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AI tools for PPC, AI search, and social campaigns: What’s worth using now

AI tools for PPC, AI search, and social campaigns: What’s worth using now

In 2026 and well beyond, a core part of the performance marketer’s charter is learning to leverage AI to drive growth and efficiency. 

Anyone who isn’t actively evaluating new AI tools to improve or streamline their PPC work is doing their brand or clients a disservice.

The challenge is that keeping up with these tools has become almost a full-time job, which is why my agency has made AI a priority in our structured knowledge-sharing. 

As a team, we’ve honed in on favorites across creative, campaign management, and AI search measurement. 

This article breaks down key options in each category, with brief reviews and a callout of my current pick.

One overarching recommendation before we dive in: be cautious about signing long-term contracts for AI tools or platforms. 

At the pace things are moving, the tool that catches your eye in December could be an afterthought by April.

AI creative tools for paid social campaigns

There’s no shortage of tools that can generate creative assets, and each comes with benefits as well as the risks of producing AI slop. 

Regardless of the tool you choose, it must be thoroughly vetted and supported by a strong human-in-the-loop process to ensure quality, accuracy, and brand alignment.

Here’s a quick breakdown of the tools we’ve tested:

  • AdCreative.ai: Auto-generates images, video creatives, ad copy, and headlines in multiple sizes, with data-backed scoring for outputs.
  • Creatify: Particularly strong on video ads with multi-format support.
  • WASK: Combines AI creative generation with campaign optimization and competitor analysis.
  • Revid AI: Well-suited for story formats.
  • ChatGPT: Free and widely familiar, giving marketers an edge in effective prompting.

Our current tool of choice is AdCreative.ai. It’s easy to use and especially helpful for quickly brainstorming creative angles and variations to test. 

Like its competitors, it offers meaningful advantages, including:

  • Speed and scale that allow you to generate dozens or hundreds of variants in minutes to keep creative fresh and reduce ad fatigue.
  • Less reliance on external designers or editors for routine or templated outputs.
  • Rapid creative experimentation across images, copy, and layouts to find winning combinations faster.
  • Data-driven insights, such as creative scores or performance predictions, when available.

The usual caveats apply across all creative tools:

  • Build guardrails to avoid off-brand outputs by maintaining a strong voice guide, providing exemplar content, enforcing style rules and banned words, and ensuring human review at every step.
  • Watch for accuracy issues or hallucinations and include verification in your process, especially for technical claims, data, or legal copy. 

Dig deeper: How to get smarter with AI in PPC

AI campaign management and workflow tools for performance campaigns

There are plenty of workflow automation tools on the market, including long-standing options, like Zapier, Workato, and Microsoft Power Automate. 

Our preferred choice, though, is n8n. Its agentic workflows and built-in connections across ad platforms, CRMs, and reporting tools have been invaluable in automating redundant tasks.

Here are my agency’s primary use cases for n8n:

  • Lead management: Automatically enrich new leads from HubSpot or Salesforce with n8n’s Clearbit automation, then route them to the right rep or nurture sequence.
  • UTM cleanup: When a form fill or ad conversion comes in, automatically normalize UTM parameters before pushing them to your CRM. Some systems, like HubSpot, store values in fields such as “first URL seen” that aren’t parsed into UTM fields, so UTMs remain associated with the user but aren’t stored properly and require reconciliation.
  • Data reporting: Pull metrics from APIs, structure the data, and use AI to summarize insights. Reports can then be shared via Slack and email, or dropped into collaborative tools like Google Docs.

As with any tool, n8n comes with caveats to keep in mind:

  • It requires some technical ability because it’s low-code, not no-code. You often need to understand APIs, JSON, and authentication, such as OAuth or API keys. Even basic automations may involve light logic or expressions. Integrations with less mainstream tools can require scripting.
  • You need a deliberate setup to maintain security. There’s no built-in role-based access control in all configurations unless you use n8n Cloud Enterprise. Misconfigured webhooks can expose data if not handled properly.
  • Its ad platform integrations aren’t as broad as those of some competitors. For example, it doesn’t include LinkedIn Ads, Reddit Ads, or TikTok Ads. These can be added via direct API calls, but that takes more manual work.

Dig deeper: Top AI tools and tactics you should be using in PPC

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AI search visibility measurement tools

Most SEOs already have preferred platforms for measurement and insights – Semrush, Moz, SE Ranking, and others. 

While many now offer reports on brand visibility in AI search results from ChatGPT, Perplexity, Gemini, and similar tools, these features are add-ons to products built for traditional SEO.

To track how our brands show up in AI search results, we use Profound. 

While other purpose-built tools exist, we’ve found that it offers differentiated persona-level and competitor-level analysis and ties its reporting to strategic levers like content and PR or sentiment, making it clear how to act on the data.

These platforms can provide near real-time insights such as:

  • Performance benchmarks that show AI visibility against competitors to highlight strengths and weaknesses.
  • Content and messaging intel, including the language AI uses to describe brands and their solutions, which can inform thought leadership and messaging refinement.
  • Signals that show whether your efforts are improving the consistency and favorability of brand mentions in AI answers.
  • Trends illustrating how generative AI is reshaping search results and user behavior.
  • Insights beyond linear keyword rankings that reveal the narratives AI models generate about your company, competitors, and industry.
  • Gaps and opportunities to address to influence how your brand appears in AI answers.

