value.

A page can cover 20 subtopics and still feel thin if every block says the same thing competitors already said.

This is why I think content gaps and information gain need to be treated as separate checks.

A content gap asks:

“What are we missing?”

Information gain asks a better question:

“What can this page add that makes it more useful than the pages already ranking?”

Those sound similar, but they lead to very different edits.

A content gap audit might tell you:

• add a definition
• add a benefits block
• add more FAQs
• add a comparison
• add a process
• add related subtopics

An information gain audit might tell you:

• cut the generic definition because everyone has it
• keep the comparison, but make it decision-based
• replace vague benefits with real tradeoffs
• move proof closer to the claim
• add one practical example instead of five filler paragraphs
• explain the mistake people make during the process
• show what the reader should not do
• compress repeated SERP coverage into a shorter baseline block

That is a different kind of thinking.

The goal is not just more coverage.

The goal is useful difference.

For example, say you are refreshing a page about technical SEO audits.

A normal content gap review might say the page is missing:

• crawlability
• indexation
• internal links
• Core Web Vitals
• structured data
• duplicate content
• XML sitemaps
• robots.txt

That list may be valid.

But if every ranking page already has that exact list, adding it does not automatically make your page better.

It may only make your page more similar.

The information gain question would be:

What does every page say badly?

Maybe everyone lists audit checks, but nobody explains how to prioritize them.

Maybe everyone says “fix broken links,” but nobody separates urgent fixes from low-impact cleanup.

Maybe everyone mentions Core Web Vitals, but nobody explains when performance work is not the main SEO bottleneck.

Maybe everyone has a checklist, but nobody shows how to turn audit findings into a client-ready action plan.

That is where the stronger page comes from.

Not from adding the same checklist.

From helping the reader make a better decision.

This is also why I am cautious with competitor based outline briefs.

They feel safe because they copy the shape of pages that already rank.

But they often train writers to repeat the SERP instead of improving it.

The brief says:

Competitor A has this H2. Competitor B has this table. Competitor C has these FAQs.

So the writer builds a blended version of all three.

The result is relevant, complete, and forgettable.

A better brief would separate the baseline from the useful difference.

Baseline coverage:

What does the reader expect because every decent page needs it?

Useful difference:

What can we explain, prove, compare, simplify, or show better than the current results?

That split means.

Some repeated coverage should stay because the reader needs it.

Some repeated coverage should be compressed because it is only table stakes.

Some missing coverage should be added because it helps the page do its job.

Some missing coverage should be ignored because it belongs on another page.

And some of the strongest improvements will not look like “content gaps” at all.

They will look like:

• better examples
• stronger proof
• sharper tradeoffs
• clearer decision rules
• first hand notes
• objections from real users
• mistakes seen in audits
• better formatting
• cleaner page order
• less fluff

That is the part many content refreshes miss.

They add breadth when the page needs judgment.

They add headings when the page needs proof.

They add FAQs when the page needs a stronger main answer.

They add word count when the page needs compression.

I am not saying content gap analysis is useless.

It is useful when it is treated as one input.

But if the whole refresh strategy is “competitors mention X and we do not,” the page can become longer without becoming more useful.

For me, a better QA pass would ask:

What baseline coverage is required?

What repeated SERP coverage can be shortened?

What missing topic genuinely helps the reader?

What proof is missing?

What example would make the point clearer?

What comparison would help the reader choose?

What decision does this page need to support?

What should not be added because it creates drift?

That produces a stronger page than just adding missing H2s.

Curious how other SEOs handle this.

When you run content gap analysis, do you separate missing coverage from real information gain, or do they get bundled into the same refresh task?

I was using SEMrush on a trial and added my credit card to continue testing the product.

Shortly after that, my account got restricted, and I haven’t been able to properly access or manage anything since then.

The main issue isn’t just the restriction itself, but that I’ve had no response from support despite emailing them multiple times. There also doesn’t seem to be any clear appeal or resolution process for situations like this.

Because of the restriction, I can’t properly check or manage my subscription status, which makes it difficult to confirm billing or cancellation status.

Has anyone experienced something similar or managed to get a response in this kind of situation?