Google’s anti-privacy bill push sparks outrage among advertisers

Google is being criticized for sending emails to small business owners urging them to oppose California Assembly Bill 566, legislation that would strengthen consumer privacy protections in digital advertising.

The outreach campaign, which asks recipients to sign a Connected Commerce Council letter opposing the bill, has prompted marketing professionals to publicly rebuke the tech giant’s tactics on LinkedIn.

Why we care. The dispute highlights growing tensions between digital advertising platforms and privacy advocates as California lawmakers consider new regulations on data collection practices.

AB 566 would require browsers and mobile operating systems to offer a built-in setting allowing users to easily opt out of data collection

Political misinformation. Google’s request was met with rejection by Navah Hopkins, brand evangelist of Optmyzr. In a LinkedIn post, she encouraged support for AB 566, arguing that businesses should build “consent-driven conversations” with customers rather than assuming entitlement to user data.

“We deserve the right to opt out of sharing our information and as marketers, we can absolutely ‘make do’ without perfect data,” she wrote, expressing disappointment in what she called “political misinformation” from Google.

Other advertisers speak up. Hopkins wasn’t the only one with concerns about this request.

Performance marketer Louis Halton Davies said that Google keeps stacking the chips in its favor when it comes to consent rules:

  • “Another sad thing is that having consented data is incredibly valuable to Google and not having it is just annoying for SMBs. Appreciate Google is a commercial business but they really take the mick stacking the chips so far in their favor.”

Lead generation specialist Julie Friedman Bacchini said that companies should get express agreement for what will be done with user data. If more people knew exactly what was being done, they would reject having their data collected, she said:

  • “Google is pretty notorious for astroturfing issues like this. I have long said that if you cannot get people to actively agree to what you might/want to do with their data then you should not be doing it. The argument that people don’t object is not a fair one as most people have no idea that companies they buy from or provide information to might upload that information to an ad platform like Google Ads. If they did, most would say no thank you, just like they have with Apple’s ATT prompts.”

The other side. In its email campaign, Google claims:

  • California Governor Gavin Newsom vetoed similar legislation last year.
  • AB 566 would mandate “new and untested technology” that might confuse consumers.
  • The bill would force businesses to “waste money showing ads to people who live far away or aren’t in the market” for their products.

What to watch. How Google responds to this push back could signal its approach to similar privacy legislation in other states, as the company navigates growing public concern over data collection practices while protecting its core advertising business.

Read more at Read More

Temu pulls its U.S. Google Shopping ads

Google shopping ads

Temu completely shut off Google Shopping ads in the U.S. on April 9, with its App Store ranking subsequently plummeting from a typical third or fourth position to 58th in just three days.

The company’s impression share, which measures how often their ads appear compared to eligibility, dropped sharply before disappearing completely from advertiser auction data by April 12.

The timing coincided with the Trump administration’s hardened stance on Chinese imports, raising tariffs to 125% while maintaining a more moderate approach to other trading partners.

First seen. Mike Ryan, head of ecommerce insights at Smarter Ecommerce, shared this news on LinkedIn:

Between the lines. Temu’s business model relied on heavily subsidized orders from parent company PDD to drive market share growth, despite operating at a loss on individual sales.

  • New tariffs, combined with crackdowns on “de minimis” import loopholes, have severely undermined Temu’s direct-from-manufacturer approach.
  • The company’s inability to maintain app performance without advertising for even a single day demonstrates the fragility of its market position.

Why we care. Ecommerce advertisers may experience temporary relief in digital advertising costs as Temu’s aggressive spending vanishes from auction platforms. Similar rapid market exits (e.g., Amazon during early pandemic lockdowns) led to drops in cost-per-click metrics. Some reduction in CPM rates is expected, potentially lowering both CPC and cost-per-conversion for remaining advertisers.

Tariffs. The underlying causes of Temu’s retreat (tariffs and import restrictions) could ultimately prove more damaging to the ecommerce landscape, particularly for small and medium-sized businesses.

Bottom line. Unlike failed competitor Wish.com, Temu’s parent company remains fundamentally sound. With U.S. trade policy still in flux and facing internal opposition even within the administration, Temu’s retreat may not be permanent.

Read more at Read More

Google AI Overview-organic ranking overlap drops after core update

AI Overviews are now less likely to cite pages that rank in Google’s top 10 organic positions, according to new BrightEdge data. This change was observed following Google’s March 2025 core update.

By the numbers. The overlap between AI Overview citations and Google’s top 10 organic positions dropped from 16% to 15% following the March 2025 core update. Shift by industry:

  • Travel industry: 6.6 percentage point increase in regular result citations (from 12.9% to 19.5%).
  • Entertainment: 4.9 percentage point increase (from 8.8% to 13.7%) for movie queries.
  • Restaurants: 4.6 percentage point increase (from 9.5% to 14.1%) for dining content.

Why we care. Tens of millions of searches per day now feature AI-generated summaries that don’t cite the highest-ranked results from organic search. The good news? Pages ranking outside Google’s top 10 positions now have a better shot at being cited in AI Overviews.

But. This appears to be a major shift in how Google is synthesizing information via its AI-generated answers. The change could pose new visibility and attribution (and even more rank tracking) challenges. Other ongoing challenges:

The big picture. Google’s John Mueller confirmed that Google AI Overviews are impacted by core updates. BrightEdge’s latest finding seems to be further confirmation of that. Mueller said last August:

  • “These are a part of search, and core updates affect search, so yes.”

What to do? Here are two areas to focus on, according to Jim Yu, founder and executive chair of BrightEdge:

  • Create complementary content that answers the next logical question(s). Your content addressing follow-up questions now has better chances of being cited even if it doesn’t rank in the top 10 regular results.
  • Don’t choose between ranking well or appearing in AI Overviews – aim for both. Track your presence in both areas to get a complete picture of your search visibility.

