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Why TikTok Is Expanding Its Premium Ads Push and What That Means for You

Key Takeaways

  1. TikTok launched four new or expanded premium ad formats at its 2026 Newfronts: Logo Takeover, Prime Time, TopReach, and expanded Pulse offerings.
  2. More than 200 million Americans are on TikTok, and the platform reaches 1.99 billion monthly active users globally.
  3. Early results on Logo Takeover showed double-digit lifts in brand awareness and purchase intent.
  4. TikTok’s engagement rate of 3.7 percent is nearly eight times higher than Instagram and twenty-five times higher than Facebook.
  5. The platform is positioning itself as a full-funnel engine, with commerce and lower-funnel capabilities maturing alongside its reach.
  6. TikTok-native creative authenticity remains essential, even within premium placements.

TikTok-native creative authenticity remains essential, even within premium placements TikTok’s 2026 IAB NewFronts presentation made one thing clear: the platform is no longer asking brands to treat it as a social experiment. It is asking for a seat at the table alongside TV and streaming budgets, and the new ad products it unveiled give it a credible case to make.

If you are still running TikTok as an afterthought in your media mix, it is time to reassess.

The New Formats, Explained

TikTok’s NewFronts announcement introduced a set of formats specifically designed to capture premium brand investment.

Logo Takeover places your brand at the moment users open the app, before anything else on the screen competes for attention. It is co-branded with TikTok itself, which carries an implicit credibility signal alongside the raw reach. Early tests showed meaningful lifts in both awareness and purchase intent, giving advertisers an actual benchmark to work from rather than just a pitch.

The logo takeover format.

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Prime Time is a sequential format that delivers up to three ads from the same brand to the same user within a 15-minute window, timed to high-engagement periods or major cultural moments. The ability to tell a continuous story across multiple exposures in a short window has historically been a TV strength. TikTok is bringing that capability to a mobile-first, creator-driven environment.

TopReach combines two existing high-visibility placements into a single buy: the first ad users see when opening the app, and the first in-feed ad in the For You feed. For brands running a major launch or trying to dominate a cultural moment, maximizing unique daily reach through a single purchase is a genuine efficiency gain.

The Top Reach format.

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The expanded Pulse offerings include Pulse Mentions, which places brands adjacent to conversations already happening about their category, and Pulse Tastemakers, which lets brands align their ads with specific creator communities. Both formats lean into what TikTok does better than any other platform: making ads feel like they belong inside the content experience rather than interrupting it.

Pulse Mentions.

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TikTok Has Grown Past Its Early Reputation

There is still a version of TikTok in many marketing budgets that looks like a niche social channel with unpredictable ROI. That picture is outdated.

The numbers tell a different story. TikTok generated $33.1 billion in global advertising revenue in 2025, a 43 percent increase from the year before. Its engagement rate of 3.7 percent sits well above every major social competitor. More than half of TikTok users have purchased from brands after seeing their products featured on the platform. TikTok Shop generated $15.82 billion in U.S. sales in 2025, growing at 108 percent year over year.

Only 26 percent of marketers currently run TikTok campaigns. For brands not yet on the platform in a serious way, that gap is the opportunity.

Commerce capabilities have matured to the point where lower-funnel performance is genuinely measurable. Creator-led storytelling has proven to drive purchase behavior in ways that traditional video placements often cannot. And now, with premium formats designed to deliver the kind of reach and sequential storytelling that TV has historically owned, TikTok is a legitimate alternative for budgets flowing toward linear and streaming video.

The brands that shifted budget toward digital video early, before it was obvious, built advantages that took competitors years to close. The same opportunity exists here.

Why Cost Efficiency Matters

Beyond reach and engagement, the cost structure of TikTok advertising makes it worth serious consideration. TikTok ads average a CPM of around $9, compared to Meta’s average Facebook CPM of roughly $15. That cost advantage combined with the platform’s higher engagement rate means dollars spent on TikTok tend to produce more interaction per dollar than on competing platforms.

TikTok vs Meta vs Google comparison.

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That advantage will not last forever. As more advertisers move budget onto the platform, auction competition will increase and CPMs will rise. The brands that establish their TikTok presence and learn what works now will be building that knowledge at a lower cost than those who wait.

How to Approach This

The most common TikTok mistake is importing creative from other channels. A CTV spot or a YouTube pre-roll that performs well will not automatically translate. TikTok rewards content that feels like it was made for the platform and the moment. Even within premium placements, the native feel of the content matters.

Research backs this up. Spark Ads deliver 34 percent higher conversions than standard in-feed ads. The best-performing brand content on TikTok does not look like advertising. It looks like something a person would make and share. Getting that balance right, particularly within premium, high-production formats, is the creative challenge.

That does not mean sacrificing production quality. The new format are built for exactly the intersection of high production value and platform-native storytelling. Getting both right is the challenge, and it requires thinking about creative from a TikTok-first perspective rather than adapting assets designed for other channels.

A few practical steps worth taking now:

  • Test Logo Takeover and TopReach early, while competition for the placements is lower and cost benchmarks are more favorable.
  • Revisit your media mix model. If TikTok is still sitting in a social budget silo, it may be underweighted relative to what it can deliver against video and streaming objectives.
  • Align your paid social and commerce teams. TikTok’s lower-funnel capabilities only deliver their full value when both sides of the house are working toward the same goals with the same data.
  • Pay attention to creator selection. Pulse Tastemakers gives you the ability to align placements with specific creators. Treat that as a targeting decision, not a creative one. The right creator community for your brand will outperform a broad placement every time.

FAQs

How is TikTok’s ad audience different from other platforms?

TikTok reaches 1.99 billion monthly active users globally, with the 25 to 34 age group now its largest single cohort at 40 percent of users. The audience is maturing, meaning the perception that TikTok skews very young is increasingly outdated. The platform also sees daily active users return an average of five to fifteen times per day, making frequency of exposure higher than most other social channels.

What makes TikTok advertising different from Meta or YouTube?

The key difference is how ads fit into the platform experience. TikTok’s ad formats, at their best, look and feel like the content people are already watching. This native quality drives higher engagement and, in many cases, better conversion performance. The platform’s algorithm also rewards content quality over account size, which means strong creative can reach audiences far beyond your existing follower base.

Is TikTok Shop worth investing in alongside paid ads?

Yes. With $15.82 billion in U.S. sales in 2025 and 108 percent year-over-year growth, TikTok Shop has crossed the threshold from experiment to serious commerce channel. Research shows that 25 percent of users who bought from TikTok Shop found the item through a TikTok ad. Paid media and shop strategy work best when they are planned together.

What budget should I start with on the new premium formats?

There is no universal answer, but the general principle applies: treat initial spend on new formats as learning investment rather than expecting immediate ROAS. Get in early while competition is lower, build benchmarks, and scale from a position of knowledge rather than guesswork.

Conclusion

TikTok is not pitching itself as a social media platform with ad inventory, but a full-funnel engine where entertainment, commerce, and performance meet. The numbers back that up: global ad revenue growing at 43 percent year over year, engagement rates eight times higher than Instagram, and a commerce operation that grew by more than 100 percent in a single year.

The brands that take that seriously now and build creative and budget strategies to match will be harder to catch as the platform continues to mature. The window for establishing a cost-efficient early presence is still open. It will not stay that way indefinitely.

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Google Is Testing Sponsored Shops in SERPs: What This Means for Advertisers

Key Takeaways

  1. Google is testing “Sponsored Shops,” a format that groups multiple products from a single retailer into one branded unit inside Shopping results.
  2. This moves competition from the product level to the retailer level, changing what it takes to win visibility.
  3. Feed quality, seller ratings, and assortment depth become more critical than ever.
  4. The format introduces multiple click paths within one ad unit, which could complicate attribution and traffic flow.
  5. Performance Max is a likely vehicle through which Sponsored Shops placements will be accessible when the format formally launches, but nobody knows for sure.
  6. Brands that build strong store-level signals now will be better positioned if and when this rolls out broadly.

