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How a 200-Person Company Competes with a $160B Giant in AI Search

February 20, 2026/0 Comments/in Web Design News/by

At just under 200 employees, Descript is not the biggest name in video editing software.

It’s not the most robust or the most popular, either.

But it’s punching way above its weight, competing with much bigger companies (like Adobe, and CapCut) in LLM search.

Using Semrush’s AI Visibility score, you can see that Descript is competing closely with giant brands like Adobe.

Semrush – AI Visibility – Competitor Research – Descript

Descript found the way in.

And so can you.

In this SaaS LLM visibility case study, we’ll break down exactly how Descript is getting seen.

And more importantly, what you can copy to improve visibility for your own product.

Choosing Clear Niche Messaging

For years, Descript has been known as a podcast editing tool.

That matters.

Because when people talk about podcast editing, Descript comes up naturally.

In blog posts.

In forums.

And now, in AI answers.

This isn’t accidental. Descript is clear about who it’s for, and their content reflects that focus.

Their product pages and blog posts consistently speak to one core audience: people who want to edit podcasts easily.

Here’s why this matters:

When I asked Google’s AI Mode for the best software to edit podcasts — specifically as someone with no video editing skills — Descript was one of the first tools mentioned.

And what shows up second in the list of sources?

One of Descript’s own blog posts about podcast editing.

Across Descript’s own website and other third-party sources, this tool is regularly mentioned as ideal for podcasters.

This matters because of a key difference between AI search and traditional SEO.

LLMs don’t just surface pages. They based their answers on query fan-outs.

Here’s what that means: AI creates multiple searches after the original query, and tries to find an answer that is most directly matched to what was asked.

That’s why even articles and websites that aren’t ranking well in Google can still get cited by AI when they provide the most relevant, specific answer to what users are asking.

Because Descript’s content is tightly focused on one audience, one use case, one problem, it maps cleanly to those AI queries.

That doesn’t necessarily correlate to higher ranking in traditional search. In fact, Descript’s traffic from traditional SEO has been steadily decreasing since its peak in 2024:

Organic Rankings – Descript – Estimated Traffic Trend

But at the same time, branded traffic has increased.

So even while the brand isn’t succeeding in traditional search, more people are becoming aware of Descript and searching for the brand name specifically.

Why? In part, because the brand is known for exactly what it does: podcast editing.

AI knows that too. And I would bet that a higher amount of mentions in AI search is helping with brand recognition and influencing that increase in branded search traffic.

Here’s the point: Descript isn’t just checking off boxes of what to talk about.

The way they write — and the way they present their product — shows exactly who they’re speaking to. They match the way their audience talks.

Take the blog article on podcast editing that we mentioned above as an example.

The copy flows naturally, includes quotes from an internal expert in the way she describes the problem and solution, and speaks in an easy way that matches the tone of the audience.

As a byproduct of this natural way of writing and clear product position, their copy and content semantically matches what their audience is searching for.

And their AI mentions keep increasing.

Visibility Overview – Descript – AI Visibility

Action Item: Identify and Focus on Your Niche Market

Effort vs. Impact: Medium effort. High impact.

If you’re trying to be all things to everyone, AI is less likely to recommend you for anything specific.

Instead, narrow your focus like Descript does:

Of course, you also want to find balance.

For example, “Podcast editing software for true crime hosts who only record on Thursdays,” may be a bit too niche.

To get the narrowest viable version of your core audience, look at your most successful customers.

Ask:

Who gets the most ROI from our product?
Who uses it weekly — or daily?
Which customers have become vocal advocates?
What do those users have in common? (Role, company size, industry, workflow)

That overlap is your niche.

Once that’s clear, your messaging gets easier.

You stop being an “All-in-one AI-powered platform for creators and teams.”

And start anchoring your product to a specific job: “Edit podcasts and spoken audio, without technical complexity.”

Then, your product becomes easier for AI systems to understand — and recommend — for specific use cases.

Further reading: Learn how to do deep audience research, along with a free audience research tracker template.

Developing Seriously Helpful Content

Once you know who you’re talking to, the next step is obvious: Help them.

That idea isn’t new.

Helpful content has long been a ranking factor in traditional search.

And in 2024, Google confirmed that their algorithm changes had reduced the appearance of low-quality content in search results by 45%.

But Descript’s example (and plenty of others) shows how this also applies to AI search.

Because clear, useful, unique content also drives LLM visibility.

Descript doesn’t rely on shallow blog posts or surface-level explanations.

They create:

Instructional blog content that answers real questions
Help Center pages that actually solve problems
Product pages that clearly explain what features do — and who they’re for

They also publish content that isn’t strictly about their product, but is highly relevant to their audience.

For example:

When I asked Google’s AI Mode how much YouTubers actually make, one of the cited sources was a Descript blog post on the topic.

Google AI Mode – How much YouTubers make

That article includes:

Data from recent studies
Real-world examples
A YouTube earnings calculator

It’s comprehensive. And it’s written from an expert perspective.

Here’s another example: When I asked how much it costs to start a YouTube channel, I was again directed to an article from Descript.

Google AI Mode – Starting YouTube channel

That page includes a detailed FAQ and embedded video content from Descript’s own YouTube channel.

The pattern is clear.

Depth gets cited. Surface-level content gets ignored.

Action Item: Focus Your Content on Being Helpful

Effort vs. Impact: High effort. Medium impact.

Once you’ve defined your niche, focus your content on what actually helps them.

Descript doesn’t target video editing professionals. So, they don’t show up in those searches.

ChatGPT – AI tends toward bigger players

They focus on content creators and podcasters. And their content reflects that.

To do the same:

Talk to people in your niche industry
Ask about their workflows, goals, and sticking points
Learn what slows them down

Pro tip: If you can’t speak directly to people in your audience or customer base, talk to your customer-facing teams. Customer success and sales teams have daily contact with your core audience. So, they’re in a better position to give you insights into what this audience cares about.

Online research also helps.

Find relevant subreddits to see what people are talking about. Check the comments section of relevant YouTube videos.

Look for recurring questions and complaints.

For example, the Descript team might peruse the r/podcasting subreddit to learn about their audience’s questions and opinions.

The goal: understanding.

When you deeply understand your audience’s day-to-day reality, creating helpful content becomes much easier.

And your content can become the source for AI answers.

Of course, getting citations back to your website isn’t the same as getting direct brand mentions. However, it’s still an opportunity to build awareness and authority.

Plus, building content around relevant core topics helps reinforce your niche messaging.

Further reading: Read the full guide on how to create helpful content.

Showcasing Images and Videos of Their Product

LLMs don’t just read text anymore.

They interpret visuals too.

With image-processing models like contrastive language–image pre-training (CLIP,) AI systems can understand what’s happening inside screenshots and videos — not just the words around them.

And those visuals now show up directly in AI answers. Especially for SaaS product queries in tools like ChatGPT.

For example, when I search for “best CRM software for a small business,” the top AI result includes images of the actual product interface.

ChatGPT – Best CRM software for a small business

That’s a shift.

Highly polished mockups matter less. Real, in-product visuals matter more.

Which is why Descript shows up like this in ChatGPT:

ChatGPT – Best software to edit podcasts

Descript consistently shows real product images and videos across product pages, Help Center articles, and blog content.

These aren’t decorative.

They show:

What the product looks like
How features work
What users should expect when they log in

As a result, those same images and videos get pulled into AI answers — often with a link back to Descript’s site.

In this case, the link goes back to a very in-depth Help Center guide to getting started with podcast editing.

And most Interestingly, that’s a near-perfect semantic match to the original query.

Action Item: Include In-Product Images in Your Marketing Content

Effort vs. Impact: Low effort. Medium impact.

Start with the basics.

For every feature you highlight, ask one question: Can someone see this working?

Then act on it. Add real screenshots of your core product screens to key product pages. Replace abstract diagrams with in-product visuals where possible.

Next, expand beyond product pages.

Mention a feature in a blog post? Include a screenshot of it in use.

Explaining a workflow in a Help Center article? Show each step visually.

Descript – Importing a Zoom recording into a new project

Teaching a process? Record a short screen capture instead of relying on text alone.

The goal is clarity.

Clear visuals help users understand your product faster. And they give AI systems concrete material to reuse in answers.

Which makes your product easier to recommend — and easier to recognize — inside AI search.

Creating Detailed MoFu/BoFu Content

Content mapped to different awareness levels performs especially well in AI search.

Descript understands this.

They don’t just publish top-of-funnel guides. They create content for product-aware and solution-aware searches, too.

When you search in ChatGPT for video creation or editing tools, Descript often appears in the results.

But more importantly, their own content is cited as a source.

ChatGPT – Video Creation & Editing Tools

In this example, the cited source is a Descript-owned “best of” article comparing video tools.

Instead of generic recommendations, the page:

Breaks tools down by specific use cases
Includes clear pros and cons
Explains who each option is best for

Descript follows this same pattern with multiple “best of” lists and comparison pages against their main competitors.

The payoff?

When I asked AI to compare podcast video editing tools, Descript appeared with clear labels explaining:

Who it’s best for
Key features
When it makes sense to choose it

That context helps AI recommend Descript to the right people (not everyone).

Action Item: Create Citable MoFu and BoFu Content

Effort vs. Impact: High effort. High impact.

Different awareness levels need different content.

To increase product-level AI visibility, focus on Product Aware and Solution Aware queries.

For Product Aware audiences, create:

Comparison pages
“Best alternative” posts
Owned “best of” lists

Want more ideas?

Talk to your sales team.

Ask them: What features are convincing people to buy? Which competitors are commonly brought up in sales conversations?

Those answers map directly to comparison content AI likes to cite.

For Solution Aware audiences, focus on how-to content that naturally features your product.

For example, when I asked Google’s AI Mode how to reduce background noise from a microphone, it referenced a Descript how-to article.

This same pattern repeats itself across many of Descript’s blog posts: Find a clear problem, give a clear solution, add product mentions naturally.

It’s all about finding the right questions to answer.

To find these opportunities faster, use Semrush’s AI Visibility Toolkit. This data is powered by Semrush’s AI prompt database and clickstream data, organized into meaningful topics.

Head to “Competitor Research” and review:

Shared topics where competitors appear
Prompts where they earn more AI visibility than you

AI Visibility – Competitor Research – Descript – Topics & Prompts

Then, dig into the specific questions behind those prompts.

AI Visibility – Competitor Research – Descript – Prompt

The goal isn’t simply “more content”.

It’s answering the right questions — at the right stage — with content AI can confidently cite.

Building Positive Sentiment With Digital PR and Affiliate Marketing

AI visibility isn’t earned on your website alone.

LLMs look for signals across the web.

This is what we call consensus. And it means that positive sentiment has to exist outside your owned channels.

Descript is doing this in two ways:

Digital PR on sites AI already trusts
A creator-friendly affiliate program that drives third-party mentions

Here’s how it works: Google’s AI Mode tends to favor certain websites to source when answering queries about software.

Semrush’s visibility research for AI in SaaS from December 2025 shows these sites dominate citations:

Zapier
PCMag
Gartner
LinkedIn
G2

Semrush – AI Visibility – Google AI Mode

Here’s what’s interesting.

Descript is mentioned in articles across nearly all of these top sources.

For example, in software listicles like this one on Zapier:

Or in real-world experience articles like this one on Medium:

Or in their clear listings on reviews sites like Gartner and G2:

When AI systems cite those favored sources, Descript comes along for the ride.

Not because it’s the biggest brand.

But because it’s present where AI is already looking.

Google AI Mode –Software for video transcription

The second lever is Descript’s affiliate program.

It’s simple:

$25 per new subscriber
30-day attribution window
Monthly payouts
No minimums

Those are solid incentives.

And they lead to more creator-driven content across the web.

For example, a YouTube walkthrough from VP Land explains how to use Descript and includes an affiliate link in the description.

YouTube-video – Descript – Affiliate link in description

When I later asked Google’s AI Mode how to use Descript, that exact video was cited as a source.

That’s the pattern.

Affiliate content creates citable, trusted references that AI systems reuse.

Action Item: Build a Strategy to Get More Mentions Online

Effort vs. Impact: High effort. High impact.

Getting third party mentions is all about building relationships.

First, build relationships with publishers, starting with the ones AI already trusts.

Even if you’re not an enterprise SaaS company with a full-sized PR team, this is still possible.

Granted, it’s not the easy route — but when you find the right websites and perform regular outreach to those teams, you can get your brand on these sites.

Before you start outreach, get your bearings.

Start by going back to Semrush’s AI Visibility Toolkit. Head to the “Competitor Research” tab and select “Sources.”

AI Visibility – Competitor Research – Descript – Sources

This shows you:

Which sites LLMs cite for your category
Where competitors are already getting mentioned
Gaps where your brand doesn’t show up (yet)

Those sites become your shortlist.

Outreach works better when you’re aiming at sources AI already relies on.

Second, build relationships with creators.

Affiliate programs work when creators want to talk about you.

So, build an affiliate program people actually want to be part of.

This means the program has to be easy to join, with clear terms that make it worth their time.

At a minimum, make sure you have:

A simple signup
Transparent tracking
Reliable payouts

Pro tip: Use a tool like PartnerStack to handle all of the details automatically. Better signups, better tracking, and automated payouts build trust with your affiliates.

If you need inspiration, research top affiliate programs to learn more about the conditions creators expect.

But most importantly: Treat affiliates as distribution partners, not just a side channel.

This means enabling them with clear positioning on your product, example use cases, demo workflows, screenshots they can reuse, and other resources.

The better you equip them, the stronger their recommendations will be.

Once you have this set up, track the results.

Use AI visibility data to see:

Which publisher relationships are turning into citations in AI search
Which creators show up in AI answers
Which formats perform best

Then, double down.

Now that we’ve discussed what Descript is doing well, let’s look at where there’s room for improvement.

Where Descript Could Improve: Reddit Marketing

Descript is doing a great job in many areas that are important for AI search visibility.

That said, there’s one area they’re missing out on: Reddit.

And yes, Reddit matters. A lot.

It’s still one of the most-cited sources in Google’s AI Mode.

And in almost all of the searches I tested above, Reddit was cited as a source (especially conversations in the r/podcasting subreddit).

Here’s the problem: right now, Reddit is not doing Descript any favors.

Here are a few thread titles I found just by searching for Descript in a podcasting subreddit:

Reddit – r/podcasting – Negative threads

And yes, there are positive mentions of Descript. But they’re buried under a wave of negative sentiment.

When LLMs scan Reddit for sentiment, that unbalance matters.

AI wants to see consensus. So when Reddit skews negative, recommendations may weaken, and alternatives get surfaced instead.

Even when the product is strong.

That’s why, while Descript’s AI visibility is good, it’s still not as good as it could be. And that vulnerability could hurt them in the long run, even if they’re still doing everything else right.