What’s next. We will continue to watch how Google’s AI Overviews and core updates impact organic traffic (that “necessary evil” which makes it possible for websites to exist and Google to have all the fresh, helpful content it needs).

Dig deeper. Google AI Overviews spiked during March 2025 core update

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How to track visibility across AI platforms

How to track visibility across AI platforms

AI has changed how people search – and what it means to be “visible” in results. 

Links and rankings still matter, but they’re no longer the full picture.

Now, it’s about mentions, citations, and whether your brand even shows up in the conversation.

Most SEO tools haven’t caught up. This makes tracking that kind of visibility hard – but not impossible. 

Here’s how to rethink visibility in the age of AI.

Why tracking AI visibility is so tricky

Remember when SEO was (relatively) simple? 

People typed in short phrases like:

  • “Best project management tool or SEO tips 2020.” 

You knew how they searched, what they were probably looking for, and how to optimize for it.

Fast-forward to today, and that same user might type: 

  • “Act as a SaaS expert and give me the top 3 project management tools for remote teams with a $50/month budget.”

Welcome to the era of conversational search – where queries sound more like DMs to a colleague than keyword strings. 

Tools like ChatGPT, Gemini, Perplexity, and Claude have normalized full-sentence prompts and pushed search behavior into a new territory. 

We’ve seen glimpses of this shift before with voice search, but AI has made it feel seamless, fast, and dangerously convenient.

That’s great for users – until it’s not. 

AI-generated answers don’t always cite their sources. 

Even when they do, the links might be missing, vague, or tossed in like an afterthought. 

As a result, people often end up back on Google to double-check facts, dig deeper, or figure out if the AI just hallucinated an entire case study. 

Still, many users are happy to take the shortcut – even if it means missing context or nuance – because who wants to read 20 blog posts when ChatGPT gives you an instant TL;DR?

This has created a hybrid search habit: start with AI, fact-check with traditional search, and hope the truth lives somewhere in between. 

Or at least, this is the current situation. There is no guarantee it will be the same in six months. 

But even now, for SEOs, it’s chaos. Visibility is no longer just about ranking in Google’s top 10.

Your brand might be mentioned in a Perplexity answer or your website cited in Google’s AI Overviews

AI visibility Tools per Perplexity

And the tools we’ve relied on? 

They’re still stuck in the exact-match keyword era, blissfully unaware of how users are actually searching in these new environments.

The result: SEO teams are flying blind. 

You can’t optimize for what you can’t see – and right now, most of what’s happening in AI-driven search is happening in the dark.

It doesn’t sound great, right?

So, what’s the next move?

We can’t just sit and hope for the best. 

We should start from somewhere. The first step is to understand what matters in the AI era.

Dig deeper: Answer engine optimization: 6 AI models you should optimize for

Capabilities that matter in the AI era

When it comes to tracking AI visibility, your needs will depend on your business size, market focus, and available resources. 

A small team may get by with basic tracking or even manual checks (something we have tried and I won’t recommend). 

But if you’re operating at mid-size or enterprise level – especially in a competitive niche – you’ll need more advanced features to get real value.

Here’s a checklist of potential capabilities to look for when evaluating tools or building a solution in-house.

Custom prompt tracking

You should be able to import your prompts, not just rely on a default list. 

Without this, you’re measuring performance on queries your customers may never actually use. 

AI tools are smart, but your team knows the audience better. 

Multi-country and language support

AI answers can vary widely by region and language. 

If you work on a website with multiple languages without localization, your visibility data might be incomplete or even wrong. 

For example, when you search in English, results in the U.S. and the UK might be completely different.

Cross-platform tracking

Your audience doesn’t live on one AI tool. A proper solution should cover ChatGPT, Gemini, Perplexity, and others. 

Otherwise, you’re only seeing part of the picture. 

Especially if you are a B2B business, some of your potential customers might be already “married” to Microsoft’s or Google’s ecosystem and unwilling to pay for another platform.

Competitor identification

You need the ability to set your known competitors and discover others based on how often they’re mentioned in the answers to the prompts. 

If you miss this, you might not realize who’s gaining ground.

Historical data access

AI results change fast. 

You’re not the only one optimizing your website – your competition is not sleeping. 

Tracking historical performance is essential for spotting trends. No history means no real benchmarking.

Topic and platform breakdowns

Not all mentions are equal. 

You should be able to slice your visibility data by topic, category, or platform. Without this, your reporting stays surface-level.

Exportable answer sets

Make sure you can export the full AI responses tied to your prompts. 

This is critical for internal analysis, validation, and documentation. If you can’t export it, you don’t own it.

Visual dashboards

To make sense of your data and communicate it effectively, you’ll need clear visualization by time, prompt, platform, or topic. 

Otherwise, you’re stuck sifting through raw tables and spreadsheets.

Most tools on the market don’t do all of this perfectly, or if they promise that they can do it – their features come with a high price. 

Unfortunately, building something in-house also takes time and technical expertise. 

The key here is to prioritize based on your team’s goals – whether that’s: 

  • Improving brand presence.
  • Monitoring competitors.
  • Understanding how AI tools are shaping the customer journey. 

Also, be mindful of your own resources

Buying a tracking tool won’t increase your capacity for optimizations, and it will just show you partly the right path.

Once you’ve defined the right capabilities, the next step is knowing what to actually measure.

Dig deeper: AI optimization – How to optimize your content for AI search and agents

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What to measure when rankings don’t matter

In AI-driven search, you’re no longer measuring rankings or CTRs – you’re measuring brand exposure. 

Traditional SEO metrics still matter, but they won’t tell you how often your brand is mentioned or cited in AI-generated answers.

Many of the metrics SEOs now need to track look more like PR KPIs: 

  • Mentions.
  • Citations.
  • Share of voice. 

Visibility is less about position and more about presence – and whether you’re being referenced as an authority.

