I’ve discovered the art of AEO content writing, and it’s all about structure, thorough research, and establishing authority signals. This approach can significantly boost the chances of your content being cited by LLMs such as ChatGPT, Gemini, and Perplexity.
Inspired by this post on HiGoodie Blog.
I recently dived into how Google’s new AI Max setting is changing the game for search term matching and reporting. It’s like an adventure where advertisers find themselves facing challenges in maintaining precise keyword control.
Why AI Max Might Not Be Ideal It’s important to note that AI Max isn’t necessarily negative. However, if broad match has underperformed in your past account history, or if your budget already limits top exact or phrase match keywords, then AI Max might require a second thought.
If you dislike text customization or Final URL expansion, which are inherent features of AI Max, you might want to reconsider.
You can maintain control by adding broad match keywords manually if they suit your objectives.
Understanding AI Max and Your Keywords From the Adalysis test, I learned that even when your campaigns lack a broad match version, AI Max behaves as if it includes one, distributing impressions and clicks to your existing keywords. This can obscure match-type reporting, crediting AI Max for traffic already earned by exact and phrase match terms.
To achieve clearer reporting, I recommend adding broad match versions of core keywords.
Trouble with Search-Term Reporting By checking search terms under AI Max, I’ve observed issues like brand terms matching non-brand queries and vice versa. Even with brand filters, misspellings and variants might sneak in. Strong negative keywords remain a vital defense line.
AI Max Isn’t Always Unearthing New Searches More often, AI Max is merely claiming credit for existing queries and can override Google’s usual matching hierarchy, misallocating impressions to less relevant ad groups.
This could partly explain why its metrics seem inflated.
The Mystery Bucket I’ve found that AI Max sometimes generates search terms not aligned with any current keyword or past searches. This might relate to Google’s keywordless technology, although confirmation is pending.
Adalysis advises de-duplicating search terms across match types to pinpoint real performance enhancements.
Decoding Google’s Priority Order Though Google asserts that exact matches should take precedence when search terms are identical, our tests sometimes revealed AI Max taking over. This inconsistency necessitates adding exact matches for even minor spelling variations to protect valued search queries.
Why It Matters This journey with AI Max highlights how it can blur match types and reporting clarity. This murkiness makes it difficult to discern the true drivers of results, hindering budget optimization and protection of brand traffic.
Final Thoughts The Adalysis test strongly suggests that while AI Max offers campaign scaling opportunities, its structure can deceive with inflated metrics by reallocating impressions from original match types.
If you’re using AI Max or planning to test it, ensure to include broad match versions, differentiate traffic with strong negatives, and keep exact match for your key queries while watching for mixed search terms. Managing search terms is as crucial now as it has always been to align your spending with high-performing searches.
Explore Further For more insights on AI Max, check these valuable reads:
Inspired by this post on Search Engine Land.
I’ve discovered that Meta’s recent research highlights the potential of native Reels ads to significantly enhance purchase intent and brand interest. This insight could be a game-changer for advertisers looking to harness the power of Reels.
Reels have rapidly become a favorite format for entertainment, education, and discovery. What I’ve learned from Meta is that advertisers must prioritize native creative content—forget about recycling old assets. Embrace 9:16 framing, platform-first audio, and swift storytelling aimed at capturing attention in a swipe-driven world.
Brand advertiser insights from Meta’s data:
Direct response advertiser takeaways:
Why I care. Reels are not just a current trend; they’re fundamentally shaping the way we approach short-form storytelling. With more than half of Instagram users spending their time on Reels, and video consumption up over 30% annually, the importance of native content is undeniable. Meta’s research makes it clear that success on Reels demands creative strategies that are tailored specifically for the platform. Early and strong branding, multiple product showings, a blend of audio and speech, relatable content, and clear CTAs are all crucial for maximizing results.
The lesson here is straightforward: ads crafted with Reels in mind, rather than repurposed from other formats, achieve the best results. Structured testing and continuously evolving creative approaches are essential for anyone aiming to capture the Reels audience effectively.
Meta’s bottom line. Reels aren’t fading away; they’re integral to the advertising landscape, with effective ads looking and behaving like native Reels content. The more seamlessly integrated the creative, the better the outcome.
What’s next. Meta recommends that advertisers develop a robust “test and learn” program, focusing on elements like incrementality measurement and A/B testing. The objective is to discern which combination of format, messaging, and creative content drives the most significant impact for their offerings. The guiding principle is to iterate quickly, validate what’s effective, and refine approaches tirelessly.
The takeaway. Successful brands on Reels are not just crafting short clips; they are designing specifically for the medium. Meta’s new data provides a roadmap, but it’s up to advertisers to test, learn, and continually adapt their creative strategies to fully realize the advantages available.
Inspired by this post on Search Engine Land.
