I’ve been truly amazed at how Conductor’s AEO intelligence is now seamlessly integrated into Optimizely, providing a powerhouse of pre-built agents that are all set to take quick action.
The fusion of these two technologies feels like having an AI ally in my corner, transforming visibility into actionable insights with remarkable efficiency. It’s a game-changer for anyone serious about leveraging AI in their optimization strategies.
The integration is not just powerful; it’s incredibly user-friendly, making it easier than ever to harness the full potential of AI-driven insights directly within Optimizely’s platform.
As I delve deeper into the world of ad platforms, it’s fascinating to see how algorithms are transforming the landscape—putting a spotlight on marketers and their expertise.
Managing ad accounts today is a whole new ball game compared to just a few years back. With automation taking center stage through tools like Meta’s Advantage+ and Google’s Performance Max, the skills needed for effective account management have evolved significantly.
Recently, I’ve conducted numerous B2B account audits, for companies both in-house and those transitioning from agencies. It’s striking how rare it is to find operators who truly grasp these systems.
Even with advanced technology at our fingertips, many proficient B2B marketers continue to make costly errors. Here are the frequent mistakes I’ve identified recently on LinkedIn and Google.
Mistakes Advertisers Are Still Making on LinkedIn
LinkedIn is indispensable for B2B outreach, yet some advertisers persist with avoidable blunders like these.
1. Ignoring Audience Targeting
Shockingly, I still see this rookie error. Ads end up targeting entry-level individuals, students, and irrelevant businesses instead of the intended audience.
2. Neglecting to Adjust Targeting Over Time
LinkedIn’s professional targeting can be excellent, but not infallible. It often lets unrelated roles and titles slip through, wasting budget on irrelevant audiences.
3. Over-relying on Automated Settings
This is more common than a mere oversight. Keeping options like audience expansion active leads to ads appearing in less relevant, low-quality spots.
4. Stagnating Creative Content
Veteran LinkedIn marketers know that LinkedIn’s ad structure complicates creative testing, emphasizing the need for creative refreshes and theme variations.
Here are the top pitfalls I’ve discovered in Google Ads accounts recently.
5. Using Conversion Strategies Without Adequate Data
Ad accounts employing conversion-based strategies without any conversion data often mislead algorithms, targeting the cheapest and least effective actions.
6. Defaulting to Display and Search Partners
This blunder reallocates budget from high-intent searches to less effective, lower-quality placements, wasting resources.
7. Forgetting Sitelinks
Omitting sitelinks diminishes the potential to occupy more space in search results, reducing appeal to potential customers.
8. Ignoring Non-Converting Keywords
Failure to scrutinize and modify ads for non-converting keywords results in wasted investment.
9. Overusing Broad Match
Exclusively using broad match without employing phrase or exact match can lead to unnecessary expenditure.
10. Unrestricted PMax Utilization
While PMax for B2B has evolved, neglecting enhanced conversions and educational setup is a recipe for low-quality leads.
11. Discounting AI Max Testing
With Google constantly innovating ad serving and optimization through AI, early adoption can offer a competitive advantage.
Critical gaps often occur post-data collection, where follow-up sequences and accurate data linking are necessary to optimize Return on Ad Spend (ROAS).
Focusing solely on past practices like granular negative keywords without adapting to newer automation capabilities highlights inefficiencies.
Mistakes might appear minor, but they signal deeper mismanagement when left unattended, stressing the need for skilled supervision even in an automated environment.
Remember, effective ad management still necessitates quality inputs, accurate measurement, and vigilant oversight.
I’m thrilled to share some fantastic news with you. We’ve just launched support for Claude Fable within Profound, and it’s an upgrade that I’m genuinely excited about.
Incorporating Claude Fable into our system not only enhances user experience but also brings a new level of efficiency to our platform. This integration is designed to provide seamless functionality and improve overall productivity.
I’m confident that this addition will greatly benefit all users by offering enhanced capabilities and features that are both intuitive and powerful. Stay tuned for more updates as we continue to innovate and evolve.
In early 2026, a significant shift unfolded in the world of search engines—68.01% of Google searches ended without a click. I discovered this intriguing fact through a study by SparkToro, which utilized Similarweb clickstream data. This percentage marks a noticeable rise from 60.45% in 2024, a 7.56-point increase over two years.