Here are some ways that Descript (and you) could turn the tides on Reddit:

Avoid promoting and start participating: Reddit punishes marketing language. Helpful, honest comments perform better than posts.
Respond to criticism directly (when appropriate): Not defensively, but with clear explanations and fixes
Be present before there’s a problem: Accounts that only show up during damage control don’t build trust
Focus on comments, not posts: High-value comments in active threads outperform standalone branded posts
Monitor brand mention weekly: Focus especially on high-intent subreddits. In Descript’s case, that could be r/podcasting.

To be fair, it seems like Descript is taking steps in the right direction.

As of December 2025, the Descript team has taken control of a dedicated brand subreddit, with PMM Gabe at the helm.

And the team’s responses feel very Reddit-friendly, not using marketing jargon or being pushy.

But popular threads here still have very little interaction with the Descript team. And there seems to be very few (if any) comments from the Descript team outside of this branded subreddit.

It’s a step in the right direction, but there’s still a lot to work on.

Done right, Reddit becomes a sentiment stabilizer and a stronger input source for AI answers.

Ignore it, and Reddit can become a liability.

Remember: for AI visibility, silence isn’t neutral.

Further reading: If Reddit feels like a whole other world, we’ve got you covered. Read our full guide to Reddit Marketing.

What You Can Take Away from This SaaS LLM Visibility Case Study

Descript isn’t winning AI visibility because it’s the biggest brand.

It’s winning because it’s clear, focused, and consistently helpful.

None of that is accidental.

And none of it requires massive scale.

You can get started on this today by choosing one key action to work on.

Use the effort vs. impact lens from this article to choose where to start.

Add in-product screenshots and videos: Low effort, medium impact
Tighten your niche messaging: Medium effort, high impact
Build citable MoFu/BoFu content: High effort, medium impact
Invest in digital PR, affiliates, and community participation: High effort, high impact
Create seriously helpful content: High effort, high impact

Pick one, start there. AI search visibility tools for SaaS companies — like Semrush’s AI Visibility Toolkit — can help you see exactly where you stand today, and where you can improve.

Remember: LLM visibility isn’t about chasing algorithms.

It’s about making your product easier to understand, easier to trust, and easier to recommend.

Do that consistently — and AI search will follow.

Want to learn how it all works on a deeper level? Read our LLM visibility guide to discover even more ways to increase your brand mentions and citations in AI search.

The post How a 200-Person Company Competes with a $160B Giant in AI Search appeared first on Backlinko.

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Why AI optimization is just long-tail SEO done right

February 17, 2026/0 Comments/in Search Engine Land, Web Design News/by

The return of long-tail SEO in the AI era

If you look at job postings on Indeed and LinkedIn, you’ll see a wave of acronyms added to the alphabet soup as companies try to hire people to boost visibility on large language models (LLMs).

Some people are calling it generative engine optimization (GEO). Others call it answer engine optimization (AEO). Still others call it artificial intelligence optimization (AIO). I prefer large model answer optimization (LMAO).

I find these new acronyms a bit ridiculous because while many like to think AI optimization is new, it isn’t. It’s just long-tail SEO — done the way it was always meant to be done.

Why LLMs still rely on search

Most LLMs (e.g., GPT-4o, Claude 4.5, Gemini 1.5, Grok-2) are transformers trained to do one thing: predict the next token given all previous tokens.

AI companies train them on massive datasets from public web crawls, such as:

Common Crawl.
Digitized books.
Wikipedia dumps.
Academic papers.
Code repositories.
News archives.
Forums.

The data is heavily filtered to remove spam, toxic content, and low-quality pages. Full pretraining is extremely expensive, so companies run major foundation training cycles only every few years and rely on lighter fine-tuning for more frequent updates.

So what happens when an LLM encounters a question it can’t answer with confidence, despite the massive amount of training data?

AI companies use real-time web search and retrieval-augmented generation (RAG) to keep responses fresh and accurate, bridging the limits of static training data. In other words, the LLM runs a web search.

To see this in real time, many LLMs let you click an icon or “Show details” to view the process. For example, when I use Grok to find highly rated domestically made space heaters, it converts my question into a standard search query.

Dig deeper: AI search is booming, but SEO is still not dead

The long-tail SEO playbook is back

Many of us long-time SEO practitioners have praised the value of long-tail SEO for years. But one main reason it never took off for many brands: Google.

As long as Google’s interface was a single text box, users were conditioned to search with one- and two-word queries. Most SEO revenue came from these head terms, so priorities focused on competing for the No. 1 spot for each industry’s top phrase.

Many brands treated long-tail SEO as a distraction. Some cut content production and community management because they couldn’t see the ROI. Most saw more value in protecting a handful of head terms than in creating content to capture the long tail of search.

Fast forward to 2026. People typing LLM prompts do so conversationally, adding far more detail and nuance than they would in a traditional search engine. LLMs take these prompts and turn them into search queries. They won’t stop at a few words. They’ll construct a query that reflects whatever detail their human was looking for in the prompt.

Suddenly, the fat head of the search curve is being replaced with a fat tail. While humans continue to go to search engines for head terms, LLMs are sending these long-tail search queries to search engines for answers.

While AI companies are coy about disclosing exactly who they partner with, most public information points to the following search engines as the ones their LLMs use most often:

ChatGPT – Bing Search.
Claude – Brave Search.
Gemini – Google Search.
Grok – X Search and its own internal web search tool.
Perplexity – Uses its own hybrid index.

Right now, humans conduct billions of searches each month on traditional search engines. As more people turn to LLMs for answers, we’ll see exponential growth in LLMs sending search queries on their behalf.

SEO is being reborn.

Your customers search everywhere. Make sure your brand shows up.

The SEO toolkit you know, plus the AI visibility data you need.

Start Free Trial

Get started with

Dig deeper: Why ‘it’s just SEO’ misses the mark in the era of AI SEO

How to do long-tail SEO with help from AI

The principles of long-tail SEO haven’t changed much. It’s best summed up by Baseball Hall of Famer Wee Willie Keeler: “Keep your eye on the ball and hit ’em where they ain’t.”

Success has always depended on understanding your audience’s deepest needs, knowing what truly differentiates your brand, and creating content at the intersection of the two.

As straightforward as this strategy has been, few have executed it well, for understandable reasons.

Reading your customers’ minds is hard. Keyword research is tedious. Content creation is hard. It’s easy to get lost in the weeds.

Happily, there’s someone to help: your favorite LLM.

Here are a few best practices I’ve used to create strong long-tail content over the years, with a twist. What once took days, weeks, or even months, you can now do in minutes with AI.

1. Ask your LLM what people search when looking for your product or service

The first rule of long-tail SEO has always been to get into your audience’s heads and understand their needs. This once required commissioning surveys and hiring research firms to figure out.

But for most brands and industries, an LLM can handle at least the basics. Here’s a sample prompt you can use.

Act as an SEO strategist and customer research analyst. You're helping with long-tail keyword discovery by modeling real customer questions.

I want to discover long-tail search questions real people might ask about my business, products, and industry. I’m not looking for mere keyword lists. Generate realistic search questions that reflect how people research, compare options, solve problems, and make decisions.

Company name: [COMPANY NAME]
Industry: [INDUSTRY]
Primary product/service: [PRIMARY PRODUCT OR SERVICE]
Target customer: [TARGET AUDIENCE]
Geography (if relevant): [LOCATION OR MARKET]

Generate a list of 75 – 100 realistic, natural-language search queries grouped into the following categories:

AWARENESS
• Beginner questions about the category
• Problem-based questions (pain points, frustrations, confusion)

CONSIDERATION
• Comparison questions (alternatives, competitors, approaches)
• “Best for” and use-case questions
• Cost and pricing questions

DECISION
• Implementation or getting-started questions
• Trust, credibility, and risk questions

POST-PURCHASE
• Troubleshooting questions
• Optimization and advanced/expert questions

EDGE CASES
• Niche scenarios
• Uncommon but realistic situations
• Advanced or expert questions

Guidelines:
• Write queries the way real people search in Google or ask AI assistants.
• Prioritize specificity over generic keywords.
• Include question formats, “how to” queries, and scenario-based searches.
• Avoid marketing language.
• Include emotional, situational, and practical context where relevant.
• Don't repeat the same query structure with minor variations.
• Each query should suggest a clear content angle.

Output as a clean bullet list grouped by category.

You can tweak this prompt for your brand and industry. The key is to force the LLM (and yourself) to think like a customer and avoid the trap of generating keyword lists that are just head-term variations dressed up as long-tail queries.

With a prompt like this, you move away from churning out “keyword ideas” and toward understanding real customer needs you can build useful content around.

Dig deeper: If SEO is rocket science, AI SEO is astrophysics

2. Use your LLM to analyze your search data

Most large brands and sites don’t realize they’ve been sitting on a treasure trove of user intelligence: on-site search data.

When customers type a query into your site’s search box, they’re looking for something they expect your brand to provide.

If you see the same searches repeatedly, it usually means one of two things:

You have the information, but users can’t find it.
You don’t have it at all.

In both cases, it’s a strong signal you need to improve your site’s UX, add meaningful content, or both.

There’s another advantage to mining on-site search data: it reveals the exact words your audience uses, not the terms your team assumes they use.

Historically, the challenge has been the time required to analyze it. I remember projects where I locked myself in a room for days, reviewing hundreds of thousands of queries line by line to find patterns — sorting, filtering, and clustering them by intent.

If you’ve done the same, you know the pattern. The first few dozen keywords represent unique concepts, but eventually you start seeing synonyms and variations.

All of this is buried treasure waiting to be explored. Your LLM can help. Here’s a sample prompt you can use:

You're an SEO strategist analyzing internal site search data.

My goal is to identify content opportunities from what users are searching for on my website – including both major themes and specific long-tail needs within those themes.

I have attached a list of site search queries exported from GA4. Please:

STEP 1 – Cluster by intent
Group the queries into logical intent-based themes.

STEP 2 – Identify long-tail signals inside each theme
Within each theme:
• Identify recurring modifiers (price, location, comparisons, troubleshooting, etc.)
• Identify specific entities mentioned (products, tools, features, audiences, problems)
• Call out rare but high-intent searches
• Highlight wording that suggests confusion or unmet expectations

STEP 3 – Generate content ideas
For each theme:
• Suggest 3 – 5 content ideas
• Include at least one long-tail content idea derived directly from the queries
• Include one “high-intent” content idea
• Include one “problem-solving” content idea

STEP 4 – Identify UX or navigation issues
Point out searches that suggest:
• Users cannot find existing content
• Misleading navigation labels
• Missing landing pages

Output format:
Theme:
Supporting queries:
Long-tail insights:
Content opportunities:
UX observations:

Again, customize this prompt based on what you know about your audience and how they search.

The detail matters. Many SEO practitioners stop at a prompt like “give me a list of topics for my clients,” but this pushes the LLM beyond simple clustering to understand the intent behind the searches.

I used on-site search data because it’s one of the richest, most transparent, and most actionable sources. But similar prompts can uncover hidden value in other keyword lists, such as “striking distance” terms from Google Search Console or competitive keywords from Semrush.

Even better, if your organization keeps detailed customer interaction records (e.g., sales call notes, support tickets, chat transcripts), those can be more valuable. Unlike keyword datasets, they capture problems in full sentences, in the customer’s own words, often revealing objections, confusion, and edge cases that never appear in traditional keyword research.

Get the newsletter search marketers rely on.

See terms.

3. Create great content

The next step is to create great content.

Your goal is to create content so strong and authoritative that it’s picked up by sources like Common Crawl and survives the intense filtering AI companies apply when building LLM training sets. Realistically, only pioneering brands and recognized authorities can expect to operate in this rarefied space.

For the rest of us, the opportunity is creating high-quality long-tail content that ranks at the top across search engines — not just Google, but Bing, Brave, and even X.

This is one area where I wouldn’t rely on LLMs, at least not to generate content from scratch.

Why?

LLMs are sophisticated pattern matchers. They surface and remix information from across the internet, even obscure material. But they don’t produce genuinely original thought.

At best, LLMs synthesize. At worst, they hallucinate.

Many worry AI will take their jobs. And it will — for anyone who thinks “great content” means paraphrasing existing authority sources and competing with Wikipedia-level sites for broad head terms. Most brands will never be the primary authority on those terms. That’s OK.

The real opportunity is becoming the authority on specific, detailed, often overlooked questions your audience actually has. The long tail is still wide open for brands willing to create thoughtful, experience-driven content that doesn’t already exist everywhere else.

We need to face facts. The fat head is shrinking. The land rush is now for the “fat tail.” Here’s what brands need to do to succeed:

Dominate searches for your brand

Search your brand name in a keyword tool like Semrush and review the long-tail variations people type into Google. You’ll likely find more than misspellings. You’ll see detailed queries about pricing, alternatives, complaints, comparisons, and troubleshooting.

If you don’t create content that addresses these topics directly — the good and the bad — someone else will. It might be a Reddit thread from someone who barely knows your product, a competitor attacking your site, a negative Google Business Profile review, or a complaint on Trustpilot.

When people search your brand, your site should be the best place for honest, complete answers — even and especially when they aren’t flattering. If you don’t own the conversation, others will define it for you.

The time for “frequently asked questions” is over. You need to answer every question about your brand—frequent, infrequent, and everything in between.

Go long

Head terms in your industry have likely been dominated by top brands for years. That doesn’t mean the opportunity is gone.

Beneath those competitive terms is a vast layer of unbranded, long-tail searches that have likely been ignored. Your data will reveal them.

Review on-site search, Google Search Console queries, customer support questions, and forums like Reddit. These are real people asking real questions in their own words.

The challenge isn’t finding questions to write about. It’s delivering the best answers — not one-line responses to check a box, but clear explanations, practical examples, and content grounded in real experience that reflects what sets your brand apart.

Dig deeper: Timeless SEO rules AI can’t override: 11 unshakeable fundamentals

Expertise is now a commodity: Lean into experience, authority, and trust

Publishing expert content still matters, but its role has changed. Today, anyone can generate “expert-sounding” articles with an LLM.

Whether that content ranks in Google is increasingly beside the point, as many users go straight to AI tools for answers.

As the “expertise” in E-E-A-T becomes table stakes, differentiation comes from what AI and competitors can’t easily replicate: experience, authority, and trust.

That means publishing:

Original insights and genuine thought leadership from people inside your company.
Real customer stories with measurable outcomes.
Transparent reviews and testimonials.
Evidence that your brand delivers what it promises.

This isn’t just about blog content. These signals should appear across your site — from your About page to product pages to customer support content. Every page should reinforce why a real person should trust your brand.

Stop paywalling your best content

I’m seeing more brands put their strongest content behind logins or paywalls. I understand why. Many need to protect intellectual property and preserve monetization. But as a long-term strategy, this often backfires.

If your content is truly valuable, the ideas will spread anyway. A subscriber may paraphrase it. An AI system may summarize it. A crawler may access it through technical workarounds. In the end, your insights circulate without attribution or brand lift.

When your best content is publicly accessible, it can be cited, linked to, indexed, and discussed. That visibility builds authority and trust over time.

In a search- and AI-driven ecosystem, discoverability often outweighs modest direct content monetization.