I’ve explored recent UX testing that reveals Google AI Mode doesn’t eliminate high-value clicks. Users still visit websites when choosing services like doctors and dentists.
In the SEO world, there’s a prevailing belief: Google’s AI Mode fails to drive traffic. The worry is that if it becomes the default search tool, websites might miss out on crucial clicks.
However, there’s a catch—most studies highlighting traffic loss focus on informational queries.
Imagine someone curious about the appearance of basal cell carcinoma; AI might indeed reduce those clicks.
But what about when someone needs to book an appointment with a dermatologist?
No studies had ventured into this territory yet, so I took the initiative.
I conducted the first UX study focusing on transactional intent within AI mode, observing 52 participants across the U.S. and Canada over nearly 22 hours as they searched for high-commitment services.
Here’s what I uncovered.
During transactional searches, such as finding a dentist or dermatologist, 69% of sessions in AI Mode led to a website visit.
Through our study, only 27% felt “ready to make a decision” solely from the AI summary, with 4% checking traditional Google Search and social media for more info.
Users rely on AI Mode to form a consideration set rather than to follow its directive blindly.
For decades, holding the top SEO spot was like hitting the jackpot.
AI Mode has redefined this dynamic: in our study, 89% of users clicked on multiple businesses.
Users aren’t looking for just one suggestion; they want a selection to consider. On average, participants checked 3.7 results per session, and only 10% looked at just one business.
This shift is enormous.
You no longer need to expend all efforts to be at the top but rather aim to secure a spot within the top three to five results. Clicking the competitors is common, too.
It’s often assumed users don’t scroll.
This isn’t true for AI Mode users. 84% of participants scrolled down to explore options.
Because AI results are seen more as curated lists, users are keen to browse and find the best fit.
Only 21% of users looked at photos in Google Business Profiles, even for services like Botox, which saw a slight increase to 24%.
What’s the main draw for clicks? Social proof.
74% of users read reviews before deciding, emphasizing the weight of textual information over visuals.
Crucially, AI Mode won’t strip you of your most valuable traffic: those ready to invest in your services.
With AI Mode, it’s essential to reframe how we view SEO goals:
If your business depends on ‘how-to’ traffic, there might be cause for concern.
However, if you’re a local business leveraging local SEO, remain calm.
The study: 69% of Transactional Searches in AI Mode Drive Traffic
Inspired by this post on Search Engine Land.
I recently delved into the enigmatic world of Google’s Daily Hub, a complex system aiming to redefine how we interact with search. At its core, Daily Hub sought to seamlessly integrate embeddings, entities, and real-time context. Unfortunately, the system crumbled under the weight of its own complexity.
The Daily Hub is far more intricate than many of us originally thought. It represents a broader trend toward hyperpersonalization we’ve seen lately. Elements like Preferred Sources and followable profile pages in Discover are steadily headed toward predicting what I need even before I type my queries.
Tracing its roots, Daily Hub extends from the “News Digest and Daily Brief” agent, which surfaced during my exploration into Google’s vast, ongoing AI initiatives. This system launched with much fanfare on the Pixel 10, yet was swiftly paused due to its intricate technical web.
Imagine Google’s system as a grand conductor, coordinating a diverse ensemble in real-time harmony. This is precisely the vision for Daily Hub.
Daily Hub’s foundation is built on two key document types, forming its memory.
The MemoryDocument encapsulates full content units, complete with structured text, entity identifiers from the Knowledge Graph, comprehensive embeddings, and essential technical metadata.
There’s also the MemoryEntityDocument, a leaner form that embodies each specific entity highlighted in the content.
In practice, if Daily Hub processes an article about “Lionel Messi joining Inter Miami,” it constructs a MemoryDocument for the article and various entity documents for involved topics like “Lionel Messi” and “Inter Miami CF.”
Various systems power the personalization aspect of Daily Hub, ensuring its response to personalized searches and updates is both swift and attuned to individual preferences.
Nephesh, known for refining user interests, AIP_TOP_ENTITIES, and TAPAS_USER_PROFILE each contribute to crafting a unique user interaction experience by leveraging behavior and contextual data.
In this realm, the AmbientRanking system oversees card presentations, using metadata to refine user experiences based on relevance and timeliness.
For example, sports scores and calendar events are prominently displayed when their relevance is at its peak, ensuring my engagement with timely content.
Andell’s documentation of Gemini’s prompts offers unparalleled insights into the system’s strategic thinking.
With precise formatting and numerous constraints, this prompt identifies and summarizes pertinent news while meticulously adhering to laid down thematic boundaries.
The prompt logic considers only the top interests and excludes unnecessary themes, maintaining focus solely on pertinent areas.
Despite its hiccups, Daily Hub is a prototype that embodies Google’s goal to create an assistant capable of forecasting our needs through sophisticated data integration and hyper-personalized content delivery.