Google is running a Shopping test that could change how brands compete for visibility in product search. If it scales, the rules shift, and advertisers who see it coming will have a head start.

Here’s what’s happening and what you should be doing about it right now.

What Is Google Actually Testing?

Google’s Sponsored Shops test groups several products from one retailer into a single ad unit inside Shopping results, alongside the store name, ratings, and brand signals. Think of it as a mini storefront sitting directly inside the search results page, rather than a row of individual competing products.

Sponsored shops results for backpack.

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It is still a test. Google has not confirmed a broad rollout. The direction it points toward matters, though, and Shopping advertisers should be paying close attention.

The test does not exist in isolation. It is part of a broader shift Google has been building toward for a while: more brand-centric, discovery-oriented, and AI-mediated shopping experiences. In 2025, Google introduced the Merchant Brand Profile feature, which lets retailers build brand-presence pages in search with lifestyle images, videos, and business descriptions. 

An example business in Google Sponsored shops.

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Sponsored Shops looks like the logical next step in that direction, bringing brand identity directly into the Shopping ad unit itself.

Why the Format Change Is a Bigger Deal Than It Looks

Right now, Shopping competition is largely a product-level game. Your listing competes against a competitor’s listing. Better feed, stronger bid, you take the placement.

Sponsored Shops changes the terms of that competition. Instead of a single product earning a spot, your entire store is on display at once: assortment, brand presence, and ratings together. A competitor with a stronger catalog and better seller signals will have a structural advantage that no amount of bid optimization can fully offset.

That’s a meaningful shift. Brands that have been winning through finely tuned individual product listings will need to think harder about how their store presents as a whole. Brands that have invested in feed quality, customer experience, and assortment depth will find that investment paying off in ways it didn’t before.

There’s also a measurement angle worth flagging. A single ad unit with multiple clickable elements (store name, individual products, ratings) creates multiple potential click paths. How traffic splits across those paths, and how that maps to your current attribution model, is an open question every Shopping advertiser should be thinking through before this format scales.

What This Signals About Where Google Is Headed

Google has been explicit about where it wants Shopping to go. In its own communications about 2026 priorities, the company described its goal as making search “a more powerful tool for discovery, where ads can inspire and answer all at once.” AI Mode already surfaces organic shopping recommendations based on query relevance, and Google has confirmed it is testing a new ad format inside AI Mode that showcases retailers offering relevant products, clearly marked as sponsored.

A ChatGPT result for men's running shoes black.

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Sponsored Shops fits squarely into that roadmap. It moves Shopping slightly up the funnel, making it as much about brand discovery as product comparison. Rather than a format designed purely to capture demand-ready buyers, it is designed to let brands show up with range and identity in front of people who are still forming their consideration set.

For users, the format is intuitive. Browsing several products from the same retailer without leaving the results page is a better experience than clicking in and out of individual listings. Google tends to expand formats that improve user experience. That’s worth taking seriously.

The PMAX Connection

As of right now, we don’t know what vehicle is going to power sponsored shops. Performance Max is a likely bet based on volume and Google’s push for PMax adoption, but nothing is confirmed. PMax already accounts for roughly 62 percent of Google Shopping spend among major advertisers, and it is already designed to surface both store-level and product-level assets dynamically across Google’s ecosystem.

With this said, though, AI Max for shopping is still in beta, so that might impact what plays a role. We also know that Google does tend to favor some of their newer products which likely helps adoption rate (e.g. AI Max, PMax, & Broad being eligible for AIO ad placements).

What to Do Before This Rolls Out

You do not need to wait for a full launch to get ahead of it.

Start with your product feed. Feed quality has always mattered in Shopping, but a storefront format makes weak data much more visible. Every title, description, image, and availability signal is part of how your store presents in that unit. Get it right now. Research consistently shows that product titles, images, and product identifiers are the three highest-impact feed optimizations, and all three will matter even more in a store-level display format.

Google results for gymshark tshirts.

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Take stock of your seller ratings. In a storefront format, ratings are far more prominent than they are in individual listings. If you have not been actively managing reviews and customer experience signals, that needs to change. A store-level placement that leads with a weak rating is a self-defeating ad.

Look at assortment depth. A Sponsored Shops unit showing three products when a competitor shows ten is a losing presentation. Review whether your full catalog is properly represented in your feed and close any gaps.

Audit your PMax asset groups. Given that PMax is the likely vehicle for Sponsored Shops placements, your asset groups should be fully built out with all image formats, high-quality lifestyle images alongside product images, accurate brand descriptions, and audience signals that represent your full customer base rather than just buyers of individual products.

Revisit your attribution setup. Multiple click paths inside a single unit means your current reporting may not capture traffic flow accurately. Think about how you will measure this before the format exists in your account at scale.

FAQs

What exactly is a Sponsored Shops unit?

A Sponsored Shops unit groups multiple products from a single retailer into one ad block inside Google Shopping results, displayed alongside the store name, ratings, and brand signals. Rather than individual product listings competing side by side, the format presents a mini storefront for a single brand.

Is Sponsored Shops live now?

As of now, Sponsored Shops is still in testing. Google has not confirmed a broad rollout timeline. The format is worth preparing for regardless, since the steps that improve your eligibility for it also strengthen your existing Shopping performance.

Which campaign type will Sponsored Shops use?

Performance Max is the most likely vehicle, given that it already accounts for the majority of Shopping spend and dynamically surfaces store-level and product-level assets across Google’s ecosystem. Making sure your PMax asset groups are fully built out is the right preparation move.

Will smaller retailers be disadvantaged?

Formats that reward assortment breadth, seller ratings, and feed quality tend to favor established retailers with larger catalogs and more customer reviews. That said, a well-optimized feed and a strong seller rating matter more than raw catalog size. Smaller retailers with tight assortments and excellent customer experience signals are not automatically excluded.

What should I do right now?

Focus on feed quality, seller ratings, and PMax asset completeness. These are the fundamentals that will determine Sponsored Shops eligibility and performance when the format expands, and they are also the fundamentals that determine your current Shopping performance.

Conclusion

Sponsored Shops is still in testing. Google Shopping is clearly moving toward a model where brands compete as storefronts, not just as individual products. The shift fits a broader pattern: more AI-mediated discovery, more brand-level visibility signals, more emphasis on the full store experience rather than the individual listing.

The time to build those store-level signals is before the competition catches up, not after. The good news is that everything you do to prepare for Sponsored Shops makes your existing Shopping campaigns stronger right now. There’s no downside to starting.

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Google zero-click searches hit 68% in early 2026: Study

Google zero

Google searches ended without a click 68.01% of the time in the U.S. during the first four months of 2026, according to new SparkToro research based on Similarweb clickstream data. That’s up from 60.45% in 2024, a 7.56-point increase in two years.

Fewer searches result in clicks. The share of searches generating at least one click fell 9.51 percentage points between 2024 and 2026 (a 22.9% decline), according to SparkToro. This includes clicks to organic results, paid ads, and Google-owned properties such as Maps and YouTube, but excludes follow-up searches within Google.

  • Over the same period, the share of searches that led to another Google search rose 7.2 percentage points.
  • This trend reflects Google’s growing ability to answer questions directly in search results while encouraging users to refine or continue their searches within Google, according to SparkToro.

