I’ve just discovered an incredibly beneficial update from Google Ads that I’m excited to share. Now, we can see precisely where our Performance Max campaigns are running through the “Where ads showed” report. This change opens up a new world of clarity and optimization possibilities that were previously inaccessible.
What’s New? This update allows me to see exactly where my PMax ads are appearing across Google’s network, including search partners, display, and other placements. By tracking impressions by placement type and network, I can now understand the detailed performance of my campaigns like never before.
Why It Matters to Me This is a game-changer for anyone managing PMax campaigns. It brings much-needed visibility into where ads are appearing, including Google Search Partners and beyond. With access to placement, type, and impression data, I can optimize budgets and make informed decisions rather than relying on guesswork. It transforms previously opaque reporting into actionable insights.
User Reaction Digital marketer Thomas Eccel shared his experience on LinkedIn, expressing that the report was once a blank page but now displays real data.
“I finally see where and how PMax is being displayed,” he wrote, highlighting the significance of this update for clarity.
He also noted how Google Search Partners are now no longer a “blurry grey zone.”
The Bottom Line For me, and many other marketers, this update offers actionable visibility into PMax campaigns, helping us understand placement performance, optimize spend, and pinpoint which networks are yielding results — all within one comprehensive report.
I’ve discovered that Microsoft Advertising is rolling out a captivating new feature that could transform how we see Shopping campaigns in Bing search results. These multi-image ads offer eCommerce brands a unique opportunity to showcase their products more vividly, potentially capturing shopper attention even before they click.
What’s new. Now, I can include multiple product images in a single Shopping ad, allowing shoppers to preview various angles, styles, or variations directly within the search results. This approach could be a game-changer for advertisers.
The design is crafted to enhance visual engagement and provide more informative ads. It allows consumers like myself to quickly compare options without the need to leave the results page.
How it works:
I can upload additional images using the optional additional_image_link attribute in the product feed.
There is an option to include up to 10 images, which I can separate by commas.
The images will appear alongside pricing and retailer information in Shopping results.
Why we care. From my perspective, multi-image ads have the potential to boost engagement and purchase intent by offering a more comprehensive visual representation of a product. More imagery can highlight features, colors, and design elements that a single image might miss.
Discovery. This feature was initially noticed by digital marketer Arpan Banerjee, who shared it on LinkedIn.
The bottom line. For retailers like you and me, multi-image Shopping ads provide more creative freedom and give shoppers a richer context immediately. This shift has the potential to enhance ad performance and reshape how products are presented in search results.
I’ve recently discovered that Google Ads has introduced an impressive new Results tab within their Recommendations section. It’s designed to help advertisers like you and me see the actual performance impact of applied suggestions, especially when it comes to bid and budget adjustments.
After applying any bid or budget recommendation, Google analyzes the campaign’s performance one week later. It then compares the results against a baseline estimate, showing us the incremental lift such as additional conversions from raising a budget or tweaking targets. It’s a fantastic update for those of us wanting concrete data on recommendation outcomes.
Wondering where to find this information? You can spot the impact reporting right in your account’s Recommendations area. There’s a handy summary callout with recent results on the main page, plus a dedicated Results tab providing a detailed breakdown categorized by Budget and Target recommendations, with helpful filters.
Why is this an important update? As an advertiser, I’m thrilled because this lets us see whether Google’s automated recommendations truly deliver incremental results, not just predicted boosts. This is crucial for assessing the real business value of these platform suggestions.
But what should we expect going forward? The Results tab reports a seven-day rolling average, measured over 28 days following a recommendation. It zeroes in on the campaign’s primary bidding objective, be it conversions, conversion value, or clicks.
This feature introduces an added layer of accountability to automated recommendations, especially as we’re relying more on platform-driven optimizations. I find it reassuring to know there’s now more transparency.
Interestingly, this was first shared by Hana Kobzová, founder of PPCNewsFeed, who took to LinkedIn with a screenshot of the help doc.
Although there isn’t a live help doc yet, a Google spokesperson mentioned they’re running an early pilot. It’s exciting to be on the cutting edge of these developments!
