I recently discovered that Google has updated its guidelines on optimizing for AI Search, and they’ve made it clear that LLMS.txt files on your site won’t impact your search rankings. It’s a relief to know that Google Search doesn’t actually utilize these files.
The portion of Google’s update that caught my attention explains that there’s no need to create new machine-readable files, such as AI text or Markdown files, to appear in Google Search, even with generative AI. Google will still discover, crawl, and index a variety of files, but these won’t receive special treatment.
Google also mentioned that maintaining LLMS.txt files for other services is perfectly fine and won’t influence your visibility in Google Search. In short, these files neither harm nor enhance your standing in search rankings.
For those interested, here is a valuable section screenshot along with more resources on the topic:
Expressing why I care about this, there’s ongoing confusion around how Google handles such files. Remember, having them on your site won’t help but also won’t hurt your SEO efforts.
I’m excited to share that Google has introduced new methods for advertisers to expand their campaigns while keeping a close grasp on efficiency targets. This expansion in Smart Bidding Exploration is sure to be a game-changer.
Google is unveiling a new series of updates designed to help advertisers discover fresh demand, take advantage of seasonal opportunities, and achieve more consistent campaign performance. I’ve always valued predictable outcomes in advertising, and these updates seem to focus exactly on that.
What’s new. The enhancements include a larger scope for Smart Bidding Exploration, the introduction of a new Promotion Mode beta, and updates to bidding target optimization specifically for campaigns with limited budgets.
Driving discovery. This enhancement allows me, as an advertiser, to set a return on ad spend (ROAS) tolerance, so my campaigns can capture additional conversion opportunities from search queries that currently might be overlooked.
From what I’ve seen, campaigns utilizing this feature experience about an 18% boost in unique converting search query categories and a 19% increase in overall conversions.
This capability is now extended to Performance Max campaigns without product feeds and is being tested in beta for Shopping ads within both Performance Max and Standard Shopping campaigns.
Peak period bidding. The new Promotion Mode empowers advertisers to adjust ROAS targets temporarily and increase the daily budget during peak periods like seasonal events, new product launches, and flash sales. I think this is a fantastic tool for maximizing high-demand opportunities.
What else is changing. Starting August 17, Google will update bidding target optimization for budget-constrained campaigns with the aim of delivering more consistent performance. This aligns better with our CPA and ROAS targets, which is reassuring for me as a campaign manager.
Notifications will begin rolling out in Google Ads on July 6, alerting advertisers about recommended campaign adjustments. I appreciate such timely updates that help me stay ahead in planning.
Why we care. These advancements allow Google’s AI bidding systems to explore incremental conversions beyond our current keyword and audience settings. This potential unlock of new demand could be pivotal in redefining campaign success for me.
The Promotion Mode stands out for retailers and seasonal advertisers by enabling temporary adjustments to ROAS targets and budgets during peak periods without needing a complete campaign overhaul. Additionally, the changes in bidding optimization aim at making performances more predictable in campaigns limited by budget.
The bottom line. Google’s recent bidding updates are designed to help advertisers, like me, find new conversion opportunities, react more assertively during peak demand times, and maintain consistent performance as campaigns scale.
I realized early on that merely reducing the cost per lead does not guarantee more signed cases for a law firm. Leads and signed cases differ in significant ways.
What stands between an ad click and a signed retainer is the intake process, speed of follow-up, and ultimately, conversion. Relying solely on cost per lead to gauge PPC success means making decisions with incomplete data.
Having managed over 1,000 ad accounts for plaintiff-side law firms, I’ve witnessed the same issues repeatedly. The ads fuel activity, but leakage occurs at various stages in turning leads to clients.
Law firms that successfully increase signed cases are those that integrate their ad data with intake performance and client retention. This requires a shift in approach to keywords, budget distribution, landing pages, and tracking.
I found most law firms approach campaigns backward, starting with generic keywords like injury attorney, yielding high-volume but low-quality traffic.