Fewer searches are resulting in clicks. Between 2024 and 2026, the share of searches generating at least one click fell by 9.51 percentage points, representing a decline of 22.9%. This includes clicks to organic results, paid ads, and Google-owned platforms like Maps and YouTube, excluding follow-up searches within Google.
During this period, I noticed that the share of searches leading to another Google search increased by 7.2 percentage points. This trend demonstrates Google’s growing proficiency in providing direct answers within its search results, encouraging us to refine or continue our searches without leaving the platform.
AI Overviews and the zero-click phenomenon. SparkToro suggests that AI Overviews might be contributing to the rise in zero-click searches, though the study doesn’t pinpoint how much of the rise from 2024 to 2026 can be specifically attributed to these overviews.
According to the research, I’ve observed that AI Overviews now appear in over 20% of Google searches, causing click-through rates to plummet by nearly 60% when they do.
AI Mode and zero-click growth. While AI Mode seemed to play a minor role during the study period from January to April 2026, SparkToro noted that only 0.34% of searches transitioned into AI Mode. However, Google announced during I/O 2026 that AI Mode had attracted over 1 billion monthly users, with query volume more than doubling each quarter, indicating a future increase in influence on search behavior.
Historical perspective on zero-click searches. SparkToro’s long-standing tracking of zero-click searches reveals an upward trend, although constantly changing data sources mean that long-term comparisons might lack precision. Nonetheless, available data consistently indicates an increase in zero-click behavior over time.
Here are some historical insights: In 2019, 49% of Google searches ended without a click, based on Jumpshot clickstream data. By 2020, SimilarWeb data showed that the figure had risen to 64.82%. And in 2024, 58.5% of U.S. searches (59.7% in the EU) ended without clicks, according to Datos data.
Why this matters to us. These findings imply that Google is increasingly meeting user needs internally, which might reduce traffic to external websites. However, direct year-to-year comparisons should be approached with caution due to differing methodologies in SparkToro’s analyses.
The evolving role of SEO. SEO remains crucial, but it’s not the sole solution for regaining traditional levels of Google-referred traffic. Rand Fishkin, SparkToro’s co-founder, advised us to focus on building brand awareness and engagement on platforms where our audience is active, irrespective of the impact on direct site visits.
SEO is still valuable for certain categories, such as branded searches, local business inquiries, and high-intent transactional searches, according to Fishkin.
About the study data. The research utilized Similarweb desktop and mobile web panel data on U.S. Google searches from January through April 2026. SparkToro estimated two-thirds of searches occurred on mobile devices, with the remainder on desktops. Searches within Google’s mobile search app, where zero-click behavior might be higher, were excluded.
Not too long ago, I remember broad match being hailed as the future of paid search. Today, AI Max has taken on that mantle.
Over recent months, I’ve received plenty of suggestions to activate AI Max on brand campaigns, even when these campaigns are performing just as I want them to.
The reality is, many accounts still aren’t equipped with the essentials AI Max requires for optimum function. Conversion tracking issues, the lack of offline conversion imports, and budget-constrained generic campaigns are common hurdles.
AI Max thrives on robust conversion signals, adequate volume, and enough variation for effective learning. I often find that brand campaigns provide most of these signals.
However, applying AI Max to brand campaigns means layering additional automation over our most efficient and predictable traffic source.
The promise and limitations of AI Max
AI Max can broaden search targeting beyond your key phrases by using keywords, landing pages, and site content as signals instead of specific targeting criteria.
Much like dynamic search ads (DSA), AI Max can align with queries you didn’t explicitly target, and it ventures even further by transcending the intent limits set by your keyword arsenal.
Google portrays AI Max as the future of Search automation, preparing to merge DSA, automatically created assets, and broad match settings into AI Max this September.
With controls like brand exclusions, URL exclusions, text guidelines, and location targeting, AI Max might tap into growth opportunities in accounts rich with strong conversion signals and enough search volume.
Yet, many accounts haven’t reached that point.
With Google’s AI Surface eligibility expanding, it’s tempting to dive headfirst into AI Max. But it’s essential to focus on account fundamentals—measurement accuracy, conversion integrity, and solid account structures—before relying solely on AI Max.
