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Why copywriting is the new superpower in 2026

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Why copywriting is the new superpower in 2026

For the last few years, copywriting has been quietly written off.

Not with outrage. Not with ceremony.

Just sidelined. Replaced. Automated.

Words – the core material of SEO, landing pages, ads, and persuasion – were demoted during the traffic rush and later the AI gold rush.

Blog posts were generated. Product descriptions were bulked out. Landing pages were templated.

Content teams shrank. Freelancers disappeared. And a convenient narrative emerged to justify it all:

“AI can write now, so writing doesn’t matter anymore.”

Then Google made it worse.

The helpful content update, followed by AI Overviews and conversational search, didn’t just hurt SEO. It hurt the broader web.

It gutted an entire economy built on informational arbitrage – niche blogs, affiliate sites, ad-funded publishers, and content-led SEO businesses that had learned how to monetize curiosity at scale.

Now, large language models are finishing the job. Informational queries are answered directly in search. The click is increasingly optional. Traffic is evaporating.

So yes, on the surface, it sounds mad to say this:

Copywriting is once again becoming the most important skill in digital marketing.

But only if you confuse copywriting with the thing that just died.

AI didn’t kill copywriting

What AI destroyed was not persuasion. 

It destroyed low-grade informational publishing – content that existed to intercept search demand, not to change decisions.

  • “How to” posts.
  • “Best tools for” roundups.
  • Explainers written for algorithms, not people.

LLMs are exceptionally good at this kind of work because it never required judgment. It required:

  • Synthesis. 
  • Summarization. 
  • Pattern matching. 
  • Compression.

That’s exactly what LLMs do best.

This content was designed to intercept purchase decisions by giving users something else to click before buying, often with the hope that a cookie would track the stop in the journey and reward the page for “influencing” the buyer journey.

That influence was rewarded either through analytics for the SEO team or through an affiliate’s bank account.

But persuasion – real persuasion – has never worked like that.

Persuasion requires:

  • A defined audience.
  • A clearly articulated problem.
  • A credible solution.
  • A deliberate attempt to influence choice.

Most SEO copy never attempted any of this. It aimed to rank, not to convert.

So when people say “AI killed copywriting,” what they really mean is this: AI exposed how little real copywriting was being done in the first place.

And that matters, because the environment we’re moving into makes persuasion more important, not less.

Dig deeper: SEO copywriting: 5 pillars for ranking and relevance

GEO isn’t about rankings

Traditional search engines forced users to translate their problems into keywords.

Someone didn’t search for “I’m an 18-year-old who’s just passed my test and needs insurance without being ripped off.” They typed [cheap car insurance] and hoped Google would serve the best results.

This created a monopoly in SEO. Those who could spend the most on links usually won once a semi-decent landing page was written.

It also created a sea of sameness, with most ranking websites saying exactly the same thing.

LLMs reverse this process. They:

  • Start with the problem.
  • Understand context, constraints, and intent. 
  • Decide which suppliers are most relevant.

That distinction is everything.

LLMs are not ranking pages. Instead, they seek and select the best solutions to solve users’ problems.

And selection depends on one thing above all else – positioning.

Not “position on Google,” but strategic positioning.

  • Who are you for?
  • What problem do you solve?
  • Why are you a better or different choice than the alternatives?

If an LLM cannot clearly answer those questions from your website and third-party information, you will not be recommended, no matter how many backlinks you have or how “authoritative” your content once looked.

This is why copywriting suddenly sits at the center of SEO’s future.

Dig deeper: The new SEO imperative: Building your brand

From SEO to GEO: Availability beats visibility

Search engine optimization was about visibility.

Generative engine optimization is about AI availability.

Availability means increasing the likelihood that your business will be surfaced in a buying situation.

That depends on whether your relevance is legible.

Most businesses still describe themselves in static, categorical terms:

  • “We’re an SEO agency in Manchester.”
  • “We’re solicitors in London.”
  • “We’re an insurance provider.”

These descriptions tell you what the business is. 

They do not tell you what problem it solves or for whom it solves that problem. They are catchall descriptors for a world where humans use search engines.

This is where most companies miss the opportunity in front of them.

The vast majority of “it’s just SEO” advice centers on entities and semantics. 

The tactics suggested for AI SEO are largely the same as traditional SEO: 

  • Create a topical map.
  • Publish topical content at scale.
  • Build links.

This is why many SEOs have defaulted to the “it’s just SEO” position.

If your lens is meaning, topics, context, and relationships, everything looks like SEO.

In contrast, the world in which copywriters and PRs operate looks very different.

Copywriters and PRs think in terms of problems, solutions, and sales.

All of this stems from brand positioning.

Positioning is not a fixed asset

A strategic position is a viable combination of:

  • Who you target.
  • What you offer.
  • How your product or service delivers it

Change any one of those, and you have a new position.

Most firms treat their current position as fixed. 

They accept the rules of the category and pour their effort into incremental improvement, competing with the same rivals, for the same customers, in the same way.

LLMs quietly remove that constraint.

If you genuinely solve problems – and most established businesses do – there is no reason to limit yourself to a single inherited position simply because that’s how the category has historically been defined.

No position remains unique forever. Competitors copy attractive positions relentlessly. 

The only sustainable advantage is the ability to continually identify and colonize new ones.

This doesn’t mean becoming everything to everyone. Overextension dilutes brands.

It means being honest and explicit about the problems you already solve well.

This is something copywriters understand well. 

A good business or marketing strategist can help uncover new positions in the market, and a good copywriter can help articulate them on landing pages.

This is a key shift from semantic SEO to GEO.

You want LLMs to recommend your business to solve those problems.

Get the newsletter search marketers rely on.

See terms.

From SEOs’ ‘what we are’ to GEOs’ ‘what problem we solve’

Take insurance as a simple example.

A large insurer may technically offer “car insurance.” But the problems faced by:

  • An 18-year-old new driver.
  • A parent insuring a second family car.
  • A courier using a vehicle for work.
  • Are completely different.

Historically, these distinctions were collapsed into broad keywords because that’s how search worked. 

LLMs don’t behave like that. They start with the user problem to be solved.

If you are well placed to solve a specific use case, it makes strategic sense to articulate that explicitly, even if no one ever typed that exact phrase into Google.

A helpful way to think about this is as a padlock.

Your business can be unlocked by many different combinations. 

Each combination represents a different problem, for a different person, solved in a particular way.

If you advertise only one combination, you artificially restrict your AI availability.

Have you ever had a customer say, “We didn’t know you offered that?”

Now you have the chance to serve more people as individuals.

Essentially, this makes one business suitable for more problems.

You aren’t just a solicitor in Manchester.

You’re a solicitor who solves X by Y.

You’re a solicitor for X with a Y problem.

The list could be endless.

Why copywriting becomes infrastructure again

This is where copywriting returns to its original job.

Good copywriting has always been about creating a direct relationship with a prospect, framing the problem correctly, intensifying it, and making the case that you are the best place to solve it.

That logic hasn’t changed.

What has changed is that the audience has expanded.

You now have to persuade:

  • A human decision-maker.
  • A LLM acting as a recommender.

Both require the same thing: clarity.

You must be explicit about:

  • The problem you solve.
  • Who you solve it for.
  • How you solve it.
  • Why your solution works.

You must also support those claims with evidence.

This is not new thinking. It comes straight out of classic direct marketing.

Drayton Bird defined direct marketing as the creation and exploitation of a direct relationship between you and an individual prospect. 

Eugene Schwartz spent his career explaining that persuasion is not accidental – benefits must be clear, claims must be demonstrated, and relevance must be immediate.

The web environment made it possible to forget these fundamentals for a while.

AI brings them back.

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

Less traffic doesn’t mean less performance

Traffic is going to fall.

Informational traffic is being stripped out of the system.

Traffic only became a problem when it stopped being a measure and became a target. 

Once that happened, it ceased to be useful. Volume replaced outcomes. Movement replaced progress.

In an AI-mediated world, fewer clicks does not mean less opportunity.

It means less irrelevant traffic.

When GEO and positioning-led copy work, you see:

  • Traffic landing on revenue-generating pages.
  • Brand-page visits from pre-qualified prospects.
  • Fewer exploratory visits and more decisive ones

No one can buy from you if they never reach your site. Traffic still matters, but only traffic with intent.

In this environment, traffic stops being a vanity metric and becomes meaningful again.

Every click has a purpose.

What measurement looks like now

The North Star is no longer sessions. It is commercial interaction.

The questions that matter are:

  • How many clicks did we get to revenue-driving pages this month versus last?
  • How many of those visits turned into real conversations?
  • Is branded demand increasing as our positioning becomes clearer?
  • Are lead quality and close rates improving, even as traffic falls?

Share of search still has relevance – particularly brand share – but it must be interpreted differently when the interface doesn’t always click through.

AI attribution is messy and imperfect. Anyone claiming otherwise is lying. But signals already exist:

  • Prospects saying, “ChatGPT recommended you.”
  • Sales calls referencing AI tools.
  • Brand searches rising without content expansion.
  • Direct traffic increasing alongside reduced informational content

These are directional indicators. And they are enough.

The real shift SEO needs to make

For a decade, SEO rewarded people who were good at publishing.

The next decade will reward people who are good at positioning.

That means:

  • Fewer pages, but sharper ones.
  • Less information, more persuasion.
  • Fewer visitors, higher intent.

It means treating your website not as a library, but as a set of sales letters, each one earning its place by clearly solving a problem for a defined audience.

This is not the death of SEO.

SEO is growing up.

The reality nobody wants, but everyone needs

Copywriting didn’t die.

Those spending a fortune on Facebook ads embraced copywriting. Those selling SEO went down the route of traffic chasing.

The two worlds had different values.

  • The ad crowd embraced copy.
  • The SEO crowd disowned it.

One valued conversion. The other valued traffic.

We are entering a world with less traffic, fewer clicks, and an intelligent intermediary between you and the buyer.

That makes clarity a weapon. That makes good copy a weapon.

In 2026, the brands that win will not be the ones with the most content.

They will be the brands that return to the basics of good copy and PR.

The information era of SEO is over.

It’s time to get back to marketing.

Read more at Read More

Content Writing 101: 8 Skills That Set Top Writers Apart

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With AI tools at everyone’s fingertips, what does “great” content writing mean in 2026?

Content writing is about using words and psychology to deliver value, earn trust, and move readers toward action.

It includes blog posts, social media content, newsletters, and white papers. Or it can be scripts for video, podcasts, and presentations.

Content Type Purpose Key Characteristics
Blog posts Educate; build brand awareness and authority In-depth, structured, research-backed
Social media posts Engage, entertain, build community Conversational, visual, platform-specific
Email newsletters Nurture relationships; drive action Personal tone, value-driven, scannable
Video/podcast scripts Entertain; educate through audio/visual Conversational, paced for speech, engaging hooks
Presentations/webinars Educate and engage viewers for awareness Educational, crisp content presented visually

Unlike copywriting, which persuades the audience to take an action, content writing builds trust through teaching.

Thanks to AI tools, filling pages is easier and faster than ever.

And as content becomes easier to produce, attention becomes harder to earn — whether readers are scrolling social feeds, skimming search results, or asking AI tools for quick answers.

The best content writers bring a full toolkit: deep research, sharp critical thinking, strategic judgment, and the ability to apply those strengths in ways AI can’t replicate.

In this guide, you’ll learn eight content writing skills that set top performers apart, shaped by my work with leading brands and insights from my colleagues at Backlinko.

Important: Research and editing are learnable skills. But the instinct for what makes content memorable — what makes someone stop scrolling, what creates emotional resonance — that’s the human layer AI can’t recreate.

1. Build and Hone Your Research Skills

Strong research is what separates fluff from content people trust.

Here’s how to build a hands-on research process.

Start with Your Audience

Audience research is the easiest way to understand your readers: their pain points, goals, and hesitations.

Start your research in a few simple but effective ways:

  • Mine social media platforms to find emotional drivers behind buying decisions
  • Skim product reviews to learn what excites or frustrates your audience
  • Talk directly to your audience through polls, surveys, or 1:1 interviews
  • Browse community forums to see real conversations around your subject

For example, if you’re writing about the “best SaaS tools,” don’t rely on generic feature lists to inspire your content.

Go where real SaaS buyers are sharing feedback — places like G2 reviews and user-generated forums like Reddit.

Reddit – SaaS buywers sharing feedback

These insights help you create genuinely helpful content that connects with readers.

Rosanna Campbell, a senior writer for Backlinko, shares what she looks for when researching an audience:

At a minimum, I like to spend time learning the jargon, current issues, etc., affecting my target reader — usually by lurking on platforms like Reddit, Quora, industry forums, LinkedIn threads, etc. I’ll also find one or two leading voices and read some of their recent content.

But you don’t have to do all the heavy lifting yourself.

AI can speed up much of this process.

Note: AI won’t write great content for you, but it can streamline your research and editing process. Throughout this guide, I’ve included prompts to help you work smarter and faster — not let AI do the thinking for you.

For instance, Michael Ofei, our managing editor, uses a strategic prompt to aggregate audience insights from multiple channels.

Copy/paste this prompt into any AI tool to jumpstart your research (just update your topic description first).

You are a content strategist researching audience pain points for: [TOPIC DESCRIPTION]

RESEARCH SOURCES: Analyze discussions from Reddit, Quora, YouTube comments, LinkedIn posts, and People Also Ask sections from the last 12 months.

PAIN POINT CRITERIA:

  • Written as first-person “I” statements
  • Specific and actionable (not vague)
  • Include emotional context where relevant
  • Reflect different sophistication levels (beginner to advanced)

OUTPUT FORMAT: First, suggest 3-5 pain point categories for this topic’s user journey.

Then create a table with:

  1. Category (from your suggested categories)
  2. Pain Point Statement (first person)
  3. User Level (Beginner/Intermediate/Advanced – use one for each pain point)
  4. Emotional Intensity (Low/Medium/High)
  5. Semantic Queries (related searches)

Aim for 8-12 total pain points that help content rank for both traditional search and LLM responses. Provide only the essential table output, minimize explanatory text.

After using this prompt for the topic “journalist outreach,” Michael received a helpful list of pain points mapped to user level and emotional intensity.

Journalist Outreach – Audience Insights

Perform a Search Analysis

Next, it’s time to review organic search results to assess what content already exists and where you can add value.

Chris Shirlow, our senior editor, stresses the importance of looking closely at who’s ranking and how when studying search results:

Analyzing search results gives me a quick pulse on the topic: how people are talking about it, what questions they’re asking, and even what pain points are showing up. From there, I can identify gaps, spot patterns in language and structure, and figure out how to create something that adds value, rather than just echoing what’s already out there.