No matter which tool you choose, the key is to adopt one quickly. 

The more data you gather on rapidly evolving AI search trends, the more agile you can be in adjusting your strategy to capture the growing share of users turning to AI tools during their purchase journey.

Dig deeper: Scaling PPC with AI automation: Scripts, data, and custom tools

What remains true as the AI toolset keeps shifting

I like to think most of my content for this publication ages well, but I’m not expecting this one to follow suit. 

Anyone reading it a few months after it runs will likely see it as more of a time capsule than a set of current recommendations – and that’s fine.

What does feel evergreen is the need to:

  • Monitor the AI landscape.
  • Aggressively test new tools and features.
  • Build or maintain a strong knowledge-sharing function across your team. 

We’re well past head-in-the-sand territory with AI in performance marketing, yet there’s still room for differentiation among teams that move quickly, test strategically, and pivot together as needed.

Dig deeper: AI agents in PPC: What to know and build today

Read more at Read More

Think different: The Positionless Marketing manifesto by Optimove

In 1997, Apple launched a campaign that became cultural gospel. “Think Different” celebrated the rebels, the misfits, the troublemakers. The ones who saw things differently. The ones who changed the world. 

Apple understood something fundamental: the constraints that limited imagination weren’t real. They were inherited. Accepted. Assumed. And the people who broke through weren’t smarter or more talented. They simply refused to believe the constraints applied to them. 

Twenty-eight years later, marketing faces its own Think Different moment. 

The constraints are gone. Technology has removed them. AI can generate infinite variants. Data platforms deliver real-time insights. Orchestration tools coordinate across every channel instantly. The infrastructure that once required armies of specialists, weeks of coordination and endless approvals now exists in platforms accessible to any marketer willing to learn them. 

Yet most marketers still operate as if the box exists. 

They wait for the data team to run the analysis. They wait for creative to deliver the assets. They wait for engineering to build the integration. They operate within constraints that technology has already eliminated, not because they must, but because assembly-line marketing taught them that’s how it worked. 

Creative waits for data. Campaigns wait for creative. Launch waits for engineering. Move from station to station. Hand off to the next department. That was the assembly line. That was the box. 

And that box is gone. But the habits remain.  

Here’s to the marketers who refuse to wait for approval

The ones who see a customer signal at 3 p.m. and launch a personalized journey by 4 p.m., not because they asked permission but because the customer needed it now. 

The ones who don’t send briefs to three different teams. They access the data, generate the creative and orchestrate the campaign themselves. Not because they’re trying to eliminate specialists, but because waiting days for what they can deliver in hours wastes the moment. 

The ones who run experiments constantly, not occasionally. Who test 10 variants instead of two. Who measure lift instead of clicks. Who know that perfect insight arrives through iteration, not through analysis paralysis. 

Here’s to the ones who see campaigns where others see dependencies 

They don’t see a handoff to the analytics team. They see customer data they can access instantly to understand behavior, predict intent and target precisely. 

They don’t see a creative approval process. They see AI tools that generate channel-ready assets in minutes, allowing them to personalize at scale rather than compromise for efficiency. 

They don’t see an engineering backlog. They see orchestration platforms that automate journeys, test variations and optimize outcomes without a single ticket. 

They’re not reckless. They’re not cowboys  

They’re simply operating at the speed technology now enables, constrained only by strategy and judgment rather than structure and process.  

This is what Positionless Marketing means: Wielding Data Power, Creative Power and Optimization Power simultaneously. Not because you’ve eliminated everyone else, but because technology eliminated the dependencies that once made those handoffs necessary. 

And here’s what most people miss: This isn’t just about speed. It’s about potential 

When marketers were constrained by assembly-line marketing infrastructure, their job was to manage the line. Write the brief. Coordinate the teams. Navigate the approvals. Wait for each station to finish its work. The marketer’s skill was project management. Their value was orchestrating others. 

Now? Your job in marketing has changed entirely 

Your job is no longer to manage process. Your job is to enable potential. To help every person on your team (and yourself) realize what they’re capable of when the constraints disappear. To show them that the data they’ve been waiting for is accessible now. That the creative they’ve been briefing can be generated instantly. That the campaigns they’ve been coordinating can be orchestrated autonomously.  

Teach people to think outside the box by showing them there is no longer a box 

The data analyst who only ran reports can now build predictive models and operationalize them in real time. The campaign manager who only coordinated handoffs can now design, test and optimize end-to-end journeys independently. The creative strategist who only wrote briefs can now generate and deploy assets across every channel. 

This is the revolution: not that technology does the work, but that technology removes the barriers that prevented people from doing work they were always capable of. 

The misfits and rebels of 1997 saw possibilities where others saw limitations. They refused to accept that things had to be done the way they’d always been done. 

The Positionless Marketers of today are doing the same thing 

They’re refusing to wait when customers need action now. They’re refusing to accept that insight takes weeks when platforms deliver it in seconds. They’re refusing to operate within constraints that technology has already eliminated. 

They’re thinking differently. Not because they’re trying to be difficult. But because the old way of thinking no longer matches the new reality of what’s possible. 

In 1997, Apple told us: “The people who are crazy enough to think they can change the world are the ones who do.”  