I recently stumbled upon a fascinating study that shows how ChatGPT pulls most of its references from the beginning sections of content. It’s clear from this research that the AI favors straightforward definitions, a balanced tone, and densely packed entities.
According to Kevin Indig, a Growth Advisor who analyzed 1.2 million AI responses and 18,012 citations, ChatGPT has a strong preference for using citations from the top of the content. This was a revelation for me and definitely something to keep in mind when writing.
Why we care. The traditional search landscape often rewards depth and gradual payoffs. However, AI is changing that game by favoring clear entities and direct answers right at the start. If I don’t make sure my key information is front and center, it’s less likely to be cited by AI.
By the numbers. In examining various datasets, Indig’s team found a “ski ramp” pattern—44.2% of citations originate from the first 30% of content, 31.1% from the middle, and only 24.7% come from the final third, with a noticeable drop towards the end.
Breaking it down even further, I learned that at a paragraph level, AI citations largely come from the middle sentences (53%), with 24.5% from the first sentence and 22.5% from the last.
The big takeaway. This really drives home the importance of front-loading critical insights at the article level. Within paragraphs, focusing on clarity and meaningful content rather than trying to hook readers with a dramatic first sentence seems to be more effective.
Why this happens. Large language models like ChatGPT are trained on various styles of writing that prioritize a “bottom line up front” approach. It seems these models use the early sections as a framework for interpreting the rest of the data.
Efficiency and context establishment remain key priorities for these models, even though they can process large sets of data.
What gets cited. Indig noted five key traits of content frequently cited by ChatGPT: definitive language, a Q&A structure, entity richness, balanced sentiment, and business-grade clarity. Learning this has been incredibly insightful for how I craft my content.
Indig’s team looked at a massive volume of data, identifying the traits of highly cited content by analyzing 18,012 verified citations from ChatGPT responses. The study focused on where and why the AI pulls content, using advanced techniques to match responses to source sentences.
Bottom line. It seems the narrative approach of crafting an “ultimate guide” might not be the best for AI retrieval. Instead, a more structured, briefing-style format appears to be more successful.
This study convinced me that writers now face what Indig calls a “clarity tax.” We need to present definitions, entities, and conclusions upfront rather than saving them for the conclusion.
Recently, I learned that Perplexity has decided to halt its advertising initiatives. The company started experimenting with sponsored placements back in 2024, but now they’re stepping back, believing these ads might jeopardize the trust that users place in their AI answer engine.
I read the Financial Times report stating that Perplexity phased out the ads and currently has no intention of reintroducing them. It’s an intriguing approach considering the rapid evolution of AI search companies.
As someone who utilizes AI-driven platforms, I find it important to monitor these changes. If Perplexity stays ad-free, brands miss out on direct paid access to a growing audience. Imagine how brands must navigate a landscape with 780 million monthly queries without the option for sponsored placements.
Perplexity was pioneering in testing ads, placing sponsored answers beneath chatbot responses. They claimed these ads were clearly labeled, ensuring they didn’t affect the quality of information. Yet, it’s evident that perception is as crucial as policy for them.
From my perspective, the notion that users might doubt the integrity of responses if ads appear is understandable. One of Perplexity’s executives mentioned that maintaining users’ belief in receiving the best possible answer is paramount.
It’s worth mentioning that while Perplexity opts out of ads, other platforms are diving in. For instance, OpenAI is testing ads in ChatGPT for free users, and Google is running ads in AI Mode within Search, although not in Gemini. Meanwhile, Anthropic is committed to keeping Claude ad-free, which reflects different strategic approaches in the industry.
Sustainability in business is key, and Perplexity sees subscriptions as its core model. They offer both free and paid plans ranging from $20 to $200 monthly, boasting over 100 million users and approximately $200 million in annual revenue. This model reflects their focus on accuracy and providing the truth, minimizing conflict of interest.
Despite launching shopping features, Perplexity doesn’t take a transaction cut, aligning with their cautious stance on revenue models that might undermine trust.
For more detailed insights, one could explore the full report from the Financial Times, though it’s a subscription-based service.