Here’s a list of metrics that can help you understand and track your AI visibility. 

You likely won’t need (or be able) to track all of them – especially early on. 

However, knowing what’s possible can help you prioritize based on your goals and resources.

Brand mentions

The number of times your brand or the brand of your competitors is referenced in AI-generated responses, regardless of whether a link is included.

  • Why it matters: Mentions are the new impressions – a signal of awareness and authority. If your competitors are mentioned more often, you’re losing visibility at the top of the funnel.

Citations (linked references)

The number of times your website and the websites of your competitors are actually linked in AI answers.

  • Why it matters: Mentions are good, but links are better. They offer validation and can drive traffic (depending on how the platform displays links). Tracking citations helps identify which content AI models consider authoritative.

Prompt-triggered visibility

Which prompts lead to your brand being mentioned or cited? 

Which prompts trigger the same for your competitor?

  • Why it matters: It helps you understand the user intent that surfaces your brand. This is especially valuable for optimizing messaging and identifying new positioning angles.

Context of mentions

Are you listed as the top recommendation? One of 10 options? 

Are you described positively, neutrally, or vaguely?

  • Why it matters: The quality of the mention shapes user perception. Being “mentioned” isn’t always a win if you’re buried in a list or framed as a secondary option.

Share of voice (SOV)

What percentage of relevant AI answers include your brand vs. competitors?

  • Why it matters: SOV gives you a benchmark to measure your presence relative to others in your category. It’s useful for spotting gains and losses in competitive positioning.

Dig deeper: How to monitor brand visibility across AI search channels

Link destination and depth

Are the links going to your homepage, product pages, blog posts, or support content?

  • Why it matters: Shows which content is earning trust – and what type of pages you should prioritize to increase citations.

Visibility over time

Mentions and citations aren’t static. You need to track changes over time to understand trends.

  • Why it matters: It helps you measure the impact of SEO and content work, PR activity, or product updates on your AI presence.

Platform-specific performance

How does your brand visibility compare across different tools – ChatGPT, Gemini, Perplexity, etc.?

  • Why it matters: AI models pull from different data sources and respond differently to prompts. Tracking platform-specific visibility can help prioritize where to focus next.

Not every team needs to track all of these, and most tools don’t cover all of them. 

Start with the metrics that align more closely with your goals and upgrade when needed.

And now, for the fun part: finding tools that can track these metrics.

Dig deeper: Your 2025 playbook for AI-powered cross-channel brand visibility

Where to start with AI visibility tools

The good news is that the landscape of AI visibility tools is evolving rapidly. 

The bad news is that most platforms currently don’t do it all. 

Most tools are still maturing and focusing on specific aspects of the visibility puzzle, such as just one or two of the main AI platforms. 

That makes tool selection less about finding “the best” solution and more about choosing the right fit for your needs and resources.

Here are a few tools currently on the radar of SEO teams exploring AI visibility:

  • Profound: Tracks brand visibility across AI platforms like Perplexity and ChatGPT.
  • Peec AI: Designed for prompt monitoring, brand detection, benchmarking, and historical trendline.
  • Otterly: Offers prompt research, similar to the keyword research process, and tracking of selected prompts.
  • Goodie: Combines SEO data with generative AI monitoring across different models.
  • Adsmurai: Originally ad-focused, now expanding into AI visibility and performance insights.
  • RankRaven: Built for tracking brand mentions and share of voice in AI-generated answers.
  • seoClarity: The enterprise suite now offers tools to monitor visibility in AI-driven search results.

Many others are emerging – and more are launching every month. 

Some tools may eventually cover everything you need, but the price quickly becomes a factor. 

The reason is simple. For most SEO teams, this means adding yet another platform to an already crowded stack. 

Something that rarely excites stakeholders, whether you’re in-house or agency-side.

Building your own system is also an option – and it might seem cost-effective on paper. 

However, maintaining a reliable AI tracking setup requires engineering time, constant testing, and a high tolerance for platform changes. 

Depending on your scale, it may cost more in time than it saves in budget.

Some teams may end up using a combination of tools:

  • One external tool for broad coverage.
  • One internal for deeper tracking.

Whatever direction you choose, set aside time to explore, test, and watch demos. 

Most of these platforms are still evolving, and what works for your team today might need rethinking in six months. 

A flexible mindset and a willingness to experiment are just as important as the tools themselves.

Tracking AI visibility in a changing search landscape

Tracking AI visibility isn’t about figuring it all out today – it’s about laying the groundwork. 

  • Define the signals that matter.
  • Pick the tools that fit.
  • Be ready to pivot as the landscape changes.

This is an exciting time to rethink what visibility means. Take the opportunity to think outside of the box and experiment. 

Dig deeper: 6 easy ways to adapt your SEO strategy for stronger AI visibility

Read more at Read More

Make 2025 the year you take home the highest honor in search

Winning an industry award can seriously impact how customers, clients, and colleagues regard your brand. Showcase your achievements and celebrate your professional excellence by entering the Search Engine Land Awards – the highest honor in search marketing!

For the past 10 years, the Search Engine Land Awards have honored some of the best in the search industry – including leading in-house teams at Wiley Education Services, T-Mobile, Penn Foster, Sprint, and HomeToGo – and exceptional agencies representing Samsung, Lands’ End, Stanley Steemer, and beyond.

This year, it’s your turn. The 2025 entry period is now open!

Here’s what you need to know:

  • This is the 10th anniversary of the Search Engine Land Awards, a program designed to celebrate individuals, agencies, and internal teams within the search marketing community who have demonstrated excellence in executing organic and paid search marketing campaigns.
  • This year’s program features 19 unique categories, from Best Use of AI Technology in Search Marketing to Agency of the Year… click here to explore them all.
  • Applying is easier than ever – send us an executive summary that showcases, in 750 words or less, the award-worthy work you and your team performed this past year.
  • Completing your application empowers you to reflect on an impressive year of work, featuring its successes and lessons learned – an invaluable exercise for you and your team.
  • Winning a Search Engine Land Award is a unique, rewarding, and cost-effective way to put your organization a step ahead of its competitors, gain well-earned publicity, boost company morale, and more.
  • Submit your application by May 23 to enjoy Super Early Bird pricing – just $395 per entry ($300 off final rates!).
  • Not sure where to begin? Check out this helpful collection of advice straight from past judges for insights on what makes a winning application.