In 2025, the people crazy enough to think they can deliver personalized experiences at scale, launch campaigns in hours instead of weeks, and operate without dependencies are the ones who will. 

The constraints are gone. 

The assembly-line marketing box can no longer exist. 

Read more at Read More

Google Search Console performance reports adds weekly and monthly views

Screenshot of Google Search Console

Google added weekly and monthly views to Search Console performance reports. These options give you clearer, longer-term insights instead of relying only on the 24-hour view.

What it looks like. Here are a few photos I took during the announcement at the Google Search Central event in Zurich this morning:

Why we care. This small update gives SEOs, publishers, and site owners access to more detailed data. It can help you pinpoint why your performance shifted in a specific month, week, or day.

Read more at Read More

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

November pushed the industry further into AI-shaped discovery. Search behaviors shifted. Platforms tightened control. Visibility started depending less on who publishes most and more on who earns trust across the ecosystem.

AI summaries reached Google Discover. ChatGPT released a browser. TikTok exposed true attribution paths. Meta refined placements. Google rolled out guardrails for AI-written ads. Social platforms changed how your data trains models. Streaming dominated households, and schema picked up a new strategic role.

Here’s what mattered most and how to stay ahead.

Key Takeaways

• AI is rewriting the click path. Google Discover summaries and AI Overviews are reducing CTRs across categories.
• Cross-channel influence is becoming measurable. TikTok attribution now shows how much value standard reporting misses.
• Visibility depends on authority across ecosystems, not just your site. LLMs pull from places brands often ignore.
• Platforms are tightening data controls and usage rules. Expect stricter compliance requirements across ads and content.
• Structured data has moved from “SEO extra” to critical infrastructure for AI-driven search.

Search & AI Evolution

AI is now shaping what users see before they click and in many cases, removing the need to click at all.

AI summaries hit Google Discover

Google added AI-generated recaps to Discover for news and sports stories. Users now get context from summaries instead of visiting publisher sites.

Our POV: Discover has been one of the few remaining high-intent traffic drivers untouched by AI. That buffer is gone. Zero-click consumption will rise.

What to do next: Track Discover CTR in Analytics. Refresh headline structure and imagery to compete with summaries. Expand content distribution beyond traditional articles, since Discover now surfaces YouTube, X, and other formats.

ChatGPT releases an AI-powered browser

ChatGPT Atlas launched with built-in summarization, product comparison, agent actions, and persistent memory settings.

ChatGPT Atlas's interface.

Our POV: The browser itself isn’t the threat. The shift in user behavior is. People will expect AI to interpret pages for them, not just display them.

What to do next: Strengthen structured data. Audit category and product pages for clarity. Start monitoring brand visibility inside AI-driven search using LLM-aware tools.

AI Overviews drive a drop in search CTRs

A new study shows that when AI Overviews appear, both organic and paid clicks fall sharply. They currently trigger for about fifteen percent of queries, most of them high-volume informational searches.

Paid and organic CTR trends driven by AI Overviews.

Our POV: AI Overviews function like a competitor. If your content doesn’t get pulled into the summary, discovery becomes significantly harder.

What to do next: Optimize for inclusion. Use schema, succinct summaries, and expert signals. Track performance beyond rankings. Visibility inside AI answers must become a KPI you can track through tools like Profound.

Schema’s new role in AI-driven discovery

Schema moved from a snippet enhancer to a foundational layer for machine understanding. W3C’s NLWeb group is helping standardize how AI agents consume the web.

Our POV: Schema is now infrastructure. AI agents need structured context to interpret brands, products, and expertise.

What to do next: Expand schema sitewide. Prioritize entity definitions, not just rich result templates. Add relationships between key content pieces to help machines map authority.

Paid Media & Automation

Platforms are folding more automation into ad delivery. Control now comes from strategy, not settings.

Google adds Waze to PMax

PMax can now serve location-targeted ads inside Waze for store-focused campaigns.

Our POV: This extends real-world intent targeting. For multi-location brands, Waze becomes a measurable foot-traffic lever.

What to do next: Audit store listings and geo-extensions. Monitor budget shifts once Waze impressions begin flowing. Validate whether foot-traffic lifts justify expanded proximity targeting.

Asset-level display reporting rolls out

Google Ads added per-asset reporting for Display campaigns. Marketers can now evaluate individual images, headlines, and copy.

Our POV: Better visibility helps refine creative, but it’s only part of the truth. Placement, bid strategy, and audience still determine performance.

What to do next: Organize assets with naming conventions before rollout hits your account. Use data to retire low-impact creatives and test new variants.

Meta introduces limited-spend placements

Advertisers can allocate up to five percent of budget toward excluded placements when Meta predicts performance upside.

Our POV: This creates a middle ground between strict exclusions and Advantage+ automation. It reduces risk without cutting off potential high-efficiency wins.

What to do next: A/B test manual vs. limited-spend placement setups. Evaluate cost per result and incremental conversions instead of pure CPM efficiency.

Social & Content Trends

Brands are being pushed into new storytelling styles, shaped by identity, utility, and AI-assisted behaviors.

Lifestyle branding gains momentum

Consumers are gravitating toward brands tied to identity and aspiration. Affordable luxury and status signaling are driving engagement.

Our POV: Features alone don’t move people. Identity and belonging do. If your copy focuses only on product attributes, you’re leaving impact on the table.