During Airbnb’s Q4 2025 earnings call, CEO Brian Chesky shared an intriguing insight that has captured my attention: bookings from AI chatbots surpass those driven by Google in terms of conversion rates.
Chesky revealed, “And what we see is that traffic that comes from chatbots convert at a higher rate than traffic that comes from Google.” However, he was less forthcoming about the exact conversion rates or the volume of traffic these AI chatbots generate for Airbnb.
I find it fascinating that despite lacking specific conversion data, it seems clear that guests reaching Airbnb via AI chatbots are further along in the booking journey compared to those originating from Google searches.
The chatbots contributing to this traffic boom weren’t explicitly identified, but Chesky did mention well-known models like OpenAI’s ChatGPT and Google’s Gemini, among others.
This evolution is significant because AI assistants are starting to prove themselves as powerful tools in the early stages of customer engagement, potentially surpassing traditional search methods in terms of quality lead generation.
Chesky portrays these chatbots as not only similar to traditional search platforms but as vital components in the journey of customer acquisition.
He believes that, “These chatbot platforms are gonna be very similar to search. Gonna be really good top-of-funnel discoveries,” highlighting their potential in broadening Airbnb’s reach.
Airbnb is excited about what lies ahead as they envision an AI-native experience where their app evolves from merely assisting in searches to genuinely understanding user preferences.
“So AI search is live to a very small percent of traffic right now,” Chesky mentioned, emphasizing that Airbnb’s strategy involves a lot of quick iterations and experimentation rather than launching big, bold changes.
Currently, within Airbnb, AI tools are not only external but also internal assets. Their AI-powered customer service agent significantly reduces the workload by resolving nearly one-third of North American support tickets.
The company aims to expand this AI tool globally with multilingual capabilities, including voice support, with hopes of handling more than 30% of tickets within the year.
An AI-powered conversational search feature is live for a limited user base, showcasing Airbnb’s commitment to embracing AI as part of their development cycle rather than waiting for a massive roll-out.
While the idea of sponsored listings remains in the background, Chesky notes that traditional ad formats might require tweaking to align with the conversational nature of AI environments.
Previously, before generative AI and AI-powered searches became trends, Airbnb shifted its budget focus to brand marketing, reducing expenditures on search marketing, a move that now aligns with their evolving AI approach.
I’ve noticed some exciting changes coming to Google Demand Gen campaigns. Starting in March 2026, Lookalike audiences will no longer be the rigid framework we’re used to. Instead, they’ll serve as optimization signals, ushering in a new era of AI-driven campaign enhancements.
Google is updating its Help documentation to reflect this transformation where Lookalike segments shift from strict targeting to a more flexible, AI-enhanced recommendation model.
Understanding the Transition. Previously, I would choose a specific similarity tier (narrow, balanced, or broad) to dictate exactly who my campaigns targeted. That’s changing.
Now, Google will use these tiers as signals. The system will intelligently expand its reach beyond my chosen Lookalike lists to engage users predicted to convert.
Behind the Change. This transition turns Lookalikes from a barrier into an enabling tool. It allows Google’s automation to use intent signals to explore audience performance well beyond predefined limits.
Interaction with Optimized Targeting. The new Lookalike-as-signal approach resembles Optimized Targeting but doesn’t replace it. When they’re layered, Google mentions it could further expand my reach.
In practice, this means multiple automation signals will be at play, providing the algorithm more freedom to either reduce CPA or boost conversion rates.
Opting Out. If I prefer the traditional Lookalike approach, I can opt out via a dedicated form, preserving the old targeting behavior. Absent that, campaigns automatically switch to the new format.
Why This Matters. This update affects the control I have over ad targeting in Google Demand Gen campaigns. Lookalike audiences will now guide rather than confine targeting, significantly influencing scale, CPA, and performance.
Additionally, it indicates an industry-wide move toward automation, similar to shifts driven by Meta Platforms. I’ll need to test thoroughly, rethink strategies, and decide whether to embrace the added reach or opt out for tighter targeting.