This doesn’t mean content businesses can’t charge for anything. It means being strategic about what you charge for. A strong model is to make core knowledge and thought leadership open while monetizing things such as:

Tools.
Community access.
Premium analysis or data.
Courses or certifications.
Implementation support.
Early access or deeper insights.

In other words, let your ideas spread freely and monetize the experience, expertise, and outcomes around them.

Stop viewing content as a necessary evil

I still see brands hiding content behind CSS “read more” links or stuffing blocks of “SEO copy” at the bottom of pages, hoping users won’t notice but search engines will.

Spoiler alert: they see it. They just don’t care.

Content isn’t something you add to check an SEO box or please a robot. Every word on your site must serve your customers. When content genuinely helps users understand, compare, and decide, it becomes an asset that builds trust and drives conversions.

If you’d be embarrassed for users to read your content, you’re thinking about it the wrong way. There’s no such thing as content that’s “bad for users but good for search engines.” There never was.

Embrace user-generated content

No article on long-tail SEO is complete without discussing user-generated content. I covered forums and Q&A sites in a previous article (see: The reign of forums: How AI made conversation king), and they remain one of the most efficient ways to generate authentic, unique content.

The concept is simple. You have an audience that’s already passionate and knowledgeable. They likely have more hands-on experience with your brand and industry than many writers you hire. They may already be talking about your brand offline, in customer communities, or on forums like Reddit.

Your goal is to bring some of those conversations onto your site.

User-generated content naturally produces the long-tail language marketing teams rarely create on their own. Customers

Describe problems differently.
Ask unexpected questions.
Compare products in ways you didn’t anticipate.
Surface edge cases, troubleshooting scenarios, and real-world use cases that rarely appear in polished marketing copy.

This is exactly the kind of content long-tail SEO thrives on.

It’s also the kind of content AI systems and search engines increasingly recognize as credible because it reflects real experience rather than brand messaging many dismiss as inauthentic.

Brands that do this well don’t just capture long-tail traffic. They build trust, reduce support costs, and dominate long-tail searches and prompts.

In the age of AI-generated content, real human experience is one of the strongest differentiators.

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The new SEO playbook looks a lot like the old one

For years, SEO has been shaped by the limits of the search box. Short queries and head terms dominated strategy, and long-tail content was often treated as optional.

LLMs are changing that dynamic. AI is expanding search, not eliminating it.

AI systems encourage people to express what they actually want to know. Those detailed prompts still need answers, and those answers come from the web.

That means the SEO opportunity is shifting from competing over a small set of keywords to becoming the best source of answers to thousands of specific questions.

Brands that succeed will:

Deeply understand their audience.
Publish genuinely useful content.
Build trust through real engagement and experience.

That’s always been the recipe for SEO success. But our industry has a habit of inventing complex tactics to avoid doing the simple work well.

Most of us remember doorway pages, exact match domains, PageRank sculpting, LSI obsession, waves of auto-generated pages, and more. Each promised an edge. Few replaced the value of helping users.

We’re likely to see the same cycle repeat in the AI era.

The reality is simpler. AI systems aren’t the audience. They’re intermediaries helping humans find trustworthy answers.

If you focus on helping people understand, decide, and solve problems, you’re already optimizing for AI — whatever you call it.

Dig deeper: Is SEO a brand channel or a performance channel? Now it’s both

Read more at Read More

The Step-by-Step Guide to Designing Local Landing Pages That Convert

February 11, 2026/0 Comments/in Web Design/by

While the growth of artificial intelligence (AI) and global conveniences like Amazon has been a great thing for society, there’s still an undercurrent of people returning to a local, more personal-feeling shopping experience.

But this “return to local” doesn’t change the fact that we still live in an internet age. Enter local search engine optimization (SEO) and landing pages.

Local SEO tends to work best for businesses with physical locations that require direct customer contact, but it can also work for virtual online businesses that don’t necessarily meet their customers before a business transaction takes place.

This is why local landing pages are so important. They can give customers the convenience of an online transaction while still providing the trust and personal feel of a local business—if your landing page is done right, of course.

Optimizing your landing page design with the proper elements can help you attract local customers to your business, increase lead generation, and boost conversion rates.

Key Takeaways

Local landing pages only work when they’re built for real locations and real intent. One page per city or service area, with localized keywords, metadata, and copy that matches how people actually search (“service + city” or “near me”).
Trust signals drive both rankings and conversions. Consistent NAP data, real reviews from nearby customers, local photos, and clear business details help you show up in map features and convince visitors to take action.
Content needs to feel local, not duplicated. Strong local landing pages include tailored copy, location-specific frequently asked questions (FAQs), social proof, and visuals that prove you serve that area, as opposed to generic pages with city names swapped in.
Mobile optimization is nonnegotiable for local SEO. Most local searches happen on mobile and convert fast. Pages must load quickly, display contact info above the fold, and make calling or getting directions effortless.
Schema markup and clear calls to action (CTAs) turn visibility into results. Structured data helps search engines and AI tools understand your business, while strong, localized CTAs guide users to call, book, or request a quote immediately.

Why Are Local Landing Pages Important?

Local landing pages help you show up when people search for services near them, and they’re key to winning conversions in your area.

Think about how people search: “best dentist in Austin,” “roof repair near me,” or “24/7 locksmith in Chicago.”

If you don’t have dedicated pages that target these local queries, you’re invisible in search engine results. In fact, recent stats show 80% of U.S. consumers surveyed search for local businesses online once a week, with about one-third (32%) searching for local businesses multiple times a day. Google’s local algorithm prioritizes relevance and proximity, and a well-optimized local page checks both boxes.

But optimizing your local SEO and landing pages is about more than appeasing Google’s algorithm. These pages can actually convert.

When someone lands on a page with your local address and glowing reviews from nearby customers, trust builds fast. In fact, according to Uberall.com, 85% of customers visit local businesses within a week of discovering them online. 17% of those visit the very next day. That’s why smart local businesses treat these like high-converting landing pages, not just generic content dumps.

With large language models (LLMs) and AI tools pulling content to answer local questions, the need for detailed, well-structured local pages becomes even more critical. These models lean on content that clearly signals relevance and authority, something a basic homepage or generic service page won’t do.

An AI overview of what are some of the best locksmiths in Chicago.

Bottom line: if local traffic matters to you, local landing pages need to be part of your SEO and conversion rate optimization (CRO) strategy.

A chart showing top ranking factors for the Local Pack.

Step 1: Identify where your customers are located.

Local landing pages only work when you know exactly which towns, neighborhoods, or service areas you’re trying to win. Otherwise, you can rack up traffic and still feel stuck because the visits come from places you can’t serve and don’t convert.

Start by answering two questions: Which locations do you want customers to come from? And which locations are they actually coming from today? Once you have both, planning local pages gets a lot easier.

Before you even open your reports, define your real-world service area. If you’re a storefront, your address needs to match how you operate in the real world (and be consistent everywhere it appears). If you’re a service-area business (such as a plumber, cleaner, or mobile vet), set a clear service area in your Google Business Profile so you don’t waste time targeting locations you can’t support.

Then, stop relying on a single data source. Use a few location signals together:

Google Analytics 4 (GA4) to spot city/region trends for session and key events (keep in mind location and demographics reporting is aggregated and can be limited by consent).

Demographics overview for Google Analytics 4.

Source

Google Search Console to see the “intent layer”—which local queries are driving clicks and impressions.

Source

Google Business Profile to see who takes action: calls, direction requests, and website clicks.
Paid geo reporting in Google Ads to confirm what’s real versus inflated by “interest” targeting.

Finally, turn those insights into simple personas with local references, clear benefits, and social proof, so your page reads like it was made for that person in that place.

Step 2: Use localized keywords and metadata to create relevance.

Relevance still matters, but that doesn’t mean you can stuff a city name into every sentence and call it a day. Good local SEO matches what the searcher wants (intent) with what the page promises, starting right in the SERP.

Here’s the key difference: a local landing page usually targets transactional intent (“dentist in Austin,” “emergency plumber near me,” “book HVAC repair”), so your keyword + metadata strategy should read like a clear offer, not a watered-down blog headline.

Start with the basics that actually move the needle:

Title tag: Make a descriptive, concise, and unique title (Google can rewrite titles, but strong input helps). A simple formula works: Primary service + city + differentiator (and brand if it fits).
Meta description: Google primarily builds snippets from on-page content, but it may use your meta description when it better matches the query. Write unique descriptions per page, include the “what” + “where,” and add a reason to click (pricing, availability, social proof). Avoid long strings of keywords.
Meta keywords: Skip them. Google has said it ignores the keywords meta tag for web ranking.

Now, a quick warning: if you’re cranking out dozens of near-identical city pages that funnel to similar destinations, that’s exactly what Google calls doorway abuse. And lists of cities jammed onto a page can fall into keyword stuffing territory.

Step 3: Use consistent NAP data

NAP stands for name, address, and phone number, and it needs to be exactly the same everywhere your business appears online. That includes your local landing pages, your Google Business Profile, directories, and social platforms.

Why does this matter? Because Google (and users) rely on NAP consistency to trust your business is legit. Inconsistent info can hurt your rankings and knock you out of key local SERP features like the map pack.

An infographic on how to create NAP data.

Source

Make sure your NAP is crawlable text, not embedded in an image. Add it in the footer or near your CTA, and match it letter-for-letter with your business listings. Even something small, like “Street” vs. “St.”, can throw off search engines.

If you serve multiple locations, each page should have its own unique NAP. No shortcuts here. Clean data builds trust, and trust drives clicks.

Step 4: Create and publish valuable content

Implementing local landing page design best practices in your content does two things: it helps you rank for location-specific searches and gives visitors a reason to trust you.

Start with copy that speaks directly to your audience in that area. Mention the city or neighborhood naturally, highlight the services you offer there, and include local differentiators like special hours or nearby service coverage. Make it feel personal.

Next, layer in content that builds credibility. Local reviews and case studies show real proof that your business delivers. Include names, star ratings, and even short quotes to make the social proof pop. Photos help, too. Real images of your team or completed projects add authenticity.

You should also include a brief FAQ section that answers questions specific to that location. Not only does this help your readers, but it also increases your chances of showing up in featured snippets or AI-generated results.

Source

Step 5: Add an effective CTA

Every local landing page needs a clear call to action. Without it, you’re leaving conversions on the table.

The best CTAs guide visitors to take the next logical step, whether that’s calling your business, booking an appointment, or requesting a quote. To be effective, your CTA must feel local and relevant. “Get a Free Quote” is okay. “Get a Free Plumbing Quote in Phoenix” is better. It reinforces the location and makes the offer feel tailored.

Make sure your CTA stands out visually. Use buttons, bold text, and color contrast to grab attention. And don’t just put it at the bottom. Add it near the top of the page and repeat it throughout, especially after sections like testimonials or service descriptions.

If phone calls are your goal, use a click-to-call button—especially for mobile users. For forms, keep them short. Name, email, and one key question is usually enough.

Remember, your local landing page should do more than just inform, it should drive action. The CTA is where that happens.

Step 6: Optimize your local landing pages for mobile users

Mobile search isn’t just dominant, it drives action. In fact, 88% of mobile local business searches result in a call or visit within 24 hours, showing how urgent mobile intent has become.

Start with your page performance. Speed is critical. Slow mobile pages frustrate users and push them to competitors. Tools like Google PageSpeed Insights help identify bottlenecks, enabling you to improve load times by compressing images and deferring unused scripts. Fast pages mean better user experience (UX), which, in turn, leads to higher engagement.

Responsive design is nonnegotiable. Your layout must adapt to screens of all sizes with easily readable text and minimal pop-up interference. Prioritize large, clickable CTAs, and ensure your contact info is visible without scrolling.

Mobile users are often on the go. Clearly display your NAP details front and center, ideally above the fold. Clean navigation and quick access to key info make it easier for people to act immediately.

Step 7: Add schema markup

Schema markup helps search engines understand the context of your content, and that’s a big deal for local SEO.

Source

When you add local business schema to your landing pages, you’re giving Google structured data that it can easily read. This increases the chances your business showing up in rich results like the map features or AI-generated summaries. It’s not just about visibility. It’s about making your information easier to find, trust, and act on.

At a minimum, include schema for your business name, address, phone number (NAP), hours of operation, and service area. This aligns perfectly with the on-page content you’ve already built. The more complete your schema, the more signals you’re sending to Google that your business is real, local, and helpful.

You can generate local business schema using tools like Google’s Structured Data Markup Helper or Schema.org. Then either embed it as JSON-LD in the <head> of your page or use a plugin if you’re on a platform like WordPress.

Don’t forget to test it. Use Google’s Rich Results Test to make sure your markup is working as intended.

It takes a few extra steps, but schema markup is one of the easiest technical wins you can add to a local landing page. It won’t guarantee rankings, but it gives your content a better shot at being seen and trusted.

FAQs

How do I create content for local landing pages for SEO?

Start with localized keywords (e.g., “[service] in [city]”) and ensure they appear naturally in your headlines and throughout the copy. Then, write content that actually helps local visitors: include location-specific details, highlight nearby landmarks, and speak directly to the needs of that community. Bonus points if you add customer reviews or links to local pages.

How to make local SEO landing pages

Structure each page around one location or service area with unique URLs (like /plumbing-los-angeles). Don’t forget your Google Business Profile and local schema markup. They help search engines match your page with nearby searchers.

How to optimize landing page for local SEO

Use consistent NAP (name, address, phone) info across the page and the web. Add a local map, embed reviews from customers in that area, and link internally to relevant services. Make sure your page loads fast and works well on mobile because that’s where most local searches happen.

Conclusion

To maximize your search results and lead generation, make sure that you design separate landing pages for each city that you’re targeting.

Above all, create unique, location-specific copy for your landing pages. Building a local landing page requires an investment. It could be the investment of your time, money, or both.

However, it’s become a lot easier these days because of the plethora of landing page creators and landing page templates.

Read more at Read More

Why Entity-Based SEO is a New Way of Thinking About Optimization

February 10, 2026/0 Comments/in Web Design/by

Search engine optimization (SEO) was once defined by the number of keywords and synonyms scattered across your content. If you used the right word enough times, you’d rank.

Those days are long gone.

Since the launch of its Knowledge Graph in 2012, Google has been moving away from literal text matching toward deep semantic understanding.

Search engines no longer evaluate pages as collections of words. They evaluate meaning.

This goes beyond Google and search engine results pages (SERPs). Modern discovery operates on entities—distinct people, places, brands, and concepts connected through context and relationships. Search systems now interpret queries by mapping how these entities relate rather than counting keyword usage.

That’s where entity SEO comes in. Entity-based structures set the groundwork for the more intuitive search results we see today in AI platforms and large language models (LLMs). Grouping queries around one central “thing” gives these platforms a clear reference point they can connect to related concepts.

Ultimately, entity SEO helps these platforms research and provide information in a more human way. It gives us the answers we want quickly, and it powers Google’s more complex search features that take our query results beyond a simple list of blue links.

In this article, we’ll explain what entities are, how to use them, and how they’ll continue to shape the future of SEO.