By reverse-engineering our keyword strategy from signed-case data, we can protect budgets and increase conversions. Instead of defaulting to Google’s suggestions, we analyze call transcripts and CRM records to find the actual language leading to retained clients.
Over time, I’ve become adept at identifying exact phrase-match terms potential clients use, like “truck accident lawyer near me” or “wrongful death law firm Tampa.”
It’s crucial to segment every keyword by funnel stage and intent. By allocating budget to high-intent terms and testing or excluding low-intent ones, we fine-tune our ad spend.
Integrating the search terms report into my workflow is the cornerstone of effective PPC management. This report reveals the precise phrases used before ad clicks, helping decide whether a lead is worth the cost. Continuous weekly reviews keep the campaign spend efficient.
Instead of treating Google Ads as a single entity, segmenting campaigns by funnel stage, intent, budget, and conversion objectives significantly improves ROI.
According to Pareto Legal’s report, Local Services Ads are the top-converting channel for personal injury firms. They’re pay-per-lead and don’t need a landing page setup. (I’m the CEO and co-founder of Pareto Legal.)
A simple yet effective adjustment we frequently make is refining LSA category selections to more precise case types like personal injury or motor vehicle accidents.
Mid-funnel incorporates non-brand searches and Dynamic Search Ads, evaluated on the rate of qualified leads rather than sheer volume. Too many unqualified leads can drain the budget, even if the cost seems reasonable.
Strategies involving Meta and YouTube retargeting work well post-website visitations. These should expand to cold audiences only when incremental lift is proven through accurate attribution.
Consider this simple framework to dramatically boost your PPC results. For instance, one injury firm achieved 273 signed cases from $765,000 without increasing the budget, just by restructuring Google Ads.
As I discovered, sending paid traffic to mismatched pages curbs conversion rates. While effective landing pages are crucial, they remain one of the most ignored aspects of PPC management, despite being well-known.
Your aim should be relevance: Landing pages need headlines matching search intent, transparency on settlement amounts, social proof via client reviews, and immediate contact options.
These pages should load quickly and adapt to mobile screens. Each practice area and intent deserves a unique landing page design for better results.
I improved one client’s generic page by creating intent-specific pages, adding recent reviews and results, and reducing form fields, doubling conversion rates with no extra ad spend.
A significant hurdle in law firm advertising is not the cost-per-click but the deteriorating intake process. Focus should be on post-contact processes rather than CPC.
Focus on key intake KPIs such as a 90%+ answer rate, sub-60-second response times, and a signed rate of 25%-40% of qualified leads.
Consider this: Spending $20,000 monthly at $250 per lead gets 80 leads. With optimal response and conversion, 30 cases can emerge from the same spend, vastly enhancing ROI.
Ensure marketing and intake teams share KPIs, ensuring media buyers don’t act on disparate targets.
Most reporting cuts off at ad platform metrics without tapping into where the action really happens—the CRM. An integrated attribution chain from ad click to signed retainer is indispensable.
Set up your attribution system: Track traffic sources through UTMs, capture call leads, monitor web behavior with Google Analytics, and track through CRMs like Lawmatics or Clio.
The keystone metric, Marketing Efficiency Ratio (MER), evaluates the marketing ecosystem rather than viewing channels separately, crucial for budget confidence and allocation.
I recommend a streamlined dashboard with key metrics—spend, leads, qualified leads, signed cases, CPL, CPA—segmented by both channel and practice area.
Without granular reporting capability, your data might only be serving as an overview. Leveraging this tracking structure highlights effective campaigns that improve ROI sustainably.
The law firms thriving with PPC are those recognizing PPC as a comprehensive system. They apply precise keyword targeting, allocate budgets by intent, regularly scrutinize search terms, understand cost per case over cost per click, and connect ad clicks to results that matter.
I often find myself pondering how AI is changing the landscape of content strategy, especially in the realm of SEO and citations. It’s fascinating to see this shift from merely retrieving information to creating engaging and citation-worthy content.
As I delve deeper into the evolving AI search mechanisms, it’s clear that content needs to provide a stellar user experience to earn citations from LLMs like Claude and ChatGPT. The focus should be on understanding where our readers and potential customers are in their journey.