AI Overviews and zero click. SparkToro believes AI Overviews are likely contributing to the increase in zero-click searches, though the study doesn’t isolate the extent to which the overall rise between 2024 and 2026 can be attributed specifically to AI Overviews.

  • AI Overviews now appear on more than 20% of Google searches, according to the research. When they do, click-through rates drop by nearly 60%.

AI Mode and zero click. It appears to have played only a limited role during the January to April study period. SparkToro found that just 0.34% of searches transitioned into AI Mode during that time.

  • However, Google said at I/O 2026 that AI Mode had surpassed 1 billion monthly users and that query volume was more than doubling each quarter, suggesting its impact on search behavior could grow significantly.

Zero click history. SparkToro has tracked zero-click search behavior for years, though its underlying data sources have changed over time. Because the studies rely on different providers, panels, and methodologies, long-term comparisons are not directly equivalent. Still, the available data consistently points to a rise in zero-click behavior over time, according to SparkToro.

Why we care. The findings suggest Google is increasingly satisfying user needs without sending users to external websites. However, you should interpret direct comparisons across years cautiously because SparkToro’s historical analyses rely on different clickstream data providers and panels.

SEO still matters, but… SEO alone may be insufficient for many publishers seeking to regain historical levels of Google-referred traffic. SparkToro co-founder Rand Fishkin recommended investing in brand awareness and influence on the platforms where your audience already spends time, regardless of whether those efforts drive direct website visits.

  • Some categories continue to benefit significantly from SEO, including branded searches, local business queries, and high-intent transactional searches, Fishkin said.

About the data. The study used Similarweb desktop and mobile web panel data covering U.S. Google searches from January through April 2026. SparkToro assumed that two-thirds of searches occurred on mobile devices and one-third on desktops. The analysis excludes searches conducted in Google’s mobile search app, where SparkToro said zero-click behavior may be even higher.

The study. In 2026, Less than One Third of Google Searches Still Send a Click

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Web Design and Development San Diego

How AI forms opinions about your brand

How AI forms opinions about your brand

AI forms opinions about your brand from what it can see online. That’s your digital footprint.

The problem is that AI often sees only fragments of your business. It sees your website, content, reviews, and mentions, but much of the expertise, customer insight, and operational knowledge that makes your business valuable never makes it into the digital footprint.

The solution is to surface that knowledge, organize it into a single source of truth, and turn it into machine-readable signals. Here’s how to collect it, organize it into a single source of truth, and distribute it across the channels AI uses to understand, evaluate, and recommend brands.

What you feed the machines is understandability, credibility, and deliverability (UCD)

Everything you put into your footprint is fodder for three things AI has to decide about you. Together, they provide the fodder for the whole funnel.

Understandability

Does AI know who you are, what you do, and who you serve? You already know where your understandability comes from: 

  • Your about page.
  • Your product pages.
  • Your structured data. 

What often gets missed is the operational detail that explains what you actually do once a client is inside.

Credibility

Does AI believe you’re good at it? This is N-E-E-A-T-T credibility — notability, experience, expertise, authoritativeness, trustworthiness, and transparency, an extension of Google’s E-E-A-T.

You know what credibility signals you currently feed: your case studies, your credentials, and your testimonials. What many businesses don’t realize is how much N-E-E-A-T-T credibility is already embedded in their day-to-day operations.

Deliverability

Does the AI engine have the content to hand you to the subset of its users who are your audience? 

You know where your deliverability comes from: the topical content, the marketing, and the authority pieces you commission. Deliverability is often hiding in plain sight, in the content generated by your business operations and offline activities.

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5 streams of business data feeding every commercial surface

All three elements of the UCD trio are fed by the five inputs below, and how much each contributes varies by business.

The point isn’t to file each input under one letter. Organized and codified, the five together give AI the fodder it needs from top to bottom of the funnel.

5 streams of business data feeding every commercial surface

1. Products and services: What you sell, and you already do it

Your products and services data: what you sell, at what price, under what conditions, and with consistent names and identifiers. This is mostly about understandability, with credibility riding alongside it.

Most businesses already do this, so the work is in the depth, not the effort. Don’t just list what you sell. Describe who each offering is for, what problem it solves, what it costs, what it doesn’t do, and how it differs from the next option.

A thin product page tells AI a product exists. An exhaustive one tells it when to recommend that product and to whom.

Keep it accurate, complete, and consistent with everything else in your footprint. A price or product name that differs across pages reads as doubt.

2. Authority content: Your expertise, and almost everybody does it

This is the marketing you already create to show you know your field: your articles, videos, guides, data studies, and the thought leadership you publish to tick the box marked “content created.”

People put effort into it to build authority, rank, do SEO, and position themselves as experts. That’s fine. It leans toward deliverability because it’s what tells AI which territory to surface you in.

But everybody does it, which is exactly why it’s the least differentiating of the five on its own. It earns its weight only when it’s tied to the rest: the same expertise proven by your operations and corroborated by third parties, not just asserted in a blog post.

It’s necessary, but it’s not where your advantage hides.

3. Brand narrative and voice: Who you are, who you serve, and why you’re the best

All marketers create brand narratives, so the work here is about consistency and clarity rather than invention. Everybody communicates who they are, what they do, and who they serve, and keeping that clear and consistent matters enormously. 

But three things are often left out, and AI needs all of them.

  • Intent: It isn’t enough to name your ideal customer profile (ICP). You have to pair your ICP with what they’re after: the cohort-to-intent combinations from the funnel query pathway. AI has to know not just whose problem you solve, but which problem, and at which moment, before it can hand you to them.
  • Credibility: The thing that feeds your N-E-E-A-T-T. Many people leave it out because they feel awkward saying it. You have to set it out because AI won’t work out your true value on its own. Be clear and bold about why you’re credible, then make sure you can back it up with evidence.
  • Making the relationship with your clients explicit: Validation from the people you serve that you deliver on what your narrative and cohort-to-intent mapping promise. Say who you are, what you do, and who you serve. Then explain why a customer should choose you and prove it.

Voice is the part corporations get wrong most often. Narrative is what you say. Voice is how you say it. One team may write the narrative once, but voice escapes through every rep, every support reply, every social post, and every deck. 

When it drifts, and in most large companies it drifts constantly, AI reads the same brand as five different brands and loses confidence in all five.

So standardize your voice and keep it consistent everywhere. Consistency is a credibility signal in itself. Inconsistency is a tax you pay without seeing the bill.

In short, make sure your brand narrative clearly sets out your ICP, who you are, and why you’re the best fit for them, in a voice that stays consistent wherever AI finds it.

4. OPID business operations: The stream almost nobody harvests

This is everything your business generates by running: onboarding, performance, integration, devotion, and all the day-to-day activity around them. 

It’s the most powerful of the five because the material comes from your clients and from the work your team does to serve them, which is exactly the material that rarely makes it online. It sits behind closed doors, buried in a CRM, parked on a platform nobody values, and almost nobody harvests it.

It feeds all three elements of understandability, credibility, and deliverability more effectively than anything else you own. 

  • Understandability comes from the granular detail of what you actually do and the exact circumstances in which you help. Most of that is only ever discussed inside the business. A review where a client describes precisely what they got from you puts something on the record you’d never say about yourself, and the machine reads it as fact.
  • Credibility is your N-E-E-A-T-T, and this is the most convincing kind because it comes from clients themselves, not from your marketing.
  • Deliverability comes from the match. The content here aligns exactly with your cohort-to-intent combinations because it was created around the clients you attracted and served well. Whether it comes from you or from them, it fits the audience and intent you need to communicate to the engines.