Key Takeaways

Entity SEO focuses on clearly defined people, brands, products, and concepts and the relationships between them, rather than isolated keywords.
When Google understands the primary entity behind a page, it can rank that page across a broader range of relevant queries without exact-match targeting.
Site structure communicates meaning. Topic clusters, internal links, and consistent terminology help search engines map how content fits together.
AI-driven search relies on entity context to disambiguate terms and interpret intent, not keyword strings alone.
Maintaining consistent signals across pages and trusted third-party profiles strengthens entity recognition and long-term visibility.

What Is Entity-Based SEO?

Entity-based SEO uses context (not just keywords) to help users find exactly what they’re looking for.

You can see this shift in action every time you type a query. For example, when you type a common name like “Malcolm” into a search bar, Google doesn’t just look for those seven letters. It tries to determine which entity you’re looking for:

A Google search dropdown for the name “Malcolm,” showing a Knowledge Panel for author Malcolm Gladwell alongside various entity-based search suggestions like “Malcolm in the Middle” and “Malcolm X.”

Google offers suggestions to searchers to provide immediate context. It speeds up the search for those looking for popular figures like Malcolm Gladwell or Malcolm X, and it prompts others to add more specific details if their intended “thing” isn’t listed.

Once you select a specific entity, the search engine stops scanning for keywords and starts delivering a comprehensive Knowledge Panel.

A Google search results page for "Malcolm Gladwell" showcasing a comprehensive Knowledge Panel. The layout displays the subject as a defined entity with categorized data points, including a photo gallery, biographical details (age, parents), linked YouTube videos, and a list of his published books, like "The Tipping Point" and "Revenge of the Tipping Point."

This layout displays the subject as a defined entity, grouping biographical details, books, and videos into a single source. While this shift makes search more intuitive for users, it makes things slightly more complicated for content creators.

Here are three ways entity-based SEO has changed the landscape:

AI visibility: Entity SEO revolves around an entity record. These records parse dozens of data points about a particular search query, making all information easy for AI platforms to access. Brands that structure their data properly make themselves much more visible in LLM search.
Better mobile capabilities: Entities allowed SEO to improve mobile results and improved mobile-first indexing.
Translation improvements: Entities can be found regardless of homonyms, synonyms, and foreign language use, thanks to context clues. For instance, a search for “red” will include results for “rouge” or “rojo” if the searcher’s settings allow it.

Let’s dig a little deeper into entity records to understand how they connect to LLMs and search engines like Google.

To start, let’s look at a hypothetical entity record about Taylor Swift:

(Image Source)

This makes it clear how entity SEO works in practice. Search engines don’t rely on a single page or keyword to understand a brand. They aggregate structured signals across the web to build a unified view of the entity.

The reason behind this is that search systems and LLMs don’t read content the way humans do. They extract discrete facts, attributes, and relationships, then assemble them into a coherent understanding.

The example above illustrates how an entity can be broken into clear, machine-readable components.

Keywords vs. Entities: What’s the Difference

Entities might sound similar to keywords, but they’re actually quite different. Here’s how they differ and why those differences are so important.

Keywords

Keywords are words or phrases people use to express intent in search. They take many forms, including questions, sentences, or single words.

For example, users looking for makeup tutorials might search for “makeup tutorial,” “smokey eye,” “how to do a smokey eye,” or something similar.

Google search results page for “how to do a smokey eye,” showing a video carousel with multiple YouTube makeup tutorials and a step-by-step blog result below.

Today, keywords tend to work best as demand signals rather than quotas to be filled. They show how users frame their intent, whether they want to learn, compare, buy, or solve a problem, and give you language to match your content to that intent.

That’s why long-tail queries and modifiers (“best,” “near me,” “for beginners,” “price,” “vs.”) are still gold.

These modifiers provide the intent that tells a search engine how to connect a user to your brand. Your goal is to rank for these high-intent terms to drive organic traffic and establish your site as the definitive source of truth for your niche.

Long-tail and informational (what, how, why) keywords also help you line up your content with where search is heading.

Data shows that about 90 percent of influential SERP features, like AI summaries and “People also ask,” come from queries like these, making them useful inputs for LLM-powered workflows like content production plans based on real query language.

If your page answers the query fully and clearly, you’re using keywords the modern way.

Entities

Google defines an entity as “a thing or concept that is singular, unique, well-defined, and distinguishable.” They can be people, places, products, companies, or abstract concepts.

What makes entities powerful is not just what they are, but how they connect. They are defined by their relationships to other entities, which helps search engines and LLMs understand how each concept fits into the “big picture.”

Once Google is confident about what your page is about, it can rank you for searches you never explicitly targeted. That happens because entities carry built-in relationships, including attributes, categories, synonyms, and commonly associated concepts.

This is where entity SEO really starts to differ from keyword-based optimization. Essentially, entity SEO prioritizes mentions and human discussion over keywords.

For example, a search for the word “apple” could result in pages about the fruit or pages about the company. As interesting as both topics are, reading about iPhones probably won’t be too helpful if you’re trying to figure out whether apple seeds are indeed poisonous.

You need to add some keywords or modifiers to give crawlers and LLMs context.

This is also why pages sometimes rank for “weird” keywords. If your content clearly describes the entity—what it is or related terms—Google can connect you to unexpected queries that share the same underlying intent. This concept is known as latent semantic intent (LSI).

That’s not magic. It’s entity understanding plus context signals.

For entities to be useful, search engines map them into knowledge graphs, which are structured systems that connect related information across the web and make retrieval more reliable.

As of May 2024, Google’s Knowledge Graph contains 1.6 trillion facts about 54 billion entities, and about 1.6 trillion facts about them. Not only do these data points help answer complex informational or long-tail queries, but they also power Google’s Knowledge Panel. Here’s an example:

A Google Search Results Page for "Eddie Aikau" featuring a Knowledge Panel highlighted in a red box.

(Image Source)

To help search engines or LLMs make sense of which entity fits your query, you want the pages of your website to behave like solid references. Spell out defining details (names, dates, specs, locations), connect related subtopics, and use consistent terminology.

Add supporting cues like internal links to your own deeper pages and clear headings that map to common questions. Structured data is also key here, making it easier for engines to see specific information that you deem to be important on a given page, like product information, locations, or other items.

How Do Entities and Keywords Work Together?

An effective SEO strategy recognizes that keywords are the signals, but entities are the destination. On-page, you can treat your website as a mini knowledge graph that uses keywords to link to different pages on your site.

You can further validate your brand by connecting your content to established knowledge graphs like Wikipedia or LinkedIn, which are high in experience, expertise, authoritativeness, and trust (E-E-A-T). While this won’t directly affect your page rank, it can improve your page’s authority in search results.

Practically, this means your keywords should map to specific entity details (features, use cases, comparisons, FAQs, structured data). The clearer those entity connections are, the easier it is for search engines to match your page to related searches. That’s especially the case for those long-tail ones where intent is clear, but the wording is inconsistent.

How To Start Building Up Your Entity-Based SEO

The biggest upside of entity clarity is that it helps your whole site act like a connected knowledge hub. When search systems recognize your brand, products, services, locations, and experts as distinct entities, they can more accurately map your content to complex user intent.

Content Depth and Topical Relevance

Entity-based SEO nudges you away from thin, keyword-targeted pages toward deep, comprehensive content. Instead of fragmented articles, build authoritative topic clusters that cover definitions, use cases, and FAQs.

This depth reinforces the “identity” of your subject matter, signaling to search engines that your site is the definitive source for that specific entity across all related queries.

Strengthening Relationships via Internal Linking

Internal linking is the connective tissue of your entity strategy.

Consistently linking supporting content to a central entity page explicitly defines relationships for search engines. That can be as simple as connecting which services belong to which categories or which authors are connected to which brands.

This internal relationship graph is essential for earning broader semantic visibility and is a core component of reputation management, as it ensures search engines never lose the thread of who you are.

Consistency as a Signal of Authority

Your entity becomes much more powerful when your brand and authors remain consistent across the web. Using the same naming conventions, professional bios, and expertise signals makes it easier for search systems to verify your “identity.”

Consistency cuts through ambiguity to make sure your authority is attributed to the correct entity. And that goes a long way in preventing your brand from being confused with unrelated concepts.

Trust Signals and Entity Clarity

Trust signals like reviews and citations match up perfectly with entity clarity. Clear, consistent data—like name, address, phone number (NAP) details—help search engines attach your content to the right real-world entity for local SEO.

Modern algorithms prioritize clear signals like these when deciding which brands to feature in high-stakes search results and AI-generated overviews.

The Role of AI in Entity SEO

AI-driven search doesn’t “read” the web like a human. It builds a model of the world.

That model is made of entities (people, brands, products, places, concepts) and the connections between them.

That’s why entities are foundational. A keyword is just a string of text. An entity has a unique identity.

When Google sees “Jaguar,” it has to decide between the animal, the car brand, or the NFL team? AI makes that call by looking at entity context—nearby terms, linked pages, structured data, and known relationships in systems like the Knowledge Graph.

The screenshots below show how that entity resolution plays out in real search results. The same keyword produces entirely different SERPs based on which entity Google identifies as the best match.

Google search results for “jaguar animal,” showing an animal Knowledge Panel with images, facts, and Wikipedia information about the jaguar species.

This is also how AI gets better at interpreting intent.

Someone searching “best running shoes for flat feet” isn’t asking for a dictionary definition of shoes. They’re signaling a problem, a use case, a set of constraints.

Entity relationships help AI connect that query to brands, product categories, medical concepts, reviews, and comparisons before picking results that match the implied goal.

You can see the shift in your data. In Google Search Console, queries often widen into themes, with multiple variations driving impressions to the same page.

In the SERPs, features like Knowledge Panels, AI Overviews, and “People also ask” reflect entity understanding, not exact-match phrasing. Content performance aligns better with topic clusters and user journeys than with single keywords.

Entity SEO future-proofs your content by aligning with how AI systems learn.

If your pages clearly define the entities you cover, connect them with strong internal linking, and stay consistent in terminology and positioning, they’re easier to interpret, categorize, and reuse as search evolves.

How to Shift Your Strategy to Entity-Based SEO

Understanding entity SEO is only useful if it changes how you work. Here are the concrete changes that move a keyword-first strategy toward an entity-based one.

Identify Core Entities Tied to the Business

A core entity is a small, intentional set of “things” that you want Google to associate with your brand. It goes beyond what you want to rank for.

Start by pressure testing your site against three questions:

Who is this? (the brand/author entity)
What do they do? (the offering entity)
Who do they serve? (the audience/market entity)

If the answer to any of these feels fuzzy, your entities are too broad or buried within your content.

Keep core entities limited and intentional. Pick the ones that define your positioning, then give each one a clear home on the site.

An example structure might be: a homepage for the brand, service pages for offerings, an about page for brand/author credibility, and supporting content that links back to those pillars.

Build Topic Clusters Around Those Entities

One page can define the entity, but topic clusters give it depth and context. The goal is coverage, not volume.

For each core entity, build one primary page that acts as the hub (your “entity’s home”). Then publish supporting pages that answer related questions, common use cases, comparisons, and next-step topics that your audience actually searches for. This is known as the hub and spoke model.

Your supporting content should do three things:

Answer real follow-up questions.
Reinforce the same entity from different angles.
Link back to the hub page with clear, consistent anchor text.

That internal structure is what helps search engines connect the dots.

Reinforce Entities Through Internal Links and Content Structure

Internal links are how you “wire” entities together across your site. Structure matters as much as the words on the page.

Link pages with related topics, not whatever feels convenient in the moment. If two articles support the same entity, connect them. If a page is a subtopic, point it to the hub and to other closely related subtopics.

NerdWallet’s credit cards hub shows how internal linking reinforces entities, with a single category page connecting related subtopics like cash back, travel rewards, and balance transfers under one clear concept.

NerdWallet credit cards hub page showing a central “Credit Cards” category with multiple subcategory links, including cash back, travel rewards, balance transfer, and business credit cards.

Keep your anchor text consistent and descriptive. And use the entity name (or a tight variation) instead of vague links like “click here” or “learn more.”

Make sure your cluster works both ways. In other words, supporting pages should link up to the main entity page, and related supporting pages should link to each other where it genuinely helps the reader move to the next logical question.

Maintain Entity Consistency Across the Site and Beyond

One way to leverage entity-based SEO is to list your business on directories across the internet. These directory sites are a popular data source for search engine crawlers and LLMs. Your Google Business Profile, for example, is used as a data source for the Google Knowledge Graph.

Other listing services, such as Yelp, can also help create strong, authoritative backlinks for your brand and define a well-known entity.

Listing sites may vary by location, so do your research when deciding where to list. Additionally, be sure to choose sites with high domain authority to improve your search engine standing.

Ultimately, consistency is key. Listing your business in multiple locations across the internet eventually turns entity signals into trust signals, but it’s important to list your business carefully.

Avoid using multiple names for the same entity and conflicting descriptions from page to page. Also, make sure your listings stay focused on topics related to entities in your industry. Don’t lose focus or drift to unrelated topics.

Prioritize Brand Building

Brand building is another essential tactic in entity-based SEO. Offline brand signals should be mirrored online wherever search engines and AI systems look for training data.

This includes your about page, author bios, case studies, podcast/webinar pages, and third-party profiles (Crunchbase, G2, LinkedIn, industry directories, etc.). For LLM optimization, you want consistent, crawlable signals in the places models and search engines pull from.

Use the same brand description, key services, and leadership names everywhere. That consistency makes it easier for systems to connect the dots.

Common Entity SEO Mistakes

Entity SEO fails when you treat it like a checklist instead of a system. These are some of the mistakes that do the most damage:

Treating schema as a shortcut. Markup helps Google label what’s on the page. It doesn’t create authority. If the content is thin or unclear, schema just highlights that faster.
Publishing thin entity pages. A quick definition page won’t earn trust. Weak entity pages struggle to rank, and they don’t attract links or support clusters.
Chasing unrelated entities. Dropping in trendy topics or random brands dilutes relevance. It can also confuse search engines about what you actually do.
Ignoring internal linking and structure. Entities need connections. If supporting pages don’t link to the hub (and to each other where it makes sense), Google can’t map the relationship.
Sending inconsistent signals. Mixed terminology, shifting positioning, and conflicting service descriptions make your entity harder to identify.

FAQs

What are entities in SEO?

Entities are the “things” search engines recognize—people, places, brands, concepts, and more. Unlike keywords, entities have context and relationships. Google uses them to understand meaning and intent. For example, “Amazon” as a company is an entity, and it’s different from the Amazon rainforest.

How do you find SEO entities?

Start with your main topic and use tools like Google’s Knowledge Graph, Wikipedia, and Ubersuggest to identify related entities. Look for people, brands, terms, and categories commonly associated with your topic. Also, check competitor content. What entities are they connecting to? Use this to build a structured, semantically rich content plan.

What is entity SEO?

Entity SEO is the practice of optimizing content around recognizable concepts, not just keywords, so search engines better understand and rank your site.