What to do next: Rework product messaging to show how your offering fits into a buyer’s desired lifestyle. Update CTAs, social captions, and headlines to evoke identity.

LLM-briefed CTAs redefine engagement

CXL tested CTAs that include a ready-made prompt for ChatGPT. Engagement improved because users received higher-quality AI outputs.

An example of an LLM-informed CTA.

Our POV: As users ask AI to interpret brand content, shaping the question becomes part of conversion optimization.

What to do next: Experiment with prompt-style CTAs in guides, templates, and tools. Test which phrasing drives more accurate and useful AI interpretations.

Influencer partners expand beyond typical creators

Brands are leaning into unconventional creators; think niche experts, offbeat personalities, and micro-communities.

Our POV: As traditional influencer pools saturate, originality becomes a differentiator.

What to do next: Identify unexpected storytellers your competitors ignore. Prioritize people with unique voices and strong community trust over polished aesthetics.

PR, Reputation & Brand Risk

Data control, AI training, and brand representation became major flashpoints in November.

Reddit files legal action over AI scraping

Four companies allegedly scraped Reddit content through Google search results instead of its paid API. Reddit is suing.

Our POV: Reddit is a major training source for LLMs. Legal pressure will reshape how models access user-generated content.

What to do next: Monitor how your brand appears in Reddit threads. Insights from these conversations often influence AI outputs, even indirectly.

LinkedIn will use member data to train AI

LinkedIn updated its policy to allow profile content and posts to train in-house models unless users opt out.

Our POV: This raises transparency questions and could affect brand safety for professional voices.

What to do next: Review employee account settings. Update your governance policies to clarify how team-generated content may be reused.

ChatGPT reduces brand mentions

ChatGPT lowered brand references per response while elevating trusted entities like Wikipedia and Reddit.

A graphic showing reduced brand mentions by ChatGPT.

Our POV: Authority now comes from third-party validation, not just your site. If you’re missing from high-trust platforms, AI tools won’t surface you consistently.

What to do next: Strengthen your presence on Wikipedia, industry directories, and review platforms. Build citations that AI models depend on.

AI search tools mention different brands for the same queries

BrightEdge found almost zero overlap between brands recommended by Google’s AI Overview and ChatGPT.

Our POV: Each model prioritizes different signals based on its training data. Ranking in one environment doesn’t guarantee visibility in another.

What to do next: Expand Digital PR efforts beyond search. Build authority in the sources each LLM favors.

Streaming & Media Shifts

Streaming hits ninety-one percent of U.S. households

Homes now average six subscriptions and spend over one hundred dollars per month on streaming.

Our POV: Streaming is now a core channel for shaping intent long before search happens.

What to do next: Add OTT to your awareness mix. Use it to influence demand before users reach paid search or social ads.

Conclusion

AI pushed every channel toward greater automation, heavier reliance on structure, and stricter expectations for authority. Success now depends on clarity, credibility, and presence across platforms that train and inform AI, not just traditional search engines.

Brands that adapt their data, content, and distribution strategies now will stay visible as user behavior shifts.

Need help applying these insights? Talk to the NP Digital team. We’re already working with brands to navigate these changes and rebuild visibility in an AI-first world.

Read more at Read More

Web Design and Development San Diego

Introducing social channels in Search Console

Today, we are excited to announce a new experiment in Search Console that offers site owners a unified
view of their Google Search performance across their websites and social channels. With this update,
we are expanding the Search Console Insights report to include performance data not only for your website,
but also for some of your social channels. This new integration allows you to review Search performance
of social channels associated with your website directly within Search Console.

Read more at Read More

AEO vs GEO vs LLMO: Are They All SEO?

These days, your audience is every bit as likely to find answers through AI Overviews, generative summaries, and language models powering ChatGPT, Gemini, and Claude as they are traditional search, if not more so. This shift explains why AEO, GEO, and LLMO keep coming up in SEO conversations. Each represents a different way your content gets discovered and surfaced across AI-driven experiences.

With this said, these systems don’t all rank content the same way. Some want clear, direct answers. Others reward depth and authority. A few care most about consistent brand signals. Stick with classic SEO tactics alone, and you’ll miss visibility your competitors are already capturing.

The good news? You don’t need three separate strategies. You need to understand how these approaches connect, so your content performs across search engines, answer engines, and conversational AI. This guide breaks down how they overlap, where they differ, and how to prioritize without duplicating your work.

Key Takeaways

  • AEO helps your content become the direct answer for specific, question-driven searches.
  • GEO positions your content as a reliable source that AI systems and generative systems want to summarize and cite.
  • LLMO improves how language models interpret and reference entities and brands in conversational AI experiences.
  • These frameworks aren’t SEO replacements; they extend it across new AI-powered discovery surfaces.
  • Rather than picking a single one, it’s important to understand how AEO, GEO, and LLMO work together so your content earns visibility regardless of where or how people search.
  • One unified strategy can support all three without creating duplicate content or cannibalizing existing pages.

AEO, GEO, and LLMO: Quick Definitions

Before comparing these frameworks, let’s cover what each one does. This context helps you understand how they interact.

What is AEO?

AEO (answer engine optimization) focuses on making your content easy for search engines to convert into a direct answer. It grew out of featured snippets, voice search, and question-based queries. Instead of optimizing only for rankings, AEO prioritizes structure, clarity, and answer-ready formatting. Think of it as helping search engines extract the “best possible response” from your content so users get fast, accurate information.