Once you start looking, you’ll find the richest material you own:

  • Customer voice is the highest signal because it’s real questions in real language: reviews across every platform, written and video testimonials, FAQs, unpublished support questions that should become FAQs, support and sales call transcripts, onboarding and churn-exit interviews, and free-text survey responses.
  • Evidence and outcomes provide the proof you need: case studies with real before-and-after numbers, patent filings, academic deposits that are public but underused, and independent third-party studies that corroborate your claims.
  • Methodology covers the rest. SOPs, playbooks, training materials, glossaries you currently keep private, and long-form spoken content such as webinars, keynotes, and podcast appearances, transcribed.

Look for material that answers a question an assistive engine or agent actually gets asked, in the questioner’s own words, with a verifiable fact attached. 

A support ticket, churn interview, or sales call transcript will often outperform polished marketing copy in that test because it’s already phrased the way real people ask questions.

That’s the whole point of harvesting OPID business operations: taking information from a place AI can’t see and moving it to a place where it can, while making it visible to your human audience, too. It’s convincing to both because it’s true and because it matches the cohort-to-intent combination exactly.

5. Bringing the offline online: The stream almost nobody runs

This section is all about the marketing and audience engagement you do offline: the talks you give, the festivals or hackathons you sponsor to support your community, the interviews, the panels, and the rooms full of clients. It’s obvious to you, but largely invisible to AI.

Bring the offline online and feed it to the machines by publishing self-reporting content and linking to the social posts and summary articles others write. That’s a huge win most brands miss.

But it works the other way, too. Your codified source of truth can feed your offline communication, so the story a client hears from you at a conference, in a newspaper, on the radio, or face to face is consistent with the story you’re telling AI on the web.

That matters more than it seems. If the two differ, you lose the person because the gap reads as doubt to a human and as low confidence to a machine.

Clarity and consistency over time, online and offline, is the name of the game.

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Organize and codify the five into one source of truth

Once you’ve harvested all five streams, organize and codify them into a single source of truth: a database you build to output whatever format each surface needs, including HTML, schema, MCP, RDF, prose, audio, video, and images.

Organize the data once, centralize it, set up a system that codifies it on the way out, and from there you can distribute it in a few clicks while your digital footprint stays clear and consistent as it grows.

Then distribute it across your digital ecosystem in the format your human audience expects and packaged so machines can ingest it cleanly.

Where you publish affects how much the machine believes you, and the rule is simple: the less of you there is in it, the more it trusts it. You’re working across three tiers.

First-party: You claim 

You publish on your own properties, in your own voice. You state who you are and set the frame. It’s the baseline, and on its own it proves nothing because you wrote it and you published it.

Second-party: You corroborate

Here, you’re still publishing, but across a broader footprint and with other voices in the mix. Two things widen here.

  • The platform: In addition to your own entity home website, you publish on platforms where you own the account, such as YouTube, LinkedIn, Medium, and press releases. You’re stating your case the same way you would on your website, just on another property you control.
  • The voice: You can publish your own words, or you can publish what a client or user said, such as a review, quote, or case study, on your own site and across those other accounts.

It’s a step up from first-party because the substance is no longer solely your own assertion, even though you’re still the one choosing it and publishing it.

Third-party: They prove you

A third party publishes in its own voice, on its own site or social accounts, or on a neutral platform such as Trustpilot, with no involvement from you. 

Think clients and partners sharing their experiences, journalists, analysts, academics, and the long tail of user-generated content that assistive engines lean on.

It’s the strongest evidence because you had no hand in creating it.

You can’t write that third tier, but you can feed it. Your clients publish because you’ve served them well enough that they want to, so earn it.

Independent publishers can’t see inside your business, so give them something to work with: a client story they can build on, a view into your operation, or data about your business and industry they can cite.

Giving outside parties a true, detailed version of your business to publish is what PR, marketing, and content teams have always done. The only thing that’s changed is that now you do it so machines read the result as proof, not just so humans read it as coverage.

Point all three tiers at the same picture — you, your audience, and the independents — and they align into one answer the machine can’t miss.

Author x Publication

Read the grid by how much of you is in the publication.

  • First-party is all you. Your words on your own site. It’s pure claim, and the machine treats it as the baseline because you wrote it and you published it.
  • Third-party is none of you. Someone else’s words on a platform you don’t control. That’s why it’s the strongest proof.
  • Everything in between is second-party corroboration. Your own words carried onto an account you run elsewhere, or someone else’s words that you chose to publish on your own page.

The same review is second-party when you surface it on your site and third-party when the client publishes it on their own account. The words are identical. The weight is different. The difference is determined entirely by who publishes it.

Step back, and you have a powerful loop: You harvest your operations, codify them into a single source of truth, and distribute them across the tiers machines read. Then the machines recommend you, your ICP arrives, and serving them generates the next round of operations to harvest.

Each turn feeds the next, so your digital footprint compounds instead of resetting.

A simplified version of the flywheel

The mirror principle is why this is the whole game

When an AI engine recommends a brand, think of it as an impartial broker. Much as a travel agent carries every airline or a mortgage broker has the whole market on screen, an AI engine carries every brand in your category and recommends whichever it judges to be the best solution for the person asking.

That impartiality is why buyers trust it. It’s also why the engine recommends your competitor without hesitation. It was never on your side. It’s on the buyer’s.

That’s good news once you see it the right way. An AI engine can only recommend what it clearly understands and trusts. You don’t need to trick a rigged system. You need to provide the clearest, most complete picture of who you are, what you do, who you serve, and why you’re the right fit.

Build a clearer, better-corroborated case than your competitors, and, on merit, you become the name the engine reaches for throughout the funnel. Many brands aren’t losing because they’re being outspent. They’re losing because the picture AI has of them is incomplete.

And that picture comes from your digital footprint. AI forms its view of you from the world’s view of you: the reviews, coverage, and corroboration scattered across the market. What it shows about you is its opinion of the world’s opinion of you. That’s the mirror principle.

You can try to flatter the system, trick it, or lean on it, and that might work for a while. But the approach that lasts is changing what the world can see. When you do that, you’re not manipulating anything. You’re providing proof: something that was always true, but underrepresented or invisible.

That’s exactly what this article has laid out. Harvest the five streams, organize and codify them into a single source of truth, and distribute them across the channels AI reads. Do that, and you’ve provided the fullest, truest, and best-corroborated picture of your business at the moment that matters most: when someone is looking for what you sell, and AI is deciding what to recommend.

Do it consistently, across everything AI can see, and you shape how it understands your business over time.


This is the 17th piece in my AI authority series.

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Why your brand campaign may not be ready for AI Max

Why your brand campaign may not be ready for AI Max

Not long ago, broad match was positioned as the future of paid search. Today, that role belongs to AI Max.

Over the last few months, I’ve heard repeated recommendations to enable AI Max on brand campaigns, even when those campaigns are already performing exactly as intended.

The problem is that many accounts still lack the foundations AI Max needs to work well. Conversion tracking is unreliable, offline conversion imports are missing, and generic campaigns remain constrained by budget or structure.

AI Max depends on strong conversion signals, sufficient volume, and enough variation for the system to learn effectively. In many accounts, brand campaigns provide most of that signal. 

But using AI Max on brand means introducing additional automation into your most predictable and efficient traffic source.

The promise and limitations of AI Max

AI Max expands search targeting beyond your existing keyword list by using keywords, landing pages, and site content as signals rather than strict targeting parameters.

Like dynamic search ads (DSA), AI Max can match to queries you didn’t explicitly target. But it goes further, reaching beyond the intent boundaries defined by your keyword set.

Google has positioned AI Max as the next step in Search automation, with DSA, automatically created assets, and campaign-level broad match settings scheduled to transition into AI Max in September.