Why AI surface eligibility isn’t reason enough to rush into AI Max
The growing interest in AI Max is fueled by Google’s push toward AI-powered search experiences. AI Overviews now engage approximately 2.5 billion users monthly, presenting ads in 25.6% of AI Overview results, according to Semrush data.
While maintaining visibility in these surprising new fields is advisable, rushing to apply AI Max without assessing your campaign structure and conversion strategies can be detrimental.
Typically, Google Ads representatives pitch AI Max for brand campaigns to ensure their eligibility in AI Mode and associated AI Overviews. However, this isn’t always the truth.
Ginny Marvin, a Google Ads liaison, confirmed that three campaign types are eligible for AI Overviews: broad match with Smart Bidding, Performance Max (PMax), and AI Max for Search. Meanwhile, exact match keywords never qualify for AI Overviews.
Thus, PMax and AI Max generally serve the same purpose concerning AI surface eligibility. Running PMax brand campaigns already gives you the necessary coverage, without the need for adding another layer of automation.
Before adding AI Max into your mix, examine whether it’s genuinely necessary over addressing your account’s foundational needs.
Test data doesn’t fully endorse Google’s AI Max assertions
Google claims that enabling AI Max could increase conversions by 14%, and those employing exact and phrase matches might experience a 27% increase. Nevertheless, independent tests have yielded a wide array of results.
The evidence for AI Max remains mixed
In tests covering 600 accounts, Smarter Ecommerce observed AI Max produced 35% lower ROAS than traditional match types. This outcome aligns with intentional budget minimization by advertisers.
Through a four-month examination, Xavier Mantica discovered AI Max resulted in the priciest conversions compared to phrase and exact match. While Mantica noted $100.37 per conversion with AI Max, phrase match was at $43.97, and exact match was at $52.69.
Moreover, 99% of impressions during Ezra Sackett’s 30,000 search term analysis returned zero conversions under AI Max.
Significantly, none of this data is brand-focused. AI Max may provide benefits in certain settings, but a successful, exact match defensive brand campaign may not be the right candidate for testing new automation.
If your brand is still the standout performer in your account, you may want to question why the rest of your campaigns haven’t met similar standards.
What to consider before testing AI Max on brand
Ask yourself these critical questions before branching AI Max into your brand campaigns:
1. Are the conversion signals trustworthy?
Does your setup cleanly distinguish between macro and micro conversions? Are offline imports running smoothly? Does the lead quality feedback enhance platform optimization?
If the underlying signals falter, AI Max will simply magnify those issues.
2. Have you already explored generic growth?
In the accounts I review, problems like budget constraints, misaligned landing pages, outdated queries, and suboptimal structure frequently hinder generic campaign growth.
Real growth is often found within these issues, rather than an already strong brand campaign.
3. Can the account provide AI with sufficient learning data?
Remember, AI Max is not some sorcery; it mirrors the quality of the signals it receives.
Relying heavily on brand conversions will only amplify these markers and obstruct other growth pathways.
4. Are brand + modifier searches already structured properly?
Search variations like “Brand + pricing” or “Brand + reviews” ought to be treated as separate strategic campaigns. AI Max should not substitute for robust account architecture.
5. Do you have a strategic reason to expand the brand campaign?
Consider testing strategically through experiments, rather than viewing AI Max as a straightforward switch to augment visibility.
AI Max only works as efficiently as the signals guiding it
AI Max might develop into a truly beneficial tool over time, much like PMax did. Automation effective at any level still requires strong foundational signals for success.
The existing issue remains with insufficient solid foundations supporting the automation. Improved conversions, precise measurement, sound account structures, and comprehensive feedback loops are vital to making automation wiser.
Above all, don’t conflate Google’s automation agenda with your campaign objectives.
I’ve discovered that server logs hold a treasure trove of information for large websites, often uncovering technical SEO issues before they impact rankings. They offer insights into how search engines interact with our site, where we might be wasting crawl budget, server response times, and the accessibility of critical pages.
Unlike Google Search Console or third-party SEO tools, server logs capture every single request made by search engines to our infrastructure. It’s surprising how many organizations overlook analyzing them, thus missing out on valuable technical SEO data.
SEO teams often place their trust in tools like Google Search Console, Bing Webmaster Tools, and various third-party crawlers, which rely on data samples, delayed reporting, or simulated crawls. Server logs, however, document direct interactions between crawlers and our infrastructure, which is crucial for websites with a vast number of URLs.