Conclusion

Entity SEO isn’t some advanced trick. It’s how modern search actually works.

Search engines no longer rely on traditional keyword research alone. They map concepts, understand relationships, and evaluate authority across connected topics.

If you want to stay visible long term, your content needs more than keywords.

Clarity and a strong topical focus are the way to go. That’s how you build trust with Google and future-proof your branding strategy as AI continues to reshape the search landscape.

Leaning into entity-focused optimization builds a durable presence that lines up with how users search and how Google works.

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AI Content Generation for SEO: Pros, Cons & How to Use It

February 8, 2026/0 Comments/in Web Design/by

AI content generation for SEO can be a game-changer if you use it the right way.

AI tools help increase the speed of your content production, from brainstorming to drafting. And yes, we’ve built our own AI writer into Ubersuggest to make that process easier.

But here’s the thing: AI isn’t a shortcut to rankings. Without the right prompts and a human touch, AI content can actually hurt your traffic. Google’s recent updates and the rise of AI Overviews in search show just how important quality and clarity are.

So no, AI-generated content isn’t bad, but you need a strategy. Otherwise, it’s just more noise.

Key Takeaways

LLMs won’t cite your content unless it’s structured, trustworthy, and answers real user questions.
AI content generation for SEO works, but only with the right strategy and human oversight.
AI can speed up all stages of content production, but publishing without reviewing will tank your results.
Prompts matter. Clear direction on content structure and audience and strong keyword targeting separate ranking content from noise.
Human elements like originality, firsthand insights, and strong E-E-A-T signals are still non-negotiable.

AI VS Humans: Pros & Cons

With AI, we found that you can’t just publish the content it generates and go off to the races.

It still takes time to use AI.

From modifying the content to putting it in your CMS to adjusting the format, creating content takes time whether you use AI or not.

Here’s how long it takes to create content using AI versus a human.

When using AI we found that you can write content, post it into a CMS, and publish it all within 16 minutes.

Humans on the other hand took an average of 69 minutes.

But there are some issues that most people don’t talk about.

The first is AI takes what’s on the web and “regurgitates” the same old info.

People want to read something new…

The second is we found that 94.12% of the time human written content outranked AI-created content.

With that said, there is still a role for AI-generated content in an SEO strategy.

<h2>Does AI-Generated Content Support SEO? </h2>

Our findings aren’t all “doom and gloom” for AI, especially as platforms and LLMs evolve. It can absolutely support your SEO strategy, especially when it comes to scaling content or repurposing existing assets, but AI needs direction. If you feed it a vague prompt like “write a blog post about SEO,” you’ll get generic, surface-level content that won’t rank or convert.

Your prompt is essential in making AI-generated content SEO-friendly. You need to tell the tool exactly what keywords to target, what questions to answer, what structure to follow, and who the audience is. Doing that requires real marketing experience.

This is where human input and oversight still matter. You need to choose the right keywords and guide the AI to meet quality standards. AI is just guessing without that input, and that rarely ends well for SEO.

It’s also worth noting that while AI can help draft content, it won’t replace human editing. You still need someone to review for tone and voice accuracy, and depth.

<h3>Does AI-Generated Content Help with LLM Presence? </h3>

AI content won’t magically get picked up by LLMs. But with smart prompting and a clear optimization strategy, it can absolutely improve your chances.

Large language models (LLMs) like ChatGPT and Gemini pull from indexed content to generate answers. This process is known as retrieval augmented generation (RAG)

If your content is well-structured and authoritative, it has a better shot of getting cited or referenced in those answers, but generic content won’t cut it. These models are picky.

To actually earn LLM visibility, you need to create content that matches how LLMs surface information. That means answering specific questions, using structured data where it makes sense, and writing in a way that’s clear, concise, and trustworthy.

AI tools can help here, but again, prompting is key. If your AI-generated content isn’t shaped around real user questions or lacks structure that aligns with LLM output patterns, it’s unlikely to perform.

Digging deeper and learning more about LLM SEO and LLM optimization is a great way to improve your skills in this area. By understanding these concepts, you’ll learn exactly what to include in your content and how to use AI to get there.

Integrating AI Into Your Content Approach (The Right Way)

Used well, AI can help you move faster but it’s the human touches that drive results. You need to start thinking of AI as a starting point, not the whole process.

We ran an experiment across 68 sites, publishing 744 articles—half written by humans, half by AI. Five months in, the average AI article brought in 52 visitors a month.
Human-written articles? 283.

Now, sure, you could scale faster with AI, but pumping out a ton of mediocre content does more harm than good. In fact, when we pruned low-quality posts, we saw an 11 to 12 percent traffic lift.

If you’re going to use a GenAI tool to do your writing, do it with intention:

Start with smart prompts. Include keyword targets and content goals.
Feed the tool solid references like existing content, credible sources, or structured outlines.
Don’t just hit publish. Run a full human review: fact-check, rewrite weak sections, fix tone issues, and make sure it aligns with your brand.

And here’s the secret sauce: add manual value. Include firsthand insights via screenshots or updated data. Layer in trust-building elements like personal experience or expert sourcing. That’s how you build E-E-A-T—Google’s framework for judging helpful, credible content.

FAQs

Is AI-generated content good for SEO?

It can be, if you do it right. AI can help you scale content creation, but you still need a human touch to make sure it’s high-quality and helpful. Google rewards useful content, not mass-produced fluff.

Does AI-generated content affect SEO?

Yes, but how it affects your SEO depends on what you publish. If your AI content adds value and matches search intent, it can help you rank. If it’s generic or purely written for keywords, it’ll likely hurt you.

Will Google penalize SEO content generated by AI?

Google will not penalize you for using AI alone. Google doesn’t care how content is made as long as it’s useful and trustworthy. But if the content is spammy or misleading, that’s where penalties come in.

Case Study: How We Use AI

AI’s biggest impact on our content writing process isn’t even the writing part.

It’s the research part.

For example, at NP Digital, we used AI to help UTI boost its traffic.

Instead of relying on AI to write extensive content, we leveraged it to create select drafts (which then undergo our human editing process) and assist us in conducting research for all the cities in which UTI has campuses.

This allowed us to scale the creation of their local pages and ensure high quality by leveraging our human content staff to incorporate other elements that would be useful for someone performing a local search.

We even won an award for this work at the Drum Awards.

Conclusion

AI can be used to help you, the issue is most marketers are relying on it to fully create their content for them.

AI is great, but it’s not there yet to just do everything for you.

And even if AI was perfect, if it doesn’t talk about something new that people haven’t seen before it won’t produce the results you are looking for.

So, are you using AI to create your content?

Read more at Read More

January 2026 Digital Marketing Roundup: What Changed and What You Should Do About It

February 4, 2026/0 Comments/in Web Design/by

January didn’t bring flashy product launches. It brought something more valuable: clarity.

Platforms spent the month explaining how their systems actually work. Google detailed JavaScript indexing rules that matter for modern sites. Reddit opened up automation insights most platforms keep hidden. Amazon positioned itself as a legitimate cross-screen player with first-party data advantages traditional TV can’t match.

Automation kept expanding, but with firmer guardrails. AI continued to compress discovery. Zero-click experiences grew. Brands without clear expertise signals or off-site authority started disappearing from AI-generated answers.

For digital marketers, January reinforced one reality: performance in 2026 depends less on clever tactics and more on getting fundamentals right across channels.

Key Takeaways

Indexing logic must live in base HTML, not JavaScript. Google may skip rendering pages with noindex directives in initial HTML, leaving valuable content invisible even if JavaScript removes the tag later.
Performance Max channel reporting is now essential, not optional. Budget pressure is currently your sharpest lever for managing underperforming surfaces like Display or Discover.
Share of search is becoming a better demand signal than traffic alone. As AI reduces click-through rates, measuring how often people search for your brand versus competitors reveals momentum better than vanishing clicks.
Digital PR now directly impacts AI visibility. Authoritative mentions and credible coverage determine whether AI systems recognize and recommend your brand in zero-click answers.
Influencer marketing reached enterprise maturity in January. Unilever’s 20x creator expansion and 50% social budget shift prove influence at scale is baseline strategy, not experimentation.
Review monitoring must track losses, not just gains. Google’s AI is deleting legitimate reviews without notice, affecting rankings and trust faster than new reviews can rebuild them.

Search, SEO, and Indexing Reality Checks

Search teams started 2026 with clearer rules, not more flexibility. Google spent January confirming how it treats indexing signals on JavaScript-heavy sites.

Google Clarifies Noindex and JavaScript Behavior

Google confirmed that pages with a noindex directive in their initial HTML may not get rendered at all. Any JavaScript meant to remove or modify that directive might never execute.

Indexing intent belongs in base HTML. JavaScript should enhance experiences, not define crawl behavior. For headless stacks and dynamic frameworks, search engines respond to what they see first, not what you hope they’ll see after rendering.

If your site uses React, Next.js, Angular, or Vue with client-side rendering, audit how noindex tags are implemented. Server-side rendering or static generation solves most of these issues.

Google Clarifies JavaScript Canonical Rules

Google detailed how canonical tags work on JavaScript-driven pages. Canonicals can be evaluated twice: once in raw HTML and again after rendering. Conflicts between the two create real indexing problems.

Server-rendered HTML pointing to one canonical while client-side JavaScript points to another forces Google to pick. That choice often hurts rankings quietly, without throwing obvious errors in Search Console.

Teams need to decide where canonicals live and enforce consistency. One canonical after rendering. No ambiguity between server and client.

December Core Algorithm Update Wraps

Google’s December 2025 core update finished after roughly 18 days of volatility. Sites with stale content, weak expertise signals, or unclear intent lost ground. Others gained visibility by being more useful and better aligned with user needs.

Core updates no longer feel disruptive because they’re frequent. Three broad core updates rolled out in 2025 alone. The advantage now comes from consistent execution, not post-update recovery tactics.

Paid Search, Automation, and Audience Control

Paid media keeps moving toward automation. January showed where control still exists and where it doesn’t.

Using Google’s PMax Channel Report More Strategically

The Performance Max Channel Performance Report keeps evolving. You can now see performance broken down across Search, YouTube, Display, Discover, Gmail, and Maps.

You still can’t control bids or exclusions at a granular level. What you can control is budget pressure. One surface consistently underperforming? Budget becomes your corrective lever. Pull back overall spend and PMax reallocates to better-performing channels automatically.

Teams that review this report monthly make better creative and investment decisions. Track this data over time. Patterns emerge. You start understanding which channels deliver at which funnel stages, even inside automation.

Google Drops Audience Size Minimums

Google lowered minimum audience size thresholds to 100 users across Search, Display, and YouTube. Previous minimums ranged from 1,000 users down to a few hundred depending on network and list type.

This opens doors for smaller advertisers and niche segments. Remarketing lists, CRM uploads, and custom audiences that previously failed minimums now become usable.

Smart teams will use this to test tighter segmentation strategies. But don’t chase volume that isn’t there. A 100-user audience won’t scale into a growth channel overnight.

Bing Tests Google-Style Ad Grouping

Bing briefly tested a sponsored results format similar to Google’s recent changes. Multiple ads grouped under a single label, with only the first result carrying an ad marker.

The test ended quickly, but the signal matters. Search platforms are converging on similar layouts. How ads appear now affects click quality and intent, not just click-through rate.

Social Platforms and Performance Content

Social platforms spent January rewarding clarity while punishing shortcuts.

Reddit Launches Max Campaigns

Reddit introduced Max Campaigns, an automated ad product handling targeting, placements, creative, and budget allocation in real-time.

What stands out is visibility. Reddit surfaces audience personas and engagement insights that most automated systems hide. Early testers report 27% more conversions and 17% lower CPA on average.

Testing works best when anchored to existing campaigns. Replicate your best-performing Reddit campaign as a Max Campaign. Let automation prove efficiency gains with known benchmarks.

Instagram Caps Hashtags

Instagram rolled out a five-hashtag limit across posts and reels. This confirms discovery on Instagram is driven by AI-based content understanding, not hashtag volume.

Hashtags now function like keywords. They clarify intent and help Instagram’s systems categorize content. They don’t manufacture reach.

Captions, on-screen text, subtitles, and visuals do the heavy lifting. Choose five hashtags that directly describe your content. Mix specificity levels: one broad category tag, two niche topic tags, one community hashtag, one branded hashtag.

LinkedIn Shares Performance Guidance for 2026

LinkedIn reiterated that human perspective drives performance. Video continues outperforming other formats. Hashtags do not impact distribution. Automated engagement and content pods face increased scrutiny.

Posting two to five times per week remains effective. AI can support thinking, but content still needs lived experience and clear points of view.

Brand Visibility, Authority, and Demand Measurement in an AI Era

AI-driven discovery is reshaping how brands get surfaced and evaluated.

What AI Search Means for Your Business

AI-generated summaries and zero-click experiences shape early discovery now. Users often form opinions before visiting a site. Google’s AI Overviews, ChatGPT’s SearchGPT, and Perplexity answer questions directly, compressing or eliminating the need to click through.

AI favors brands with clear expertise, structured content, and external validation. Generic explanations get compressed into summaries that strip away brand identity. Thin content disappears entirely.

Optimization now includes being understandable and credible to machines, not just persuasive to human readers. That means structured data markup, clear content hierarchy, author credentials, and topical authority signals.

Share of Search Becomes a Core KPI

As AI reduces click-through rates, traffic becomes a weaker signal of demand. Share of search fills that gap.

It measures how often people look for your brand compared to competitors. That correlates strongly with market share and future growth. Brands with rising share of search typically see revenue growth follow within quarters, even if organic traffic stays flat.

Calculate share of search by tracking branded search volume for your brand and key competitors over time. Tools like Google Trends, Semrush, or Ahrefs make this accessible.

Digital PR Matters More Than Ever

AI systems recommend brands they recognize and trust. That trust is built off-site, not through on-page optimization.

Authoritative mentions, expert commentary, and credible coverage now influence visibility across AI-driven experiences. Links still matter, but reputation matters more.

PR, SEO, and content strategy can no longer operate independently. Authority compounds when they align. If you’re not investing in Digital PR alongside traditional SEO, you’re optimizing for a search ecosystem that’s rapidly shrinking.

Video, CTV, and Cross-Screen Media Strategy

Video buying is consolidating across screens.

Amazon Emerges as a Cross-Screen Advertising Player

Amazon is positioning itself as a unified advertising ecosystem across Prime Video, live sports, audio, and programmatic inventory. Layered with first-party shopper data, this creates a powerful performance and measurement advantage traditional TV buyers can’t match.

Amazon now competes higher in the funnel through premium video and live sports while retaining lower-funnel accountability through its commerce data. Interactive features let you add “add to cart” overlays directly in OTT video ads.

CTV Breaks the 30-Second Format

Streaming dominates TV consumption. Ad formats are finally catching up. Interactive and nontraditional CTV units are gaining traction, supported by early standardization efforts from IAB Tech Lab.