Industry Context. Google’s strategy echoes a broader trend toward AI-first audience expansion, aligned with similar adaptations from Meta in recent years. The advertising landscape is increasingly prioritizing machine-led optimization over detailed manual control.
The Reasoning. According to digital marketer Dario Zannoni, there are two main reasons for Google’s shift:
Stringent Lookalike targeting can limit scale and hinder performance in conversion-focused campaigns.
The complexity of maintaining high-quality similarity models makes automation a more viable option.
The Bottom Line. For performance marketers like me, this marks another step towards automation-centric strategies. Reduced control might be daunting, but similar platform changes have historically yielded performance gains. A fresh testing cycle is on the horizon as I examine the impact of expanded Lookalike signals on CPA, reach, and conversions.
Observed and Shared. Dario Zannoni initially highlighted this update on LinkedIn.
I’ve just learned that Meta has begun embedding Manus AI directly into Ads Manager, a move that drastically simplifies the way we handle reporting, research, and campaign optimization.
What’s happening: If you’re like me, you might have noticed prompts encouraging us to activate Manus AI within Ads Manager. Exciting, right?
Manus is available for everyone through the Tools menu, and some of us are also seeing pop-ups suggesting we try it as we work.
This rollout suggests even more integration in the future.
What is Manus: Manus AI acts like a supercharged assistant within our ad workflow, capable of handling tasks such as report creation and audience research.
Why it matters: By placing AI-driven automation tools directly in our hands, Manus AI speeds up key processes such as report building and audience analysis, making our campaigns more efficient.
Meta is keen on linking its AI investments to better ad performance, offering us the chance to tweak workflows for maximum gains.
The bigger picture: Meta feels the heat to showcase tangible benefits from its AI investments. By weaving Manus AI into our daily tools, it’s easier to see how AI can boost performance.
Looking ahead: This move is in line with Mark Zuckerberg’s vision to integrate AI throughout Meta’s products. By promoting Manus as an ad performance booster, Meta aims to enhance ad results and strengthen its financial narrative.
The takeaway: For us advertisers, Manus offers another layer of automation to explore. Early adopters might find significant time and efficiency savings as Meta ramps up its AI capabilities.
I’ve noticed how TikTok’s innovation never ceases to amaze me, and now they’ve launched AI-powered ads specifically aimed at us, entertainment marketers. This new tool allows us to deliver highly personalized content, increasing both engagement and conversions.
Recently, TikTok has offered us, marketers in Europe, new opportunities to precisely target audiences. By utilizing AI, we can enhance engagement and drive conversions for streaming and ticketed events.
What’s happening. TikTok has rolled out two fresh ad types for our European campaigns:
Streaming Ads: These AI-driven ads are specifically designed for streaming platforms, showcasing personalized content based on user interaction. Whether it’s a four-title video carousel or a multi-title media card, 80% of TikTok users have expressed that the app shapes their streaming decisions, and I find this incredibly impactful.
New Title Launch: This targets high-intent audiences using signals like genre preference and price sensitivity, thereby aiding us in converting cultural moments into actual ticket sales, subscriptions, or event attendance.
Context. This launch coincides perfectly with the 76th Berlinale International Film Festival, showing TikTok’s growing footprint in entertainment marketing. Just last year, TikTok users shared an average of 6.5 million daily posts about film and TV, and impressively, 15 of the top 20 European box office hits sparked viral trends on the platform.
Why we care. With TikTok’s new AI-powered ad formats, entertainment brands like mine can now target users more accurately with content that’s tailored just for them. The increase in engagement and conversions is something I am particularly excited about.
With 80% of users admitting that TikTok influences their viewing habits (as per TikTok’s own data), these tools give us the power to shape audience behaviors, turning cultural phenomena into tangible results like subscriptions, ticket sales, or even increased viewership.
The bottom line. For us, entertainment marketers, TikTok’s AI-driven ad formats are opening new avenues to captivate audiences, increase viewership, and transform trending content into quantifiable outcomes.
As someone deeply fascinated by how AI influences search engines, it’s intriguing to know that behind Google’s AI search facade, there is a robust system at work. This system diligently narrows down tens of thousands of documents to just a handful, relying heavily on traditional signals for visibility.