Logs record every server request, and when used for SEO purposes, the most revealing entries come from search engine bots like Googlebot and Bingbot. These records create a detailed history of how our site gets crawled over time.
Most technical SEO problems start as crawl inefficiencies. I’ve seen scenarios where search engines request a page but receive unexpected responses, or they follow complex redirect chains, contributing to delays and inefficiencies.
Server logs clearly expose these inefficiencies. For instance, on large ecommerce platforms, logs might show that crawl resources are wasted on parameterized URLs, while important product pages are overlooked.
Retaining logs over time provides historical visibility into trends related to migrations, infrastructure changes, and platform redesigns. This ongoing visibility is something Google Search Console does not offer.
For instance, large sites often compete internally for crawl attention, and search engines don’t treat all pages equally. Logs can reveal if our valuable category pages are getting the right amount of attention or if outdated URL structures are still consuming resources.
Without these logs, many crawl inefficiencies might remain hidden. The crawl data in logs also assists us in understanding which sections of our site need optimization for better crawl efficiency and response timing, influencing SEO and even our infrastructure.
It’s amazing how log file analysis can differentiate between temporary issues and persistent infrastructure problems, helping us focus our efforts where it truly matters.
Having extensive log data enables us to monitor site migrations effectively, understanding crawler behavior pre- and post-deployment to ensure a smooth transition.
Operating without retaining server logs is like flying blind. Logs bridge the gap that many SEO tools cannot fill, providing a comprehensive view of crawler behavior and interactions with our web infrastructure.
Building a strong digital footprint is essential for helping AI understand my expertise, recognize my credibility, and recommend my brand to potential customers.
AI forms opinions about my brand from my online presence—my digital footprint. The challenge? AI often captures only pieces of my business: the website, content, reviews, and mentions. Unfortunately, much of the expertise and customer insight I offer doesn’t always make it into that footprint.
To address this, I’ve learned to surface that hidden knowledge, organize it into a single source of truth, and convert it into machine-readable signals. Here’s my strategy for collecting, organizing, and distributing this knowledge across the platforms AI uses to understand and recommend brands.
What You Feed the Machines: Understandability, Credibility, and Deliverability (UCD)
Everything I contribute to my digital footprint feeds into three key aspects for AI: understandability, credibility, and deliverability, which together form the whole funnel.
Does AI know who I am, what I do, and whom I serve? My about page, product pages, and structured data contribute to this understanding, but the operational details that highlight my business’s value are often overlooked.
Credibility: Building Trust with AI
Does AI trust I’m proficient in what I do? This is about N-E-E-A-T-T credibility—Notability, Experience, Expertise, Authoritativeness, Trustworthiness, and Transparency. It’s an extension based on Google’s E-E-A-T.
I am aware of the credibility signals I currently utilize: case studies, credentials, and testimonials. However, many businesses, including mine, often underestimate how much of this credibility is already woven into daily operations.
Deliverability: Reaching My Audience
Is my content available to the AI engine for delivering to my target audience? I recognize that my deliverability roots lie in topical content, marketing strategies, and authority pieces. Deliverability often hides within the content my business operations generate.
With AI viewing every brand in my category impartially, my task is to build a clearer and more trustworthy picture of who I am and what I represent. By showcasing my strengths more effectively than competitors and being transparent with AI, I position myself as the top recommendation for my target audience.
I’m intrigued by Google’s decision to update its Local Services Ads on July 6. This change isn’t just a simple update—they’re renaming policies as “requirements” and aligning everything with a recent badge system overhaul.
So, what’s going on? Google is working to refine the rules governing Local Services Ads. They’re not just updating the language; they’re also aligning advertiser requirements with their new verification standards.
One key change is the renaming of “Local Services platform policies” to “Local Services Ads requirements.” It might sound administrative, but these adjustments suggest a more straightforward way for businesses to comply and earn those coveted Google Guarantee badges.
For those of us in advertising, these updates are vital. They not only promise clarity but hint at the possibility that compliance will tie directly to badge status. Agencies and local businesses must stay vigilant and ensure their credentials and standards are spot-on.
What does this mean in the grand scheme of things? Google aims to make the advertiser requirements crystal clear, aligning them with the new badge framework while simplifying the guidance on compliance.