The platform includes controls such as brand exclusions, URL exclusions, text guidelines, and location targeting. In accounts with strong conversion tracking, sufficient search volume, and reliable performance signals, AI Max may uncover incremental growth opportunities.

Many accounts haven’t reached that stage yet.

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Why AI surface eligibility isn’t a reason to rush into AI Max

Much of the recent interest in AI Max stems from Google’s push toward AI-powered search experiences.

AI Overviews now reach 2.5 billion monthly users, according to Google. Ads appear in 25.6% of AI Overview results, Semrush data shows.

As Google continues expanding AI-driven search experiences, advertisers are understandably focused on maintaining visibility across those surfaces.

That concern is reasonable. The problem is that AI Max is often presented as the solution before advertisers address the measurement, conversion, and account structure issues that determine whether the automation can succeed.

Google Ads representatives typically pitch AI Max for brand campaigns by claiming it’s necessary for eligibility in AI Mode and AI Overviews on brand searches. But this isn’t accurate.

Ginny Marvin, Google Ads liaison, confirmed that three campaign types are eligible to serve in AI Overviews: broad match with Smart Bidding, Performance Max (PMax), and AI Max for Search.

However, exact match keywords aren’t eligible to serve in AI Overviews at all, even when identical broad match keywords exist in the same account.

So, the eligibility picture looks like this:

Campaign type AI Overview eligible Query control Best use case
Exact match No Highest Defensive brand
Phrase match No Medium Controlled intent expansion
Broad match Yes Lower Generic scaling
Performance Max Yes Low Cross-network automation
AI Max Yes Lowest Mature accounts with strong signals

PMax and AI Max do broadly the same job in terms of AI surface eligibility. So if you run PMax brand campaigns, you’re already covered. Adding AI Max won’t unlock anything new, as it’ll only add another automation layer to a setup that’s already eligible.

So, when reps position AI Max on brand as the answer to AI surface eligibility, advertisers should stop and ask why this feature takes priority over fixing the account’s foundation.

Test data doesn’t support Google’s AI Max claims

When AI Max was in beta, Google stated that advertisers who activate the feature would see 14% more conversions, and those running exact and phrase match keywords would likely see a 27% increase in conversions.

Google also indicated that advertisers who enable the full AI Max feature suite see 7% more conversions on average. Independent testing has produced more mixed results.

The evidence for AI Max remains mixed

Across 600 accounts, Smarter Ecommerce found that AI Max delivered a 35% lower return on ad spend (ROAS) than traditional match types. AI Max accounted for just 0.57% of total ad spend in those accounts, indicating that advertisers kept the budget to a minimum.

After running a four-month test, Xavier Mantica found that AI Max had the most expensive conversions. While AI Max cost $100.37 per conversion, phrase match cost $43.97 per conversion, and exact match cost $52.69 per conversion. And Ezra Sackett tested 30,000 search terms with AI Max, only to find that 99% of impressions delivered zero conversions.

After a 23-test analysis of 16 advertisers, Andy Goodwin noted improved Quality Score and ROAS when advertisers used the AI Max full feature suite. But he tested mature advertisers and used text customization in only 50% of tests and URL optimization in just 44%. This suggests advertisers were cautious about enabling every AI Max feature.

However, none of this data is brand-specific. AI Max may deliver value in the right context, but an exact match defensive brand campaign that already performs well isn’t the ideal place to test a new automation product that depends heavily on signal quality. This is especially true for accounts that haven’t solved the underlying data problems feeding the automation.

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AI Max attribution gets murky on brand

AI Max doesn’t always find genuinely new search terms, according to Adalysis. In some cases, it simply takes credit for the queries that exact and phrase campaigns were already winning.

Because AI Max treats keywords as signals rather than targeting parameters, impressions that would previously have been attributed to your exact match keyword can end up attributed to AI Max instead.

This reporting issue can be significant for brand campaigns. Brand traffic is already the highest converting traffic in most accounts.

Flip on AI Max, and suddenly you see an uplift. But it’s difficult to tell if it’s incremental or if preexisting branded performance simply appears in a different automation bucket.

Brand controls don’t work consistently

Google’s pitch leans heavily on brand controls. AI Max offers inclusions, exclusions, and guardrails that supposedly keep the match type tightly focused. In practice though, these controls don’t always work well.

Adalysis notes that competitor terms occasionally slip through and brand terms sometimes match to non-brand queries. DAC reports overlap between brand and non-brand terms as well as unintended language matching. And LBBOnline finds relevance hovering around 50% in some campaigns.

Brand controls could improve over time. But the available evidence doesn’t support treating AI Max as a low-risk switch for tightly controlled defensive brand campaigns.

What to consider before testing AI Max on brand

Before expanding automation into a defensive brand campaign, ask these questions.

1. Are the conversion signals trustworthy?

Have you separated macro and micro conversions? Do offline imports work correctly? Does lead quality feed back into the platform, or does Google still optimize equally toward every form fill?

If the signal quality underneath the account is poor, AI Max will amplify it instead of fixing it.

2. Have you already explored generic growth?

In many of the accounts I audit, budget, weak landing page alignment, poor structure, and outdated query management limit generic campaigns. This is where you usually find incremental growth, not inside an already dominant brand campaign.

3. Does the account give automation enough useful learning data?

AI Max isn’t magic. It reflects the quality of the signals underneath it.

If most of the account’s meaningful conversion volume comes from brand, then turning AI Max on in a brand campaign may reinforce existing dependency on branded traffic rather than helping the account grow beyond it.

4. Are brand + modifier searches already structured properly?

“Brand + reviews,” “Brand + pricing,” “Brand + near me,” and product intent variations often deserve their own campaign strategy entirely. AI Max shouldn’t become a substitute for good account architecture.

5. Do you have a strategic reason to expand the brand campaign?

If so, test carefully using experiments. That’s a business decision, not a checkbox recommendation from a rep who hasn’t looked deeply enough at the account to understand where the real opportunities actually are.

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AI Max only works as well as the signals feeding it

AI Max may grow into something genuinely useful over time. Remember, PMax went through a similar evolution and is in a much stronger place now than it was early on.

But automation only works as well as the signals feeding it. Right now, the issue is that the foundations underneath the automation still aren’t strong enough. Better conversion frameworks, measurement, account structure, and feedback loops make automation smarter.

If brand remains the best-performing campaign in the account, the bigger question is why the rest of the account hasn’t caught up yet. 

Above all else, don’t confuse Google’s automation priorities with your account priorities.

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Web Design and Development San Diego

Google adds new Performance Max asset testing tools

Google Ads may be over-crediting your conversions- A 7-day test tells a different story

Google is expanding experimentation capabilities in Performance Max, giving advertisers more ways to test creative assets and measure campaign performance before making large-scale changes.

What’s happening. Google is rolling out new asset experiments for Performance Max campaigns, allowing advertisers to test how different creative assets affect results.

The feature enables marketers to compare entirely new asset groups, evaluate the impact of adding individual assets, or measure the performance of seasonal creative against evergreen content.

Advertisers will also be able to test assets generated through Google’s Asset Studio.

The big picture. Performance Max has long automated campaign optimization across Google’s inventory, but advertisers have had limited visibility into the impact of creative changes.

The new experiments aim to give marketers a more controlled way to evaluate creative decisions before applying them across campaigns.

Between the lines. The addition of a second success metric could be particularly valuable for advertisers balancing competing objectives, such as maximizing conversions while maintaining efficiency targets.

Rather than declaring a winner based on a single KPI, marketers will be able to evaluate how changes affect broader campaign performance.

What else is new:

  • Conversion lift studies and experiments are being brought together under one Experiments page.
  • Additional experiment and measurement capabilities are planned for future releases.
  • Expanded support for manager accounts (MCCs) and the Google Ads API is expected to begin rolling out in the coming weeks.