Traditional :15 and :30 second spots still work, but they blend into an increasingly crowded environment. Emerging formats offer differentiation in lower-clutter streaming contexts.

Brands that test early build creative and performance advantages before these formats normalize and competition increases.

Pinterest Acquires tvScientific

Pinterest’s acquisition of tvScientific connects intent-driven discovery with CTV buying. This closes a long-standing measurement gap between inspiration and awareness channels.

For brands rooted in discovery—home decor, fashion, food, travel, DIY, beauty—this creates a clearer path from interest to action.

Brand-Led Attention and Influence at Scale

Attention increasingly flows through people, communities, and culture-driven media.

Unilever’s Influencer Expansion

Unilever announced plans to work with 20 times more influencers and shift half its ad budget to social. This isn’t a test. It’s a structural reallocation signaling influencer marketing has reached enterprise maturity.

Unilever’s SASSY framework now activates nearly 300,000 creators. The company reported category-wide outperformance, attributing significant gains to influencer-driven campaigns.

Brands still treating creators as side projects will struggle to compete against organizations running influencer programs with the same rigor and budget as paid search or programmatic display.

Google’s AI Is Deleting Reviews

Google’s AI moderation is removing reviews at scale, including legitimate ones, often without notice. Business owners report hundreds of reviews disappearing overnight.

That affects rankings, conversion rates, and consumer trust. Reputation strategy now includes monitoring review loss, not just tracking new reviews.

Check your Google Business Profile weekly. Document total review count and average rating. When drops occur, investigate patterns. Better yet, diversify review platforms beyond Google.

Experimentation and Growth Discipline

Sustainable growth depends on knowing why a test exists before judging its outcome.

Growth vs Optimization: Drawing the Line

Growth experiments explore new opportunities. Optimization improves what already works. Blurring the two creates misaligned expectations and poor decision-making.

Clear intent leads to clearer measurement and stronger buy-in. Teams that label tests correctly scale with more confidence.

What Digital Marketers Should Take Forward

Platforms are clarifying rules. AI rewards authority and consistency. Measurement is shifting away from clicks alone.

The advantage in 2026 comes from alignment across teams and channels. Durable signals outperform clever workarounds.

Indexing logic must live in base HTML. Performance Max channel reporting is essential. Share of search reveals momentum. Digital PR impacts AI visibility. Influencer marketing reached enterprise maturity. Review monitoring must track losses.

This is the work we focus on every day at NP Digital.

If you want help aligning fundamentals across SEO, paid media, content, and PR in a way that compounds over time, let’s talk.

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Inspiring examples of responsible and realistic vibe coding for SEO

February 3, 2026/0 Comments/in Search Engine Land, Web Design News/by

Vibe coding is a new way to create software using AI tools such as ChatGPT, Cursor, Replit, and Gemini. It works by describing to the tool what you want in plain language and receiving written code in return. You can then simply paste the code into an environment (such as Google Colab), run it, and test the results, all without ever actually programming a single line of code.

Collins Dictionary named “vibe coding” word of the year in 2025, defining it as “the use of artificial intelligence prompted by natural language to write computer code.”

In this guide, you’ll understand how to start vibe coding, learn its limitations and risks, and see examples of great tools created by SEOs to inspire you to vibe code your own projects.

Vibe coding variations

While “vibe coding” is used as an umbrella term, there are subsets of coding with support or AI, including the following:

Type	Description	Tools
AI-assisted coding	AI helps write, refactor, explain, or debug code. Used by actual developers or engineers to support their complex work.	GitHub Copilot, Cursor, Claude, Google AI Studio
Vibe coding	Platforms that handle everything except the prompt/idea. AI does most of the work.	ChatGPT, Replit, Gemini, Google AI Studio
No-code platforms	Platforms that handle everything you ask (“drag and drop” visual updates while the code happens in the background). They tend to use AI but existed long before AI became mainstream.	Notion, Zapier, Wix

We’ll focus exclusively on vibe coding in this guide.

With vibe coding, while there’s a bit of manual work to be done, the barrier is still low — you basically need a ChatGPT account (free or paid) and access to a Google account (free). Depending on your use case, you might also need access to APIs or SEO tools subscriptions such as Semrush or Screaming Frog.

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To set expectations, by the end of this guide, you’ll know how to run a small program on the cloud. If you expect to build a SaaS or software to sell, AI-assisted coding is a more reasonable option to take, which will involve costs and deeper coding knowledge.

Vibe coding use cases

Vibe coding is great when you’re trying to find outcomes for specific buckets of data, such as finding related links, adding pre-selected tags to articles, or doing something fun where the outcome doesn’t need to be exact.

For example, I’ve built an app to create a daily drawing for my daughter. I type a phrase about something that she told me about her day (e.g., “I had carrot cake at daycare”). The app has some examples of drawing styles I like and some pictures of her. The outputs (drawings) are the final work as they come from AI.

When I ask for specific changes, however, the program tends to worsen and redraw things I didn’t ask for. I once asked to remove a mustache and it recolored the image instead.

If my daughter were a client who’d scrutinize the output and require very specific changes, I’d need someone who knows Photoshop or similar tools to make specific improvements. In this case, though, the results are good enough.

Building commercial applications solely on vibe coding may require a company to hire vibe coding cleaners. However, for a demo, MVP (minimum viable product), or internal applications, vibe coding can be a useful, effective shortcut.

How to create your SEO tools with vibe coding

Using vibe coding to create your own SEO tools require three steps:

Write a prompt describing your code
Paste the code into a tool such as Google Colab
Run the code and analyze the results

Here’s a prompt example for a tool I built to map related links at scale. After crawling a website using Screaming Frog and extracting vector embeddings (using the crawler’s integration with OpenAI), I vibe coded a tool that would compare the topical distance between the vectors in each URL.

This is exactly what I wrote on ChatGPT:

I need a Google Colab code that will use OpenAI to:

Check the vector embeddings existing in column C. Use cosine similarity to match with two suggestions from each locale (locale identified in Column A).

The goal is to find which pages from each locale are the most similar to each other, so we can add hreflang between these pages.

I’ll upload a CSV with these columns and expect a CSV in return with the answers.

Then I pasted the code that ChatGPT created on Google Colab, a free Jupyter Notebook environment that allows users to write and execute Python code in a web browser. It’s important to run your program by clicking on “Run all” in Google Colab to test if the output does what you expected.

This is how the process works on paper. Like everything in AI, it may look perfect, but it’s not always functioning exactly how you want it.

You’ll likely encounter issues along the way — luckily, they’re simple to troubleshoot.

First, be explicit about the platform you’re using in your prompt. If it’s Google Colab, say the code is for Google Colab.

You might still end up with code that requires packages that aren’t installed. In this case, just paste the error into ChatGPT and it’ll likely regenerate the code or find an alternative. You don’t even need to know what the package is, just show the error and use the new code. Alternatively, you can ask Gemini directly in your Google Colab to fix the issue and update your code directly.

AI tends to be very confident about anything and could return completely made-up outputs. One time I forgot to say the source data would come from a CSV file, so it simply created fake URLs, traffic, and graphs. Always check and recheck the output because “it looks good” can sometimes be wrong.

If you’re connecting to an API, especially a paid API (e.g., from Semrush, OpenAI, Google Cloud, or other tools), you’ll need to request your own API key and keep in mind usage costs.

Should you want an even lower execution barrier than Google Colab, you can try using Replit.

Simply prompt your request and the software will create the code, design, and allow testing all on the same screen. This means a lower chance of coding errors, no copy and paste, and a URL you can share right away with anyone to see your project built with a nice design. (You should still check for poor outputs and iterate with prompts until your final app is built.)

Keep in mind that while Google Colab is free (you’ll only spend if you use API keys), Replit charges a monthly subscription and per-usage fee on APIs. So the more you use an app, the more expensive it gets.

Inspiring examples of SEO vibe-coded tools

While Google Colab is the most basic (and easy) way to vibe code a small program, some SEOs are taking vibe coding even further by creating programs that are turned into Chrome extensions, Google Sheets automation, and even browser games.

The goal behind highlighting these tools is not only to showcase great work by the community, but also to inspire, build, and adapt to your specific needs. Do you wish any of these tools had different features? Perhaps you can build them for yourself — or for the world.

GBP Reviews Sentiment Analyzer (Celeste Gonzalez)

After vibe coding some SEO tools on Google Colab, Celeste Gonzalez, Director of SEO Testing at RicketyRoo Inc, took her vibing skills a step further and created a Chrome extension. “I realized that I don’t need to build something big, just something useful,” she explained.

Her browser extension, the GBP Reviews Sentiment Analyzer, summarizes sentiment analysis for reviews over the last 30 days and review velocity. It also allows the information to be exported into a CSV. The extension works on Google Maps and Google Business Profile pages.

Instead of ChatGPT, Celeste used a combination of Claude (to create high-quality prompts) and Cursor (to paste the created prompts and generate the code).

AI tools used: Claude (Sunner 4.5 model) and Cursor

APIs used: Google Business Profile API (free)

Platform hosting: Chrome Extension

Knowledge Panel Tracker (Gus Pelogia)

I became obsessed with the Knowledge Graph in 2022, when I learned how to create and manage my own knowledge panel. Since then, I found out that Google has a Knowledge Graph Search API that allows you to check the confidence score for any entity.

This vibe-coded tool checks the score for your entities daily (or at any frequency you want) and returns it in a sheet. You can track multiple entities at once and just add new ones to the list at any time.

The Knowledge Panel Tracker runs completely on Google Sheets, and the Knowledge Graph Search API is free to use. This guide shows how to create and run it in your own Google account, or you can see the spreadsheet here and just update the API key under Extensions > App Scripts.

AI models used: ChatGPT 5.1

APIs used: Google Knowledge Graph API (free)

Platform hosting: Google Sheets

Inbox Hero Game (Vince Nero)

How about vibe coding a link building asset? That’s what Vince Nero from BuzzStream did when creating the Inbox Hero Game. It requires you to use your keyboard to accept or reject a pitch within seconds. The game is over if you accept too many bad pitches.

Inbox Hero Game is certainly more complex than running a piece of code on Google Colab, and it took Vince about 20 hours to build it all from scratch. “I learned you have to build things in pieces. Design the guy first, then the backgrounds, then one aspect of the game mechanics, etc.,” he said.

The game was coded in HTML, CSS, and JavaScript. “I uploaded the files to GitHub to make it work. ChatGPT walked me through everything,” Vince explained.

According to him, the longer the prompt continued, the less effective ChatGPT became, “to the point where [he’d] have to restart in a new chat.”

This issue was one of the hardest and most frustrating parts of creating the game. Vince would add a new feature (e.g., score), and ChatGPT would “guarantee” it found the error, update the file, but still return with the same error.

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In the end, Inbox Hero Game is a fun game that demonstrates it’s possible to create a simple game without coding knowledge, yet taking steps to perfect it would be more feasible with a developer.

AI models used: ChatGPT

APIs used: None

Platform hosting: Webpage

Vibe coding with intent

Vibe coding won’t replace developers, and it shouldn’t. But as these examples show, it can responsibly unlock new ways for SEOs to prototype ideas, automate repetitive tasks, and explore creative experiments without heavy technical lift.

The key is realism: Use vibe coding where precision isn’t mission-critical, validate outputs carefully, and understand when a project has outgrown “good enough” and needs additional resources and human intervention.

When approached thoughtfully, vibe coding becomes less about shipping perfect software and more about expanding what’s possible — faster testing, sharper insights, and more room for experimentation. Whether you’re building an internal tool, a proof of concept, or a fun SEO side project, the best results come from pairing curiosity with restraint.

Read more at Read More

Why most SEO failures are organizational, not technical

February 3, 2026/0 Comments/in Search Engine Land, Web Design News/by

I’ve spent over 20 years in companies where SEO sat in different corners of the organization – sometimes as a full-time role, other times as a consultant called in to “find what’s wrong.” Across those roles, the same pattern kept showing up.

The technical fix was rarely what unlocked performance. It revealed symptoms, but it almost never explained why progress stalled.

No governance

The real constraints showed up earlier, long before anyone read my weekly SEO reports. They lived in reporting lines, decision rights, hiring choices, and in what teams were allowed to change without asking permission.

When SEO struggled, it was usually because nobody rightfully owned the CMS templates, priorities conflicted across departments, or changes were made without anyone considering how they affected discoverability.

I did not have a word for the core problem at the time, but now I do – it’s governance, usually manifested by its absence.

Two workplaces in my career had the conditions that allowed SEO to work as intended. Ownership was clear.

Release pathways were predictable. Leaders understood that visibility was something you managed deliberately, not something you reacted to when traffic dipped.

Everywhere else, metadata and schema were not the limiting factor. Organizational behavior was.

Dig deeper: How to build an SEO-forward culture in enterprise organizations

Beware of drift

Once sales pressures dominate each quarter, even technically strong sites undergo small, reasonable changes:

Navigation renamed by a new UX hire.
Wording adjusted by a new hire on the content team.
Templates adjusted for a marketing campaign.
Titles “cleaned up” by someone outside the SEO loop.

None of these changes look dangerous in isolation – if you know before they occur.

Over time, they add up. Performance slides, and nobody can point to a single release or decision where things went wrong.

This is the part of SEO most industry commentary skips. Technical fixes are tangible and teachable. Organizational friction is not. Yet that friction is where SEO outcomes are decided, usually months before any visible decline.

SEO loses power when it lives in the wrong place

I’ve seen this drift hurt rankings, with SEO taking the blame. In one workplace, leadership brought in an agency to “fix” the problem, only for it to confirm what I’d already found: a lack of governance caused the decline.

Where SEO sits on the org chart determines whether you see decisions early or discover them after launch. It dictates whether changes ship in weeks or sit in the backlog for quarters.

I have worked with SEO embedded under marketing, product, IT, and broader omnichannel teams. Each placement created a different set of constraints.

When SEO sits too low, decisions that reshape visibility ship first and get reviewed later — if they are reviewed at all.

Engineering adjusted components to support a new security feature. In one workplace, a new firewall meant to stop scraping also blocked our own SEO crawling tools.
Product reorganized navigation to “simplify” the user journey. No one asked SEO how it would affect internal PageRank.
Marketing “refreshed” content to match a campaign. Each change shifted page purpose, internal linking, and consistency — the exact signals search engines and AI systems use to understand what a site is about.

Dig deeper: SEO stakeholders: Align teams and prove ROI like a pro

Positioning the SEO function

Without a seat at the right table, SEO becomes a cleanup function.

When one operational unit owns SEO, the work starts to reflect that unit’s incentives.

Under marketing, it becomes campaign-driven and short-term.
Under IT, it competes with infrastructure work and release stability.
Under product, it gets squeezed into roadmaps that prioritize features over discoverability.

The healthiest performance I’ve seen came from environments where SEO sat close enough to leadership to see decisions early, yet broad enough to coordinate with content, engineering, analytics, UX, and legal.

In one case, I was a high-priced consultant, and every recommendation was implemented. I haven’t repeated that experience since, but it made one thing clear: VP-level endorsement was critical. That client doubled organic traffic in eight months and tripled it over three years.