To be clear, Google isn’t cracking down hard on policy. Instead, they’re focused on clarity and modernization, simplifying how businesses access these requirements.
In summary, Google is refreshing its Local Services Ads policies. The shift is towards “requirements,” backed by a badge-driven approach, enhancing trust and eligibility for businesses.
As part of OpenAI’s exciting expansion, I’ve learned they’re extending ChatGPT ads into five fresh markets, including the UK. Excitingly, new campaign management features are on the horizon!
I can see OpenAI ramping up its ad strategies within ChatGPT through an early test that presents the possibility for multiple advertisers to showcase their ads in a single space.
What’s happening. From what I’ve gathered, OpenAI is trialing a new multi-advertiser format over a limited number of ChatGPT ads, which was confirmed in a recent update to their advertisers.
This new approach consolidates several relevant ads into one space instead of just one sponsored result. I understand these ads will be sold using a second-price auction model, commonly employed in digital advertising.
I’m excited to share that OpenAI aims to enhance user product discovery and provide ample avenues for advertisers to connect with users during high-intent interactions.
Meanwhile, in Ads Manager Beta. There’s more good news, as OpenAI rolled out some updates to campaign management features, and here’s what caught my attention:
It’s now possible to shift existing campaigns from lifetime budgets to daily budgets, which makes budgeting more flexible.
CPM campaigns can seamlessly transition to CPC bidding with just a click.
I’ve noticed that impression-based campaigns now support customized CPM max bids.
Bulk editing right in the Ads Manager interface—how convenient is that?
Daily budgets will start working under an average daily budget system, touting weekly pacing flexibility.
There’s fantastic geographic targeting expansion, beyond the U.S., Canada, Australia, and New Zealand, now including the U.K., Japan, South Korea, Brazil, and Mexico.
Why we care. The updates are instrumental in aligning OpenAI’s ad structure with what we as marketers expect from established ad systems, easing campaign management while widening international targeting.
What to watch. This multi-advertiser test might just be the indicator of how OpenAI plans to monetize ChatGPT. If it’s successful, the strategy could be key to expanding advertisers’ reach during users’ purchasing and research phases.
The bottom line. I see OpenAI carefully crafting its advertising framework, with the introduction of multiple advertisers in a single placement potentially redefining sponsored content’s role within AI-driven conversations.
Hey there! I’ve got exciting news for advertisers like me who are constantly looking for better ways to fine-tune our Google Ads campaigns. Google has just introduced new Performance Max asset testing tools that make it easier to analyze creative performance and make data-driven decisions.
Google’s latest update is all about expanding our ability to experiment with Performance Max. Now, I can test creative assets and measure campaign performance more effectively before committing to large-scale changes.
What’s new? Google is enhancing how I run asset experiments in Performance Max campaigns. This update lets me test different creative assets to see which ones drive the best results.
The new feature allows me to compare entirely new asset groups, assess the impact of adding individual assets, or even measure how seasonal content stacks up against evergreen creatives.
I can also test assets generated through Google’s Asset Studio, opening up even more possibilities for creative experiments.
The bigger picture. While Performance Max has automated many aspects of campaign optimization across Google’s inventory, the real challenge has been understanding how creative changes impact results.
The new experiments provide a more controlled environment for evaluating creative decisions before rolling them out across all my campaigns.
Cutting through the noise. With an additional success metric, I can balance multiple objectives—like maximizing conversions and maintaining efficiency targets—by evaluating broader campaign performance rather than relying on a single KPI.
What to look out for:
All experiments, including conversion lift studies, are centralized under one Experiments page.
More experiment and measurement capabilities are on the way.
Support for manager accounts (MCCs) and the Google Ads API will start rolling out soon.
Why it matters. Creative assets are crucial in Performance Max campaigns, but testing new assets always carries some risk. With these new tools, I can validate my creative decisions using data before fully committing any budget.
Stay ahead of the curve. As Google continues to invest in automation and AI-generated creative, asset testing becomes even more vital. Being able to compare human-crafted, seasonal, and AI-generated assets provides deeper insights into what excels in Performance Max campaigns.
The takeaway. Google is empowering Performance Max advertisers like myself with sophisticated testing capabilities. I find it easier than ever to evaluate creative changes, measure results across multiple KPIs, and manage experiments from one place.
First sighted by. This update was first spotted by PPC News Feed.