Don’t miss your opportunity to participate in the only awards program recognized by Search Engine Land, the industry publication of record. Begin your application today! 

Read more at Read More

Google vision match vs. traditional search: Early insights on AI shopping tool

Vision match vs. traditional search: Early insights on Google’s AI shopping tool

With Google’s introduction of its vision match feature, users can now describe a product they’re looking for, and AI will generate suggestions similar to that item. 

The real kicker is that the product generated likely doesn’t exist. 

The AI will create something that you want, show you the product, and then try to match the AI product with items from the real world.

Google Shopping - Vision match

It sounds like a streamlined, user-friendly shopping experience that could revolutionize online shopping yet again. 

  • But does it actually enhance our shopping journey? 
  • Are these AI-generated results truly more effective than the product results we get from traditional Google search? 
  • What does this mean for advertisers? 

To find out, I tested the feature myself, comparing vision match results with standard search results for a few product categories. 

What I found may give you a new perspective on the role AI plays in online shopping.

Test 1: Vision match ‘suggested query’

To start, I choose one of vision match’s “suggested queries.”

  • Search query: “holographic platform boots with metallic highlights”

Vision match results

Vision match - holographic platform boots with metallic highlights

Sponsored shopping results

Sponsored shopping results - holographic platform boots with metallic highlights

Free listing results

Free listing results - holographic platform boots with metallic highlights

Interestingly, my AI-generated image results are more accurate than the “shop similar-looking product” results. 

While the product recommendations included some metallic holographic platform boots, they also included a mix of non-metallic boots and even a prom dress. 

So, what’s the point of the AI-generated images if they’re not actually helping me find the product I want but instead creating a fake version of it? 

Wouldn’t it be more sensible to expand the “Shop Similar” section beyond six products and give us more real options? 

What also stands out to me is the selection of retailers. 

I’ve never heard of most of these retailers. Some results come from resale platforms like eBay and Poshmark. 

And then there’s the cost difference. The average price of the AI-generated product recommendations? 

A whopping $230, with some listings going as high as $954. 

Meanwhile, the average price from a regular search? Just $75.

Looking at my shopping results, every product in the first carousel (and beyond) is a pair of holographic platform boots with metallic highlights and nothing else. 

Now, I’m not planning on attending a disco party anytime soon, but if I were, I’d know where to go: Google Search.

Contender  Grade
Vision match D+
Traditional search A

Next, I tried a broader query, hoping for more accurate results. 

Test 2: Non-specified search (statement piece)

  • Search query: “mens red button down shirt”

Vision match results

Vision match - mens red button down shirt

Sponsored shopping results

Sponsored shopping - mens red button down shirt

Free listing results

Free listing - mens red button down shirt

The first AI-generated image and product suggestions did show a red button-down shirt (though some may argue it’s an orange-red.) 

From there, I got a mix of red button-downs, some with patterns and textures. 

Overall, it wasn’t bad, but the price range was still all over the place.

I still find myself drawn to the traditional search results. 

When I search for “men’s red button-down shirt,” I get exactly what I asked for. 

No guesswork, no unnecessary variations. 

Plus, I’m given valuable details upfront, like:

  • Discount percentages.
  • Customer ratings.
  • Product attribute callouts (“comfortable,” “easy to clean”). 

These elements make the listings far more compelling and trustworthy, which incentives my click – unlike the AI-generated results, which feel more like a best guess than a true recommendation.

Contender  Grade
Vision match C
Traditional search A

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Test 3: Brand search

You’d expect that searching for a specific brand name would surface products primarily from that brand’s official website, right?

Not quite.

I ran this search multiple times just to be sure I wasn’t imagining things – and still, less than 3% of the results actually came from Nike’s own site.

  • Search query: “Nike sneakers”

Vision match results

Vision match - Nike sneakers

Sponsored shopping results

Sponsored shopping - Nike sneakers

Free listing results

Free listing - Nike sneakers

Even worse, I was given additional irrelevant results – New Balance sneakers, socks, leggings, and blue polka dot blankets – definitely not Nike sneakers.

Sponsored shopping is a different story.

Since other major retailers carry Nike products, it makes sense to see them appear alongside Nike. 

However, when looking at the free listings, all my results come from nike.com. 

So, what does this mean for brands? 

If Nike, one of the world’s largest retailers, is hardly featured in the vision match results, what does this mean for smaller brands? 

Will they struggle even more to get visibility in vision match and possibly other future AI-generated product recommendations?

Contender  Grade
Vision match D-
Traditional search A+

Test 4: A trending search

For my final test, I wanted to see how AI would handle a fashion trend rather than a standard clothing item. 

Trends come and go quickly, creating a high demand for a short period of time, unlike staple pieces like a “men’s red button-down shirt,” which remain relevant year after year. 

This felt like an important test, given how fast fashion moves and how crucial it is for shopping tools to keep up with ever-changing styles.

  • Search query: “barrel jeans”

Vision match results

Vision match - barrel jeans

Sponsored shopping results

Sponsored shopping - barrel jeans

Free listing results

Free listing - barrel jeans

If you haven’t heard of the “barrel jean” trend, don’t worry; you’re not missing much in the vision match results because they’re completely wrong. 

I only included three screenshots, but after scrolling through everything, I didn’t spot a single pair of actual barrel jeans. 

Instead, I got jeans in every color and pattern imaginable, along with some random dress slacks.