Google results for "What is Answer Engine Optimization?"

What Is GEO?

GEO (generative engine optimization) helps your content become the kind of source generative engines prefer to surface, draw insights from, or align with when producing summaries. It emphasizes depth, expertise, and freshness because generative systems prioritize trustworthy, well-supported content. GEO isn’t about giving short answers. It’s about delivering enough substance that AI systems view your content as authoritative and worth citing.

Google results for "When should I buy a house?"

What Is LLMO?

LLMO (large language model optimization) focuses on how large language models understand, interpret, and surface information about entities. Instead of optimizing for traditional SERPs, you optimize for conversational responses from tools like ChatGPT, Gemini, Claude, and Perplexity. LLMO emphasizes entity clarity, consistent terminology, strong brand signals, and original insights that models can incorporate into long-form answers.

A ChatGPT answer for "What are the best backpacks for work?"

AEO vs GEO vs LLMO: The Comparisons

AEO, GEO, and LLMO all fall under modern SEO, but they optimize for different AI-driven experiences. Here’s how they compare.

Search Intent They Serve

  • AEO: Direct, question-based intent (“what is,” “how to,” “why does”).
  • GEO: Broad informational or exploratory searches where users want deeper context.
  • LLMO: Conversational prompts and open-ended queries inside tools like ChatGPT, Gemini, Claude, or Perplexity.

Where Your Content Appears

  • AEO: Featured snippets, answer cards, PAA results, definition boxes.
  • GEO: AI Overviews, generative summaries at the top of search, AI-powered search tools.
  • LLMO: Long-form AI responses, conversational threads, citation-style outputs in LLM tools.

Content Style That Performs Best

  • AEO: Structured, scannable sections, FAQs, lists, clear definitions.
  • GEO: Long-form content with depth, sources, clarity, and E-E-A-T signals.
  • LLMO: Comprehensive guides, expert insights, consistent terminology, entity-rich content.

Optimization Focus

  • AEO: Formatting and structure so engines can extract a precise answer.
  • GEO: Trustworthiness, depth, citations, and topical authority.
  • LLMO: Brand clarity, entity consistency, and unique perspectives AI can reuse.

The Role They Play in Your Strategy

  • AEO: Captures quick answers and action-based queries.
  • GEO: Positions your content as source material for generative systems.
  • LLMO: Shapes how AI tools talk about, reference, and summarize your brand.

How AEO, GEO, and LLMO Work Together

AEO, GEO, and LLMO aren’t separate marketing channels. They form a layered system that helps your content perform everywhere people search or ask questions. Treat them as connected instead of competing, and it gets easier to build one strategy that supports all three.

AEO Sets the Structure

AEO gives your content the clarity and formatting models need to extract direct answers. It helps you win question-based queries in search, and it makes generative engines more likely to pull accurate, well-structured information. Clean headers, short definitions, and precise formatting start the chain.

GEO Adds the Depth and Authority

Once structure is in place, GEO strengthens your content with research, topical depth, and context. Generative engines favor content that demonstrates expertise and provides more than a simple answer. Your deeper sections—examples, sources, statistics, analysis—give AI tools something credible to cite.

LLMO Adds Context and Brand Understanding

LLMO builds on both layers by helping large language models understand entities, brands, terminology, and expertise. Repeat key entities consistently and appear across credible sources, and models become more likely to reference your business in conversational responses.

What Do You Prioritize First?

Not every business needs the same optimization approach. AEO, GEO, and LLMO support different goals, so your starting point depends on your business model, audience, and growth targets.

AEO should lead when your content relies on capturing direct, question-based searches. It’s the strongest fit for:

  • Local and service businesses answering specific queries
  • Product-led brands solving practical “how to” or “what is” searches
  • Companies optimizing for featured snippets or quick-answer visibility
  • Pages driving conversions from intent-heavy traffic

If immediate clarity drives results, start with AEO.

GEO plays a bigger role when your strategy depends on depth and credibility. Choose GEO first if you:

  • Publish long-form content or educational resources
  • Compete in broad, research-oriented verticals
  • Need visibility in AI Overviews and other generative results at the top of search
  • Want to strengthen your brand’s expertise through content

Businesses in SaaS, B2B, and thought leadership-heavy industries benefit most.

LLMO matters when your goal is influencing how models interpret and reference entities and brands. Prioritize LLMO first if you:

  • Want AI tools to mention your brand in long-form responses
  • Invest heavily in original research, frameworks, or analysis
  • Need consistency in how your brand and expertise are described
  • Care about unlinked mentions and semantic authority

If brand equity and expert positioning drive your strategy, LLMO should take priority.

How To Optimize for All Three

You don’t need three playbooks to optimize for AEO, GEO, and LLMO. The most efficient approach is building one content system that naturally supports all three. Structure your pages well, go deep on topics, and keep your entities consistent. That makes them easier for search engines, generative systems, and large language models to understand and reuse.

1. Start With Strong SEO Fundamentals

A fast site, clear navigation, clean URLs, and solid internal linking are still the backbone of modern visibility. These basics ensure your content is discoverable no matter which AI-driven system tries to interpret it.