My strategy now includes considering how third-party platforms perceive our brand. It’s all about consistent messaging, ensuring that AI systems like Google’s understand our brand identity, target audience, and the right moments to highlight our offerings.
Transitioning from traditional SEO to what I call “experience-based GEO” offers exciting opportunities. Instead of prioritizing SEO, I focus on creating content that speaks directly to our desired audience, ensuring our brand emerges in relevant queries.
I’ve learned that while some SEO fundamentals remain, LLMs emphasize customized user experiences. This means our content marketing should aim to resonate with individual preferences, not just optimize for search engines.
Consider this: although the client’s CEO and I share similar demographics, our wine preferences differ, indicating how personalized AI interactions have become. When I’m seeking wine suggestions from an LLM, the results are tuned precisely to my tastes, showing how AI can truly understand consumer desires.
Google is shifting too, leaning towards AI-driven personalized results. This means that I need to adapt my content, both on my site and on external platforms, to align with these new AI paradigms.
Creating a content strategy extending beyond just our website is crucial. RAG (retrieval-augmented generation) depends on authoritative sources, which means featuring our brand in trusted platforms is key.
For instance, ensuring our wine retailer clients get mentioned in niche articles with relevant talking points can help them stand out in this AI-driven content realm. I emphasize using media buys or PR for placements that matter to our buyer personas.
As an individual brand, focusing on listicles and strategic mentions where our unique selling points are highlighted is vital. This ensures our brand is noticed for the solutions we provide.
AI systems crave expertise. By continually positioning ourselves as thought leaders and reliable retailers, we enhance our reputation, allowing LLMs to recognize and trust our brand over time.
It’s clear that traditional SEO techniques aren’t obsolete; they’re evolving. Schema, server-side rendering, and appropriate content structure remain essential, helping AI systems fully grasp who we are and what we offer.
In essence, my focus is on making our site an easy-to-navigate space for both human visitors and AI systems. By surveying customers and understanding their needs, I can tailor content to align with what they truly seek.
Creating a seamless customer experience ensures that our offerings are clear to both users and search engines, potentially improving our conversions.
I’m committed to keeping up with the evolving landscape of LLMs and SEO. By maintaining consistency and adapting our strategies, we can ensure our brand remains relevant and ready for whatever technological advancements come our way.
AI Overviews and Google AI Mode are increasingly shaping the discussions within the SEO community. In this evolving landscape, search is transitioning from a mere information retrieval tool to a powerful recommendation engine.
As a travel brand, this shifts the dynamics of online discovery. It’s no longer just about making your website understandable to search engines; it’s about ensuring AI systems recognize when to recommend your business.
How AI is Revolutionizing Travel Planning
Interacting with large language models (LLMs) has become a routine for many of us. We use them to structure conversations by project, creating folders for our upcoming trips and building on previous chats to refine our preferences and travel profiles.
This is a major shift from the conventional searching methods. Traditionally, we would start our travel plans with Google searches for terms like:
“Hotels in Porto”
“Things to do in Rome”
“Best restaurants in Barcelona”
Today, the process is much more conversational. Instead of a series of disjointed searches, I might open a new folder labeled “Summer 2026” in ChatGPT and begin with a broad question, gradually sculpting it into a complete itinerary.
“Where should I stay in Porto for a quiet weekend within walking distance of the historic center?”
“Which area of Rome is best for families with young children?”
These discussions naturally expand to include restaurant recommendations, tourist attractions, accommodation options, transportation tips, and more detailed daily plans.
When I ask my AI assistant these questions, I’m not looking for a list of websites. What I truly want is an insightful recommendation.
Impact of AI Overviews on Travel Search
AI Overviews gather data from multiple points to deliver highly curated recommendations instead of just a list of links. For this reason, trust, consistency, and context have become vital factors for online visibility.
A traveler might decide to book my hotel based on an AI-generated suggestion without even visiting the website. Instead, their next steps could include a branded search or a visit to a review platform where they might finalize their booking through an OTA.