Jeff Dean, Google’s chief AI scientist, recently shared some insights on the Latent Space: The AI Engineer Podcast, where I learned how much Google’s AI still draws from its classic search engine architecture.
The architecture: filter first, reason last. In essence, for any content to be visible, it must navigate through various ranking thresholds. It starts with entering a broad candidate pool, goes through intense reranking, and only then becomes part of an AI-generated response. Essentially, AI builds on top of traditional ranking metrics.
Dean elaborated that an LLM-powered system doesn’t skim through the entire web in a single go. Instead, it begins with Google’s comprehensive index, utilizing lightweight techniques to sift through a large pool of potential documents. Dean described this process:
“You start by pinpointing a subset that seems relevant using very lightweight methods. Initially, you might have around 30,000 documents, and this number gradually refines as increasingly sophisticated algorithms and signals are applied, ultimately leading to the final 10 results or so.”
These robust ranking systems further trim this set. Consequently, it’s only after multiple filtering rounds that the most capable model steps in to analyze a significantly smaller group and generates a response. Dean continued:
“An LLM-based system isn’t vastly different. Although it processes trillions of tokens, it seeks the key 30,000-ish documents with those maybe 30 million significant tokens. From there, it derives the crucial 117 documents needed to accomplish the task.”
Dean referred to this as an “illusion” of engaging with trillions of tokens. In practice, it’s a structured pipeline: retrieve, rerank, synthesize. Dean elaborated:
“Google search isn’t about an illusion; it’s genuinely searching the internet but distilling it down to a very relevant subset.”
Matching: from keywords to meaning. Although it’s not novel, emphasizing that comprehensive topic coverage is more important than repeating exact keywords was refreshing.
Dean explicated how LLM-based representations revolutionized query-to-content matching by moving beyond word-for-word alignment. Now, Google evaluates whether pages or even paragraphs are topically relevant to a given query. He explained:
“Implementing an LLM-based text representation means we’re no longer bound by the need for specific words on a page. Instead, we delve into the topical relevance of a page or paragraph to a query.”
This paradigm shift allows Search to connect queries to answers notwithstanding different phrasings, increasingly focusing on intent and subject matter rather than mere keyword placements.
Query expansion didn’t start with AI. Dean highlighted Google’s 2001 achievement of moving its index into memory, enabling swift query expansion. He noted:
“We significantly scaled in 2001, wanting a larger index for better retrieval, accommodating growing traffic through a sharded system, evolving to fit the entire index in memory across machines. This dramatically improved query quality.”
Before this, expanding queries with additional terms was cost-intensive due to disk accesses. Once the index resided in memory, Google could enrich short queries with synonyms and variations to capture broader meanings. Dean recalled:
“Previously, term lookup was constrained by disk seek penalties. Post-memory transition, handling 50-term queries became feasible, enhancing definition and meaning extraction, far ahead of LLMs.”
This transition steered Search towards intent and semantic matching, setting the stage for today’s LLM-driven advancements, which amplify meaning-based retrieval through more refined systems and advanced computing power.
Freshness as a core advantage. Dean’s insights revealed that one of Search’s pivotal transformations involved accelerating update rates. Early on, pages refreshed monthly. Now, Google’s systems can refresh in under a minute. He observed:
“Google’s early index expansion coincided with ramping up refresh rates, now a vital parameter. Swift updates remain crucial.”
This advancement significantly enhanced news search results and overall user experience, as current data is a consumer expectation. Dean added:
“A stale index, like last month’s news, loses utility fast.”
Google’s sophisticated systems decide the frequency of page crawls, weighing potential change against the value of the latest version. Even less frequently updated important pages might be crawled often due to high update value. Dean shared:
“An intricate system determines update rates and page importance, ensuring often-updated important pages remain current.”
Why I find this crucial. The fascinating aspect is realizing that AI answers don’t bypass fundamental elements like ranking, crawl prioritization, or relevance signals. These aspects remain critical. Although LLMs reshape content synthesis and presentation, they don’t circumvent the underlying search mechanics essential for eligibility and quality.