2. Use Structure That Supports AEO

Place short definitions, question-based headers, and scannable sections near the top of your content. This makes your page extraction-friendly for answer boxes and helps generative engines pull accurate information. Key Takeaways sections are a great starting point:

An example of Key Takeaways for AEO structure optimzation.

3. Expand Depth to Support GEO

After the quick answers, build out deeper explanations, examples, research-backed analysis, and supporting context. This gives AI systems something substantial to cite and increases your authority on broader topics. The inverted pyramid method is a great way to structure content with this in mind.

A graphic detailing the importance of depth for supporting GEO.

4. Strengthen Entities to Support LLMO

Reinforce consistent terminology, expert bios, brand descriptions, and niche-specific language. The clearer your entities are, the easier it is for AI models to recognize and reuse your content accurately.

Author boxes on the Neil Patel blog.

5. Use Layouts That Work Across AI Formats

Pages should be readable by both humans and machines:

  • Short intros
  • Quick definitions
  • Logical headers and subheads
  • Lists and steps
  • Deep sections with context
  • Supporting data or examples

This format helps your content perform across search engines, answer engines, and conversational AI.

FAQs

Are AEO, GEO, and LLMO the same?

No. AEO, GEO, and LLMO all build on SEO, but they focus on different things. AEO is about making your content easy for search engines to turn into direct answers. GEO is about creating deep, trustworthy content that generative systems can summarize and cite. LLMO is about helping large language models understand entities, terminology and expertise.

Conclusion

AEO, GEO, and LLMO aren’t replacements for SEO. They’re extensions of it, shaped by how AI systems now interpret and deliver information. Structure your content for clear answers, go deep enough to be cited in generative summaries, and stay consistent so language models understand you. Do that, and you earn visibility across the entire search ecosystem.

You don’t need three separate strategies. A single, unified approach helps your content perform everywhere your audience looks for answers—on search engines, inside AI Overviews, and across conversational tools. The real opportunity isn’t choosing between AEO, GEO, and LLMO. It’s creating content that works across all of them.

If you want help implementing these strategies or need a deeper analysis of how your content currently performs across these channels, check out my SEO consulting services

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Web Design and Development San Diego

Streamline your Search Console analysis with the new AI-powered configuration

The Search Console Performance report is a powerful tool to analyze organic search traffic, but finding
the exact data you need can take more time than you’d like. Today, we’re excited to announce an experimental
feature in the Performance report designed to reduce the effort it takes for you to select, filter, and
compare your data: AI-powered configuration.

Read more at Read More

Audience Segmentation in Marketing: Definition, Types & Best Practices

If your marketing still treats everyone the same, you’re falling behind.

Audience segmentation is what turns generic campaigns into personalized, high-performing ones. Segmented email campaigns can generate a 760 percent increase in revenue compared to non-segmented ones.

That same principle applies across paid ads, social content, product messaging, and just about any other marketing channel you can think of.

Without segmentation, you’re guessing what your audience wants. That leads to wasted ad spend, and low engagement.

Segmentation gives you an edge. It helps you deliver the right message, to the right people, at the right time.

In this guide, you’ll learn what audience segmentation is, how the different types work, and how to apply them to drive better results across your funnel.

Key Takeaways

  • Audience segmentation is the process of dividing your broader audience into smaller, more specific groups.
  • Segmentation helps improve engagement, click-through rates, and conversions across every channel.
  • There are five core types: demographic, geographic, psychographic, behavioral, and firmographic (which is specifically for B2B).
  • Good segmentation starts with real data, not assumptions, and improves over time.
  • The most effective marketing strategies use segmentation to deliver more personalized and relevant messaging.

What Is Audience Segmentation?

Audience segmentation is the process of dividing your broader audience into smaller, more specific groups based on shared characteristics. These characteristics can be demographic, geographic, behavioral, or even psychographic.

The goal is simple: understand your audience better so you can speak to them more effectively.

Think of it like this: you wouldn’t send the same message to a first-time visitor and a loyal customer. And you wouldn’t talk to a 23-year-old in the same way you’d market to a 65-year-old. Segmentation helps you avoid that one-size-fits-none approach.

This isn’t just a tactic for email marketers, either. It’s a core part of building relevant campaigns across paid ads, landing pages, SMS, product marketing, and more.

Here’s what segmentation unlocks:

  • More personalized content and offers
  • Smarter ad targeting
  • Higher engagement rates
  • Better alignment across your marketing funnel

Audience segmentation often gets confused with defining your target audience. But while defining a target audience helps you understand who you’re going after at a high level, segmentation helps you break that audience down into actionable groups for more precise messaging.

Audience segmentation dashboards in action.

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Why Audience Segmentation is Essential

Most marketers aren’t struggling with a lack of data. The challenge is turning that data into action.

That’s where customer and audience segmentation creates real value. When you group your audience based on shared traits or behaviors, you can tailor your messaging, timing, and channels to what actually resonates.

Brands that use segmentation typically see:

  • Higher open and click-through rates
  • Increased customer lifetime value
  • Lower cost per acquisition (CPA)
  • More efficient use of ad budgets

65 percent of consumers expect personalization in their customer experience. And it’s not limited to email. Whether you’re running Google Ads, building a product launch campaign, or personalizing a homepage—segmentation improves performance across the board.

An infographic explaining the differences between marketing funnels wiith and without segmentation.