To win over AI model recommendations, I need to precisely define my brand. It’s crucial for AI to be certain of who I am, what I offer, whom I serve, and the contexts in which my brand is relevant.
Selecting a primary category and maintaining a clear brand position are imperative. Investing in digital PR and securing mentions beyond my own website can help too. Being featured in travel articles on relevant topics can significantly boost visibility.
Moreover, ensuring that my business information is consistent, accurate, and easy to find across my website, Google Business Profile, TripAdvisor, OTA listings, and social media is essential.
Understanding the Role of Zero Click Visibility
The methods for evaluating search performance are evolving. While traditional SEO metrics will remain relevant, it’s important for travel marketers like myself to broaden how visibility is measured.
One critical error is viewing fewer clicks as a decrease in visibility.
A traveler might learn about my property through an AI response and then decide to search for it later or visit a review profile on a platform like TripAdvisor.
That’s why seeing growth in branded searches is a promising sign of AI visibility. Monitoring AI mentions, citations, and assisted conversions is also worthwhile.
Assisted conversions highlight the channels and touchpoints that lead to bookings, even if they aren’t the final source of conversion. I can track these in Google Analytics 4 by navigating to Advertising > Attribution > Conversion Paths and Attribution Reports.
Leveraging TripAdvisor and OTA Listings
Platforms like TripAdvisor have grown beyond being review sites, and OTAs offer more than just booking services.
When someone requests AI recommendations, the system doesn’t rely on a single data point but synthesizes information from multiple avenues.
My website forms a part of this ecosystem.
AI builds confidence in its guidance by cross-referencing data across different platforms. What others say about my brand through reviews, travel guides, media references, OTA listings, or local mentions is increasingly significant. It’s large-scale reputation management.
This additional context helps AI identify when my property is relevant to specific traveler needs, like:
Family-friendly environments.
Ideal for business travelers.
Located in walk-friendly areas.
Renowned for exquisite dining.
Suitable for luxury or budget travel.
Distinguishing My Travel Brand
For example, if I manage a family-friendly hotel, it’s important to highlight features like family suites, kids’ activities, and family-oriented reviews. Alternatively, a romantic destination should emphasize aspects like cozy atmospheres, spa facilities, and exclusive packages.
Similarly, a hotel catering to business travelers should spotlight meeting rooms, workspaces, high-speed internet, and its proximity to business hubs. On the other hand, a restaurant known for its culinary excellence should consistently be mentioned in reviews, receive media attention, and third-party accolades focusing on its food quality, head chef, or dining experience.
While some businesses naturally fit various categories, having a clear primary positioning helps generative search engines easily identify when my brand is appropriate for a recommendation.
This principle holds for travel destinations too. AI-driven engines depend on signals from reviews, travel guides, local listings, and related content when suggesting where tourists should stay, visit, or explore.
Strengthening Entity Signals Across Platforms
As AI systems place more focus on entities instead of individual web pages, I must create a robust and consistent digital presence.
1. Clarifying Attributes with Structured Data
Structured data aids search engines and AI in interpreting key business details. For travel entities like mine, this includes lodging types, amenities, locations, and more.
Emphasize the attributes that truly set my property apart. This can span from family-friendly amenities to wellness-centered experiences, renowned dining options, pet-friendliness, or proximity to major landmarks.
The clearer and more structured my information is, the better the chances AI-powered experiences will spotlight my business in relevant recommendations.
2. Resolving Entity Ambiguities
It’s crucial to review third-party portrayals of my brand. Inconsistencies can diminish the trust AI systems have in my brand information, as AI pulls data from various sources.
Think of a hotel with differing phone numbers, outdated details, varying categories, or conflicting amenity information across platforms—these inconsistencies confuse AI systems.
Ensuring my business data is consistent across my website, Google Business Profile, TripAdvisor listings, and OTA profiles will reduce ambiguity and strengthen AI’s confidence.
3. Prioritizing Operational Information
Start by evaluating existing customer reviews.