Meanwhile, a simple Google Search gave me exactly what I was looking for: a variety of barrel jeans from well-known retailers available in different price ranges and washes. 

It’s pretty clear that when it comes to keeping up with fashion trends, AI might be trending, but vision match still isn’t fashion-forward.

Contender  Grade
Vision match F
Traditional search A+

The final verdict

In my experience, while vision match offers an interesting new way to search for products, it still has a long way to go in terms of accuracy and relevance. 

In each test, it struggled to provide precise matches for the products I was looking for, often offering unrelated items or a confusing mix of options. 

On the other hand, traditional search results from Google gave me exactly what I wanted: clear product options, price ranges, and relevant details that helped me make an informed decision. 

Let’s take a look at the final results of our test:

Contender  Grade
Vision match D
Traditional search A

I understand that these results are subjective, but anyone with search intent would agree that a D average is generous in this case. 

So, what does this mean for advertisers? 

As AI-generated results grow, advertisers must continue to adapt their strategies to ensure their products are accurately represented. 

However, with limited control over what’s featured in vision match, this will be very difficult. 

Given that the current vision match results seem subpar, users will likely still prefer the traditional search results, which continue to provide more accurate and relevant options. 

While vision match has potential, its current limitations likely won’t sway many users away from the search results for now.

Read more at Read More

Your 2025 playbook for AI-powered cross-channel brand visibility

Search Engine Land - Fractl Agents Header

AI is changing how people find and engage with content – but the core signals that drive visibility haven’t changed. 

This article shows how search marketers can stay competitive by combining proven SEO and content strategies with AI-powered workflows to build authority, trust, and reach across platforms.

Why authority still wins in the age of AI search

If you work in marketing, you’ve probably heard the same questions repeated throughout the past year:

  • “What’s AI’s impact on organic search?” 
  • “How can my brand appear in AI-driven search results?” 
  • “Is it safe to use AI in my content workflow?” 

While many agencies and experts are rushing to stake a claim in the new frontier of “AI brand visibility,” industry leaders are aligning around the idea that search engine and AI optimization rely on similar signals. 

Whether you’re using Google or ChatGPT, both platforms strive to surface the most authoritative, relevant content on a given subject. 

They do this by identifying which entities (brands or sources) have provided robust subject matter expertise (contextual relevance) backed by strong third-party authority signals (e.g., citations from trusted sources). 

In other words, the fundamentals that make your content rank highly on Google – expertise, authority, and trustworthiness – also increase your visibility in AI-generated answers.

If you’ve paid attention to effective content marketing strategies over the last decade, keep creating unique, valuable, educational, and engaging brand content enriched with proprietary data and expert insights. 

This approach:

  • Builds credibility and provides fresh expertise beyond what AI alone can produce, which all channels seek to surface. 
  • Earns coverage and citations from authoritative sources across diverse platforms.
  • Strengthens your brand’s authority, diversifies visibility, and drives qualified traffic and cross-channel conversions. 

Meanwhile, if you’re simply using ChatGPT to churn out regurgitated content for top-funnel informational queries – you might as well burn your marketing budget. 

In 2025, it’s critical not to get lost in another “SEO is dead, long live […AI]!” echo chamber. 

We’re 14 years into this recurring, sensationalized industry news cycle, yet search interest is still growing.

SEO is dead - Google Trends
SEO - Google Trends

If we slow down and look at the facts, Google now processes over 5 trillion searches per year (with a 20%+ YoY growth).

AI tools like ChatGPT are expanding search behavior, not replacing it. 

Up to 70% of ChatGPT prompts involve collaborative, custom tasks like code debugging or meal planning, per a recent Semrush study. 

These are things classic search wasn’t built for. 

Growth of Google searches, 2023-2024

Regardless of what we dub this era of “AI optimization,” one thing remains true: the value of cross-channel, inbound marketing reigns supreme, as it has since the dawn of digital. 

While we don’t always need a new industry buzzword, generative engine optimization (GEO) has grown in interest significantly over the last 12 months.

Agency goliaths are starting to invest in their own content strategies around it. 

Ian Lurie on Bluesky
Generative engine optimization - Google Trends

Most brand channels rely on similar ranking principles. 

So, instead of panicking about the latest platform shifts, challenge your team to pause and reflect. 

How do you use AI to scale effective, cross-channel marketing strategies and workflows to sustain your brand’s visibility?

Specifically:

  • How are we building content ecosystems that establish topic authority while earning trust and driving engagement across multiple platforms?
  • Have we done audience research to understand where our target market resides, and are we effectively using AI to scale cross-channel content syndication to those platforms? 
  • Where else should we seek to apply AI to automate our proven marketing workflows to improve efficiency, reach, and ROI? 

Here’s how my agency uses prompt engineering, custom GPTs, and proprietary AI agents to streamline digital marketing – and how your team can, too.

1. Using AI to create newsworthy campaigns with strong E-E-A-T

One of the highest ROI activities your brand can invest in now is proprietary research that produces insights AI can’t replicate. 

This type of content gets cited by publishers, builds domain expertise, and increases your chances of influencing AI training data itself.

You don’t need to build bleeding-edge agentic workflows to see real impact from AI in your marketing. 

Even teaching your team simple prompt engineering and having them build custom GPTs can help streamline your workflows. 

AI can handle tedious tasks – like deep research and data analysis – so your team can spend more time on strategy and creative thinking. 

Below are a few examples of AI-enhanced content workflows any brand can adopt. 

Reactive PR campaign prompts

Use AI to research and brainstorm campaign ideas based on breaking news that aligns with the brand’s vertical and target market. 

For example, you might prompt ChatGPT to take on a digital PR persona tasked with scanning breaking news in your industry from the past 24-72 hours and generating timely campaign ideas.

This kind of AI-assisted brainstorming ensures your content ideas are timely and poised to earn media attention without waiting for time-intensive human research and ideation sessions. 