Pay attention to:

  • Content depth: Is the content shallow (short posts) or comprehensive (long guides)?
  • Authority: Who’s ranking — big brands, niche experts, or smaller sites?
  • Visuals: What kind of visuals can make your content stand out?
  • Gaps and missing angles: What’s missing that you could add?

Google SERP – Lifecycle marketing strategy

Then, repeat the same process with large language models (LLMs) like ChatGPT, Claude, and Perplexity.

AI has changed how people discover and consume information.

This means it’s no longer enough to rank on Google; your content also needs to surface in AI-generated answers.

Traditional Search vs. AI Search

Notice the type of insights coming up in AI-generated responses, and find gaps in the results.

Pay attention to the frequently cited brands and content formats to understand what AI considers “trusted.”

Study those articles closely to see how they’re earning citations and mentions.

ChatGPT – Lifecycle marketing strategy

Map Out Key Topics with Content Tools

Tools like Semrush’s Topic Research also help you learn more about the topics your audience is interested in.

Enter a topic like “lifecycle email marketing” and you’ll get a visual map of related themes like “loyalty program” and “segmenting your audience.”

Topic Research – Lifecycle email marketing – Mind Map

This gives you insight into the subtopics to cover, questions to answer, and angles that resonate with your audience.

2. Find Fresh Angles to Create Standout Content

Don’t fall into the trap of rehashing what’s already ranking.

Find new angles and content ideas to break through the crowd.

Angles come from tension. This can be a surprising insight, a common mistake, a high-stakes story, or a view that challenges the norm.

Without tension, you’re just adding to the noise. Here’s how to find them.

Find Gaps in Existing Content

Study the top-ranking and frequently cited articles for your topic, and see what’s missing.

It could be:

  • Shallow sections that need a deeper analysis
  • Topics explained without visuals, examples, or case studies
  • Predictable “safe takes” that ignore alternative perspectives and bold advice

Use this framework to document these gaps.

Content Gap What to Assess
Depth Is the content surface-level? Are key topics rushed, repetitive, or missing nuance?
Evidence Are claims backed by credible proof like examples, case studies, data, or visuals?
Perspective Does it repeat what everyone else is saying, or bring a fresh angle?
Format Is the information structured logically and easy to scan?

Consider Opportunities for Information Gain

Information gain adds unique value to your content compared to the existing content on the same topic.

Think original data, free templates, and new strategies.

Basically, it helps your content stand out from the crowd. And creates an “aha” moment for your readers.

Backlinko – AI Search Strategy – Article

Use these tips to add information gain to your articles:

  • Find concrete proof: Support your claims with original research, case studies, quotes, or real examples from your own experience or industry experts
  • Expand on throwaway insights: Take loosely discussed ideas and cover them in detail with additional context, data, and actionable takeaways
  • Counter predictable advice: Stand out with contrarian perspectives, exceptions, or overlooked approaches
  • Address unanswered questions: Find what confuses readers and fill those gaps with your content

At Backlinko, our writers and editors consider information gain early in outlining to uncover gaps and add value from the start.

Here’s how our senior editor, Shannon Willoby, approaches it:

I try not to default to common industry sources when gathering research. Everyone pulls from these, which is why you’ll often see industry blogs all quoting the same people, statistics, and insights. Instead, I look for lesser-known sources for information gain, like podcasts with industry experts, webinar transcripts, niche newsletters, and conference presentations. AI tools can also help with this task, but you’ll have to thoroughly vet the recommendations.

In my own article on ecommerce SEO audits, I proposed a simplified, goal-based structure for the outline, with an actionable checklist — something missing from existing content.

Information Gain – Ecommerce AEO Audits

This approach gave readers a clearer roadmap instead of just another generic audit guide.

Use AI as a Creativity Multiplier

AI content tools make great sparring partners that enhance your thinking.

For instance, Shannon shares her process for using AI to refine her research.

Once I’ve drafted my main points, I’ll ask ChatGPT or Claude a question like, ‘What’s the next question a reader might have after this?’ This helps me spot gaps and add supporting details that make the article more valuable to the audience.

The following prompts can help you find deeper angles and improve your audience alignment:

How to use AI to improve content Prompts
Find blind spots Here’s my research for an article on [topic]. What questions or objections would readers still have after going through this? List gaps I should address to make it feel more complete.
Challenge assumptions I’m arguing that [insert your point]. Play devil’s advocate: what would be the strongest counterarguments against this view, and what evidence could support them?
Explore alternative perspectives Rewrite this idea as if you were speaking to: (a) a total beginner, (b) a mid-level practitioner, and (c) a skeptic. Show me how each group would interpret or question it differently.

3. Back Up Your Points with Evidence

Evidence-backed content gives weight to your arguments and makes abstract ideas easier to digest.

It also helps your content stick in readers’ minds long after they’ve clicked away.

This includes firsthand examples, data, case studies, and expert insights.

Backlinko – AI Optimization – Semrush research

The key is using reputable, industry-leading sources in your content writing. And backing up claims with verifiable proof.

Pro tip: LLMs favor evidence-backed content when generating responses — boosting both your authority as a writer and your clients’ visibility.

Here’s how different types of evidence can strengthen your content:

  • Recent research data: Backs up trends and industry shifts with hard numbers
  • Case studies: Proves outcomes are achievable with real-world results
  • Expert quotes: Adds credibility when challenging assumptions or introducing new ideas
  • Examples: Makes abstract concepts concrete and relatable

Back up your claims with proof points

4. Structure Your Ideas in a Detailed Outline

An outline organizes your ideas and insights into a clear structure before you start writing.

It maps out the key sections you’ll cover, supporting evidence, and the order in which you’ll present your points.

For example, here’s the outline I created for my Backlinko article on subdomains vs. subdirectories:

Subdomains vs. Subdirectories

I included a working headline, H2s, and main points. I also added my plans for information gain.

This shows clients or employers how you’ll deliver unique value — and keeps you focused on differentiating your content from the start.

Add expert quotes to emphasize each point

To get started with your outline, think of your core argument: what’s the most important takeaway you want readers to leave with?

From there, use the inverted pyramid to create an intuitive structure.

Include the most important details at the start of every section, then layer additional context as you go.

The Inverted Pyramid Approach for Outlining Content

Pro tip: Save time with Semrush’s SEO Brief Generator. Add your topic and keywords, and it generates a solid outline instantly. From there, you can refine it with your own research and insights.

5. Develop Your Unique Writing Voice

Two people can write about the same topic.

But the one with a distinct voice is the one people quote, bookmark, and remember.

Assess Your Writing Personality

To define your writing personality, start by analyzing how you naturally communicate.

Look at your emails, Slack messages, and social posts.

Notice patterns in tone, humor, pacing, analogies, pop-culture references, or how often you use data and stats.

LinkedIn – Shreelekha Singh – Status

Then, distill these insights into a few adjectives that describe how you want to sound.

Like professional, insightful, and authoritative.

Use these to guide your writing voice.

Describe your voice with adjectives

For example, let’s say your adjectives are conversational, humorous, and authentic.

Here’s how that might look in practice:

  • Conversational: Short sentences with casual, relatable language. “Let’s be real — writing your first draft is 90% staring at a blinking cursor.”
  • Humorous: Use wit or funny references to engage readers. Instead of “Most introductions are too long,” you might say, “Most intros drag on longer than a Marvel end-credit scene.”
  • Authentic: Add stories from your lived experiences to make people feel seen. “When I first launched my blog, my mom was my only reader for six months.”

Get Inspired by Your Favorite Writers

To keep sharpening your voice, study writers you admire.

Pay attention to their rhythm, tone, and structure.

What terms do they use? How do they hold your attention — whether in a long-form blog post or a quick LinkedIn update?

LinkedIn – Get inspired by your favorite writers

Borrow what works, then put your own spin on it so it still sounds like you.

Adapt to Your Clients’ Voices

As a content writer, clients and employers will often expect you to adapt your writing to their brand voice.

This might mean adjusting your tone, pacing, or word choice to match their brand’s personality.

Study a few of their blog posts or emails to understand their style.

Backlinko – Increase Website Traffic – Article

Note patterns in rhythm and vocabulary, and mirror those in your draft — without losing what makes your writing yours.

AI tools can help you check how well your draft matches your client’s voice.

Upload both the brand’s voice guidelines and your draft to an LLM and use this prompt:

I’ve added the brand voice guidelines and my draft for this brand.

Compare my draft against the guidelines and tell me:

  • Where my tone, word choice, or style drifts away from the brand voice
  • Specific sentences I should rewrite to better match the guidelines
  • Suggestions for how to make the overall flow feel more consistent with the brand voice

6. Add Rich Media to Improve Scannability

Even the best ideas lose impact when hidden behind walls of text.

Plus, research shows that most people skim web pages. Their eyes dart to headlines, opening lines, and anything that stands out visually.

That’s why adding visual breaks, such as images, screenshots, and tables, is so important.

Backlinko – Is SEO dead? – Article

Visual content works well when you want to illustrate a point.

It also simplifies or amplifies ideas that are hard to convey with text alone.

As Chris Hanna, our senior editor, puts it:

Often, words alone just won’t make full sense in the reader’s mind, or they won’t have the desired impact on their own. Anytime you’d personally prefer to see a visual explanation, it’s worth thinking about how you can convey it through visuals. If you can imagine watching a video on the topic you’re writing about, use that as your guide for how you could illustrate it with graphics.

Here are a few places where infographics can supplement your writing:

    • Comparisons:

Tables or side-by-side visuals

  • Frameworks and models:

 

Diagrams or matrices

  • Workflows and processes:

 

Flowcharts or timelines

  • Abstract concepts:

 

Layered visuals (like Venn diagrams)

Use infographics to supplement your writing

At Backlinko, we track visual break density (VBD) — the ratio of visuals to text.

Our goal is a visual break density of 12% or higher for every article.

That’s about 12 visuals (images, GIFs, callout boxes, or tables) per 1,000 words to keep content easy to scan and engaging.

Here’s how this looks in practice:

We do this to improve the readability, retention, and engagement of our articles, from start to finish.

7. Understand How to Sell Through Your Content

Every piece of content sells something — a product, a signup, a return visit.

But good content doesn’t read like a pitch.

It gently nudges people to take action by building trust and solving real problems.

Lead with Value

This is what Klaviyo, an email marketing platform, does through its blog content.

Klaviyo – Christmas email marketing

They include helpful examples, original data, and actionable tips in their content writing.

But they also weave in product mentions that feel helpful, not salesy.

There are case studies, screenshots, and examples that show how real clients used their platform to increase revenue.

Klaviyo – Case studies, screenshots & examples

This is smart for a few reasons.

It proves their expertise, reinforces how their product solves real problems, and delivers value — even if the reader never becomes a customer.

Focus on Outcomes, Not Features

People don’t care what a company offers — they care what it helps them achieve.

Features talk about what you offer. Outcomes show people how they can benefit.

Here’s what this looks like in practice:

Feature-driven writing Outcome-driven writing
“Redesigned homepage using Figma and custom CSS” “After my redesign, load time dropped to 2 seconds and conversions jumped 40%. Here’s how I planned it.”
“Our tool automates monthly reporting.” “One agency cut reporting time from 5 hours to 1 and reinvested those 4 hours into client growth. Let’s break down this workflow to help you achieve similar results.”

Show people you understand their frustrations by baking their pain points into your content writing.

When readers sense you’ve been in their shoes, they’re more open to your advice.

Take this HubSpot CRM product page, for example.

​​It highlights real frustrations — setup hassles, messy migrations, lost data — the exact headaches their audience feels.

HubSpot – CRM Product Page

Then, it shifts to outcomes with copy like “unified data” and higher productivity from “day one.”

That’s outcome-driven content writing. It connects with the audience immediately and makes the benefits crystal clear.

Share Your Firsthand Struggles

Authority matters, but so does humility.

Be honest about your wins and failures. It makes your content feel real.

Here’s an example from one of my Backlinko articles where I shared my struggles with creating a social media calendar:

Backlinko – Social Media Calendar – Shared struggles

I relate to the audience with language like “too many tabs” and “overwhelming categorization.”

And provide a free calendar template so readers can apply what they learn.

Backlinko – Social Media Calendar – Free template

Pro tip: Free resources, such as tools, frameworks, and templates, make your content more actionable. Even a simple checklist or worksheet can help readers take the next step, and make your work far more memorable.

8. Finalize Your Work

Here’s the truth: your first draft is never your best draft.

Editing is where your content truly comes alive.

Step Away from Your Draft

One of the simplest editing tricks in the book? Give your draft some breathing room.

Chris Shirlow, our senior content editor, explains why:

Spend too much time in an article and you lose all perspective. Take a walk, sleep on it, or do something totally unrelated. When you come back, you’ll see what’s working — and what’s not — much more clearly.

It may take a few rounds of editing and refining before you get everything just right:

  • Round 1 (quick wins): Go through the article. Does it flow logically? Is it easy to understand? Do your examples clearly illustrate the core ideas?
  • Round 2 (structure): Ask AI for editing feedback. What are you missing? Does the structure/writing flow naturally? Is there any room to add more value?
  • Round 3 (polish): Tighten sentences, transitions, audience alignment, and examples

Here’s a prompt you can use for Round 2:

You are an expert editor specializing in long-form content writing. Please analyze my draft on the topic [ADD TOPIC] for its structure, flow, and reader experience.

Specifically, give feedback and suggestions on:

  1. Structure: Are the sections ordered logically? Does each section build on the previous one?
  2. Depth and focus: Which parts feel under-explained or too detailed? How can I tighten or expand them to improve the flow?
  3. Reader journey: Where might readers drop off or lose context?

Summarize your feedback into 3–5 actionable editing priorities.

Pro tip: AI suggestions feel generic? Train the tool on your style first. Both Claude and ChatGPT let you upload writing samples and guidelines so their suggestions align with your voice.

Prioritize Clarity Over Cleverness

If your audience has to re-read a sentence to understand it, you’ve lost them.

As Yongi Barnard, our senior content writer, says:

A clever turn of phrase is nice, but the goal is for readers to understand your point immediately. Edit out any language that makes them pause to figure out what you mean.

Take a quick litmus test: Is this sentence/phrase/word here because it helps my audience, or because I like how it sounds?

You’ll know a sentence/phrase needs to be cut if it…

  • Slows down the flow
  • Makes the point harder to understand
  • Is redundant

Common issues in content writing (and how to fix them) include:

Problem Areas Weak Example Strong Example
Wordiness “At this point in time, in order to improve your rankings, you need to be focusing on the basics of SEO.” “To improve rankings, focus on SEO basics.”
Jargon “We need to leverage synergies across verticals.” “We need different teams to work together.”
Abstract Claims “Content quality is important for SEO success.” “Sites that publish in-depth content (2,000+ words) rank higher than thin pages.”

Build Your Personal Editing Checklist

Every writer has blind spots: repeated grammar errors, overused words, or formatting mistakes.