What did they enjoy most during their visit?
What made their stay memorable?
What areas need improvement?
Such feedback provides insight into what genuinely differentiates my brand. Details about amenities, accessibility features, business hours, parking, and pet policies help AI address specific travel-related queries with confidence.
Google Business Profile is another vital source for operational data. The categories, attributes, amenities, and working hours mentioned on the profile enhance AI’s ability to answer travel queries accurately and helpfully.
To provide further context, I can also use Google Business Profile to publish posts that link back to my site’s content. Consistently posting on Google Business Profile can boost engagement, increase profile visits, and encourage customer interaction, ensuring my listing remains updated with fresh content about my offerings.
Cultivating AI-Trusted Signals
Generative search levels the playing field more than traditional search. AI favors recommending businesses, not just their websites. Visibility isn’t solely determined by what transpires on my site; it encompasses the comprehensive digital footprint that my brand projects.
For travel brands, this means I must think broader than just rankings and clicks. Reviews, OTA listings, travel guides, media mentions, and business profiles all contribute to how AI recognizes and recommends my brand.
It’s time to get creative, try new approaches, and collaborate with complementary businesses. Most crucially, it’s time to build the trust signals that AI systems rely on.
In this report, I’m excited to share the findings from a research study I conducted with my team on the emerging field of Agentic Search Optimization, or ASO. We’ve developed a strategic framework that businesses and marketing agencies can leverage to stay ahead in this dynamic landscape.
What is Agentic Search Optimization?
Agentic Search Optimization, often referred to as Agentic GEO, involves optimizing your online presence so AI agents choose your products or services on behalf of users. Unlike Generative Engine Optimization (GEO), which focuses on gaining human trust after an AI recommendation, ASO targets conversions by persuading AI to recognize your offering as the best choice for users.
ASO might seem similar to GEO since both aim to drive leads or purchases, but there’s a significant difference: GEO involves human decision-making, while ASO transfers that responsibility to intelligent bots.
For instance, in ASO, a user doesn’t ask ChatGPT for the best gift card platforms. Instead, they might say, “Send $50 holiday gift cards to my remote team at their preferred stores”. The AI agent interprets, evaluates options, and makes the purchase autonomously.
So far, the ASO landscape hasn’t been thoroughly researched to identify universally accepted best practices. Our study attempts to build a framework outlining agentic search stages, determinants of company selection, and actionable tactics to influence search results.
The Study
Between March 4, 2026, and June 10, 2026, our research team conducted 2,417 agentic search commands using popular AI agents across the U.S. These commands were task delegations such as purchases, bookings, quote requests, or vendor shortlists, rather than just informational quests. We observed the entire behavior chain of agents, including sub-queries, source retrieval, candidate evaluation, and the final action or inaction.
Our analysis revealed that ASO follows three key stages: Retrieval, where AI scans the web (primarily Google) for top results and compares them to its beliefs; Evaluation, where the best company, product, or service is chosen to fit user needs; and Action, where the task is completed, often involving a transaction.
Through our research, we’ve identified three crucial insights:
Agents Review Complete Results: Across all commands, AI agents opted for the platform’s top-ranked recommendation 44.6% of the time. However, they selected options ranked 4th or lower in 38.2% of cases, demonstrating a choice based on suitability over rank.
Agents Possess Predetermined Brand Beliefs: In 81.6% of evaluations, agents relied on pre-existing brand beliefs established during their training or via web searches, indicating that brand perception heavily influences ASO.
Agents Forfeit Companies Unable to Transact: If a conversion page was machine-actionable, agents completed 78.3% of attempts. When not, completion fell drastically to 9.6% with many agents substituting transactable competitors without user input.
This study further explores the ASO process in detail, showcasing tactics that our team tested and validated in early 2026.
The Three Stages of Agentic Search
When I delegate tasks to an AI agent, it performs query interpretation, creating an average of 6.3 sub-queries. The process proceeds through three stages: Retrieval, where it constructs a result set; Evaluation, narrowing choices to the best fit; and Action, executing the conversion. During this, agents cross-reference claims with multiple sources; inaccuracies result in immediate rejection of a candidate.