Why we care. Creative remains one of the biggest levers available to Performance Max advertisers, yet testing new assets often involves risk. The new experimentation tools provide a structured way to validate creative decisions with data before fully committing budget.

What to watch. As Google continues investing in automation and AI-generated creative, asset testing is becoming increasingly important. The ability to directly compare human-created, seasonal, evergreen, and AI-generated assets could offer advertisers deeper insight into what drives performance across Performance Max campaigns.

The bottom line. Google is giving Performance Max advertisers more sophisticated testing capabilities, making it easier to evaluate creative changes, measure results across multiple KPIs, and manage experiments from a centralized location.

First spotted. The update was first spotted by PPC News Feed.

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Web Design and Development San Diego

OpenAI to expand ChatGPT ads to new markets & test multi-advertiser placements

OpenAI ChatGPT ad platform

OpenAI is expanding its advertising ambitions inside ChatGPT, beginning an early test that allows multiple advertisers to appear within a single ad placement.

What’s happening. The company is testing multi-advertiser ad units across a small subset of ChatGPT ads, according to a product update sent to advertisers.

Rather than displaying a single sponsored result, the new format will group multiple relevant ads together in one placement. Eligible ads will be sold through a second-price auction model, a common pricing mechanism used across digital advertising platforms.

OpenAI says the goal is to improve product discovery for users while creating more opportunities for advertisers to engage with users during high-intent conversations.

Meanwhile, in Ads Manager Beta. OpenAI also announced several new campaign management features for advertisers:

  • Advertisers can now convert existing campaigns from lifetime budgets to daily budgets.
  • CPM campaigns can be cloned and converted to CPC bidding with one click.
  • Impression-based campaigns now support custom CPM max bids.
  • Bulk editing is available directly within the Ads Manager interface.
  • Daily budgets will transition to an average daily budget model with weekly pacing flexibility.
  • Geographic targeting is expanding beyond the U.S., Canada, Australia, and New Zealand to include the U.K., Japan, South Korea, Brazil, and Mexico.

Why we care. The updates bring OpenAI’s ad platform closer to the functionality marketers expect from mature advertising ecosystems, reducing campaign management friction while expanding targeting opportunities internationally.

What to watch. The multi-advertiser placement test could provide an early signal of how aggressively OpenAI intends to monetize ChatGPT. If successful, the format may become a larger part of the platform’s ad inventory strategy while offering advertisers more opportunities to reach users during purchase and research journeys.

The bottom line. OpenAI is steadily building out its advertising stack, but the biggest development may be its experiment with showing multiple advertisers in a single ChatGPT ad placement — a move that could reshape how sponsored content appears within AI conversations.

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Google to update Local Services Ads policies in July

Google Local Services Ads vs. Search Ads- Which drives better local leads?

Google is changing the rules framework that governs Local Services Ads, updating policy language and aligning advertiser requirements with its new badge system.

What’s happening. On July 6th Google will update its Local Services Ads policies to improve readability, revise terminology, and remove requirements that no longer apply to advertisers.

As part of the update, Google will rename “Local Services platform policies” as “Local Services Ads requirements.”

The changes build on the company’s recent overhaul of the Local Services Ads badge system, including updates to Google Guarantee badges and advertiser verification standards.

Why we care. While these changes are mostly administrative, advertisers should pay attention because the new “requirements” framework could make it easier for Google to tie compliance standards directly to badge status in the future. For agencies and local businesses, it’s another indication that maintaining verification credentials and meeting platform standards will remain critical for competing in LSAs.

The big picture. Google says the policy refresh is intended to better align advertiser requirements with the new badge framework while making compliance guidance easier to understand.

The company is not positioning the update as a major policy crackdown. Instead, the focus appears to be on simplifying existing rules and modernizing the way requirements are communicated to businesses.

The bottom line. Google is refreshing the policy framework for Local Services Ads, replacing “platform policies” with “requirements” and aligning advertiser guidance with a new badge-driven approach to trust and eligibility.

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Web Design and Development San Diego

Google AI Brief may be the replacement keywords never had

Google AI Brief may be the replacement keywords never had

People have been calling the keyword dead since at least 2010. Yet here we are in 2026, still using keywords to show ads on Google.

Advertisers weren’t wrong to equate the loss of control with the death of the keyword. The keyword simply couldn’t disappear until Google had something better to replace it.

At Google Marketing Live (GML) last month, we may have seen that replacement. AI Brief is a Gemini-powered control layer that lets you steer AI Max using prompts-first language.

At first glance, AI Brief may seem like just another AI Max feature. AI Max is still trying to gain traction among advertisers. So couldn’t advertisers simply ignore it and stick with keywords?

Probably not.

When users shifted to mobile, Google eventually pushed advertisers toward Enhanced Campaigns. The conditions may now be in place for a similar transition, this time from keywords to prompts.

Consider the other announcements from GML. AI Mode surpassed 1 billion monthly users. The search box is getting its biggest redesign in 25 years. Users in AI Mode are also submitting queries that are, on average, three times as long as traditional searches.

Whether advertisers like it or not, people are increasingly using prompts instead of keywords to find information.

With AI Brief, the replacement for the keyword finally exists. We can now target prompts with prompts. Combined with the consumer-driven shift away from keyword-based searches, that makes the keyword’s obituary much easier to believe.

The keyword is dying because users stopped using it

Most “keywords are dead” arguments over the past decade were supply-side stories. Google reduced broad match’s control, made RSAs decide the best ad variation, and let Smart Bidding set bids to help any keyword deliver on its underlying financial goals. They also stopped showing every query in search terms reports, all steps framed as Google taking the keyword away.

Now it’s different. The pressure is coming from the demand side.

People are asking Google longer, more conversational questions because Google built a search experience that invites them to. The new search box, the biggest upgrade in 25 years, dynamically expands as you type. You no longer pick a “mode” before you ask. The interface itself is telling consumers that “running shoes” is no longer the only way to ask for what they really want.

If you’re an advertiser, the question stops being “Do I want to use keywords?” It becomes “How do I show up in a query a keyword can’t possibly match?” Trying to capture a paragraph of context with three positive match types and one negative is, let’s be real, increasingly absurd.

Optmyzr’s 2026 Match Type Study shows the same pattern from the spend side. We analyzed 30,000 Google Ads accounts in February 2026 across all Search campaigns with active keyword spend. (Disclosure: I’m the cofounder and CEO of Optmyzr.)

Exact match has lost nearly 10 percentage points of spend share since 2022, while broad match has climbed steadily to become the dominant match type by budget. 

Phrase match, meanwhile, consistently punches above its weight, holding the largest share of non-branded spend and leading on conversion rate in both ecommerce and lead-gen segments. 

Advertisers are clearly growing more comfortable trusting Google’s AI with broader targeting, a shift attributed to Smart Bidding’s maturation rather than exact match losing its performance edge.

The other tell is that Google isn’t alone here. We recently started managing ads on ChatGPT, and OpenAI’s ad surface is keyword-optional from day one. 

When the company that invented keyword advertising and the company reinventing search both ship a keyword-optional product, that means something. At this point, we’re just arguing about how fast the keyword is dying.

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AI Brief is a technical replacement for keywords

Unsurprisingly, AI was the topic that drove nearly every announcement at GML 2026. At I/O the day before, Sundar Pichai, Google’s CEO, even said that Google’s migration to become an AI-first company was nearing completion, with AI agents providing the final push and rewriting the last remaining code. Downstream from all the talk about AI is the realization that consumers now prompt rather than search with keywords.