That’s why Yongi suggests creating a personal editing checklist that includes common errors and recurring feedback from editors.

Chris Hanna suggests going through the checklist before submitting your draft:

Run a cmd+F (Mac) or CTRL+F (Windows) search in the doc each time. It’ll help you catch the most important but easy-to-fix errors.

Over time, you’ll naturally make fewer mistakes.

Here’s an editing checklist to get you started:

The Self-Editing Checklist

Big picture

  • Does the piece serve the reader (not me)?
  • Is the main takeaway crystal clear from the start?
  • Does the flow make sense, with each section leading naturally to the next?

Clarity and value

  • Is every section genuinely useful, not filler?
  • Did I back up claims with examples, data, or stories?
  • Did I explain the ideas simply enough that my target readers would get it?

Language and style

  • Am I prioritizing clarity over cleverness?
  • Are any sentences too long or clunky — could I cut or split them?
  • Did I cut filler words (actually, very, really, in order to, due to the fact that)?

Engagement

  • Did I vary sentence lengths?
  • Does the tone feel human — not robotic, not overly formal?
  • Is there at least a touch of personality (humor, storytelling, relatability)?

Polish

  • Are transitions smooth between sections?
  • Did I run a spell-check and grammar-check?
  • Did I read it out loud (or edit bottom-up) to catch awkward phrasing?
  • Did I run through my personal “repeat offender” list (words/phrases I overuse)?

Final Pass

  • Did I add relevant internal links?
  • Does the article end with a clear, valuable takeaway?
  • Did I include a natural next step (CTA, resource, or link) without sounding pushy?

Pro tip: Use a free tool like Hemingway Editor to tighten your writing. It gives you a readability grade and highlights long sentences, passive voice, and other clarity issues.

Hemingway App – Free editor

How to Become a Content Writer: A Quick Roadmap

If you’re starting from scratch, don’t worry — every great content writer began exactly where you are.

Here’s how to build momentum and get noticed.

Find a Niche You’re Passionate About

The fastest way to level up as a writer? Specialize.

Niching down builds authority — and makes clients trust you faster.

So, pick a niche (or two) and become an expert.

Rosanna Campbell – B2B SaaS Content Marketing

A good niche checks three boxes:

  • Passion: You care enough to keep learning and writing when it gets tough
  • Potential: There’s growing demand for this information
  • Profitability: Businesses invest in content on this topic

Three Ps of Blog Niches

Pro tip: Validate before you commit. Check job boards, freelance platforms, and brand blogs to see who’s hiring and publishing in that niche. If both interest and demand line up, you’ve found a winner.

Build Expertise and Authority in Your Niche

Once you pick a niche, become a trusted voice.

This gives you multiple advantages:

  • Traditional and AI search engines see your content as authoritative
  • Readers are more likely to trust what you say
  • Your content is more likely to be shared and quoted

Start with what you know. Draw from your own experiences to add depth and credibility.

For example, the travel writer India Amos built her authority by writing firsthand reviews.

Her Business Insider piece about a ferry ride is grounded in real experience, making the content trustworthy and relatable.

Business Insider – Piece

But don’t limit yourself to content writing for clients. Get your name out there.

  • Contribute guest posts and expert quotes to reputable sites
  • Speak at conferences or webinars
  • Share insights on social media

The more you’re cited as an expert, the stronger your credibility.

Build authority as a niche writer

Learn SEO Fundamentals

SEO basics remain essential to content writing: keyword research, competitive analysis, and on-page optimization.

But you’ll also want to know how to write and structure LLM-friendly content.

YouTube videos, blogs, and courses can help you understand these topics quickly.

Backlinko Hub SEO

It’s also helpful to familiarize yourself with popular SEO tools.

Clients often expect you to know platforms like:

  • Clearscope, Surfer SEO, MarketMuse: Content optimization and readability scoring
  • Semrush, Ahrefs, Moz: Keyword research and competitive analysis
  • Perplexity, ChatGPT, Gemini: AI search insight and prompt-based content discovery

Clearscope – Example

Pro tip: Consider pursuing niche-specific certifications to stand out. This is especially helpful in “Your Money or Your Life” (YMYL) fields like finance, health, or law, where expertise and trust matter most.

Show Proof of Work with a Portfolio

A portfolio showcases what you bring to the table and provides proof of your accomplishments as a writer.

But you don’t have to spend weeks (or months) building one.

What matters most is what’s inside your portfolio, such as:

  • A short intro about who you are and what you offer
  • Writing samples that showcase your expertise
  • Testimonials or references
  • Contact information

Essential Elements for a Writing Portfolio


Tools like Notion, Contra, Authory, and Bento let you design a portfolio in minutes.

For instance, here’s my Authory portfolio:

Authority – Shreelekha Singh

I like this platform because it automatically adds all articles credited to my name.

You can also invest in a website for more control and search visibility.

I did both — having a portfolio and website helps me improve my online visibility:

Shreelekha Singh – Work samples

LinkedIn can also double as your portfolio.

Add details about each client and link to your articles in the “Experience” section of your profile.

LinkedIn – Shreelekha Singh – Experience

Share your on-the-job insights, feature testimonials, and engage in relevant conversations.

And don’t forget to post your favorite work, from blog posts to copywriting.

Unlike a static site, LinkedIn keeps you visible in real time.

LinkedIn – Shreelekha Singh – Post

Future-Proof Your Content Writing Skills

Use what you’ve learned here to create content that builds your reputation and lands clients.

Because great content writing doesn’t just fill pages. It opens doors.

And as AI continues to reshape the content world, the best writers don’t resist it — they evolve with it.

So, don’t fear artificial intelligence as a writer. Use it to your advantage.

Read our guide: How to Use AI to Create Exceptional Content. It’s packed with practical workflows, expert insights, and handy prompts that will help you work smarter and stay ahead.

The post Content Writing 101: 8 Skills That Set Top Writers Apart appeared first on Backlinko.

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Social-First Ranking Strategies

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The way people discover brands has changed faster than most teams realize. Visibility does not start on your website anymore. It begins in the places where people trade unfiltered opinions such as Reddit threads, TikTok videos, YouTube reviews, niche forums, expert interviews, creator breakdowns, and news articles.

These are the signals AI tools and search engines now rely on. They mirror the conversations people trust most. If your brand is not present in those conversations, you are handing visibility to someone else.

Digital PR sits in the middle of this shift. Not as a press release machine, but as the strategy that fuels the narrative across the platforms and communities that feed Google, TikTok, Instagram, YouTube, and every major language model.

This article breaks down how to use digital PR, social media content, and community engagement to increase discoverability everywhere people search today.

Key Takeaways

  • People move across 11 or more platforms while researching, comparing, and validating decisions. Your brand needs to meet them across those touchpoints.
  • Forums like Reddit and niche communities carry firsthand experience, which is why Google and language models are pulling them directly into search results.
  • Short-form video has become a high-impact discovery surface both inside social platforms and on Google page one.
  • Digital PR fuels AI visibility by supplying the fresh, authoritative, third-party information that language models prefer.
  • SEO becomes more powerful when PR, social content, and community insights work together.

The New Discovery Journey and Why Visibility Starts Beyond Your Website

A few years ago, someone looking for a new espresso machine would have gone straight to Google. They would have clicked a few product pages and made a decision. That journey looks nothing like what consumers do today.

A result for best expresso machine in Google.

Now they might ask ChatGPT for recommendations, check TikTok for short-form reviews, watch long breakdowns on YouTube, scan Reddit for real-world pros and cons, and then head to Google for price comparisons or final research.

By the time someone reaches your website, they already formed opinions based on content across half a dozen platforms.

This is the messy middle. It is where brands win or lose visibility.

An infographic explaining the "Messy Middle in terms of marketing and purchase journeys.

People are searching more often and in more places. Google reflects this change. Page one now includes short-form videos, Reddit threads, social carousels, media articles, and AI Overviews. This is not a reinvention of search. It is Google responding to real user behavior.

A diagram showing how Google is changing information.

To stay visible, your brand needs to show up where people learn, evaluate, and talk, not just where they click.

Why Forums and Community Conversations Matter More Than Ever

Reddit and niche forums are not fringe communities anymore. Reddit alone is projected to pass 1.5 billion monthly active users in 2026. The scale matters, but the reason it impacts search runs deeper.

Reddit monthly active users in a graph.

Source

Forums contain the firsthand experiences that AI and search engines trust.

Let’s talk a bit more about Reddit. Reddit content appears in at least four locations.

  1. Reddit search.
  2. Subreddit communities.
  3. Reddit Answers, which is the platform’s AI search tool.
  4. Google’s search results, especially in the Discussions and Forums section.
Where Reddit content appears.

This means a well-written Reddit thread can live for years and continue influencing decisions long after the original post.

There are other reasons why forum and community content is so important today.

Forums Shape Brand Perception Faster Than Brands Realize

These conversations happen with or without you. People share frustrations, recommendations, and detailed use cases that no brand site ever captures.

Many brands worry Reddit is hostile. In reality, it performs well when brands participate genuinely and respectfully. NP Digital activated Reddit profiles for two major brands. One saw one hundred percent positive or neutral sentiment on every comment. The other reached ninety-eight point seven percent positive. Yet general Reddit conversation about the same brand sat around forty-one percent positive.

How NP Digital drove results on Reddit.

The difference was authentic participation that added value to the community.

Reddit Drives Traffic and Influences Search Behavior

Some brands have seen organic declines this year because users spend more time researching on social platforms and forums. Reddit helps fill that gap by driving referral traffic. If people are searching for “best espresso machines Reddit”, you want your brand involved in those discussions or at least contributing useful insights.

With these notes in mind, you don’t want to rush into a Reddit strategy. Follow a progression that respects the community. At NP Digital, we recommend sticking to a crawl-walk-run strategy.

Crawl

Listen first. Join subreddits. Build karma slowly. Understand rules and norms.

Walk

Answer questions honestly. Participate in low-risk threads. Add context or correct misinformation. Avoid promotion entirely.

Run

Launch a brand subreddit if needed. Build content pillars. Create new threads that contribute information. Scale moderation and community responses.

Once your brand understands the culture and adds value, Reddit becomes a powerful discovery engine and insight tool.

Social Search as a Visibility Engine

Social is no longer just a place to publish helpful content. It has become a core part of the search journey for both consumers and business decision-makers. Sixty-seven percent of social users rely on social search at some point in their purchase process. That shift alone explains why Google has started indexing more social posts in page one results, including TikToks, LinkedIn posts, YouTube Shorts, and Instagram videos.

For example, if you search for a term like VPS hosting, you will often see a carousel labeled “What People Are Saying” that blends Reddit threads, TikToks, LinkedIn posts, and YouTube content in one feed. 

Results you find when searching for What People Are Saying.

Google is pulling from the places people already trust. It is a direct signal that social engagement and social authority now influence how visible a brand becomes across multiple search surfaces.

Social search depends on two things. You need keywords people are actively searching for, and you need content that earns engagement. Keyword research happens inside each platform. TikTok’s Creator Search Insights, Instagram’s autocomplete, YouTube’s search can all reveal the questions and topics users care about. Once you know those keywords, place them where platforms can detect them. Captions, spoken audio, on-screen text, subtitles, and alt text are all signals that help social platforms and search engines understand your content.

Engagement plays the second role. Social performs well when content feels timely, helpful, or relatable. It does not require studio production. You need clear audio, a strong hook, and information that teaches or entertains. Short-form video remains one of the most effective ways to earn reach, both inside social platforms and across search results. It is visible, digestible, and easy for people to interact with quickly.

How you can expand visibility using social search.

How Platforms Understand Keywords

Platforms are using audio transcription, text recognition, caption scanning, and behavioral signals to understand what your content is about. Hashtags still help in some cases, but they are not the main factor anymore. If you say the keyword in your video, write it on screen, and include it in your caption, platforms know the topic and can connect your content to the right search behavior.

Why UGC and Employee-Generated Content Matter

User-generated content has been an effective marketing tool for years because it feels relatable and trustworthy. Now it plays a role in discoverability as well. Employee-generated videos carry even more authority because they combine authenticity with expertise. They help social content rise faster and make your presence stronger across search and AI surfaces.

Social search works best when keyword strategy, content quality, and audience signals all point in the same direction. When those elements align, your videos and posts can appear across multiple platforms, gain reach quickly, and support the rest of your visibility strategy.

How Digital PR Fuels AI Overviews, LLM Citations, and Brand Visibility

AI search tools and language models work by gathering information from sources they consider reliable. They scan news articles, expert commentary, public forums, brand websites, and structured datasets. The goal is simple. They want to provide information that feels trustworthy, current, and grounded in real experience.

Digital PR supports this optimization ecosystem by producing brand information that is easy for AI tools to interpret and cite. Data studies, surveys, annual roundups, expert insight, and product comparisons all fall into content types that language models treat as credible. When this information exists across reputable publications, media outlets, and authoritative websites, AI systems have more material to work with. That increases the chances of being referenced when users search for answers.

How PR helps brands get featured.

Recency also plays a strong role. In one example, news coverage tied to a press release led to AI Overview citations within a couple of days. This shows how quickly language models can incorporate new information when it comes from a trusted source. Fresh material signals relevance.

How DPR led to AI overview citations.

Where coverage appears also influences visibility. Some publishers partner with AI companies or contribute more frequently to the datasets models learn from. Securing placement with these outlets increases the likelihood that the information will be integrated into AI responses. Add community discussions from platforms like Reddit or structured content from first-party research, and brands create a multi-layered presence that AI tools can draw from.

How to get featured on publishers most likely to pull in AI.

This approach has measurable impact. Several brands that we’ve worked with at NP Digital saw substantial growth in referrals from AI tools. The increases ranged from nearly one thousand percent to more than sixteen hundred percent within a year. 

Digital PR is a key part of all this success, helping supply the authoritative signals, data, and context that help AI tools understand a brand’s expertise. As search expands across platforms and models, PR becomes part of the information layer that shapes how brands are represented wherever users look for answers.

Bringing It All Together: How to Build a Unified Search Everywhere Strategy

With this in mind, let’s talk about using a strategy that leans on all these different levers to ensure an article on say, the most secure web browser, earns the most value by appearing where the ideal audience would be, versus forcing the fit.

Here is what a unified workflow would look like in those circumstances:

  1. Create a first-party study that tests browser security.
  2. Turn the findings into multiple assets. YouTube videos, Shorts, TikToks, Reels, media pitches, bylines, and Reddit threads.
  3. Join relevant subreddit conversations that mention browser security and contribute insights or data.
  4. Pitch journalists covering the topic.
  5. Reach out to writers of existing articles to provide updated data that improves the piece.
  6. Repurpose your content across newsletters, blog posts, paid ads, and social channels.
An example of a uniified content workflow.