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It also allows you to meet customers where they are in their journey. Someone new to your brand might need education. A returning customer may be ready for an upsell. With segmentation, you can deliver the right message at the right moment.

Types of Audience Segmentation

There are several ways to segment your audience. Each type gives you a different lens into what drives your customers’ behavior. The best strategies use a mix of these, depending on your goals, product, and data.

An infographic explaining types of audience segmentation.

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Here are the five most common types of audience segmentation:

Demographic Segmentation

This is the most straightforward method. You segment based on traits like:

  • Age
  • Gender
  • Income level
  • Education
  • Marital status

Example: A clothing brand might promote its premium line to high-income professionals while marketing basics to students or entry-level workers.

Geographic Segmentation

Here, you group users by physical location:

  • Country or region
  • Climate
  • City size
  • Urban vs. rural

Example: A food delivery app might market lunch deals to users in busy cities while promoting family meals in suburban areas.

Psychographic Segmentation

This method looks at the “why” behind your customer’s actions:

  • Personality traits
  • Interests and hobbies
  • Lifestyle choices
  • Core values

Example: A fitness brand might market high-performance gear to athletes and eco-friendly materials to sustainability-minded shoppers.

Behavioral Segmentation

Segment based on how people interact with your brand:

  • Purchase history
  • Engagement level
  • Brand loyalty
  • Product usage

Example: A SaaS company might send upgrade offers to heavy users and reactivation emails to inactive accounts.

Firmographic Segmentation (B2B Only)

This is the B2B version of demographic segmentation:

  • Company size
  • Industry
  • Revenue
  • Location
  • Decision-maker role

Example: A software vendor might offer enterprise features to large corporations and budget-friendly plans to startups.

Real-World Segmentation Examples Across Channels

Segmentation works across every channel you’re using. The tactics change, but the principle stays the same: send the right message to the right person.

Email Marketing: New subscribers get your welcome series. Inactive customers (90+ days) get a win-back offer with a discount. Same list, different messages based on engagement level.

An email encouraging a reader to look at an abandoned cart.

Paid Advertising: Cart abandoners see retargeting ads featuring the exact product they left behind. Cold audiences see brand awareness content and educational posts. Match the ad creative to where they are in the funnel.

Content Personalization: SaaS visitors see automation guides and workflow content. E-commerce brands see conversion optimization and retention posts. Your CMS can handle this with simple behavioral tags based on past visits.

Product Rollouts: Power users get early beta access to new features. Light users get the stable release later with more documentation. This reduces your support burden and makes heavy users feel valued.

SMS Marketing: Previous buyers in specific zip codes get flash sale alerts for local stores. First-time visitors get a welcome discount. High intent plus geographic relevance equals higher conversion rates.

An example of SMS marketing.

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The channel doesn’t matter. What matters is matching the message to the person and where they are in their journey.

How To Segment Your Audience, Step-By-Step

Getting started with segmentation doesn’t have to be complex. Here’s a simple process you can use to organize your audience into actionable groups.

1. Start With Data You Already Have

Look at what’s in your CRM, email platform, or analytics tool. Useful data often includes location, purchase history, on-site behavior, and sign-up source.

2. Define Your Most Important Attributes

Based on your goals, decide which traits matter most. For an e-commerce brand, it could be past purchase behavior. For a SaaS company, it might be usage level or company size.

3. Build Initial Segments

Group your audience using filters like:

  • “Has purchased in last 30 days”
  • “Visited pricing page but didn’t convert”
  • “Signed up from Facebook campaign”

Start simple. You can get more granular later.

4. Map Each Segment to the Customer Journey

Think about where each group is in their decision-making process. Someone early in the funnel needs education. A returning visitor might need an incentive.

If you haven’t done this yet, use customer journey mapping to connect segments to meaningful actions.

5. Test, Learn, and Refine

Segmentation isn’t one-and-done. Use A/B testing to refine your messaging, offers, and timing by segment. Drop what doesn’t work. Scale what does.

Best Practices for Audience Segmentation (That Actually Work)

Anyone can slice up an email list but effective segmentation goes beyond basic filters. Here are a few proven tips to get better results without overcomplicating your strategy.

Use Real Data, Not Assumptions

Avoid guessing what people care about. Use actual behavior, survey responses, or analytics to guide how you group your audience.

Keep Segments Useful, Not Just Accurate

A perfect audience profile is useless if it’s too small to act on. Prioritize segments that tie directly to your business goals—like conversions, upsells, or retention.

Don’t Over-Personalize

Over-segmentation can create unnecessary complexity. You don’t need 30 different versions of the same email. Focus on meaningful variations that actually move metrics.

Update Your Segments Regularly

Customer behavior changes. Segments should too. Review and refresh your data often to avoid targeting stale or irrelevant groups.

Align Segments With Personas

Your audience groups should reflect the same needs and motivations as your core buyer personas. If you don’t have a clear set, start with this guide to building an accurate customer persona.

Examples of customer personas.

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Common Segmentation Mistakes to Avoid

I see the same mistakes over and over. Avoid these pitfalls to get better results from your segmentation strategy.

Segmenting too early. You need data before you can segment effectively. If you’re working with a brand-new list or product, focus on collecting behavioral data first. Premature segmentation based on assumptions will waste time and money.