AI Brief is one way to operationalize the required evolution for advertisers to keep up with consumer behavior. Powered by Gemini, it lets you describe, in your own words, what your business is, what your messaging should and shouldn’t say, the searches you want to capture or avoid, and the audience you’re trying to reach. 

Google calls these messaging guidelines, matching guidelines, and audience guidelines. Internally, I think of it as: tell the model what you’d tell a new media buyer on their first day.

Then AI Brief echoes back how it understood your requests and shows preview samples of the assets and queries it thinks you meant. You push back if it’s off. You iterate. When you’re happy, you lock in the brief.

That’s a meaningfully different interaction model than a keyword list. A keyword list is a static artifact. A brief is a negotiation. It can adapt as your business changes without you reuploading hundreds of new keywords.

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There’s a parallel in the world of coding, where AI has arguably had the biggest impact with agentic code writers and vibe-coding systems like Lovable.dev. The idea is that the code we write to have software achieve an outcome should be merely a temporary artifact reflecting the current abilities of the tech. 

Coders should focus on writing the prompts that describe the goals of the web page rather than the code needed to achieve those goals. The prompt instructs the software what it should do and how to do it safely. AI can then write the code that executes the task on demand, using the latest capabilities while staying grounded in the prompts that define its purpose.

This is what Sam Altman called “software on demand” at the GPT-5 launch, the idea that AI can “instantaneously create an entire piece of computer software for you.”

Google echoed the same vision at I/O 2026, where Pichai described Search using Gemini and Antigravity to build custom experiences, dynamic layouts, and persistent mini apps on the fly. Software generated in response to what each user needs, in the moment they need it.

People need to be purposeful about work. Your purpose at work isn’t to write emails and work with spreadsheets. It’s to achieve certain outcomes, and writing emails and using spreadsheets is how that gets done. Stop worrying about how and start thinking about the real goal: growing your ad revenue by 10% while maintaining similar margins.

Keywords are the “how,” not the “why.” AI Brief is actually closer to letting us manage the “why” while letting AI figure out the “how.”

How to try AI brief now

AI Brief is rolling out in English for AI Max for Search first, then Performance Max and AI Max for Shopping. Existing text guidelines will migrate into AI Brief automatically as messaging guidelines. 

So yes, this is starting as an AI Max feature, and you may not be using AI Max because several practitioners note that AI Max can pull in junk traffic on lead-gen accounts, competitor-heavy verticals, and new campaigns with thin signal. Some veteran marketers have been turning AI Max off in those situations.

The practical playbook shared during a recent PPC Town Hall is solid: start new campaigns in Phrase, promote the winners to Exact, and layer Broad and Smart Bidding on top once you have data. 

With the advent of AI Brief’s matching guidelines, advertisers can further tweak their targeting by saying, “prioritize searches for X, avoid Y.” But this strategy still requires a human who knows the account to pull that lever. So don’t unplug your keyboard just yet.

The new funnel, and why short keywords still have a job

Andrew Lolk and Kirk Williams pushed me on a real edge case in the LinkedIn discussion that led to this piece: the newborn photographer whose entire business depends on someone in their city typing “newborn photographer” and converting on the first ad that shows.

Short, transactional queries won’t disappear. So why not keep traditional search campaigns with keywords around to handle these types of queries? I think it’s reasonable to have two campaign types for different jobs. But their relationship is a funnel, not a parallel.

Here’s how I see it shaping up:

  • AI prompts for discovery: “I just had a baby and I want to remember this period. What are some ideas?”
  • AI prompts for research: “Compare lifestyle newborn photographers to studio newborn photographers in the Bay Area.”
  • Short keyword to buy: “Newborn photographer Los Altos.”

If you only show up at the bottom of that funnel, you’re betting your entire business on being the first short-keyword click. If you’re not present in the discovery and research prompts above it, you’re not in the consideration mix when the short query happens. 

The reason a user may do that short query is that they already know more or less who they’d buy from, and they’re now looking for the best offer from a shortlisted set of options. The conversational layer feeds the transactional layer. Ignore it, and the transactional volume eventually stops coming to you.

This is also why I don’t think Google maintains two parallel systems forever. The short-keyword volume will keep shrinking relative to AI prompt volume, and at some point, the economics of supporting both stop working. 

Further, AI-first campaign types will soon be great at converting agentically, using the Universal Commerce Protocol and other new methods being developed to allow agents to transact for their humans.

What AI Brief does to the four human PPC roles

I’ve argued for years that PPC pros take on four roles in an automated world: 

  • Teacher.
  • Doctor.
  • Pilot.
  • Restaurateur. 

These roles continue to explain the PPC manager’s world quite well, but with some new nuance.

The teacher 

This role is the most direct analogy. You used to teach the machine what to target by handing it the end result: a keyword. 

The funny part is that for many of us, that keyword was already generated by feeding an LLM a prompt and cleaning up the output. 

AI Brief lets you skip the lossy translation step. Hand the machine the prompt itself, not the artifact it produced. The teaching gets richer because nothing gets lost.

The doctor

The shift is from “prescribe Drug X” to writing down, in structured language, what the patient actually needs. 

The treatment can then evolve as the patient’s condition and the available solutions change. Keywords were restrictive: one symptom, one prescription. 

Briefs and prompts allow freedom and evolution. That’s what good medicine looks like, and that’s what good targeting looks like now.

The pilot

We need a new instrument panel. If we’re not aiming at keywords anymore, the search query report stops being the right gauge of how well Google is matching intent. 

We’ll probably see more search themes (buckets of intent that AI Brief is mapping into) replacing the line-by-line query list.

The restaurateur 

You write the menu and the concept brief so the chef (the AI) cooks. AI Brief is almost literally the concept brief. 

You define the cuisine, the values, the things the chef must never serve, and the kind of guest you’re cooking for. Then you taste, correct, and iterate. The kitchen runs.

If you want the longer-form version of where I think digital marketing automation is heading, I wrote it up earlier this year as AI skills, the next layer of marketing automation.

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Why AI Brief feels different

The keyword isn’t dying because Google decided to kill it. It’s dying because consumers stopped phrasing their needs in a couple of words.

AI Brief is the first structural replacement that seems to allow advertisers to express their intent in as rich a manner as consumers can now express theirs to a chatbot. That’s why this GML announcement felt like a more serious nail in the coffin of the keyword than the last several.

Control was about dictating keywords to Google. Leverage is about feeding the engine the right brief and letting the auction execute at a scale no human team can match.

We don’t have to escape automation. We have to coexist with it on better terms. AI Brief is a great eventual replacement for the keyword. Hand it your prompt. Watch what it does. 

Push back. Lock it in. Then you can move on to the parts of the job a machine can’t do, like knowing your customers and working on the goals that move their business in the direction they want.

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Web Design and Development San Diego

Stop looking for the perfect PPC budget split

Stop looking for the perfect PPC budget split

Most PPC budget discussions focus on finding the right split between brand awareness and conversion-focused campaigns. That’s usually the wrong goal.

The optimal balance changes constantly based on business stage, market saturation, seasonality, competitive pressure, and revenue objectives.

Yet many teams still treat the funnel split as a fixed decision: 40% upper funnel, 60% lower funnel, set it and forget it. That might be the right ratio today and completely wrong in six months.

Every budget conversation eventually comes down to the same argument. Someone wants to cut brand awareness spend because it doesn’t convert directly. Someone else warns that if you only chase conversions, the pipeline dries up in 12 months.

Both are right, which is what makes this so difficult.

The lower-funnel case is easy to make

When most PPC managers talk about the lower funnel, they mean Shopping, Performance Max, and high-intent Search. 

Someone typing “buy running shoes new york” has already decided they want the product. Shopping shows the right SKU at the right price. PMax chases the conversion signal across every Google surface. The attribution is clean, the ROAS is visible, and the CFO is happy.