This is not just traditional SEO. This is visibility architecture. You are building a presence across every surface where people search, compare, and validate decisions. Search engines and AI tools follow those signals.

FAQs

How quickly can a brand appear in AI Overviews?

When a brand distributes fresh, authoritative information and earns credible media coverage, inclusion can happen in a matter of days. 

Should every brand activate on Reddit?

Every brand should at least listen on Reddit. Activation makes sense once you understand the community and can contribute meaningfully. Listening alone offers valuable insights into customer needs, sentiment, and content opportunities.

Does social content influence search visibility?

Yes. Google increasingly pulls social posts and short-form videos into search results. High engagement on social platforms often correlates with stronger visibility across search surfaces.

What makes AI cite one brand more often than another?

Language models cite sources that appear reliable, current, widely referenced, and easy to interpret. Digital PR accelerates this by producing data, expert insight, and media mentions that models treat as credible.

Conclusion

People search everywhere now. They ask questions on social platforms, browse forums, follow creators, read news, and use AI tools before they ever visit a brand’s website. The brands winning visibility are shaping conversations in those places. They are publishing authoritative content, creating engaging social experiences, and participating in the communities that influence decisions.

Digital PR, social content, and community engagement support all of this. When these channels work together, your brand becomes easier to find across every surface where people search.

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A 5-step framework for year-end PPC reports that resonate with leadership

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A 5-step framework for year-end PPC reports that resonate with leadership

The new year is here, which means it’s time to deliver your end-of-year (EOY) PPC report.

But an EOY report isn’t just a longer version of your monthly performance check-in. 

It’s typically read by a different audience – often leadership teams who don’t see your regular reporting – and it needs to tell a different story.

Done well, your year-end report sets the tone for 2026, earns buy-in for your strategy, and positions you as a strategic partner rather than just a campaign manager. 

Done poorly, it creates confusion and undermines confidence in your work.

Here’s how to build an EOY PPC report that speaks to leadership and sets your work up for success in the year ahead.

1. Identify your audience and their priorities

You wouldn’t launch a new campaign without clearly defined goals and audiences. 

Don’t do it with your EOY report, either. Different stakeholders evaluate performance through very different lenses.

For example, among the clients I’m preparing reports for this year are:

  • A leadership team I’ve never met (despite working with this client for eight years) that wants a maximum five-page report at a very high level.
  • A data-driven CEO who wants a clear narrative connecting PPC spend, decisions, and outcomes.
  • A new director who wants context on the competitive landscape, performance, and specific opportunities for next year.

If I were to use a carbon-copy EOY report template for all of them, I’d have at best one happy leadership team and two confused or frustrated teams. 

I don’t care for those odds. Instead, I’m customizing each report to match the readers and their specific needs.

Sample PPC reports

If you don’t know the recipients (and if you’re at an agency, there’s a good chance you don’t), ask your primary contact questions like: 

  • Who will be receiving the report?
  • What do they care about most? 
  • What’s top of mind for them heading into next year?
  • What decisions will they be making based on this report? 

The answers should directly inform the report’s structure, depth, metrics, and length. 

When your audience is clearly defined upfront, the final report is far more likely to drive clarity, alignment, and confidence.

2. Create an easy-to-read executive summary

Your executive summary has one job – help leadership quickly understand how PPC performed across key metrics. 

Think of it as the “at a glance” page that sets the context for everything that follows.

If you studied communications formally, you probably learned to write executive summaries last, even though they appear first. 

Since you’re pulling data rather than crafting prose, flip that approach. 

Build this section first to guide the flow of what comes next.

Lead with the KPIs that matter most

Start with the metrics your audience actually cares about – the ones that were established as priorities at the beginning of the engagement or year. 

This will usually include revenue, leads, and conversions, but your mileage may vary. 

If your leadership team obsesses over market share or engagement, lead with those instead.

Include meaningful benchmarks

Unless your leadership team is dialed into PPC goals and performance, you need to give them benchmarks so they have a comparison tool. 

Use at least one of these three key benchmarks:

  • Year-over-year performance: How did this year stack up against last year?
  • Performance against target: Did we hit the goals we set out to achieve?
  • Industry benchmarks: How do we compare to competitors or industry averages?

In the example below, I’m showing revenue, ROAS, and cost for the year, with both percentage changes and raw numbers from the previous year.

2025 paid search performance - executive summary

This format does the heavy lifting for busy executives. 

At a quick glance, they know what happened and whether it’s good news. 

More importantly, it sets the stage for invisible CTAs and the deeper analysis that follows.

3. Break down performance details

In the following section, you’ll move from “what happened” to “why it happened and what we learned.” 

The executive summary told your reader whether the year was successful. Now you need to show them the engine under the hood.

The level of detail will depend on the format. A five-page executive report may only have room for a few pivotal moments, while a more comprehensive report can get into the weeds. 

In either case, selectivity is critical. You can’t — and shouldn’t — document every metric, test, or optimization from the year.

Instead, focus on insights that either explain the results in your executive summary or point directly to opportunities for the year ahead.

Here are some categories to get you started.

What performed best 

Show them the winners: your best-converting creative, highest-revenue products, or most efficient channels. 

Leadership loves to see what’s working, and it can point to where to double down. 

How resources were allocated

Break down spend distribution across campaign types, the split between brand and non-brand, or platform-specific investments like Google versus Bing. 

Leadership wants to know if you’re putting money where it matters most, and this section answers that question.

Google Ads 2025 spend breakout

What you tested and learned

Highlight new initiatives, strategic experiments, or incrementality tests. 

Did you test a new platform? Try a different targeting approach? 

These insights show you aren’t just managing campaigns, you’re advancing the strategy.

Trends that shaped the year

Include year-over-year comparisons, seasonal patterns, and performance trends over time. 

If Q3 saw unusual momentum or holiday performance differed from previous years, explain why.

Performance through the funnel

Show how users moved through your conversion funnel and where the biggest opportunities or bottlenecks exist.

Tracking and conversion changes

Changing what is counted as a conversion will affect just about everything else. 

If you made tracking or conversion definition changes during the year, call them out here.

Leadership needs to understand if a metric shift reflects actual performance or a measurement change.

2025 paid search performance in review

Keep this section platform-specific and substantive. Each insight should clearly tie back to the executive summary. 

Use visuals (charts, trend lines, and comparison tables) to make complex data easier to interpret. And resist the temptation to include everything you track. 

If a metric doesn’t explain results, answer a question from leadership, or inform future strategy, leave it out.

Get the newsletter search marketers rely on.

See terms.

4. Evaluate external factors

You’ve already explained what happened in the account and why performance moved the way it did. 

Now you need to zoom out and show leadership what else was happening. What external forces shaped your results, for better or worse?

This is where you separate execution from environment. 

Without this context, strong strategic work can look mediocre in a difficult year, or weak decisions can hide behind tailwinds. 

Leadership needs to understand what you controlled versus what you were responding to.

Think of it this way: performance details add context to your KPIs. External factors add context to your performance.

Digital marketing factors

What influenced performance that was external to paid search execution, but internal to the broader marketing ecosystem?

  • Non-PPC marketing initiatives: Product launches, pricing changes, promotions, website redesigns, or messaging shifts can all impact conversion rates and search behavior – positively or negatively.
  • Non-PPC channel performance: Performance in organic search, email, social, affiliates, or offline channels can meaningfully influence paid search results. It also provides a clearer picture of market factors beyond paid channels.
Purchase revenue by channel (medium)
  • Platform and policy changes: Google Ads feature rollouts, automation shifts, policy enforcement, or reporting changes often affect performance in ways that aren’t immediately visible in metrics alone.
  • Competitive dynamics: New entrants, aggressive bidding, creative shifts, or changes in competitor behavior can alter auction pressure and efficiency over time.

Macro-economic factors

What forces outside the marketing organization shaped demand, behavior, and constraints?

A useful way to structure this analysis is with a lightweight PESTEL lens, adapted for a marketing context.

  • Political: Gov actions and policy shifts (e.g., tariffs, shutdowns).
  • Economic: Market conditions (e.g., inflation, spending slowdowns).
  • Social: Behavior and lifestyle trends (e.g., travel, demographics).
  • Technological: Platform/tech changes (e.g., AI, privacy updates).
  • Environmental: Weather and seasonality (e.g., storms, climate shifts).
  • Legal: Regulations and compliance (e.g., privacy laws, labor rules).

You don’t need to address every category. The goal is to highlight the factors that materially influenced performance and decisions during the year.

In a volatile year like this one, it can even make sense to highlight big events that didn’t have an impact on performance, just to assuage any worries.

PESTEL analysis example

Doing this helps stakeholders understand what factors contributed to performance.
​
And just as important, it positions you as someone who sees beyond the interface to meaningful business implications.

5. Answer the ‘what’s next?’ question

Leadership wants to know what to expect for next year. 

They’re not necessarily expecting a crystal ball, but they do want confidence that there’s a plan, even if the path changes.

The reality is that most paid search strategy isn’t mapped a year in advance. 

Platforms change, competitors react, budgets shift, and new constraints appear with little warning. 

What matters isn’t having every answer upfront, it’s having a clear way to decide what to do next when conditions change.

This section of your EOY report is your opportunity to show that decision-making framework, and get your audience excited to work with you on what’s to come.

Next steps and recommendations

These are the initiatives you’re committed to pursuing; the strategic moves grounded in last year’s data:

  • Applied learnings: How insights from the past year are shaping priorities, structure, and decision-making going forward.
  • Identified opportunities: Areas where data consistently pointed to upside: channels, audiences, products, or tactics that warrant attention.
  • Known risks: Challenges leadership should expect, along with how you’re monitoring or mitigating them.
  • Resource clarity: What additional budget, tools, or support would enable — and what remains constrained without them. Be concrete: “With X additional budget, we can test Y based on Z insight from last year.”

These recommendations should feel inevitable; the logical next steps given what happened last year.

Next steps in EOY report

Testing pipeline

Then there’s the other category: things you’re watching, interested in, or ready to jump on if circumstances align. 

These scratch leadership’s itch for innovation and cutting-edge solutions without overcommitting:

  • New platform features you’ll test when they’re released.
  • Emerging platforms or initiatives worth monitoring.
  • Competitive tactics you’ve identified but need more validation.
  • Opportunistic tests if budget or priorities shift.

Frame these as “if/then” scenarios or “things we’re monitoring” rather than firm commitments. 

Leadership gets to feel like you’re on top of industry trends without expecting guarantees.

Augmented reality opportunities

A final pass through a leadership lens

You’ve covered a lot of ground. 

This final pass is about tightening credibility and making sure this work pays dividends in the coming years, not just this one.

Give your report a final pass

Before sending, review the report the way leadership will:

  • Source your data clearly: Label where each chart’s data came from and when it was pulled. This prevents follow-up questions and builds trust.
  • Address negatives head-on: Leadership expects challenges. What erodes confidence isn’t bad news, it’s unexplained bad news. Show what didn’t work, why, and what you did about it.
  • Pressure-test against the brief: Review your stakeholders’ original requests. Did you actually answer their questions? Ask a colleague (or AI) for a second set of eyes.

Make next year easier

Now that you’ve done the heavy lifting, leverage this work going forward:

  • Turn your EOY report into a client-specific template: A single format won’t work across all clients, but once you find a structure that resonates with a given audience, reuse it year over year. Incorporate feedback and refresh the data, but keep the core framework consistent.
  • Track big issues as they happen: Document key events as you progress through the year. Keep a running list, outside of emails and reports. Even the biggest issues today will be hard to accurately remember in 12 months without this.

Year-end reports take real effort. Make sure yours actually resonates. 

Follow these steps to strengthen stakeholder relationships and position yourself as a strategic partner for 2026 and beyond.

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How to use LinkedIn targeting in Microsoft Advertising

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How to use LinkedIn targeting in Microsoft Advertising

LinkedIn targeting in Microsoft Advertising exists to help brands message-map their best creative with the ideal audience. 

When approached thoughtfully, it allows you to apply professional understanding to intent‑driven inventory without breaking the bank.

The key is knowing how the targeting methods work together across the various campaign types.

What follows is a practical guide to using LinkedIn data inside Microsoft Advertising, including:

  • LinkedIn in Search campaigns (includes Multimedia ads).
  • Using LinkedIn insights to inform broader audience strategy.
  • Performance Max targeting signals.
  • Directional insights into audience reach and composition through Audience Planner.

Disclosure: I am a Microsoft employee. I attempted to keep this article as objective as possible, focusing on how LinkedIn targeting works as well as action items for targeting, reporting, and creative message mapping.

LinkedIn profile targeting in search

LinkedIn profile targeting is fully available in Microsoft Advertising search campaigns and lets you layer professional attributes on top of keyword targeting.

The supported attributes are:

  • Company.
  • Industry.
  • Job function.

These audiences apply across Microsoft‑owned environments such as Bing Search, Microsoft Edge, Microsoft Start, and other eligible search surfaces, as long as users are signed in.

In search, LinkedIn targeting works best as a contextual guide, not a standalone target. 

The keywords still do the heavy lifting. LinkedIn data helps you respond differently when professional relevance is present.

LinkedIn profile targeting in search

How to approach it

  • Start with keywords that already convert: LinkedIn targeting can help amplify existing intent on proven keywords. Apply bid adjustments to campaigns/ad groups where search terms already demonstrate business value. This might mean a 10%-15% increase if you’re bidding aggressively, or a more aggressive bid adjustment if your impression share lost to rank is high.  
  • Choose one professional dimension first: Begin with either company, industry, or job function – not all three. If you’re targeting someone who works for a company in an industry you’re also targeting, it’s very easy to bid on them twice. 
  • Use bid‑only mode to establish a baseline: Observation gives you performance clarity before you make delivery decisions. Treat this as audience research on who is engaging with you in a profitable way.

Dig deeper: LinkedIn Ads retargeting: How to reach prospects at every funnel stage

LinkedIn Professional Demographics in Audience ads

Audience ads support LinkedIn Professional Demographics as both a targeting and observation layer – bringing professional context into native, display, and video formats designed for scalable reach.

While Audience ads are not driven by keyword intent, Professional Demographics provide a way to anchor delivery and insights in a real‑world business context, bridging the gap between broad reach and professional relevance.

Audience ads allow you to apply company, industry, and job function as professional audience layers. 

These can be used either to observe performance trends or to influence delivery, depending on campaign objectives.

LinkedIn Professional Demographics in Audience Ads

Unlike search, where intent is explicit, Audience ads rely more heavily on audience signals and creative relevance. 

LinkedIn Professional Demographics help ensure that reach is oriented toward users who are more likely to be operating in a business mindset, even when browsing content.