Unfortunately, the in-house SEO team is just another team that might not get the chance to excel. Placement is not everything, but it is the difference between influencing the decision and fixing the outcome.

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Hiring mistakes

The second pattern that keeps showing up is hiring – and it surfaces long before any technical review.

Many SEO programs fail because organizations staff strategically important roles for execution, when what they really need is judgment and influence. This isn’t a talent shortage. It’s a screening problem

The SEO manager often wears multiple hats, with SEO as a minor one. When they don’t understand SEO requirements, they become a liability, and the C-suite rarely sees it.

Across many engagements, I watched seasoned professionals passed over for younger candidates who interviewed well, knew the tool names, and sounded confident.

HR teams defaulted to “team fit” because it was easier to assess than a candidate’s ability to handle ambiguity, challenge bad decisions, or influence work across departments.

SEO excellence depends on lived experience. Not years on a résumé, but having seen the failure modes up close:

Migrations that wiped out templates.
Restructures that deleted category pages.
“Small” navigation changes that collapsed internal linking.

Those experiences build judgment. Judgment is what prevents repeat mistakes. Often, that expertise is hard to put in a résumé.

Without SEO domain literacy, hiring becomes theater. But we can’t blame HR, which has to hire people for all parts of the business. Its only expertise is HR.

Governance needs to step in.

One of the most reliable ways to improve recruitment outcomes is simple: let the SEO leader control the shortlist.

Fit still matters. Competence matters first. When the person accountable for results shapes the hiring funnel, the best candidates are chosen.

SEO roles require the ability to change decisions, not just diagnose problems. That skill does not show up in a résumé keyword scan.

Dig deeper: The top 5 strategic SEO mistakes enterprises make (and how to avoid them)

When priorities pull in different directions

Every department in a large organization has legitimate goals.

Product wants momentum.
Engineering wants predictable releases.
Marketing wants campaign impact.
Legal wants risk reduction.

Each team can justify its decisions – and SEO still absorbs the cost.

I have seen simple structural improvements delayed because engineering was focused on a different initiative.

At one workplace, I was asked how much sales would increase if my changes were implemented.

I have seen content refreshed for branding reasons that weakened high-converting pages. Each decision made sense locally. Collectively, they reshaped the site in ways nobody fully anticipated.

Today, we face an added risk: AI systems now evaluate content for synthesis. When content changes materially, an LLM may stop citing us as an authority on that topic.

Strong visibility governance can prevent that.

The organizations that struggled most weren’t the ones with conflict. They were the ones that failed to make trade-offs explicit.

What are we giving up in visibility to gain speed, consistency, or safety? When that question is never asked, SEO degrades quietly.

What improved outcomes was not a tool. It was governance: shared expectations and decision rights.

When teams understood how their work affected discoverability, alignment followed naturally. SEO stopped being the team that said “no” and became the function that clarified consequences.

International SEO improves when teams stop shipping locally good changes that are globally damaging. Local SEO improves when there is a single source of location truth.

Ownership gaps

Many SEO problems trace back to ownership gaps that only become visible once performance declines.

Who owns the CMS templates?
Who defines metadata standards?
Who maintains structured data? Who approves content changes?

When these questions have no clear answer, decisions stall or happen inconsistently. The site evolves through convenience rather than intent.

In contrast, the healthiest organizations I worked with shared one trait: clarity.

People knew which decisions they owned and which ones required coordination. They did not rely on committees or heavy documentation because escalation paths were already understood.

When ownership is clear, decisions move. When ownership is fragmented, even straightforward SEO work becomes difficult.

Dig deeper: How to win SEO allies and influence the brand guardians

Healthy environments for SEO to succeed

Across my career, the strongest results came from environments where SEO had:

Early involvement in upcoming changes.
Predictable collaboration with engineering.
Visibility into product goals.
Clear authority over content standards.
Stable templates and definitions.
A reliable escalation path when priorities conflicted.
Leaders who understood visibility as a long-term asset.

These organizations were not perfect. They were coherent.

People understood why consistency mattered. SEO was not a reactive service. It was part of the infrastructure.

What leaders can do now

If you lead SEO inside a complex organization, the most effective improvements come from small, deliberate shifts in how decisions get made:

Place SEO where it can see and influence decisions early.
Let SEO leaders – not HR – shape candidate shortlists.
Hire for judgment and influence, not presentation.
Create predictable access to product, engineering, content, analytics, and legal.
Stabilize page purpose and structural definitions.
Make the impact of changes visible before they ship.

These shifts do not require new software. They require decision clarity, discipline, and follow-through.

Visibility is an organizational outcome

SEO succeeds when an organization can make and enforce consistent decisions about how it presents itself. Technical work matters, but it can’t offset structures pulling in different directions.

The strongest SEO results I’ve seen came from teams that focused less on isolated optimizations and more on creating conditions where good decisions could survive change. That’s visibility governance.

When SEO performance falters, the most durable fixes usually start inside the organization.

Dig deeper: What 15 years in enterprise SEO taught me about people, power, and progress

Read more at Read More

Is SEO Dead in 2026?

February 2, 2026/0 Comments/in Web Design/by

Is traditional SEO is dead? Not exactly. But definitely evolving. Google still controlled a whopping 89% of all U.S. web traffic in 2025. It’s still a search powerhouse, no doubt, but it isn’t the only show in town anymore.

SEO as we know it is no more. The way people find information is changing dramatically.

Google’s rolling out 12-plus algorithm changes per day. At the same time, platforms like TikTok, Amazon, and generative AI tools like ChatGPT and Claude are becoming major players in the search game.

Let’s face it. Traditional SEO tactics aren’t always the best option.

To succeed, you must adapt.

In 2026, it’s less about search engine optimization and more about search everywhere optimization.

Let’s dig into the data for a pulse check on SEO in 2026.

Key Takeaways

SEO isn’t dead, but traditional tactics alone won’t cut it. To stay visible, your strategy must account for AI Overviews, zero-click searches, and shifting user behavior across platforms.
AI Overviews and SERP features now dominate page one. If your content isn’t cited or structured for AI, you risk being invisible—no matter your ranking.
Brand signals like search volume, authority, and trust matter for AI visbility. Google favors entities, not just pages. Build real-world credibility if you want to rank.
Optimize for LLMs and SEO at the same time. Clear formatting, concise answers, and fact-rich content help you rank and get quoted in generative results.
Search is no longer just on Google. Users discover content through social media, marketplaces, and AI engines—your optimization strategy needs to reach beyond traditional search.

Is SEO Dead?

Google doesn’t share its search volume data. However, approximations place it in the tens of billions, somewhere over 15 billion per day.

This shows that SEO still holds weight, but AI and LLM searches are growing. Currently, these platforms account for about 6% of global search volume, which doesn’t seem like much. But when you consider that the number is about triple what it was a year ago, it makes marketers start to take notice.

According to SmartInsights, the top 3 positions carry double-digit click-through rates, but these drop drastically for positions lower down the page. Just look at the chart below:

A graphic showing Google CTR growth for featured snippets.

This drastic drop highlights how Google’s been steadily moving toward a “zero-click” search experience.

Does this mean AI Overviews are surely going to kill SEO? Well, no, but they’re definitely shaking things up. In fact, Google’s been moving toward its “answer engine” model and its new AI mode for a while now.

Features like featured snippets and answer boxes already provide concise information directly on the search results page, reducing the need for users to click through to websites.

This trend is driven by the rise of “zero-click content”—content that’s so comprehensive and informative that it satisfies user intent right on the search engine results page (SERP).

Essentially, users can find their answers without visiting a website.

AI Overviews take the zero-click approach to a whole new level, providing even more content directly in the search results.

A graphic saying "What are the top trends in digital marketing."

So, how do we come to grips with both truths—that zero-click search directly results in less engagement with SEO results and that organic search is still a significant driver of traffic?

A common concern for marketers is that emerging AI engines, like ChatGPT, will kill the industry as we know it. But consider this: AI search engines still rely on Google and other algorithm-driven engines for information.

Instead of assuming SEO is dead, we should consider how SEO works today in conjunction with these trends.

The Face of the New SEO Campaign

To understand what success looks like in the new world of search, let’s look at a successful campaign of one of our NP Digital clients, RefiJet.

RefiJet has quickly become a leader in the motorcycle and auto loan refinancing space over the last decade. But to grow further, they needed to differentiate themselves from competitors and grow their digital footprint, all while AI search was changing the very way the game is played. Their company also faced macroeconomic challenges as high interest rates pushed many borrowers to the sidelines.

Our strategy for them blended new AI search principles with traditional SEO best practices. We focused on traditional technical SEO aspects such as crawlability, site speed, and structured data optimization. These moves boosted RefiJet’s inclusion in AI overviews.

Next, we launched on-page optimization tactics. These were aimed at catching traditional long-tail, high-intent search queries. We also leveraged retrieval-augmented generation (RAG) to showcase RefiJet’s authority in its space and boost citations across the web.

Stats showing the result of NP Digital's campaign with RefiJet.

This blended approach helped RefiJet achieve some pretty eye-catching results:

Their SERP features increased 30,800% (that’s not a typo) since May 2024
Their rankings in the highly coveted 1-3 slots in Google increased 522% year-over-year
Traffic from LLMs is up 2012% and site-wide page views from LLMs are up 7144% year over year.
Most importantly, RefiJet’s funded loans from organic search and LLMs are up 178% year over year.

So, no. Traditional SEO is not dead. The “new” strategy just takes a modern, blended approach to modern search problems.

SEO Isn’t Dying (It’s Just Changing)

So, is SEO dead? At this point, I think you know my answer.

That would be a resounding no.

SEO isn’t going anywhere. However, for brands to find success with SEO strategies, there are specific things to keep in mind when developing campaigns.

We know Google functions more as a discovery engine but here is what else you need to know to dominate SERPs.

AI Is Taking Up A Larger Portion of the SERPs

If you’ve searched for anything on Google lately, you’ve probably seen it. That big, AI-generated box right at the top—pushing organic results further down the page.

Google’s AI Overviews are live, and they’re eating up prime SERP real estate. For certain keywords, especially broad informational ones, they dominate. And if your content doesn’t get cited in those summaries? You might not even show above the fold.

But it’s not just AI Overviews. Google has been quietly expanding other SERP features too, like interactive knowledge panels, visual product listings, “Discussions and Forums,” and even its experimental AI mode inside Search Labs. The days of ten blue links are long gone.

This shift doesn’t mean SEO is over. It means we have to rethink how we optimize. Your content still needs to be the best answer, but now it also needs to be the kind of content Google’s AI is willing to quote.

If you haven’t yet, start digging into how AI Overviews work. See which types of pages Google is pulling from. Understand the patterns.

SEO isn’t dying. But the way we earn visibility is shifting. Fast.

Technical Fundamentals Still Matter

Google’s focus isn’t backlinks, keyword density, or a specific SEO metric. Instead, the focus is on a seamless and enjoyable user experience.

What metrics does Google use to gauge user experience?

Using a clear navigation structure is a good place to start. If you want people to spend a lot of time on your site, you need to understand how users navigate. This includes using a clear URL structure, enabling breadcrumbs, and linking internally.

Core Web Vitals—a set of standardized metrics Google uses to measure real-world page performance—is another good launchpad. These include:

Largest Contentful Paint (LCP): The time from when a user starts loading a page until the largest image or text block is visible in the viewport. Goal: 2.5 seconds or less.

Interaction to Next Paint (INP): The time between a user action, like a click or key press, and the next time it takes for the page to respond. Goal: 200 milliseconds or less.

Cumulative Layout Shift (CLS): How much a webpage’s layout unexpectedly shifts during loading. Goal: A CLS score of less than 0.1.

Other important user experience metrics include dwell time, time spent on page, bounce rate, and exit rate. You can find these metrics in Google Analytics.

So, how can you improve user experience? There are a few steps you can take that will positively impact the metrics mentioned above:

Improve site speed: The faster your site loads, the better experience the user will have (We saw the impact this can have in our RefiJet example). You can gauge site speed with tools like PageSpeed Insights and Pingdom.

Optimize for mobile: You can’t afford to not optimize for mobile, as it accounts for more than 50% of web traffic. Tools like PageSpeed Insights can give you the information you need to start, like eliminating render-blocking resources or reducing unused code. You will also want to consider a responsive design if you’re not already using one.

Errors found in Google PageSpeed Insights.

Social Search Is Taking A Larger Share

Google remains a powerful tool, but, as we’ve established, it’s no longer the sole player in search and discovery.

Platforms like TikTok, Reddit, and even voice search engines—such as Alexa and Siri—are reshaping SEO. The question is: Are you reshaping your strategies to match them?

When Google is deciding what to rank and where to rank it, it looks past its own dataset toward other spots online, like the platforms mentioned above.

Source: https://sproutsocial.com/insights/social-media-search/

All these platforms have one thing in common: They cater to users who prefer quick, conversational, or visual content.

So, what does optimizing your content strategy to leverage these platforms look like in practice? Each app has its own wrinkles you need to consider to maximize your performance across channels:

TikTok: Create engaging, short-form videos with trending hashtags.

Reddit: Participate in relevant subreddits and provide value without overtly promoting.

YouTube: Create a combination of long-form and short-form videos, targeting different users on the platform.

Voice search: Focus on conversational keywords and provide clear answers to common questions.

You may be asking, why don’t users just use those platforms to find what they need?

They do, but before you say “SEO does not matter,” remember while Google is a search engine, it can provide results from other platforms, making them relevant. We’ve seen this recently with Reddit results surging to the top of the SERPs, and showing up in a whopping 97.5 percent of Google search queries for product reviews.

As  younger audiences use social media or videos more for discovery, Google will continue to update and adapt to meet user needs. And since Google pulls from so many different spaces (not just social), it still offers more reliable results on topics people want to find.

Take Reddit, for example. It shows up in a whopping 97.5 percent of Google search queries for product reviews.

Google Loves Brands

As your brand grows, you’ll find your rankings climb because Google takes authority, trustworthiness, and relevance into account. Typically, well-established brands have a higher authority and level of trustworthiness. Branded search volume is the number of searches for keywords containing your brand name on a search engine. This is one of the metrics for tracking growth because it reflects user’s interest and awareness of your brand.

Let’s consider what happens when you type “men’s running shoes” into Google’s search bar. Here is an example of what you might get:

A list of results for men's running shoes.

Brands, brands, and more brands.

If you search my name, Google assumes you want to look at my website, businesses, and information about me or my social accounts.

Results of a Google search for Neil Patel.

Often, Google assumes that people searching for these terms already know what they want (and likely plan to make a purchase). This is especially the case if a customer is searching for an already well-established brand.

So, how do you establish your brand?

Aligning with the E-E-A-T framework is a good start. When your brand exudes Expertise, Experience, Authoritativeness, and Trustworthiness, Google will notice (and so will users).