As these technical hurdles are addressed, I anticipate a transformation in how I interact digitally, setting a new standard for search interfaces.
From today’s suspended project to tomorrow’s blueprint for digital interaction, Google’s vision pivots on delivering a groundbreaking consumer experience.
Inspired by this post on Search Engine Land.
As someone deeply involved in PPC marketing, planning and managing budgets across various paid media channels has become a vital skill in my toolkit.
I’m perpetually tasked with determining how to allocate spending across channels, handling significant budget fluctuations, and deciding whether to set total or daily budgets.
In the world of AI-driven ad platforms, campaign budgets are one of the few areas I still have full control over, and so they demand thoughtful attention.
Depending on my business model, I may have varying degrees of input into the overall paid media budget, but I usually have the reins when it comes to distributing that budget across channels and campaigns.
My strategy begins with assessing the total budget available. It’s unwise to spread a modest budget across too many campaigns, as this limits the platforms’ capacity to gather data and drive effective results.
However, with a larger budget, exploring new testing channels or campaign styles becomes feasible.
Dig deeper: PPC budget planning: Aligning business goals, ad spend, and performance
For instance, if my efforts in paid search are maxed out and additional budget is available, I might allocate some to Google Demand Gen or social channels to see how they perform.
Considering the brand’s current awareness level is crucial. If building credibility is still ongoing, focusing on social prospecting could enhance visibility and audience building for future retargeting.
Another factor is my ability to support campaigns requiring creative assets. If getting creative approved is challenging, keeping budget in paid search might be more pragmatic, with plans to expand to other channels once assets are ready.
When making budget decisions, I ensure not to view individual channels or campaign types in isolation. It’s important to understand how they might affect each other and leverage data to guide these decisions.
For instance, if I launch a YouTube campaign that raises product awareness, I might notice improved conversion rates in search, with video viewer remarketing audiences performing well.
Even if direct conversions from YouTube are minimal, data might show improved overall conversion efficiency, justifying an ongoing budget for both YouTube and search.
When mapping out annual budgets, aligning them with peak buying times or potential slumps specific to the industry at hand is vital.
Ecommerce brands may raise budgets around holiday seasons, while B2B brands might choose to invest earlier in the year.
Historical data can be a guide, and tools like Google Trends offer insights into monthly trends for relevant keywords.
Unexpected budget shifts are common, whether due to financial constraints or last-minute fiscal year decisions. I’m prepared to adapt by pausing campaigns or reallocating budgets where they’ve proven efficient.
Opportunities to increase budgets prompt a focus on campaigns that are currently capped and performing efficiently. However, I avoid increasing budgets too rapidly, to prevent inefficiencies.
Dig deeper: How to manage a paid media budget: Allocation, risk and scaling
Finally, selecting between total or daily budget types is a frequent consideration. Short campaigns or ones with strict budget limits benefit from a total budget, while ongoing campaigns are better suited to daily budgets.
I’m mindful of spending spikes and aim to avoid overspending, especially when adjusting budgets mid-month.
Dig deeper: How to optimize B2B PPC spend when budgets and confidence are low
Having a budget strategy that’s adaptable to ongoing and exploratory efforts, while considering the unique nuances of each platform, is key to successful paid media campaign management.
Inspired by this post on Search Engine Land.
The position of ads is more crucial than ever. I’ve recently come across new data that underscores how Google AI Overviews are reshaping paid search visibility and click-through rates (CTR).
In my experience, Google’s AI Overviews have dramatically altered the search landscape almost overnight. As someone deeply invested in paid search, I’ve noticed the battle for visibility isn’t just about ad rank anymore—it’s about appearing above the AI results.
This change is part of a rapid surge in AI Overviews, which I discovered in Adthena’s earlier study. My analysis found that AI Overviews are now trespassing into short, high-volume commercial searches.
The underlying mechanism causing this is pretty clear to me: AI Overviews intercept user attention, slash CTRs, and push both organic and paid listings lower down the page. As a result, clicks and revenue take a hit.
From what I’ve seen in Adthena’s latest research, it accurately identifies how often advertisers secure top ad positions above AI Overviews across seven major industries, device types, and query categories. The research highlights clear leaders and provides actionable strategies for the rest of us in paid search.
The industry benchmark table below reveals how fierce the fight is for the top spot. It shows us the percentage of ads that appear either above or below AI Overviews across seven industries.
From what I’ve gathered, device-specific data indicates that ads are more likely to be displaced by AI Overviews in a mobile setting due to limited screen space.
Generally, I’ve observed that long queries tend to be more informational and thus more likely to activate AI Overviews, while shorter ones are typically transactional. The table below unfolds a surprising industry pattern related to this.
This table reveals the connection between query complexity (or user intent) and AI Overviews’ dominance, spread over query lengths from one to ten words.