How to approach it

  • Start in observation to understand natural performance: Use Professional Demographics in observation mode to learn which industries, job functions, or company types naturally engage with your Audience ads before applying delivery constraints.
  • Let LinkedIn data inform creative, not just delivery: Because Audience ads appear in feed‑based and content‑rich environments, creative matters more than targeting alone. Use insights from high‑performing professional segments to inform tone, examples, and value framing in messaging.
  • Align format choice with professional mindset: Different formats serve different roles:
    • Native and display perform well for awareness and education within professional segments
    • Video supports storytelling and category framing, particularly when aligned with industry‑specific narratives
    • Professional Demographic insights help guide which formats are most appropriate for different business audiences.

LinkedIn data in Performance Max: Guiding automation with purpose

LinkedIn profile targeting is available inside Performance Max campaigns, where it functions as an audience signal.

Within Performance Max, these signals help the system understand which professional profiles have a high probability for profit to your business and help influence how budget is allocated across inventory.

Professional signals are most effective in Performance Max when they are representative and directional, not exhaustive. 

They work best when they give the system a strong starting point rather than a narrow definition of success.

LinkedIn data in Performance Max: Guiding automation with purpose

How to approach it

  • Select signals that reflect your best customers, not every customer: Use LinkedIn attributes that describe your most valuable segments, not the full universe of potential buyers. This is especially important if the different personas represent different ROAS/CPA goals, as all asset groups in a PMax campaign will share the same ROAS/CPA bidding. 
  • Pair LinkedIn signals with strong conversion definitions: Automation performs better when professional context is reinforced by clear success metrics. It’s critical to ensure there are at least 30 conversions in a 30-day period for any autobidding.
  • Allow time for learning: Audience signals need sufficient volume to influence delivery. Avoid frequent changes in the first learning period (two weeks). Once you clear this, budget changes of up to 15% can be made without triggering learning period fluctuation. 

Dig deeper: Google and Microsoft: How their Performance Max approaches align and diverge

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Reporting: Turning audience data into decisions

Aggregated reporting for LinkedIn audiences is broken down by company, industry, and job function, allowing you to see how different professional segments contribute to performance across campaigns.

LinkedIn reporting can be found in Reporting > Professional demographics, and includes any LinkedIn targeting or audiences applied through predictive targeting.

LinkedIn reporting - professional demographics

How to approach it

  • Look for consistency across time, not single spikes: Patterns that repeat across weeks or months are more actionable than short‑term anomalies. Give “observation” audiences the time to prove themselves out. If you don’t have time for that, lean on Audience Planner to help you make informed decisions at scale.
  • Use reporting to inform creative and bids together: When a professional segment outperforms, examine both messaging and bidding before making changes. It’s possible that the audience really resonated with the creative, but you also want to confirm you didn’t overbid on a particular group. 
  • Avoid over‑segmentation early: Too many audience cuts can dilute signal strength (especially if you’re running up against conversion scarcity). 

Bidding with LinkedIn audiences

In Microsoft Advertising, you can use bid adjustments alongside automated bidding strategies, giving flexibility in how LinkedIn audiences influence auctions.

Because users can belong to multiple professional dimensions, bid adjustments may compound when audiences overlap within auctions, making overlap awareness an important part of bid strategy.

Bidding adjustments are most effective when they are incremental and reversible. The goal is calibration, not acceleration.

How to approach it

  • Keep initial bid adjustments small: Single‑digit percentage changes preserve learning while still allowing differentiation.
  • Audit audience overlap before increasing bids: Review how company, industry, and job function audiences intersect within campaigns.
  • Apply bid changes gradually and sequentially: Adjust one audience dimension at a time to understand its individual impact.
  • Reassess after enough volume accumulates: Make decisions only after performance reaches statistical relevance.

Dig deeper: The future of remarketing? Microsoft bets on impressions, not clicks

Creative strategy: Professional relevance without narrow assumptions

LinkedIn targeting shapes who is more likely to see your ads. Creative determines whether those impressions turn into engagement.

Professional cohorts include a wide range of experiences, identities, and perspectives. Effective creative respects that diversity while remaining relevant to the shared context.

Creative works best when it reflects professional empathy – acknowledging challenges, goals, and constraints without relying on shortcuts or stereotypes.

How to approach it

  • Anchor creative in shared problems, not titles: Focus on challenges that span roles and seniority levels within a LinkedIn targeting segment.
  • Keep language inclusive and adaptable: Avoid assumptions about background, experience, or decision‑making authority.
  • Use AI tools to localize, not homogenize: Adapt tone or examples by region or industry while preserving message intent.
  • Test creative alongside audience layers: Evaluate messaging performance within LinkedIn segments to refine both together.

Extending LinkedIn insights across B2B campaigns

LinkedIn targeting in Microsoft Advertising presents an opportunity to combine professional expertise with intent-driven media in a way that is scalable, privacy-conscious, and economically sustainable.

For teams already running LinkedIn Ads, it also provides a practical way to extend learnings into additional inventory through automation, supporting reach and efficiency beyond search.

The value doesn’t come from complexity. It comes from alignment – between data, mechanics, and human behavior.

Key takeaways:

  • LinkedIn profile targeting is fully available across Search and Performance Max on Microsoft‑owned surfaces.
  • Professional attributes function as targeting layers in search and as optimization signals in Performance Max.
  • Observation‑first approaches build understanding before commitment.
  • Aggregated reporting supports informed optimization without exposing individual data.
  • Small, intentional bid adjustments preserve performance stability.
  • Creative grounded in empathy strengthens professional relevance.

When LinkedIn data is used with curiosity and care, it becomes a way to see audiences more clearly rather than control them more tightly. 

For B2B advertisers navigating complex buying journeys, that clarity is often the most valuable optimization of all.

Dig deeper: 5 LinkedIn Ads mistakes that could be hurting your campaigns

Read more at Read More

December 2025 Digital Marketing Roundup: What Changed and What You Should Do About It

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December made one thing clear: AI is no longer a feature layered on top of marketing. It is the system deciding what gets seen, what gets skipped, and what earns trust.

Search pushed deeper into zero-click behavior. Paid ads lost prime real estate. Influencer content matured into a full‑funnel channel. Platforms added tools while quietly tightening control. At the same time, security and data ownership became real business risks, not abstract concerns.

This roundup breaks down what actually mattered in December and how to adjust before these shifts harden in 2026.

Key Takeaways

  • Google accelerated AI-first search with Gemini 3, AI Mode, and AI-powered Search Console reporting.
  • AI Overviews and AI Mode are pushing both organic and paid clicks down, reshaping SERP strategy.
  • Influencer marketing expanded beyond Gen Z, pulling older, high-value audiences into creator ecosystems.
  • LinkedIn doubled down on video and events, reinforcing its position as the B2B growth platform.
  • Security threats like Google Ads MCC hijacks escalated, making account governance a priority.

Search & AI

AI is now deciding what gets seen before a click ever happens. December’s updates show Google tightening its grip on discovery while pushing brands to earn visibility inside AI systems.

Search Console Gets AI-Driven Reporting

Google rolled out AI-powered configuration in Search Console, allowing users to request custom reports using natural language. Instead of manually stitching filters together, teams can now ask questions the way they think about performance.

Google Search Console's AI-powered search configuration.

Our POV: This changes who gets access to insight. Reporting no longer bottlenecks around technical SEO or analytics specialists. Strategy conversations can happen faster, and closer to the business question that triggered them.

What this unlocks: Faster pattern recognition across large sites, quicker validation of hypotheses, and fewer reporting cycles spent just getting the data into shape.

What to do next: Standardize a small set of executive-level prompts tied to growth questions (discovery, decline, opportunity). Use this to shorten the distance between signal and decision.

Gemini 3 Lands Directly in Google Search

Google deployed Gemini 3 straight into Search across 120 countries, delivering richer answers, visuals, and interactive elements without requiring users to leave the results page.

Our POV: This is Google asserting itself as the destination, not the doorway. Content that once earned traffic by being explanatory or comparative now competes with Google’s own synthesized answers.

Strategic impact: Informational content becomes less about volume and more about authority. If your content is interchangeable, it becomes invisible.

What to do next: Identify where your content overlaps with Gemini-style answers. Invest more heavily in insight, proprietary data, and perspective that AI cannot compress without losing value.

Google Embeds AI Mode Into the Search Flow

When users tap “show more” under an AI Overview, Google now routes them into a full AI chat experience rather than expanding citations.

Our POV: This confirms that Google is intentionally reducing outbound traffic in favor of guided, AI-mediated discovery.

Strategic impact: Attribution gets murkier. Influence matters more than visits. Brands that only measure success by clicks will underinvest in visibility where decisions actually form.

What to do next: Start treating AI inclusion as a visibility channel. Track brand mentions, citations, and presence inside AI responses alongside traditional KPIs.

AI Overviews Push Ads Below the Fold

Research shows that roughly a quarter of search results now place paid ads beneath AI Overviews, with mobile SERPs most affected.

AI overview stats being pushed above the fold.

Our POV: Paid search is losing guaranteed prominence. Bidding harder no longer guarantees being seen.

Strategic impact: Paid media performance becomes dependent on how well it aligns with AI-generated context, not just auction dynamics.

What to do next: Re-evaluate high-value keywords where ads routinely fall below AI content. Coordinate paid and organic teams so messaging reinforces what users see first.

Branded Query Filtering and Chart Notes Arrive in GSC

Search Console now separates branded and non‑branded queries automatically and allows chart-level annotations.

Branded and non-branded queries being seperated in GSC.

Our POV: This finally closes long-standing reporting gaps that distorted SEO performance narratives.

What to do next: Capture a baseline brand vs non‑brand split now. Add annotations for launches, migrations, PR wins, and algorithm shifts to preserve institutional knowledge.

Paid Media & Risk

Automation keeps increasing, but so does exposure. December highlighted how fragile performance can be without strong governance and clear safeguards.

OpenAI Pauses ChatGPT Ads

OpenAI halted its early test of native ads inside ChatGPT after users struggled to distinguish sponsored content from AI-generated answers.

Our POV: This pause is less about ads failing and more about timing. Conversational interfaces collapse the distance between advice and influence, which raises the bar for trust.

Strategic impact: Future AI advertising will not behave like traditional display or search ads. Brands will compete on usefulness, credibility, and contextual fit rather than interruption.

What to do next: Start pressure-testing what value-driven, answer-oriented advertising could look like for your category. Focus on scenarios where a brand genuinely helps a user decide, not just where it can appear.

Google Ads MCC Hijacks Surge

Phishing attacks targeting Google Ads manager accounts increased sharply, allowing attackers to drain budgets and lock out advertisers within hours.

Our POV: This is no longer an edge case. As accounts scale, risk compounds.

Strategic impact: Performance gains mean little if governance fails. Security lapses can erase months of optimization and undermine executive confidence in paid media.

What to do next: Treat access control as part of your growth strategy. Limit permissions aggressively, audit users regularly, and align security reviews with budget planning.

Product, Design & UX

Product and design updates are quietly shaping how fast teams can ship, test, and iterate. December brought one change that materially reduces friction between design and development.

Figma Introduces CSS Grid-Like Layout Controls

Figma rolled out a new grid system that more closely mirrors how CSS Grid and Flexbox behave in production. Designers can now edit rows and columns directly, reposition elements with keyboard controls, and build layouts that respond more like real front-end frameworks.

Our POV: This narrows the long-standing gap between design intent and shipped experience. Fewer handoff mismatches mean faster iteration and fewer compromises downstream.

Strategic impact: Design systems become more scalable when layouts behave predictably across breakpoints. Teams that rely on rapid experimentation benefit most.

What to do next: Revisit your design system and layout standards. Align designers and developers on grid conventions so prototypes map cleanly to production.

Social & Creator Economy

Creator content is no longer niche or youth-driven. Platforms are shaping social into a full-funnel, multi-generational influence engine.

LinkedIn Sees Another Video Surge

LinkedIn reported continued double-digit growth in video uploads and watch time, with short-form content driving disproportionate reach.

Our POV: LinkedIn has quietly become a daily content destination, not just a professional directory.

Strategic impact: B2B visibility increasingly depends on consistent, human-led storytelling. Brands that delay video adoption will find it harder to build authority as the feed fills up.

What to do next: Commit to a repeatable LinkedIn video cadence. Prioritize clarity and expertise over production polish, and measure engagement trends over time.

LinkedIn Upgrades Event Ads

New integrations with ON24 and Cvent allow LinkedIn Event Ads to capture and route leads directly into CRMs.

Linkedin Event Ads

Our POV: Events are moving out of the brand bucket and into the revenue conversation.

Strategic impact: This blurs the line between awareness and pipeline, making events accountable in ways they historically avoided.

What to do next: Reframe events as performance channels. Align messaging, registration, and follow-up under a single measurement framework.

Influencer Content Expands Beyond Gen Z

New data shows that more than half of adults aged fifty-five to sixty-four now watch influencer content weekly, often via connected televisions.

Our POV: Influencer marketing has crossed into mainstream media behavior. This is no longer a youth or trend-driven channel.

Strategic impact: Influencers are shaping consideration and trust for higher-value purchases, not just discovery for impulse buys.

What to do next: Test creator partnerships that emphasize expertise and credibility. Treat influencer content as a mid-funnel and upper-funnel asset, not just awareness.

Meta Enhances the Creator Marketplace

Instagram expanded its Creator Marketplace with better discovery, AI recommendations, and stronger paid amplification tools.

Our POV: Meta is positioning creators as a scalable performance input, not just an organic reach lever.

Strategic impact: The line between influencer marketing and paid social continues to erode. Creative quality and creator trust now directly affect efficiency.

What to do next: Identify creators whose content already performs organically. Use paid support to scale what works instead of forcing performance from scratch.

PR, Media, and Trust

As AI pulls from third-party sources, brand credibility is being shaped outside your owned channels. Relationships and presence matter more than volume.

Journalists Push Back on AI Pitches

Surveys show most journalists still prefer human-led outreach, citing AI-written pitches as generic and misaligned with their coverage needs.

Our POV: Efficiency without judgment damages relationships.

Strategic impact: As AI-generated noise increases, thoughtful and relevant outreach becomes a stronger differentiator.

What to do next: Use AI for research and preparation, not substitution. Preserve human insight where trust and creativity matter most.

Discord Emerges as a Media Hub

PR teams are increasingly using Discord servers as live, on-demand press rooms.

Our POV: This flips traditional outreach from push to pull.

Strategic impact: Brands that make themselves accessible become resources journalists return to, not just sources they react to.

What to do next: Pilot a controlled Discord environment for media. Offer clear channels, real access, and timely updates without overwhelming participants.

Platform Playbooks

Smaller platform updates often hide the most practical gains. December delivered clear lessons on how context and native execution drive results.

Reddit Releases Dynamic Product Ad Guidance

Reddit published best practices showing that focused optimizations can lift Dynamic Product Ad performance meaningfully.