Sample prompt 

  • Role: You are a digital PR strategist for [Brand], tasked with identifying trending, high-authority news stories in [Topic/Industry/Region] from the past 24-72 hours and generating timely, data-driven campaign ideas that use the brand’s expertise to secure mainstream media coverage.
  • Campaign criteria:
    • Trend-driven: Focus on viral/trending topics from major outlets, TikTok, Reddit, X, Google Trends.
    • Brand-relevant: Align with [Brand]’s domain expertise.
    • Timely and actionable: Campaign can be executed within 24-72 hours.
    • Data-backed: Use rapid methods – pulse surveys, social scraping, Google Trends, proprietary data.
    • Emotionally compelling: Ideas must be timely, educational, emotional, or entertaining.
  • Campaign structure:
    • Title: A concise, engaging campaign title.
    • Description: Explain the campaign idea, key insights/questions, target audience, and tie to current news + brand’s expertise.
    • Methodology: Outline how you will gather and analyze data (e.g., surveys, social scraping, government datasets, trends).

Dig deeper: Reactive PR and AI: How to capitalize on trending topics faster

Data journalism campaign prompts

Consider training a custom AI model on your own archive of successful content marketing campaigns (or case studies from your industry) to generate fresh campaign ideas. 

An internal ideation agent could be fed thousands of past campaign briefs, articles, or link building projects along with their performance outcomes. 

The AI can then generate new ideas tailored to your brand’s vertical, following patterns that historically earned high authority backlinks and engagement. 

You might guide it with criteria. For instance, the idea must:

  • Have a high likelihood of attracting authoritative .edu/.gov/.com links.
  • Be data-driven and unique.
  • Align with your business goals.
  • Be timely or seasonal.
  • Include a visually engaging component. 

This way, the AI isn’t pulling generic ideas from thin air – it’s remixing elements of proven hits to suggest the next big content piece. 

Sample prompt

  • Role: You are a creative data-driven PR strategist, tasked with generating newsworthy, high-authority campaign ideas that earn backlinks from top-tier media (.com), government (.gov), and educational (.edu) sites. You focus on creating unique, engaging, and data-backed campaigns tailored to [Brand]’s specific vertical (e.g., education, tax, aviation, hosting, creative industries).
  • Campaign criteria:
    • High-authority potential
    • Data-driven
    • Creative and unique
    • Aligned with goals
    • Timely 
    • Visually engaging 
  • Approved methodologies
  • Campaign structure:
    • Title 
    • Description 
    • Methodology 

Ideation scoring prompt

Another valuable use of AI is evaluating and refining newsworthy brand content at scale. 

Marketing teams constantly brainstorm ideas for data journalism campaigns, blog posts, and social content. 

Based on learned criteria, an AI agent can rapidly assess each idea for “newsworthiness” or virality potential. 

For example, you can program an AI to act as an editorial panel that:

  • Scores ideas on a scale for promotional viability.
  • Suggests which statistics or angles would make the idea more compelling to the press.
  • Even recommends how to execute the methodology more rigorously.

This doesn’t replace your decision-making, but it helps streamline a recurring process that has a proven framework that AI can help scale. 

Sample prompt

  • Role: You are a data journalism and PR expert tasked with evaluating the newsworthiness and promotional viability of data-driven campaign concepts, including surveys, studies, meta-rankings, and analyses. Your role is to rate the idea’s media potential, suggest the best promotional angles (headlines/takeaways), and refine the methodology to ensure data accuracy and media appeal.
  • Evaluation process:
    • Promotional viability score 
    • Potential headline-worthy takeaways
    • Suggested name 
    • Methodology recommendation

Beyond these examples, dozens of other AI applications can streamline your content workflow. 

Forward-thinking teams are deploying custom AI assistants for tasks such as:

  • Writing survey questions.
  • Building campaign briefs.
  • Identifying typos or brand guideline violations.
  • Discovering unique data sources or variables for new research.
  • And so much more. 

An AI agent can improve any repetitive or data-intensive part of content creation. 

Once you’ve used AI to assist in creating high-quality, E-E-A-T-rich content, the next step is to ensure that the content gets in front of the right audience. 

This is where AI can also play a game-changing role in distribution and PR.

2. Scaling digital PR with AI

Building your brand’s authority and trust through earned media has become more critical in an era of AI-driven search results. 

Google’s algorithm and AI models prioritize widely cited and trusted content, favoring brands with strong E-E-A-T signals. 

Digital PR helps secure high-authority backlinks and trusted media mentions that improve search rankings. 

These efforts also increase the likelihood of being featured in AI-generated results, as LLMs are trained on well-cited, newsworthy sources.

In short, earning mainstream news and authoritative, niche-relevant brand coverage simultaneously strengthens your visibility across search, social, and emerging AI platforms.

AI holds enormous potential for PR teams. It can:

  • Research journalists.
  • Personalize outreach.
  • Even draft pitches in seconds. 

Still, we must pair scale with skill.

Relying too much on automation can lead to spammy, robotic pitches that journalists ignore (or resent). 

Rather than blasting out “just another AI-generated pitch,” smart PR teams use an AI-powered, human-perfected workflow.

AI handles the heavy lifting – research, pattern-based tasks, and first drafts – while humans focus on strategy, messaging, and real relationship-building. 

The key is scaling the repeatable parts with AI and reserving human effort for creativity, judgment, and authentic personalization.

AI and humans in marketing

Here are a few high-impact ways PR professionals can use AI today.

AI pitch strategy prompt

One of the easiest prompts to create is a digital PR strategy generator that mimics the pitch templates your team uses to earn authoritative brand coverage.

Incorporating training guides, sample pitch templates, industry pro tips, and other proprietary knowledge is crucial. 

This is key to building a GPT or agent that helps your PR team stand out in a sea of sameness.