To build those signals:

Create content that shows off your real-world experience and authority. Think in-depth tutorials or original research. Customer stories help, too.
Earn mentions and backlinks from reputable sources. Digital PR matters here.
Foster community. Social proof, like reviews, forum engagement, or user-generated content, tells Google and users that your brand is alive and active.

While some argue SEO is dead, building brand authority proves otherwise. In the age of AI and zero-click searches, it’s your ticket to higher rankings and increased visibility.

To be clear, E-E-A-T doesn’t only help for branded terms. Be sure to optimize for branded and non-branded terms to get in front of the most users.

Intent Is More Important Than Ever

Google is getting better at understanding intent, and users expect results that feel tailored to what they actually want, not just what they typed. If someone searches “best running shoes,” are they looking to buy now, compare options, or read reviews? If your page doesn’t match that intent, it’s not going to rank or convert.

It’s not just about categories like “informational” or “transactional” anymore, either. Google’s updates and AI enhancements have made search more personalized. Things like location and device type all influence which results appear and in what format.

That means one-size-fits-all content just won’t cut it. You need to build pages that solve specific problems for specific searchers, and make it obvious within the first few seconds that your content delivers the answer.

Look at the top results for your target terms. What kind of experience is Google rewarding? Long guides? Product roundups? Local directories?

When you align with intent, you’re not just improving your SEO, you’re giving users what they came for. And that’s how you win in the long run.

Create Content That’s Friendly For LLMs and SEO (there’s crossover)

Your niche is where your product or services fit in the market. What do you offer, and LLM tools like ChatGPT and Perplexity are changing how people search. Users have the ability to ask a question and get an instant summary. That means your content has to be referenced in this zero-click section of the search.

This is where LLMO (large language model optimization) comes in. It overlaps with SEO in a lot of ways. Clear structure and concise answers are good for both. But there are key differences.

LLMs don’t care about keyword density; they care about relevance and clarity. They’re more likely to pull from well-organized content (read as “easy to parse”) and rich in facts. Formatting matters. Use short copy blocks and bulleted lists to increase readability, and, on the technical side, clean HTML and schema markup help machines understand your content even more.

When it comes to backlinks, they still matter for SEO, but LLMs are more influenced by how well your content explains a topic.

If you want to future-proof your content, think about both: ranking high in search and being the source that LLMs pull from when people skip the SERPs entirely. For example below, well-known and reviewed medical sources pop up for this medical question.

Smart content creators are already optimizing for both worlds. Don’t get left behind.

User-Generated Content/Original Content Matters

Have you noticed that when you search on Google, your results are different than those Google is focusing on rewarding content that actually shows experience.

That’s why original content, especially from real users, is more valuable than ever. Some good examples of this type of content are:

Customer reviews
Community Q&As
Case studies
Proprietary research
Photos from your team.

Here’s an example of a successful UGC campaign from GoPro:

Source: https://www.sevenatoms.com/blog/ugc-marketing-examples

These types of content act as trust signals that feed directly into Google’s E-E-A-T framework (experience, expertise, authoritativeness, and trustworthiness).

If you haven’t already, get familiar with E-E-A-T. It’s the lens Google uses to figure out if your content deserves to rank. And in a world where LLMs are regurgitating the same surface-level info, showing firsthand knowledge is how you stand out.

User-generated content helps with that. So does publishing original insights—whether that’s internal data, lessons learned, or your unique take on industry trends. This is the kind of material Google can’t find anywhere else. It’s also what LLMs prefer to cite when pulling answers.

If you’re just rephrasing what’s already out there, you’re invisible. But if you create something worth referencing, both humans and machines will take notice.

Start building a content library that’s not just SEO-optimized, but undeniably your brand.

Focus Metrics Are Changing

Clicks and rankings used to be the gold standard in SEO. Not anymore.

Today, traditional SEO numbers like clicks and rankings only tell part of the story. With AI Overviews and zero-click searches taking over the SERPs, it’s possible to “rank” without getting any traffic. In this new environment, the way we measure success needs to evolve.

Instead of obsessing over position one, look at visibility across AI and SERP features. Are you showing up in AI summaries? In featured snippets? In the “People Also Ask” box? These touchpoints matter more now because they shape user behavior before a click even happens.

Engagement metrics are shifting, too. Scroll depth, dwell time, and interaction with on-page elements can reveal more about content quality than bounce rate ever did. The same goes for branded search volume and return visits—both strong signs that your content is resonating.

Search Everywhere Optimization Has Taken Center Stage

Search engines no longer corner the market on search. Non-search platforms, like social media and generative AI engines, are increasingly being used for search and discovery, disrupting traditional SEO norms.

This is what search everywhere optimization is all about.

You can no longer assume that users are only using search engines to find services and products that they need. They’re also using marketplaces (e.g., Amazon, Walmart), social media (e.g., TikTok, Pinterest), and generative AI (e.g., ChatGPT).

This means you need to expand your search optimization efforts, well, everywhere! Here’s how:

Social media: Platforms like TikTok and Instagram prioritize engaging, visual content. Optimize by using trending hashtags, creating shareable posts, and collaborating with influencers. Forums like Reddit are also highly cited in LLM results.

Generative AI engines: Tools like ChatGPT are shaping search behavior by delivering conversational and context-aware responses. Businesses should focus on producing concise, relevant, and authoritative content to rank within these engines.

Marketplaces: Amazon and similar sites act as search engines for product discovery. Ensuring optimized product titles, descriptions, and reviews is crucial.

We’ve seen this trend of the expanded search surface for a while now, but in 2026, your audience can be found across more platforms than ever. That’s why finding where your audience hangs out, and spending time to see how they’re interacting within the community, is such an important part of modern marketing strategy.

The traditional direct path of top-of-funnel to mid-funnel to bottom-of-funnel doesn’t play anymore. Your audience can convert from virtually anywhere in today’s market. Understand where they are, understand how they’re interacting, and understand the nuances of marketing on each platform, and you’ll be good to go.

FAQs

Is local SEO dead?

Not even close. Local SEO remains essential for businesses that rely on local customers. In fact, features like the local pack, Google Business Profiles, and map results are highly influential, especially on mobile. What’s changing is how users find you. Optimize for reviews and local content to stay competitive.

How long will SEO exist?

SEO is here to stay, but it continues to evolve. As long as people use search engines, social platforms, and AI tools to discover information, SEO will exist, even if tactics evolve.

Conclusion

SEO isn’t dead; it’s adapting to how people search today.

With AI reshaping the SERPs and user behavior shifting fast, what worked five years ago won’t cut it now. But the fundamentals still matter: create useful content, match search intent, and build trust with your audience.

If you’re unsure where to start, look at your content strategy. Are you prioritizing originality and structure? That’s what both Google and LLMs are rewarding.

Now’s also the time to rethink how you measure success. Traffic is great—but brand signals like engagement and trust are carrying more weight, and will only grow in importance in the future.

Want more tactical advice? Check out our guides on search engine trends and how to improve your SEO rankings.

Modern platforms like AI didn’t kill SEO; they just made it smarter. And we all need to follow suit.

Read more at Read More

AI-Powered Functionality in Google’s SEO Tools

January 29, 2026/0 Comments/in Web Design/by

Google’s been quietly upgrading Search Console and Analytics with AI. No fanfare. Just better data filtering. They sit quietly inside platforms you already use, like Search Console and Google Analytics, and they change how data is surfaced, filtered, and interpreted.

These updates don’t power AI Overviews or conversational search. They work behind the scenes in platforms you already use. Google is using AI to reduce manual analysis, surface issues faster, and help marketers understand complex datasets without exporting everything to spreadsheets.

Indexing patterns and performance trends are easier to spot, even if the underlying work still requires human judgment. Google’s automating the diagnostics. You still handle the strategy.

Key Takeaways

Google’s embedding AI into Search Console and Analytics 4 to cut down on manual data analysis. The AI handles filtering and pattern detection—you still make the decisions.
AI-powered features focus on filtering, pattern detection, and prioritization rather than execution.
Google Search Console AI helps surface performance insights faster.
Google Analytics 4 uses AI for anomaly detection, predictive metrics, and guided analysis.
Predictive metrics in GA4 (like churn probability) give you directional guidance, not guarantees. Use them to build hypotheses, not to replace analysis.

Why Google Is Embedding AI in SEO Tools

Google’s SEO tools have always produced more data than most teams can realistically analyze. As sites grow, so do performance reports and behavioral metrics. AI helps Google address that scale problem.

AI-Powered configuration in Google Analytics.

The main shift is from reactive analysis to proactive surfacing of insights. Instead of expecting marketers to manually filter reports, compare date ranges, and segment data, Google is using AI to highlight patterns and outliers automatically.

Search Console now groups issues more intelligently, with clearer prioritization, and more context around what matters. Analytics delivers automated insights, anomaly detection, and predictive metrics.

An example of Search Console grouping with AI.

The most practical benefit is time savings. AI-powered filtering lets you type what you want to see instead of clicking through multiple dropdowns. You can ask for specific trends, segments, or anomalies and let the system do the slicing for you. That alone removes a lot of friction from daily SEO work.

Your SEO expertise still matters. AI just handles the mechanical steps that used to slow you down. Google’s goal is to help marketers spend less time finding the signal and more time deciding what to do with it. For teams managing complex sites, this automation is table stakes.

If you want to understand how AI fits into broader SEO workflows, check out our guide on AI SEO.

AI Features in Google Search Console

Google Search Console has gradually introduced AI-assisted functionality that focuses on diagnostics and data interpretation rather than automation.

As a start, Search Console’s performance reporting benefits from smarter analysis. The platform highlights notable changes in clicks, impressions, and rankings without requiring manual comparison. This helps teams catch traffic drops or unexpected gains earlier, before they become larger problems.

Conversational-style filtering saves even more time. Instead of manually applying multiple filters, marketers can describe what they want to see, and Search Console narrows the data automatically. This reduces the time spent digging through reports just to answer basic questions.

Here’s how it works in practice: Instead of clicking Performance > Filters > Query > Contains > ‘product name’ > Apply, you type ‘show me queries for product pages with declining CTR.’ The AI interprets your request, applies the right filters, and shows you the data. That’s the time savings—going from five clicks to one typed question.

Note: Conversational filtering is rolling out gradually and may not be available in all Search Console accounts yet.”

AI won’t fix your indexing issues or update your site. It finds problems faster so you can fix them yourself. The value comes from speed and clarity, not automation. For SEO teams, this shortens the path between detection and action without removing human oversight.

AI Features in Google Analytics 4

This is partly because GA4 handles more complex event-based data and cross-device behavior.

Analytics Advisor is the most visible AI feature. Currently in Beta and not available for everyone yet, It automatically flags unusual patterns, such as sudden traffic spikes, drops, or changes in engagement. These insights appear without manual configuration and are designed to draw attention to potential issues or opportunities.

Source

To access Analytics Advisor, click the lightbulb icon in the top right corner of any GA4 property. The insights refresh daily and highlight metrics that deviate from your baseline. You might see ‘Pageviews from organic search increased 47% compared to last week’ with a link to explore the affected pages. That’s faster than manually comparing week-over-week reports.

Predictive metrics add another layer. Examples include purchase probability, churn probability, and revenue prediction for eligible properties. These metrics help teams forecast outcomes based on historical behavior rather than relying purely on past performance.

Predictive metrics require at least 1,000 positive and 1,000 negative examples of the target event over 28 days. If your site doesn’t meet that threshold, you won’t see predictions for purchase probability or churn. This makes the feature more useful for high-traffic e-commerce sites than small content publishers.

Another important use of AI in GA4 is automated anomaly detection. The platform monitors metrics continuously and alerts users when behavior deviates from expected patterns. This can surface tracking issues, campaign impacts, or site problems more quickly than manual review.

GA4’s AI points you toward what matters. You still handle the investigation. Teams still need to validate data quality, understand context, and decide how insights should influence strategy.

Other Google Tools Getting Smarter With AI

Beyond Search Console and GA4, other Google tools now have AI-supported features. Several other Google tools marketers use regularly now rely on machine learning to guide decisions and reduce manual work.

Google Analytics 4’s predictive metrics extend beyond reporting. They influence how audiences are built and activated, especially when connected to Google Ads. This allows marketers to target users based on likely future behavior rather than past actions alone.

Google Ads leans on machine learning to suggest budget shifts, adjust bids automatically, and test creative variations. You can accept or reject these suggestions, the control stays with you. These systems focus on optimization suggestions rather than forced changes, leaving final control with advertisers.

Here’s what matters: diagnostic AI explains what’s happening now. Predictive AI estimates what comes next. Diagnostic AI explains what is happening now and why. Predictive AI estimates what might happen next. Both influence how marketers act, but they serve different purposes. Understanding which type of insight a tool provides helps teams decide how much weight to give its recommendations.

This changes your daily workflow. Instead of checking reports manually and looking for problems, you respond to flagged issues. Instead of building audience segments from scratch, you refine AI-generated segments. The shift is from ‘find the problem’ to ‘validate the finding.’ That’s faster, but it requires trust in the system’s baseline accuracy.

Should You Trust AI to Support Your Reporting?

Google’s using AI to decide what you see first in your reports. That raises control questions. These tools influence what you see first, what gets flagged, and what feels urgent.

Trust the insights. Verify the recommendations. AI supports reporting by prioritizing information, not by defining truth. Understanding its role helps teams use it effectively without losing oversight.

Is AI Taking Too Much Control?

One concern is that AI-driven data points could push marketers into autopilot mode. When tools highlight issues automatically, it’s tempting to assume they reflect the full picture.

AI helps you see more. It surfaces technical problems and data anomalies that teams often miss because they’re buried in reports or obscured by volume. AI helps surface data anomalies that teams might miss due to scale or limited time. It reduces the chance that important issues stay hidden in reports.

Don’t follow every data point blindly. AI recommendations are based on models and thresholds that may not reflect business context. Treat insights as starting points, not final answers. Validation still matters.

Who Really Gets the Advantage?

People assume big brands with more data get better AI insights. Not true. Everyone has access to the same tools.

The advantage goes to teams that actually use the insights. A local contractor who spots a data anomaly flagged by Search Console and acts on it outranks a national franchise that ignores the same alert.

AI lowers the barrier to analysis, but it doesn’t guarantee better outcomes. Interpretation and execution still determine results.

FAQs

Does AI in GA4 replace manual analysis?

No. AI highlights anomalies and predictions, but analysts still need to validate findings and decide how to act.

Are predictive metrics in GA4 always accurate?

Predictive metrics are estimates based on historical data. They provide directional guidance, not certainty.

Conclusion

AI makes Google’s SEO tools more efficient. It doesn’t replace the need for strategy. You still need to validate insights, understand your business context, and decide how to act on recommendations. The teams winning with these tools treat AI as an assistant, not an autopilot.

They use automated insights to find problems faster, then apply their own expertise to fix them. That combination (AI-powered detection plus human strategy) is what drives results. Start by exploring the AI features already available in your Search Console and GA4 accounts. Check what Analytics Advisor has flagged. Look at how Search Console groups your indexing issues.

See if the insights align with what you’re already tracking manually. Then decide where automation saves you real time.

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