Our POV: Reddit rewards relevance over polish.

Strategic impact: Brands that adapt creative to platform norms outperform those that recycle ads from other networks.

What to do next: Speak directly to subreddit context, keep messaging tight, and test incrementally to isolate what actually moves performance.

Conclusion

December reinforced a hard truth: visibility is no longer owned. It is earned repeatedly across AI systems, platforms, and communities.

The brands that win in 2026 will build authority machines, not traffic hacks. They will secure their data, design for AI interpretation, and show up consistently wherever decisions are shaped.

If you want help translating these shifts into a durable growth strategy, the NP Digital team is already doing this work every day.

Read more at Read More

2026 PPC trends to get ahead of now

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4 PPC trends to monitor in H2 2025

The PPC landscape in 2025 shifted faster than ever, with updates arriving at a pace unmatched in the industry’s 20-year history. At SMX Next, a panel of industry experts broke down what’s working, what’s failing, and what advertisers should prepare for in 2026 and beyond.

The state of PPC

The panelists agreed that 2025 marked a major shift, especially in how quickly Google responded to advertiser feedback.

Ameet Khabra, founder of Hop Skip Media, called the year “interesting” and said he was genuinely surprised by Google’s willingness to listen to advertisers, especially on channel reporting for Performance Max.

  • “It was really cool to see the people who were in that room sit there and be like, this is exactly what we asked for,” she noted, referring to discussions at Google Marketing Live.

Chris Ridley, head of paid media at Evoluted, said 2025 wasn’t just about Google listening — it was the year AI and AI search truly took off.

  • “Everyone is now talking about the different platforms available, like Perplexity, ChatGPT, Gemini, and they just seem to be dominating. AI Overviews have kind of taken over as well.”

Reva Minkoff, founder and president of Digital4Startups, called 2025 “the year of the max,” pointing to Performance Max, AI Max, and the growing list of “max” campaign types. She said more features launched this year than at any other time in her 20-year search career.

  • “It’s just every day there’s a new thing, which is really exciting. But there’s definitely a lot happening now.”

What’s working in PPC

Back to basics: Structure and signals

All panelists stressed that success in 2025 came from returning to the fundamentals.

Minkoff stressed the importance of proper campaign structure and quality signals:

  • “It’s still important to have a good search campaign with keywords that you control and ads you create that goes to an audience that you think it should be going to.”
  • Minkoff noted that Performance Max is working well, but only when the signals are right — “if you’re not putting good stuff in, you won’t get good stuff out.”

She also pointed to strong results from Demand Gen (formerly Video Action campaigns), user-generated content, and influencer marketing:

  • “I think people want to hear from real people.”

Khabra stressed the importance of using scripts and automation oversight to catch issues before they turn into problems.

  • “We’ll have scripts in place that are like anomaly detectors, just so we know that tracking is off. The broken URL script is a lifesaver, honestly — how many times have we had a developer push a change and we didn’t even know it happened?”

The human touch in creative

Ridley underscored the need for authentic creative in an AI-driven landscape:

  • “Going back with our authentic user-generated content is getting really good results compared to this slick, polished stuff, especially with AI coming out now and people questioning whether it’s real or not. Having that human touch is really working for us.”

Client communication

Beyond tactics, Ridley emphasized better client communication:

  • “Making sure that we understand what their business objectives are rather than just their ROAS and CPAs” has been essential for success.

What not working in PPC

Automatically created assets (ACAs)

The panel unanimously agreed that Automatically Created Assets are problematic, primarily from a brand safety perspective.

Khabra was particularly critical:

  • “We can’t put in guidelines. We’re not allowed to approve things beforehand. So we really have to sit there and kind of just figure out what the system has created for us.”

She referenced a quote from Amy Hebdon:

  • “AI is a pattern matcher, not a creator. It’s going to generate the most probable thing, not something that’s actually new or exciting, or even correct.”

Minkoff echoed these concerns:

  • “A lot of clients need to be able to control their brand story and what they’re talking about, and the words that they use. I just don’t trust the automatically generated anything to reflect those guidelines.”

Minkoff noted that automatically generated content often misses business nuances, such as which products deserve budget and which items shouldn’t be advertised at all.

User interface and UX issues

Ridley voiced frustration with ongoing platform user interface (UI) and user experience (UX) changes.

  • “Having to click campaign, campaign, campaign makes no sense. I’m finding everything a lot easier to do in Editor now or using tools like Optmyzr where it kind of skips that UI.”
  • He apologized to Google representatives on other panels but maintained that UI changes are “counterproductive in terms of making it quicker, easier, more natural for people to find what they need.”

The problem is compounded by gaps between the UI and Editor, forcing advertisers to jump back and forth between the two.

Learning periods and flexibility

Minkoff pointed to extended learning periods as a major challenge, especially for smaller campaigns or time-sensitive moments like Black Friday and Cyber Monday.

  • “How do you navigate a learning period on these platforms that feel no longer designed to let you do those pushes for one day that are honestly a real part of the business calendar?”

Measurement challenges

Khabra flagged measurement as a major pain point, especially for small business owners with limited budgets and data.

  • “Trying to figure out how to make that work with automation that needs a lot of it has been really, really interesting.”

Khabra warned that Google’s modeled conversions reflect a “best possible outcome” scenario that business owners may mistakenly treat as reality.

Biggest surprises of 2025

Google Marketing Live announcements

Ridley said Google Marketing Live was his biggest surprise, noting that Google “announced loads of new things for small and medium businesses, but also big things we’ve been asking for.” Key announcements included:

He called the changes “game-changing” for small businesses.

Performance Max channel reporting

Minkoff was caught off guard by channel reporting for Performance Max:

  • “I did not see that coming. I think it’s very exciting, although the next step is going to be being able to do something about it, which is kind of what I’m hoping for come soon.”

Waze pins in Performance Max

Khabra’s biggest surprise was the most recent: Waze pins as a placement in Performance Max.

  • “That was definitely not on my bingo card. I would’ve never, ever in a million years thought the Waze pins would be a placement in PMax.”

The speed of AI/LLM rollout

Minkoff was struck by how quickly AI Overviews and LLMs became ubiquitous.

  • “It felt like overnight in a way. It was kind of coming out and then it was out and it’s there a good chunk of the time. The cat is out of the bag and it is very out of the bag and not coming back.”

The channel reporting debate

The Performance Max channel reporting discussion exposed tension between what advertisers want and what the platform was built to do.

The problem

Minkoff explained that many campaigns now see 95% or more of their budget flowing into a single placement, usually display:

  • “I just don’t think that was the point of PMax. The thing that I’ve always liked about PMax is that it can fill the whole funnel, that it can fill these different placements, that it wasn’t gonna be completely overrun by one.”

The fence-sitting position

Khabra acknowledged sitting on the fence:

  • “It was meant to be a black box this entire time. Although I’m really happy about the channel reporting, there was a little piece of you that was like, were we supposed to — should this have actually happened?”

She worried that everyone is now trying to manipulate the system in ways that defeat its purpose:

  • “We’re supposed to put in clean data, we’re supposed to put in good signals, and it’s supposed to do its job.”

Potential solutions

Ridley raised an intriguing idea: What if Google offered media mix controls that let advertisers suggest percentage splits — like 20% search and 30% display — as guidance rather than hard limits?

Minkoff suggested bid adjustments as a middle ground:

  • “Bid up, bid down. I want more of this, I want less of this. I’m not even necessarily asking for me to figure it out because if I was right, I would just run them in the other campaign. But more a matter of like, do a little more of this, do a little less of this.”

The consensus was clear: until better controls exist, advertisers should focus on sending the right signals so Google can make smarter decisions on the backend.

Biggest struggles right now

Controlling automated AI features

Ridley called the automatic rollout of AI recommendations and features the biggest challenge:

  • “Even sometimes after you turn it off and you go through the whole review, the campaign setup, you see it turned back on.”

He pointed to Matt Beswick’s recent experience, where forgetting to disable search partners led to most of the budget being spent on wasted traffic.

The challenge goes further with hidden toggles and hard-to-find settings, creating constant stress for advertisers trying to stay in control.

Finding hidden settings

Minkoff echoed this concern:

  • “A lot of the boxes are hidden, so it’s hard to even find where it was turned on or turned off, or the option that you can adjust it.”

Measurement for small businesses

Khabra’s biggest concern remains measurement challenges, especially with privacy concerns making tracking increasingly difficult:

  • “I think that’s just gonna continually become more of an issue.”

What we’ll be talking about in 2026

The unknown unknown

Minkoff offered a fascinating perspective: “My favorite thing about this question is that I honestly don’t know. And I feel like this is the first time I can say that—the first time where I felt like things were changing that quickly.”

She emphasized that the biggest thing we’ll discuss in a year probably hasn’t even been released yet:

  • “We have to make sure that we have budget, we have flexibility to factor that into our planning. I really think it’ll be something completely new, which is super exciting, but also kind of crazy.”

The antitrust trial

Khabra is watching the Google antitrust trial closely:

  • “They lost the first part of it. They’re appealing it. I’m really curious just to see what happens on that front and what the implications are.”

Ads within AI platforms

Ridley expects AI to remain the focus a year from now, but with ads running inside AI platforms.

  • “Ads within each of the AI platforms as well, and probably Google and other platforms integrating them as network partners as well.”

The only certainty in PPC is uncertainty

PPC changed at an unprecedented pace in 2025. Google finally listened to advertisers while pushing deeper into AI-driven automation. The advertisers who performed best embraced automation without giving up strategic control, prioritized quality signals over volume, and stayed agile enough to adapt to changes that seemed to come weekly, rather than quarterly.

As 2026 approaches, platforms are evolving faster than ever, and the biggest changes likely haven’t even been announced yet. Advertisers who build flexibility into their strategies, stick to strong fundamentals, and feed high-quality signals into automated systems will be best positioned to succeed — whatever 2026 brings.

Watch: 2026 PPC trends to get ahead of now + Live Q&A

Here is the full panel discussion from SMX Next 2025:

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How to earn brand mentions that drive LLM and SEO visibility

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How to earn brand mentions that drive LLM and SEO visibility

Remember when link building was all the rage in SEO? 

While it never disappeared, its role evolved as Google introduced clearer guidelines and placed greater emphasis on quality, relevance, and intent.

Today, as AI search reshapes the organic landscape, link building has shifted into a closely related – and increasingly prioritized – initiative: brand mentions.

You might think of brand mentions as “citations,” but in the context of AI search, citations describe how brands are referenced by LLMs. 

Brand mentions are the input that leads to those citations. To avoid confusion, this article uses brand mentions to describe the tactic itself.

Beyond their role as a leading – if not the leading – factor influencing AI search citations, brand mentions are also gaining weight in traditional SEO algorithms. 

To build durable organic visibility for your brand or clients, brand mentions should be a priority in 2026.

Let’s break down what that looks like in practice.

How and why to prioritize brand mentions

Brand mentions have moved from a nice-to-have tactic to core infrastructure in an AI search environment. 

LLMs look beyond links, so this is not a return to the backlink strategies that once dominated SEO. 

Instead, they evaluate mentions, context, and repeated co-occurrence between your brand and the topics you want to rank for.

Brand mentions are part of the ranking moat. 

They compound over time, and they matter even more when competitors are not investing in the same signals.

From a prioritization standpoint, brand mentions should come:

  • Right after technical and content fundamentals are in place, including crawlability, structured data, and on-page clarity.
  • Before heavy long-form expansion or content produced for its own sake. You can publish 200 articles, but without a citation footprint, LLMs have little reason to surface them.

Dig deeper: In GEO, brand mentions do what links alone can’t

How to find high-priority brand mention opportunities

Like backlinks, brand mentions vary widely in influence depending on their source.

At my agency, we look beyond standard SEO tools to identify high-priority opportunities, including:

  • Using Profound to surface existing brand mentions tied to prompt topics that matter.
  • Reviewing the links that appear in AI Overviews for key SEO queries.
  • Examining Reddit threads ranking on Page 1 to see which entities show up most often for important keywords.

In AI Overviews, you can find links to source articles by clicking the chain-link icon shown here:

best CMS for SaaS companies - AI Overviews

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How to drive passive brand mentions

Passive brand mentions occur when something you produce fills a gap in the broader information ecosystem. 

The goal is to make your brand the easiest source to reference.

They are earned by creating referenceable assets, not just content. Examples include:

  • Original data or insights: Think mini research drops, annual reports, or proprietary trends. These stand out from the generic web, and LLMs are effective at finding and citing them even when overall citation volume is limited.
  • Highly scannable definition or explainer pages: When a brand becomes the canonical definition of a concept, it is cited disproportionately. The objective is to become the primary source, as I’ve been saying for a while now.
  • Useful tools, templates, or calculators: These encourage habitual linking from blogs, forums, and communities, helping brands surface broadly for relevant queries.
  • Active participation on visible platforms, including Reddit and industry forums, approached as a knowledgeable contributor rather than a brand billboard. These discussions are scraped and can surface in LLM training data.

Dig deeper: A smarter Reddit strategy for organic and AI search visibility

How to actively solicit brand mentions

The most effective outreach for earning brand mentions is relationship-driven and anchored in information value.

Key guidelines include:

  • Lead with the asset, not the ask: For example: “We published new proprietary data on [X] and thought it might support your upcoming coverage.”
  • Use narrative relevance, not conditional relevance: Pitch journalists and creators who have recently covered the topic, not those who might someday.
  • Deliver a clear angle: Providing a ready-made hook, such as a specific data comparison, significantly increases the likelihood of brand inclusion.
  • Blend outreach with thought leadership: Podcasts, community AMAs, expert panels, and webinars increase surface area for discovery and research.
  • Follow up with new value, not reminders: If there is nothing new to add, wait until there is.

The long-term objective is to build an outreach engine by developing relationships with writers and personalities who are more likely to reference your brand in future work. 

In some cases, there is added value when those relationships extend into content collaboration.

When to bring on a PR resource

Beyond budget considerations, PR support is most effective for building brand mention momentum when:

  • A strong story or data engine exists, but distribution is limited.
  • Brand mentions need to scale quickly, such as for fundraising, major launches, or highly competitive categories.
  • Internal teams are not structured for ongoing media relationship management.
  • Credibility from tier-one sources, such as The Wall Street Journal or TechCrunch, is needed to strengthen perceived authority in LLM evaluations.
  • The category is reputation-driven, where trust and authority directly affect rankings. Health, finance, legal, property management, and AI fall into this group.

If technical SEO fundamentals are still unresolved or reference-worthy assets are not yet in place, PR is premature. 

When a brand is ready to function as a source, PR accelerates the signal flywheel.

Dig deeper: How to build search visibility before demand exists

Building brand mentions that compound across search engines and LLMs

Many of the long-standing lessons from link building still apply. 