Below are the areas you should hone in and expand when designing a PR strategy prompt.

  • Role: You are PPS savant, a digital PR Expert specialized in generating a complete pre-pitch strategy (PPS) for data-driven PR studies and media outreach. Your job is to extract the most compelling statistics from a provided study or campaign and generate a fully developed, press-ready outreach strategy, including subject lines, email copy, and a targeted media list.
  • Distill:
    • Compelling statistics 
    • Subject lines 
    • Email pitch structure 
    • Follow-up pitch structure 
    • Targeted media outlets 
    • Tone and focus 

Media list builder

Finding the right outlets and contacts is a time-consuming part of PR that AI can dramatically improve. 

Instead of manually searching media databases (or paying for expensive platforms that quickly go out of date), an AI-driven media list builder can scan recent articles and news to identify journalists and publications relevant to your content. 

For example, given a summary of your campaign or a PDF of your research, an AI agent could compile a list of the 30–50 most relevant publishers and reporters, including:

  • The outlet name.
  • A link to a recent similar article (to prove relevance).
  • The journalist’s name and beat.
  • Even metrics like the outlet’s traffic. 

This increases the odds of getting interest and saves thousands of dollars that might have been spent on static media databases. 

Sample prompt

  • Role: You are a digital PR publisher expert who analyzes brand studies to identify the 40 most relevant, high-authority news outlets based on the provided resource. Your focus is on matching the content (attached PDF) to vertical-specific, top-tier outlets and ensuring maximum relevance and link potential.
  • Publisher recommendations should include:
    • Publisher name
    • Root domain (clickable URL)
    • Vertical section (e.g., “All Finance”, “Lifestyle”, “Health”, “Education”, etc.) 
    • Domain authority (DA) 
    • Site traffic 
    • Relevant post 

Blog search

Beyond mainstream media outlets, much of the “long tail” of PR success comes from niche blogs and industry influencers who syndicate or share your content. 

Here, too, AI can make a huge difference. 

To solve this, we built a “blog search agent” that runs a semantic search across 20,000+ active (non-spam) blogs from Kagi’s Small Web to help uncover niche-specific influencers who regularly update their smaller, mid-tier sites for highly relevant audiences. 

Blog search - AI agent

By using AI in these ways, PR teams can significantly increase the speed and quality of their outreach, leading to more authoritative coverage. 

Top brands already use these tactics to land stories in national newspapers and specialized trade publications.

More than algorithmic efficiency, effective PR requires credible, newsworthy content and authentic human relationships. 

AI can help you write 10 pitches in the time it used to take to write one.

Still, if the core story isn’t strong or you haven’t bothered to personalize it, journalists will delete your email. 

Get the newsletter search marketers rely on.



3. Streamlining social content syndication with AI

New search and social platforms will inevitably rise and fall

However, one principle remains constant: marketers must repurpose and syndicate their best content across the diverse platforms where their audience engages. 

In the age of AI, diversification is key for building defensible brand visibility and traffic. 

A blog post that earns links and ranks on Google can be adapted into an X thread, a Reddit post, a LinkedIn article, a TikTok script, or a YouTube video.

Each extension reinforces your expertise and reaches new pockets of your audience. 

Consistent cross-platform visibility boosts SEO and engagement and trains AI models to recognize your brand’s authority everywhere.

Share of social media referrals to the web

Many content teams excel at creating high-value content, but they often lack the bandwidth or distribution tools. 

By automating parts of the syndication process and optimizing content for each channel, AI ensures your work actually gets seen. 

Here are a few ways AI can amplify your cross-channel content strategy.

Reddit advice tool 

With Reddit dominating the SERPs, it’s a crucial time to evaluate this social platform as another avenue for your content syndication. 

This agent helps your social team:

  • Identify relevant subreddits for your brand content.
  • Develop suggested titles and justify why that style would resonate with each specific community.
  • Generate article summaries to get you started. 
Reddit advice tool 

AI image creation 

AI is making creating eye-catching graphics, illustrations, or photos to accompany your content easier than ever.

That said, the jury is still out on the best models and prompts that can make or break your brand’s output. 

A few of my personal favorites include DALL-E 3, Midjourney, and Stable Diffusion, which can produce custom images that were unimaginable a few years ago. 

Still, the key difference is the quality of your prompt engineering.

Often, the best prompt designers are those with a creative eye, like graphic designers, who will know how to coax the model toward a desired style or factual accuracy. 

The result: your content stands out in crowded feeds without the cost or time of a full photoshoot or graphic design cycle for every piece.

AI generated image post by John Mueller

Dig deeper: How to create images and visuals with generative AI

Diversifying and repurposing content across channels is no longer optional. It’s essential for building a resilient brand presence. 

AI makes this far easier by taking on the heavy lifting of format adaptation. 

Your most valuable content assets can live multiple lives: a research report can spawn dozens of social posts, videos, and media pitches. 

A single great insight can become an infographic, a blog, a webinar, and a Reddit AMA. 

With AI handling the transformation and distribution at scale, you can ensure that no piece of content potential goes untapped. 

The payoff is greater reach, more engagement, and a brand that appears ubiquitously wherever your audience (or the algorithms) might look.

AI tools and agents will transform 2025 marketing strategies and workflows 

If there’s one overarching lesson in all of this, it’s that AI isn’t replacing great marketers – it’s amplifying their proven workflows and freeing up time for even greater innovation. 

The brands that outpace their competition in the next 1–3 years will use AI to scale proven marketing workflows.

Humans will stay relentlessly focused on driving creativity, building community, and establishing trust. 

Forward-thinking teams are already investing in comprehensive AI toolkits that touch every aspect of marketing: 

  • Content research and clustering.
  • Content optimization.
  • Digital PR outreach.
  • Technical SEO analysis.
  • Social media scheduling.
  • Sentiment analysis.
  • And much more. 