Creating too many micro-segments. A segment with 47 people isn’t actionable. Keep your segments large enough to matter. If a group is too small to justify custom creative or messaging, fold it into a larger segment.

Using outdated data. Someone who bought six months ago isn’t in the same segment as someone who bought yesterday. Refresh your segments quarterly at minimum. Monthly is better for fast-moving businesses.

Segmenting but not personalizing. Building segments means nothing if you send the same message to everyone. Each segment should get tailored copy, offers, or creative. Otherwise, you’re just organizing your list for no reason.

Ignoring overlap between segments. People can belong to multiple groups. A high-value customer might also be geographically close to your store. Think about how segments intersect and prioritize which message matters most.

Not testing segment performance. Track metrics by segment. If one group consistently underperforms, either refine the segment definition or adjust your messaging. Segmentation without measurement is guesswork.

FAQs

What is audience segmentation?

Audience segmentation is the process of dividing your broader audience into smaller groups based on traits like behavior, interests, demographics, or location. It helps you deliver more targeted and relevant marketing.

What are the types of audience segmentation?

The most common types include demographic, geographic, psychographic, behavioral, and firmographic segmentation. Each one gives you a different way to understand and connect with your audience.

How do you segment your audience effectively?

Start with data you already have—like purchase history or engagement. Then group users based on shared traits, align segments to the customer journey, and continuously refine based on performance.

Conclusion

Audience segmentation isn’t a tactic you add later. It’s where effective marketing starts.

By breaking your audience into meaningful groups, you gain the ability to tailor messages, prioritize the right channels, and improve your results across the board. Whether you’re building email campaigns, running paid ads, or planning content, segmentation keeps your strategy focused and relevant.

Start with the data you already have. Pick one or two segments that align with your goals. Then test, learn, and scale.

The more precise your segmentation, the more personal your marketing will feel and the better it will perform.

Need help building a segmentation strategy that actually drives results? Check out my consulting services for hands-on support.

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Google pushes deeper into lifecycle targeting with new GA audience templates

Google is expanding its customer lifecycle capabilities in Google Analytics, launching new audience templates and dynamic remarketing features designed to make high-value targeting and re-engagement easier for advertisers.

Driving the news. Google has introduced two new suggested audience templates in GA to help advertisers instantly build lifecycle segments:

  • High-Value Purchasers — powered by purchase count or lifetime value, with Google adding a new LTV percentile field so marketers can isolate their top-tier customers.
  • Disengaged Purchasers — defined by days since last purchase, giving Google a built-in way to help brands re-engage lapsed buyers.

Google designed these templates to sync directly with Google Ads customer lifecycle goals, including high-value new customer acquisition and re-engagement modes.

Google’s next move: dynamic remarketing inside GA. Google is also bringing display dynamic remarketing directly into Analytics, letting brands show personalized product-based ads to past site visitors without needing to build remarketing setups externally.

Once advertisers implement Google’s recommended eCommerce event collection, Analytics will automatically share dynamic remarketing data with linked Google Ads accounts — as long as personalized advertising is enabled.

Why we care. Google is making it much easier to target the customers who matter — high-value buyers and lapsed purchasers — without building complex audiences from scratch. These new templates and dynamic remarketing tools create faster, smarter ways to drive acquisition, retention, and repeat purchases directly from Google Analytics.

Google is giving you more precise lifecycle targeting with less manual work, and that can translate directly into better performance and more profitable campaigns.

The big picture. Google is tightening its ecosystem, giving advertisers more automated ways to identify, activate, and re-engage customers — all fueled by audience intelligence built inside Google Analytics.

The bottom line. Google is doubling down on lifecycle marketing by turning Google Analytics into an even stronger audience engine for Google Ads.

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Site Kit by Google insights now available for all Yoast SEO Premium users 

Since its launch in June we have been rolling out our integration with Site Kit by Google. Every Yoast SEO Premium customer now has access to it. The update brings key Google Analytics and Search Console insights directly into your Yoast Dashboard, giving you a clear view of your site’s performance without switching between tools or tabs.

Previously, only users of Yoast SEO (free) and Yoast SEO Premium who already had the Site Kit plugin installed could use the integration. Access is now available to all Yoast SEO Premium customers even if Site Kit is not installed, and it will become available to remaining Yoast SEO (free) users soon.

What you can do with the new integration 

Connect once to see an immediate overview of your most important metrics. View organic traffic, impressions, clicks, and bounce rates in one place. Spot opportunities faster and understand where to focus your SEO work. 

Benefits 

  • See how your site is performing without switching between tools 
  • Quickly spot what needs attention with a clear site wide overview 
  • Dig into categories or individual pages to understand patterns and save time 
  • Group content by type to focus on the areas that matter most
  • Find new opportunities to grow traffic by combining Yoast insights with Google data

Learn more about the benefits on the Yoast SEO Dashboard integration page.

How to get started 

Update Yoast to the latest version (26.5), open your Yoast Dashboard, follow the steps in the Site Kit widget, and your insights will appear right away. 

If you want step by step guidance on how to connect Site Kit by Google insights to Yoast SEO, please visit this help article on how to set up.

For Yoast SEO (free) users 

We will continue rolling out access to the integration with Site Kit by Google for free users. Keep an eye on your Yoast Dashboard as it becomes available over time.  

The post Site Kit by Google insights now available for all Yoast SEO Premium users  appeared first on Yoast.

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