The problem is that this demand already exists. These campaign types harvest intent. They don’t create it. Every conversion you get from a high-intent search term or a Shopping click is the result of awareness that was built somewhere else: 

  • A YouTube pre-roll.
  • A friend’s recommendation.
  • A social post.
  • Years of brand presence in the market. 

You’re collecting fruit from a tree you didn’t plant.

Search is worth treating separately here because it doesn’t sit neatly at the bottom of the funnel. A query like “best running shoes for marathon training” is informational. 

The person is researching, not buying. AI Max and broad match expansion in Google Ads are pushing Search campaigns further into this territory, meaning Search can serve both ends of the funnel depending on how it’s configured and which queries it actually captures. 

It’s worth auditing your Search terms regularly through this lens: How much of your Search spend is closing existing demand versus reaching people earlier in their decision-making process?

This works until it stops working. And the signal that it’s stopping usually arrives too late. 

When branded search volume flatlines, CPCs on your core terms keep climbing because the same pool of high-intent users is getting more expensive to reach, and new customer acquisition starts to plateau while retention holds steady. These are the symptoms of a brand that’s been living off existing demand without replenishing it.

Lower-funnel efficiency is real. But it’s also borrowing against the future.

Dig deeper: PPC budget planning: Aligning business goals, ad spend, and performance

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The reseller trap: When your lower funnel depends on someone else’s brand

There is a version of this problem that’s specific to resellers and multi-brand ecommerce, and it doesn’t get discussed enough.

If you sell branded products you don’t own, your lower funnel can work extremely well in the short term. 

Shopping and Search campaigns for established brands convert efficiently because the brand owner has already done the awareness work. You’re harvesting demand that Nike, Adidas, or whoever else has spent years and significant budgets building.

The structural risk is that you have no control over that demand. If the brand owner reduces its marketing investment, pulls out of a market, or simply fades in relevance, your Shopping and Search volume follows. 

You can’t counter it with your own PPC spend because the underlying interest isn’t there to harvest. The tree stops producing fruit, and you never owned it.

This creates two strategic imperatives that are easy to deprioritize when the lower funnel is performing well. 

  • Own-brand development: products or lines that you control, where you own the brand equity and can invest in awareness independently. 
  • Reseller brand building: investing in the upper funnel to make your own name well known, so customers think of you as the destination regardless of which brands you carry. A consumer who searches for your store name rather than a specific brand is much more resilient than one who only finds you through a branded product query.

Both require some form of upper-funnel investment. Own-brand development needs awareness campaigns to build product recognition from scratch. Reseller brand building needs a consistent presence across Demand Gen, YouTube, and Display to make your name synonymous with the category, not just the brands within it. That’s only within Google’s ecosystem. 

To complete the picture, you might also include SEO, word of mouth, pop-up events, local advertising, and more. Brand building has no limits.

Neither of these investments shows up in this month’s ROAS report. Both show up in next year’s business resilience.

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Upper funnel is inventory management

Brand awareness spend is often framed as the soft, hard-to-measure part of the budget. The part you do when you have money left over. That framing gets it exactly backward.

Upper-funnel investment is how you build the pool of future converters. Every person who sees a Demand Gen ad on YouTube or Google Display today and doesn’t click isn’t a failed impression. They’re a potential high-intent searcher in three weeks. You’re filling the top of the pipeline that your Shopping and Search campaigns will harvest later.

Google’s Demand Gen campaigns make this dynamic particularly visible within a single platform. You can run Demand Gen to reach in-market audiences who don’t yet know your brand, then watch Search impression share and branded query volume respond over the following weeks. The lag is real and measurable. 

Upper-funnel spend today shows up in lower-funnel performance next month, not this week. That delay is why it gets cut first when budgets tighten, and why cutting it tends to hurt six to eight weeks later rather than immediately.

Teams that manage this well think of Demand Gen not as brand spend, but as pipeline investment. The question isn’t “What is the ROAS on this campaign?” It’s “How much qualified demand am I creating for my Shopping and Search campaigns to close?”

Dig deeper: Paid media efficiency: How to cut waste and improve ROAS

Why a fixed split is the wrong answer

The 70/30 or 60/40 rules you read about are averages across many businesses in many contexts. They’re useful as a starting point and useless as a long-term policy.

Consider what changes the optimal split.

  • A new product launch needs heavy upper-funnel investment upfront because awareness is zero. 
  • A mature product in a saturated category needs it, too, because every competitor is also harvesting the same pool of high-intent searchers, and the only way to grow is to expand the pool. 
  • A seasonal business approaching peak needs to have already done its upper-funnel work before the peak hits because awareness doesn’t respond fast enough to be built in-season.

Equally, a business in financial distress or facing a short-term revenue target can’t afford to wait eight weeks for upper-funnel investment to mature. The right answer in that moment is to focus on the lower funnel, accept the trade-off consciously, and plan to reinvest in awareness as soon as the pressure lifts.

The point is that both of these decisions are correct in context. A fixed split ignores context entirely.

Building a dynamic split logic

Rather than a fixed ratio, the most useful framework is a set of conditions that trigger a shift in either direction.

Shift budget toward upper funnel when:

  • Branded search volume is flat or declining quarter over quarter.
  • New customer acquisition cost is rising while retention metrics hold.
  • You’re entering a new market or launching a new product.
  • Competitors are visibly increasing their brand presence.
  • You’re approaching a peak season with at least six to eight weeks of runway.
  • You’re a reseller whose top brands are showing declining search interest or reduced marketing activity.

Shift budget toward lower funnel when:

  • You have a short-term revenue target that can’t wait.
  • Upper-funnel campaigns have been running long enough to build measurable awareness, and the conversion window is now.
  • Cost per acquisition on Shopping or Search is below target, and scaling makes sense.
  • Audience saturation on Demand Gen is high, meaning you’re reaching the same people repeatedly without expanding reach.

Within Google Ads, the data to monitor this is available without external tools. Branded query volume in Search Terms, impression share trends on non-branded terms, Demand Gen reach and frequency metrics, and new versus returning customer segmentation in conversion data together give you a reasonable picture of where the funnel is healthy and where it isn’t.

The review cadence matters as much as the metrics. Monthly is the minimum for a funnel split review. Quarterly is too slow. By the time a quarterly review catches a declining branded search trend, you’ve already lost several weeks of pipeline-building time.

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The conversation nobody wants to have

The reason funnel balance stays broken in most organizations isn’t analytical. It’s political.

Lower-funnel spend is easy to defend in a meeting. The ROAS is there, the conversion numbers are there, and the CFO can see a direct line between spend and revenue. 

Upper-funnel spend requires a different kind of argument: “This investment will make our Shopping and Search campaigns work better in six weeks.” That argument is harder to make, easier to cut, and almost impossible to defend when someone asks for a quick win.

The answer isn’t to stop making the argument. It’s to change the evidence you bring to it. 

  • Track branded search volume as a leading indicator. 
  • Build a view that shows Demand Gen reach in month one and Search conversion volume in month two alongside each other. 
  • Make the lag visible and the relationship concrete. Once the data tells the story, the conversation gets easier.

Budget allocation isn’t a one-time decision. It’s an ongoing signal about what kind of growth you’re building. 

Optimizing purely for this month’s ROAS is a choice. So is investing in the demand that will drive next quarter’s revenue. 

And if you’re a reseller, it’s also a decision about whether your business is built on a foundation you control or one you’re renting from brand owners who have their own priorities.

The best PPC teams do both, and they know when to lean in each direction.

Dig deeper: How to optimize B2B PPC spend when budgets and confidence are low

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