These early adopters recognize that nearly every marketing workflow will have some element that AI can improve. 

By experimenting now, they’re building a foundation of AI-augmented processes that will be standard practice for everyone else a few years later.

The message for marketing leaders is clear: don’t wait. 

  • Encourage your team to pilot AI in different parts of your operation and see what boosts your efficiency or results. 
  • Create internal case studies of what works (and share these insights with peers, contributing to industry knowledge). 

Remember, the goal isn’t to hand everything over to machines. It’s to let machines do what they’re great at so that humans can do what they’re great at. 

The winning formula is AI + human, not AI vs. human.

In 2025 and beyond, success in SEO and cross-channel marketing will come down to this balance. 

The hype cycles will continue – new tools, algorithms, and platforms – but the fundamentals remain.

  • Know your audience.
  • Create real value.
  • Earn trust.
  • Be everywhere your audience is looking. 

AI is simply the newest (and arguably most powerful) set of tools to help you execute on those fundamentals at scale. 

Those who embrace these tools thoughtfully will:

  • Safeguard their brand’s visibility.
  • Reclaim precious time to focus on strategy and big ideas.
  • Foster the human connections that truly build brands. 

And that’s a winning playbook, no matter how search evolves.

Bottom line? 

AI will boost your growth strategy if you don’t shy away from being an innovator on the technology adoption curve. 

Innovators vs. laggards

Your 2025 brand goal is simple.

  • Repurpose your most valuable content to achieve cross-channel brand visibility, authority, and engagement where your target market resides.
  • Hedge against the rapidly evolving AI landscape that will reshape consumer behavior over the next 12-36 months.

Read more at Read More

Google Search Console updates its Merchant opportunities report

Google announced it has refreshed and renamed the Search Console report now known as the Merchant opportunities report. Previously, this report was named the Search Console Shopping tab listings report, when it was introduced in November 2022.

The Merchant opportunities report within Google Search Console can show you recommendations for improving how your online shop appears on Google.

What Google said. Google posted on LinkedIn about this report saying:

“Today we’re refreshing the Search Console Shopping tab listings report to also include details about payments methods and store ratings. To bring the report name in line with its functionality, we’re renaming it to be the “Merchant opportunities report”.

Check it out and make sure to add important info about your store, so customers can see it when shopping on Google.”

The report. Here is a screenshot of this report:

As you can see, this report will tell you what you are missing when it comes to Google Merchant Center and your fields in that area. The help document goes on to explain:

Adding store information can improve the display of your products and help people when they’re shopping on Google. If you’ve created and associated your Merchant Center account under Merchant opportunities in Search Console, you’ll see suggested opportunities, including: 

You can return to the report to see if your information is pending, approved, or flagged for issues that need fixing.

Why we care. If you sell product on your site, this is a report you want to make sure to review and see what opportunities you are missing with your e-commerce site setup. You can then plug those items and hopefully get more exposure within Google Search, Shopping and even local results.

Read more at Read More

The uncontested paid search problem—and what it’s costing you by Edna Chavira

Join us live!
Save your spot!

Are you paying too much for branded search ads, even when you’re the only one bidding?

A hidden flaw in Google Ads could be driving up your CPCs unnecessarily. You’re not alone. And you’re not imagining it. 

Join Jenn Paterson and John Beresford from BrandPilot AI for The Hidden Cost of Google Ads: Solving the Uncontested Paid Search Problem where they’ll reveal:

  • Why CPCs are inflated on branded terms—without active competition
  • How this self-bidding trap happens, and what it’s costing you
  • The true scale of wasted spend across paid search accounts
  • How to stop this problem and take back control of your brand budget

This is the insight your campaigns—and your budget—deserve. Save your spot here!

Read more at Read More

Advertisers pull back from TikTok, boost Meta amid ban uncertainty

TikTok search

Already facing a sale or ban order, TikTok faced additional pressure in Q1 as major brands scaled back advertising spending and shifted budgets to Meta, according to new data.

By the numbers:

  • TikTok’s U.S. cost-per-thousand-impressions (CPMs) dropped sharply, posting double-digit declines since January.
  • 8 of TikTok’s top 10 advertiser categories cut spending in Q1 2025 compared to Q1 2024.
  • User activity dipped in January during a temporary service blackout, further rattling confidence.

The big picture. Despite the turbulence, TikTok is expected to rake in $14.8 billion in U.S. ad revenue this year, according to eMarketer. That puts it behind Facebook’s $36.9 billion but ahead of YouTube’s $9.9 billion.

Behind the shift. Advertisers aren’t abandoning TikTok entirely, but many are hedging. They’re shifting parts of their budgets to platforms with less uncertain futures.

  • “No one’s acting like TikTok is gone – but no one’s pretending it’s business as usual, either,” said Raul Rios, head of strategy at independent creative agency Saylor.
  • “Brands that swiftly resumed advertising on TikTok post-ban are staying the course, but many remain hesitant, keeping media spend lower despite months of attractive incentives,” said Toni Box, EVP of brand experience at Assembly.

Meta’s moment. While TikTok wobbles, Meta is cashing in. Facebook and Instagram have leaned hard into short-form video:

  • Facebook and Instagram’s short-form video CPMs are rising fast as advertisers reallocate spend.
  • “It’s undeniable that Facebook and Instagram have made CPM growth a key initiative,” said Jason Krebs, GM of media at Varos.

Why we care. This shifting ad spend carries immediate financial risks and strategic disruption. TikTok’s lower CPMs may seem like a short-term bargain for brands. However, if the platform faces a full ban after mid-June, those investments could evaporate. For brands that have optimized for TikTok’s unique algorithm and culture, moving to other platforms could mean starting over – new content formats, new metrics, new audiences.

What’s next. All eyes will be on Washington and ongoing acquisition talks. If a deal saves TikTok, advertisers may return. If not, Meta’s gains could become the new normal.

Read more at Read More