Avoid low-quality publications, and do not confuse volume with impact. 

With a prioritized source list and a disciplined approach, brands can:

  • Earn more mentions.
  • Increase AI search citations.
  • See meaningful improvements in search rankings.

Read more at Read More

A 90-day SEO playbook for AI-driven search visibility

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A 90-day SEO playbook for AI-driven search visibility

SEO now sits at an uncomfortable intersection at many organizations.

Leadership wants visibility in AI-driven search experiences. Product teams want clarity on which narratives, features, and use cases are being surfaced. Sales still depends on pipeline.

Meanwhile, traditional rankings, traffic, and conversions continue to matter. What has changed is the surface area of search.

Pages are now summarized, excerpted, and cited in environments where clicks are optional and attribution is selective. 

When a generative AI summary appears on the SERP, users click traditional result links only about 8% of the time.

As a result, SEO teams need a clearer playbook for earning visibility inside generative outputs, not just around them.

This 90-day action plan outlines how to achieve this in a phased, weekly execution, with practical adjustments tailored to the specific purpose of the website.

Phase 1: Foundation (Weeks 1-2)

Define your ‘AI search topics’

Keywords still matter. But AI systems organize information around entities, topics, and questions, not just query strings.

The first step is to decide what you want AI tools to associate your brand with.

Action steps

  • Identify 5-10 core topics you want to be known for.
  • For each topic, map:
    • The questions users ask most often
    • The comparisons they evaluate
    • “Best,” “how,” and “why” queries that indicate decision-making intent

Example:

  • Topic: AI SEO tools
  • Mapped query types:
    • Core questions: What are the best AI SEO tools? How does AI improve SEO?
    • Comparisons: AI SEO tools vs traditional SEO tools.
    • Intent signals: Best AI SEO tools for content optimization.

Where this shifts by website type

  • Content hubs (media brands, publishers, research orgs) should prioritize mapping educational breadth – covering a topic comprehensively so AI systems see the site as a reference source, not a transactional endpoint.
  • Services/lead gen sites (agencies, consultants, local businesses) should map problem-solution queries prospects ask before converting, especially comparison and “how does this work?” questions.
  • Product and ecommerce sites (DTC brands, marketplaces, subscription ecommerce, retailers) should map topics to use cases, alternatives, and comparisons – not just product names or category terms.
  • Commercial, long-funnel sites (B2B SaaS, fintech, healthcare) should anchor topics to category leadership – the “what is,” “how it works,” and “why it matters” content buyers research long before demos.

If you can’t clearly articulate what you want AI systems to associate you with, neither can they.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

Create AI-friendly content structure

Generative engines consistently surface content that is easy to extract, summarize, and reuse. 

In practice, that favors pages where answers are clearly framed, front-loaded, and supported by scannable structure.

 High-performing pages tend to follow a predictable pattern.

AI-friendly content structures include: 

  • A short intro (2-3 lines) that establishes scope.
  • A direct answer placed immediately after the header, written to stand alone if excerpted.
  • Bulleted lists or numbered steps that break down the explanation.
  • A concise FAQ section at the bottom that reinforces key queries.

This increases the likelihood your content is:

  • Quoted in AI Overviews.
  • Used in ChatGPT or Perplexity answers.
  • Surfaced for voice and conversational search.

For ecommerce and services sites in particular, this is often where internal resistance shows up. Teams worry that answering questions too directly will reduce conversion opportunities. 

In AI-driven search, the opposite is usually true: pages that make answers easy to extract are more likely to be surfaced, cited, and revisited when users move from research to decision-making.

Dig deeper: Organizing content for AI search: A 3-level framework

Phase 2: Generative engine optimization (Weeks 3-6)

Optimize for AI answers (GEO/AEO)

In generative search, content that gets surfaced typically resolves the core question immediately, then provides context and depth. 

For many commercial teams, that requires rethinking how early pages prioritize explanation versus persuasion – a shift that’s increasingly necessary to earn visibility at all.

This is where GEO (generative engine optimization) and AEO (answer engine optimization) move from theory into page-level execution.

  • Add a 1–2 sentence TL;DR under key H2s that can stand on its own if excerpted
  • Use explicit, question-based headers:
    • “What is…”
    • “How does…”
    • “Why does…”
  • Include clear, plain-language definitions before introducing nuance or positioning

Example:

What is generative engine optimization?

Generative engine optimization (GEO) helps content get selected as a source in AI-generated answers.

In practice, GEO is the process of structuring and optimizing content so AI tools like ChatGPT and Google AI Overviews can interpret, evaluate, and reference it when responding to user queries.

How does answer-first structure change by site type?

  • Publishers benefit from definitional clarity because it increases citation frequency.
  • Lead gen sites see stronger mid-funnel engagement when prospects get clear answers upfront.
  • Product sites reduce friction by addressing comparison and “is it right for me?” questions early.
  • B2B platforms establish category authority long before a buyer ever hits a pricing page.

Add structured data (high impact, often underused)

Structured data remains one of the clearest ways to signal meaning and credibility to AI-driven search systems. 

It helps generative engines quickly identify the source, scope, and authority behind a piece of content – especially when deciding what to cite.

At a minimum, most sites should implement:

  • Article schema to clarify content type and topical focus.
  • Organization schema to establish the publishing entity.
  • Author or Person schema to surface expertise and accountability.

FAQ schema, where it reflects genuine question-and-answer content, can still reinforce structure and intent – but it should be used selectively, not as a default.

This matters differently by site type:

  • Content hubs benefit when author and publication signals reinforce editorial credibility and reference value.
  • Lead gen and services sites use schema to connect expertise to specific problem areas and queries.
  • Product and ecommerce sites help AI systems distinguish between informational content and transactional pages.
  • Commercial, long-funnel sites rely on schema to support trust signals alongside relevance in high-stakes categories.

Structured data doesn’t guarantee inclusion – but in generative search environments, its absence makes exclusion more likely.

Get the newsletter search marketers rely on.

See terms.

Phase 3: Authority and trust (Weeks 7-10)

Strengthen E-E-A-T signals

As generative systems decide which sources to reference, demonstrated experience increasingly outweighs polish alone. 

Pages that surface consistently tend to show clear evidence that the content comes from real people with real expertise. 

Meaning, signals associated with E-E-A-T – experience, expertise, authoritativeness, and trust – remain central to how generative systems decide which sources to reference.

Key signals to reinforce:

  • Clear author bios that establish credentials, role, or subject-matter relevance.
  • First-hand experience statements that indicate direct involvement (“We tested…”, “In our experience…”).
  • Original visuals, screenshots, data, or case studies that can’t be inferred or synthesized

This is where generic, AI-generated content reliably falls short. 

Without visible signals of experience and accountability, AI systems struggle to distinguish authoritative sources from interchangeable ones.

How different site types should demonstrate experience and authority

  • Media and research sites should reinforce editorial standards, sourcing, and author attribution to support citation trust.
  • Agencies and consultants benefit from foregrounding lived client experience and specific outcomes, not abstract expertise.
  • Ecommerce brands earn trust through real-world product usage, testing, and visual proof.
  • High-ACV B2B companies stand out by showcasing practitioner insight and operational knowledge rather than marketing language alone.

If your content reads like it could belong to anyone, AI systems will treat it that way.

Dig deeper: User-first E-E-A-T: What actually drives SEO and GEO

Build ‘citation-worthy’ pages

Certain page types are more likely to be cited in AI-generated answers because they organize information in ways that are easy to extract, compare, and reference. 

These pages are designed to serve as reference material – resolving common questions clearly and completely, rather than advancing a particular perspective.

Formats that consistently perform well include:

  • Ultimate guides that consolidate a topic into a single, authoritative resource.
  • Comparison tables that make differences explicit and scannable.
  • Statistics pages that centralize data points AI systems can reference.
  • Glossaries that define terms clearly and consistently.

Pages with titles such as “AI SEO Statistics (2025)” or “Best AI SEO Tools Compared” are frequently surfaced because they signal completeness, recency, and reference value at a glance.

For commercial sites, citation-worthy pages don’t replace conversion-focused assets. 

They support them by capturing early-stage, informational demand – and positioning the brand as a credible source long before a buyer enters the funnel.

Dig deeper: How generative engines define and rank trustworthy content

Phase 4: Multimodal SEO (Weeks 11-12)

Optimize beyond text

Generative systems increasingly synthesize signals across text, images, and video when assembling answers. 

Content that performs well in AI-driven search is often reinforced across formats, not confined to a single page or medium.

  • Add descriptive, specific alt text that explains what an image shows and why it’s relevant.
  • Create short-form videos paired with transcripts that mirror on-page explanations.
  • Repurpose core content into formats AI systems can encounter and contextualize elsewhere:
    • YouTube videos.
    • LinkedIn carousels.
    • X threads.

How this supports different site goals

  • Publishers extend the reach and reference value of core reporting and explainers.
  • Services and B2B sites reinforce expertise by repeating the same answers across multiple surfaces.
  • Ecommerce brands support discovery by contextualizing products beyond traditional listings and category pages.

Track AI visibility – not just traffic

As generative results absorb more of the discovery layer, traditional click-based metrics capture only part of search performance. 

AI visibility increasingly shows up in how often – and where – a brand’s content is referenced, summarized, or surfaced without a click.

With 88% of businesses worried about losing organic visibility in the world of AI-driven search, tracking these signals is essential for demonstrating continued influence and reach.

Signals worth monitoring include:

  • Featured snippet ownership, which often feeds AI-generated summaries.
  • Appearances within AI Overviews and similar answer experiences.
  • Brand mentions inside AI tools during exploratory queries.
  • Search Console impressions, even when clicks don’t follow.

For long sales cycles in particular, these signals act as early indicators of influence. 

AI citations and impressions often precede direct engagement, shaping consideration well before a buyer enters the funnel.

Dig deeper: LLM optimization in 2026: Tracking, visibility, and what’s next for AI discovery

Recommended tools

These tools support different parts of an SEO-for-AI workflow, from topic research and content structure to schema implementation and visibility tracking.

  • Content and AI SEO 
    • Surfer, Clearscope, Frase
    • Used to identify gaps in topical coverage and evaluate whether content resolves questions clearly enough to be excerpted in AI-generated answers.
  • Schema and structured data 
    • RankMath, Yoast, Schema App
    • Useful for implementing and maintaining schema that helps AI systems interpret content, authorship, and organizational credibility.
  • Visibility and performance tracking 
    • Google Search Console, Ahrefs
    • Essential for monitoring impressions, query patterns, and how content surfaces in search – including cases where visibility doesn’t result in a click.
  • AI research and validation 
    • ChatGPT, Perplexity, Gemini
    • Helpful for testing how topics are summarized, which sources are cited, and where your content appears (or doesn’t) in AI-driven responses.

The rule that matters most

AI systems tend to favor content that provides definitive answers to questions. 

If your content can’t answer a question clearly in 30 seconds, it’s unlikely to be selected for AI-generated answers.

What separates teams succeeding in this environment isn’t experimentation with new tactics, but consistency in execution. 

Pages built to be understandable, referenceable, and trustworthy are the ones generative systems return to.

Read more at Read More

Fashion AI SEO: How to Improve Your Brand’s LLM Visibility

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AI chat is changing how people shop for fashion — fast.

Before AI, buying something as simple as casual leggings meant typing keywords into Google. Then, sifting through pages of results.

Comparing prices. Reading reviews. Getting overwhelmed.

In fact, 74% of shoppers give up because there’s too much choice, according to research by Business of Fashion and McKinsey.

Now?

A shopper submits a query. AI gives one clear answer — often with direct links to products, reviews, and retailers. They can even click straight to purchase.

Google AI Mode – Women's leggings

So, how do you make sure AI recommends your fashion brand?

We analyzed how fashion brands appear in AI search. And why some brands dominate while others disappear.

In this article, you’ll learn how large language models (LLMs) interpret fashion, what drives visibility, and the levers you can pull to get your brand visible in AI searches (plus a free fashion trend calendar to help you plan).

Note: The data in this article comes from Semrush’s AI Visibility Index, August 2025.

The 3 Types of AI Visibility in Fashion

There are three ways people will see your brand in AI search: brand mentions, citations, and recommendations.

3 Types of AI Visibility in Fashion

Brand mentions are references to your brand within an answer.

Ask AI about the latest fashion trends, and the answer includes a couple of relevant brands.

ChatGPT – Top trending fashion looks – Brands

Citations are the proof that backs up AI answers. Your brand properties get linked as a source. This could be product pages, sizing guides, or care instructions.

AI Search Visibility

Citations also include other sites that talk about your brand, like Wikipedia, Amazon, or review sites.

Product recommendations are the most powerful form of AI visibility. Your brand isn’t just mentioned; it’s actively suggested when someone is ready to buy.

For example, I asked ChatGPT for recommendations of aviator sunglasses:

ChatGPT – Aviator sunglasses recommendations

Ray-Ban doesn’t just show up as a mention — they’re a recommended option with clickable shopping cards.

How AI Models Choose Which Fashion Brands to Surface

If you’ve ever wondered how AI chooses which fashion brands to surface, here are the two basic factors:

  • By evaluating what other people say about you online
  • By checking how consistently factual and trustworthy your own information is

Let’s talk about consensus and consistency. Plus, we’ll discuss real fashion brands that are winning at both.

Consensus

If you ask all your friends for their favorite ice cream shop, they’ll probably give different answers.

But if almost everyone coincided in the same answer, you trust that’s probably the best place to go.

AI does something similar.

First, it checks different sources of information online. This includes:

  • Editorial websites, like articles in Vogue, Who What Wear, InStyle, and others
  • Community and creator content, including TikTok try-ons, Reddit threads, and YouTube product roundups
  • Retailer corroboration, like ratings and reviews on Amazon, Nordstrom, Zalando, and more
  • Sustainability verification from third parties like B Corp, OEKO-TEX, or Good On You

After analyzing this information, it gives you recommendations for what it perceives to be the best option.

Here’s an example of what that consensus looks like for a real brand:

Brand Consensus

Carhartt is mentioned all over the web. They appear in retail listings, editorial pieces, and in community discussions.

The result?

They get consistent LLM mentions.

ChatGPT – Jacket recommendations

Consistency

AI also judges your brand based on the consistency of your product information.

This includes:

  • Naming & colorways: Identical names/color codes across your own site, retailers, and mentions
  • Fit & size data: Standardized size charts, fit guides, and model measurements
  • Materials & care: The same composition and instructions across all channels
  • Imagery/video parity: The same SKU visuals (like hero, 360, try-on) on your site and retailer sites
  • Price & availability sync: Real-time updates during drops or restocks to avoid stale or conflicting data

For example, Lululemon does a great job of keeping product availability updated on their website.

If you ask AI where to find a specific product type, it directs you back to the Lululemon website.