Adthena’s research highlights that the threat of Google AI Overviews is fragmentary. Precision is key: know when and where your ads can outrank AI Overviews, adjust your bids and content accordingly.
From my ongoing observations, as the frequency of AI Overviews rises, these ad position percentages might swing. I advise regularly auditing profitable keywords to effectively handle changes in the AI-driven search landscape.
Here are three game-changing steps we can take:
I’ve realized that mobile often amplifies visibility loss from AI Overviews, notably in sectors like automotive.
I recommend considering a device-specific strategy, especially for campaigns severely impacted by AI Overviews.
Data on word counts reveals unexpected possibilities. Industries like Gaming and Automotive often see robust ad placements with long-tail queries (four words or more) above AI Overviews.
This signals high-visibility traffic in mid- to upper-funnel searches that our competitors may be ignoring.
AI Overviews can miss out on brand nuances and emotional resonance.
To captivate users, ads must deliver what AI can’t: a strong, compelling reason to choose you over Google’s summary. Using messaging that includes trust, guarantees, or urgency can clearly differentiate from AI’s generic style.
Convey transactional incentives like deals, free shipping, or scarcity (“Limited stock, grab yours!”), and use emotional elements like customer testimonials to build trust and convey your unique brand narrative.
The search landscape has evolved. Adthena’s data suggests that marketers who rapidly analyze and adjust their ad strategies in response to AI Overviews will thrive.
Adthena gives you the precise data on ad appearances in relation to AI Overviews, helping you adapt to changes in AI search performance. Book a demo to see where your ads rank today.
Inspired by this post on Search Engine Land.
I’m excited to share some thrilling news with you. Profound’s Agent Analytics now fully supports WordPress with a custom-made plugin. This integration offers seamless AI observability for WordPress sites, which means your team can easily track AI crawlers and agents as they interact with your content, even if traditional CDN log drains are unavailable.
This development is a significant step forward for anyone using WordPress, making it easier to understand and optimize AI-driven interactions. Whether you’re on a managed hosting platform or running your own setup, this plugin is designed to enhance your capabilities in managing AI observability effortlessly.
Inspired by this post on Try Profound Blog.
In my recent dialogue with Slava Kravchuk, the Founder and CEO of Atwix, we delved into the future of B2B eCommerce and the key elements businesses need to thrive beyond 2026. Drawing from Atwix’s 15+ years of industry experience with manufacturers, distributors, and wholesalers, Slava shared invaluable insights on the rapidly evolving marketplace.
Slava started his journey with Atwix back in 2006, and the transformation in the B2B eCommerce realm since then has been nothing short of remarkable. Initially, the majority of B2B firms lacked a digital presence, but today, eCommerce is indispensable. Slava highlighted how COVID-19 rapidly accelerated digital transformation, compressing a decade’s worth of changes into mere quarters. This urgency pushed countless businesses to embrace digital commerce as a means of survival and growth.
We discussed the ongoing debate about selecting the right eCommerce platform. Slava emphasized that it’s not about the ‘best’ platform but choosing one that meets a business’s specific needs. Atwix offers expertise across various platforms like Adobe Commerce, Magento, Shopify Plus, and Shopware, because of their diverse capabilities. For complex B2B operations, Adobe Commerce and Shopware are often ideal due to their flexible architecture suited for intricate business requirements.
Another crucial aspect of B2B eCommerce is effective ERP integration. Slava insists that seamless eCommerce and ERP connectivity is vital to avoid data errors and ensure customer satisfaction. Atwix’s proprietary tool, Sirius, allows businesses to integrate their ERPs with their eCommerce frontends smoothly. This tool has transformed customer experiences, enabling real-time order tracking and payment capabilities.
We also touched on the decision-making process between building a custom solution or utilizing a platform. Slava advised starting with a platform due to the efficiencies and flexibility they offer. He stressed the importance of customizing smartly to avoid technical debt and ensuring a platform can evolve with the business’s future needs.
Slava’s approach is one of partnership. He believes in engaging with clients to map out a strategic vision before any development begins. This foresight helps prevent costly setbacks and aligns the technology with long-term business goals. For example, Byrne Electrical’s rapid development during the pandemic was successful due to careful, phased planning upfront.
Looking ahead to 2026 and beyond, Slava predicts that AI, integrated experiences, and personalization will be the driving forces of change in B2B eCommerce. AI advancements are already shaping product discovery and customer interactions. Meanwhile, customers now expect integrated, personalized experiences akin to B2C interactions.
For businesses contemplating digital transformation, Slava’s advice is clear: start with a minimal viable product and continuously refine it based on feedback. Choosing the right partner who understands your industry is crucial for building lasting, adaptable eCommerce solutions. The time for B2B companies to embark on their digital journey is now.
Inspired by this post on First Page Sage Blog.