Google AI Mode – Specific product type

This happens because Lululemon’s site provides accurate, up-to-date information.

Plus, it’s consistent across retailer pages.

The Types of Content That Dominate Fashion AI Search

Mentions get you into the conversation. Recommendations make you the answer. Citations build the credibility that supports both.

The brands winning in AI search have all three — here’s how to diagnose where you stand.

AI Visibility Diagnostic

Let’s talk about the fashion brands that are consistently showing up in AI search results, and the kind of content that helps them gain AI visibility.

Editorial Shopping Guides and Roundups

Editorial content has a huge impact on results.

Sites like Vogue, Who What Wear, and InStyle are regularly cited by LLMs.

TOP Sources Analysis Fashion & Apparel

These editorial pieces are key for AI search, since they frame products in context — showing comparison, specific occasions, or trends.

There are two ways to play into this.

First, you can develop relationships with editorial websites relevant to your brand.

Start by researching your top three competitors. Using Google (or a quick AI search), find out which publications have featured those competitors recently.

Then, reach out to the editor or writers at those publications.

If they’re individual creators, you might send sample products for them to review.

Looking for mentions from bigger publications?

You might consider working with a PR team to get your products listed in articles.

To build consistency in that content, provide data sheets with information about material, fit, or care.

Who What Wear – Provide information

​​

Second, you can build your own editorial content.

That’s exactly what Huckberry does:

Huckberry – Build your own editorial content

They regularly produce editorial-style content that answers questions.

Many of these posts include a video as well, giving them more opportunity for discovery in LLMs:

YouTube – Huckberry wardrobe 2025

Retailer Product Pages and Brand Stores

Think of your product detail page (PDP) as the source of truth for AI.

If you don’t have all the information there, AI will take its answers from other sources — whether or not they’re accurate.

Product pages (your own website or a retailer’s) need to reflect consistent, accurate information. Then, AI can understand and translate into answers.

Some examples might include:

  • Structured sizing information
  • Consistent naming and colorways
  • Up-to-date prices and availability
  • Ratings (with pictures)
  • Fit guides (like sizing guides and images with model measurements and sizing)
  • Materials and care pages
  • Transparent sustainability modules

For example,Everlane provides the typical sizing chart on each of its products. But they take it a step further and include a guide to show how a piece is meant to fit on your body.

You can even see instructions to measure yourself and find the right size.

Everlane – Size Guide

That’s why, when I ask AI to help me pick the right size for a pair of pants, it gives me a clear answer.

And the citations come straight from Everlane’s website.

ChatGPT – Suggesting a size

Everlane’s product pages also include model measurements and sizing.

So when I ask ChatGPT for pictures to help me pick the right size, I get this response:

ChatGPT – Pictures to help

However you choose to present this information on your product pages, just remember: It needs to be identical on all retailer pages as well.

Otherwise, your brand could confuse the LLMs.

User Generated Video Content

What you say about your own brand is one thing.

But what other people say about you online can have a huge influence on your AI mentions.

Of course, you don’t have full control over what consumers post about you online.

So, proactively build connections with creators. Or, try to join the conversation online when appropriate.

This can help you build a positive sentiment toward your brand, which AI will pick up on.

Not sure which creators to work with?

Try searching for your competitors on channels like TikTok or Instagram. See which creators are mentioning their products, and getting engagement.

You can also use tools like Semrush’s Influencer Analytics app to discover influencers.

Search by social channels, and filter by things like follower count, location, and pricing.

Semrush Influencer Analytics App

Here’s an example: Aritzia has grown a lot on TikTok. They show up in creator videos, fit checks, and unboxing-style videos.

In fact, the hashtag #aritziahaul has a total of 32k posts, racking up 561 million views overall.

TikTok – Artizia

Other fashion brands, like Quince, include a reviewing system on their PDPs.

This allows consumers to rate the fit and add pictures of themselves wearing the product.

LLMs also use this information to answer questions.

Quince – Reviwing system

Creator try-ons, styling videos, and similar content can help increase brand mentions in “best for [body type]” or “best for [occasion]” prompts.

Pro tip: Zero-click shopping is coming. Perplexity’s “Buy with Pro” and ChatGPT’s “Instant Checkout” hint at a future where AI answers lead straight to one-click purchases. The effects are still emerging, but as with social shopping, visibility wins. So, make sure your brand shows up in the chats that drive buying decisions.

Reddit and Community Threads

Reddit is a major source of information for fashion AI queries.

This includes information about real-world fit, durability, comfort, return experiences, and comparisons.

For example, Uniqlo shows up regularly in Reddit threads and questions about style.

Reddit – Fashion community threads

You can also find real reviews of durability about the products.

Reddit – Real review of durability

As a result, the brand is getting thousands of mentions in LLMs based on Reddit citations.

Plus, this leads to a ton of organic traffic back to the Uniqlo website.

Semrush – AI Visibility – Uniqlo – Cited Sources

Obviously, it’s impossible to completely control the conversation around your brand. So for this to work, there’s one key thing you can’t miss:

Your products need to be truly excellent.

A mediocre product that has a lot of negative sentiment online won’t show up in AI search results.

And no amount of marketing tactics can fool the LLMs.

Further reading: Learn how to join the conversation online with our Reddit Marketing guide.

Lab Tests and Fabric Explainers

This kind of content shows the quality of your products.

It gives LLMs a measurable benchmark to quote on things like pilling or color fastness.

This content could include:

  • “6-month wear” style videos
  • Pages that explain the fabrics and materials used
  • Third party tests
  • Clear care instructions

For example, Quince has an entire page on their website talking about cashmere.

Quince – About cashmere

And in Semrush’s AI Visibility dashboard, you can see this page is one of the top cited sources from Quince’s website.

Semrush – Visibility Overview – Quince – Cited Pages

Another option is to create content that shows tests of your products.

Here’s a great example from a brand that makes running soles, Vibram.

They sponsored pro trail runner Robyn Lesh, and teamed up with Huckberry to lab test some of their shoes.

YouTube – Vibram – Lab test of the product

This kind of content is helping Vibram maintain solid AI visibility.

Visibility Overview – Vibram – AI Visibility

And for smaller brands who don’t have Vibram’s sponsorship budget?

Try doing product testing content with your own team.

For example, have a team member wear a specific product every day for a month, and report back on durability.

Or, bury a piece of clothing underground and watch how long it takes to decompose, like Woolmark did:

Instagram – Woolmark decompose clothing

Get creative, and you’ll have some fun creating content that can also help your brand be more visible.

Want to check your brand’s AI visibility?

Try the AI Visibility Toolkit from Semrush to see where your brand stands in AI search, and learn how to optimize.

Start by checking your AI visibility score. You’ll see how this measures up against the industry benchmarks.

Visibility Overview – Ray-ban – AI Visibility – Industry avg

You can prioritize next steps based on the Topic Opportunities tab.

There, you’ll see topics where your competitors are being mentioned, but your brand is missed.

Visibility Overview – Ray-ban – Topic & Sources

Then, jump to the Brand Perception tab to learn more about your Share of Voice and Sentiment in AI search results.

You’ll also get some clear insights on improvements you can make.

Semrush – Brand Performance – Sentiment & Share of Voice

Comparisons and Alternatives Content

AI loves a good comparison post (and honestly, who doesn’t?). So, creating content that compares your products to other brands is a great way to get more mentions.

This is part of LLM seeding.

It helps you get brand exposure without depending on organic traffic dependence. Plus, it helps level the playing field with bigger competitors.

How does LLM Seeding Work

For instance, Quince is often cited online as a cheaper alternative to luxury clothing.

I asked ChatGPT for affordable cashmere options, and Quince was the first recommendation.

ChatGPT – Affordable cashmere options

So, why is this brand showing up consistently?

One reason is their comparison content.

In each PDP, you’ll see the “Beyond Compare” box, showing specific points of comparison with major competitors.

Quince – Beyond Compare

The right comparisons are handled honestly and tastefully.

Focus on real points of difference (like Quince does with price). Or, show which products are best for certain occasions.

For example: “Our sweaters are great for hiking in the snow. Our competitors’ sweaters are better for indoor activities.”

Comparisons give AI a reason to recommend your fashion brand when someone asks for an alternative.

What This Shift Means for Your Fashion Brand

AI search has changed the way people discover products, and even their path to purchase.

Before, this involved multiple searches, clicking on different websites, or scrolling through forums. Now, you can do this in one simple interface.

So, how is AI changing fashion, and how can your brand adapt?

Editorial, Retailer, and PDP Split

AI search doesn’t treat every source of information equally.

And depending on which model your audience uses, the “default” source of truth can look very different.

ChatGPT leans heavily on editorial and community signals.

It rewards cultural traction — what people are talking about, buying, and loving.

For example, articles like this one from Vogue are a prime source for ChatGPT answers:

Vogue – Fashion trends

Meanwhile, Google’s AI Mode and Perplexity skew toward retailer PDPs.

They look for structured data like price, availability, or fit guides. In other words, they trust whoever has the cleanest, richest product data.

The most visible brands win in both arenas: cultural conversation and PDP completeness.

Here’s What You Can Do

To show up in all major LLMs, you need two parallel pipelines.

  1. Cultural traction: Like press mentions, creator partnerships, and community visibility
  2. Citation-ready proof: For example, complete and accurate PDPs across retailer channels

Here’s an Example: Carhartt

Carhartt is a great example of a brand that’s winning on both sides.

First, they get consistent cultural visibility.

For instance, Vogue reported that the Carhartt WIP Detroit jacket made Lyst’s “hottest product” list. That led to searches for their brand increasing by 410%.

This makes it more likely for LLMs to recommend their products in answers:

Google AI Mode – Womens workwear jacket

This is the kind of loop that works wonders for a fashion brand.

AI TrenD Loop

At the same time, Carhartt is also stocked across a huge range of retailers. You can find them in REI, Nordstrom, Amazon, and Dick’s, plus their own direct-to-consumer website.

So, Google AI Mode has an abundance of PDPs, videos, reviews, and Q&A to cite.

This makes Carhartt extremely “citation-friendly” in both models.

No wonder it has such a strong AI visibility score.

Visibility Overview – Carhartt – AI Visibility

Trend Shocks and Seasonal Volatility

Trend cycles aren’t a new challenge in the fashion industry. But it becomes a bigger challenge to maintain visibility when those trends affect which brands appear in AI search.

Micro-trends pop up all the time, triggering quick shifts in how AI answers fashion queries.

When the trend heats up, LLMs pull in brands that appear online in listicles or TikTok roundups.

ChatGPT – When the trend heats up

And when the trend cools? Those same brands disappear just as quickly.

Here’s What You Can Do

To stay present during each trend swing, you need a content and operations pipeline that speaks in real time to the language models are echoing.

  1. Build a proactive trend calendar: Map your content to seasonal moments, like spring tailoring, fall layers, holiday capsules, back-to-school basics, and so on
  2. Refresh imagery and copy to mirror trend language: Update PDPs, on-site copy, and retailer description to match the phrasing used in cultural content
  3. Create rapid-fire listicles and lookbooks: Listicle-style content, creator videos, and other trend-related mentions can help boost visibility. This includes building your own content and working with creators and publications to feature your product in their content.

Download our Trend Calendar for Fashion Brands to plan ahead for upcoming trends and create content that matches.

Here’s an Example: UGG

Anyone who was around for Y2K may have been shocked to see UGG boots come around again.

But the brand was ready to jump onto the trend and make the most of their moment.

Vogue reported that UGG made Lyst’s “hottest products” list in 2024.

Since then, they’ve been regularly featured in seasonal “winter wardrobe essentials” style roundups.

One analyst found that there had been a 280% increase in popularity for the shoes. Funny enough, that trend seems to be a regular occurrence every year once “UGG season” rolls around.

In fact, on TikTok, the hashtag #uggseason has almost 70k videos.

TikTok – Uggseason videos

UGG stays visible even as seasons trends shift. That’s because the brand is always present in the content streams that LLMs treat as cultural indicators. By partnering with influencers, UGG amplified its presence so effectively that the boots themselves became a moment — something people wanted to photograph, share, and join in on without being asked.

The result?

They have one of the highest AI Visibility scores I saw while researching this article.

Visibility Overview – Ugg – AI Visibility

(As a marketer, I find this encouraging. As a Millennial, I find it deeply disturbing.)

Pro tip: Want to measure the results? Track how often your brand or SKUs appear in new listicles per month, plus how they rank in those roundups. Then use Semrush’s AI Visibility Toolkit to track your brand’s visibility using trend-related prompts.

Sustainability and Proof (Not Claims)

Sustainability has become one of the strongest differentiators for fashion brands in AI search.

But only when brands back it up with verifiable proof.

LLMs don’t reward vague eco-friendly language. Instead, they surface brands with certifications, documentation, and third-party validation.

Models also pull heavily from Wikipedia and third-party certification databases. These pages often act as trust anchors for AI search results.

Here’s What You Can Do

You need to build a clear, credible footprint that models can cite.

  1. Centralize pages on materials, care, and impact: Make them brief, structured, and verifiable. Include materials, sourcing, certifications, and repair/resale info.
  2. Maintain third-party profiles: Keep your certifications up-to-date. This includes things like Fair Trade, Bluesign, B-Corp, GOTs, etc.
  3. Standardize sustainability claims across all retailers: If your DTC site says “Fair Trade Certified” but your Nordstrom PDP doesn’t? Models treat that as unreliable.

Here’s an Example: Patagonia

Patagonia is the ruler of AI visibility with a 21.96% share of voice.

Top 20 Brands Fashion & Apparel

In part, this is because of their incredible dedication to sustainability. They basically own this niche category within fashion.

Patagonia’s sustainability claims are backed up by third-party certifications.

And they’re displayed proudly on each PDP.

Patagonia – Sustainability Certs

They’re also transparent about their efforts to help the environment.

They keep pages like this updated regularly.

Patagonia – Progress This Season

These sustainable efforts aren’t just big talk.

Review sites and actual consumers speak positively online about these efforts.

Gearist – Patagonia Repair Review

They’ve made their claim as a sustainable fashion brand.

So, Patagonia shows up first, almost always, in LLMs when talking about sustainable fashion:

ChatGPT recommends Patagonia

That’s the power of building a sustainable brand.

Make AI Work for Your Fashion Brand

You’ve seen how the top fashion brands earn AI visibility.

The path forward is simple: Consensus + Consistency.

Build consensus by getting people talking: Create shareable content, encourage customer posts, or work with creators and publications.

Build consistency by keeping your product info aligned across your site and retail partners.

To get started, download our Fashion Trend Content Calendar to plan your strategy around seasonal trends.

Want to go deeper? Check out our complete guide to AI Optimization.

The post Fashion AI SEO: How to Improve Your Brand’s LLM Visibility appeared first on Backlinko.

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