To benefit from agentic search, companies must achieve two goals: securing the #1 rank on AI platforms, aiding the Retrieval stage, and clearly defining their fit, crucial for Evaluation. Technical prowess ensures seamless Action.
Stage 1: Retrieval
The Retrieval stage encompasses traditional GEO: agents scan the web and build a pool of companies or products. All previous GEO strategies apply here—Comparison blogs, metric pieces to boost rankings, and brand authority statements that AI platforms might trust help form this candidate set.
What’s innovative in ASO is understanding the AI’s pre-existing beliefs. This necessitates mapping the AI Belief Landscape, an audit scoring AI model beliefs about a brand, alongside sentences exemplifying these beliefs.
This assessment guides marketers in pinpointing areas where their brand falls short in the eyes of AI, a crucial step in adjusting perceptions during ASO.
Tactic: AI Belief Correction
AI Belief Correction involves publishing evidence to transition model beliefs from weak to strong. For instance, for a skincare brand like Rejuve, enhancing its perception involved producing detailed scientific explanations onsite and acquiring third-party verification offsite, establishing credibility.
Stage 2: Evaluation
Evaluation diverges drastically from traditional SEO. Agents, not humans, select candidates based on user knowledge. Our study showed agents broke user commands into prioritized categories: Hard Requirements, Important, Nice to Have, and Optional, with evaluations leading to a “Fit Verdict.”
Properly communicating fit information is crucial. Content detailing product suitability increases selection odds.
Tactic: Suitability Pages
Suitability Pages—criterion-specific pages that declare who a product is suited for and, critically, who it isn’t—are vital. Noting “non-fit” conditions paradoxically increases credibility by adding authenticity, improving agentic evaluation rates.
Stage 3: Action
Achieving the third stage requires technical readiness: machine-readable pages and APIs enable seamless agent transactions. The disparity in conversion rates between machine-actionable and non-actionable setups is significant, underscoring the importance of technical preparation.
The Future of Agentic Search Optimization
I anticipate that AI-driven commercial transactions will rise dramatically in the coming years. As that shift occurs, here’s what I foresee:
Suitability content will become essential: Just as landing pages are vital for SEO today, clearly defined fit will become mandatory for ASO visibility.
Tougher verification layers: Securing third-party endorsements will become even more critical, emphasizing PR’s value in ASO.
Selection share will surpass rankings: The focus will shift to actual AI agent selections over mere recommendation visibility.
Marketers excelling in GEO are already poised for agentic success, but comprehensive strategy across all stages is necessary for ultimate triumph.
Downloading This Report & Inquiries
Got questions or need a PDF copy of this report? Feel free to contact us here.
Discover more about our Agentic Search Optimization services by reaching out here.
Appendix A: Command Categories in Agentic Search Study
Category
Commands
Ecommerce purchasing
612
B2B software evaluation & signup
489
Travel booking
343
Professional services inquiries
291
Consumer & local services
274
Financial products
213
Healthcare services & products
195
Total
2,417
Appendix B: # of Commands Issued in Agentic Search Study
AI Agent
Commands Issued
Notable Behavior
ChatGPT (agent mode)
884
Most likely to verify claims against third-party sources before acting
Gemini (agentic tasks)
519
Strong integration with data feeds; likely to abandon when pages aren’t machine-actionable
Claude (browsing & computer use)
397
Thorough evaluator; applies the largest number of distinct criteria per command
Perplexity Comet
462
Widest retrieval fan-out; often selects options ranked outside top 3
Other browser agents
155
Diverse behavior observed; included for completeness
I’ve recently discovered that Google is expanding its Limited ad serving policy across its Search platform. This change gives Google more control to restrict ad impressions from advertisers deemed unqualified or who might create confusion for users.
The implication of this update is significant. For newcomers, brands receiving negative feedback, or those not clearly presenting their identity in ads, the frequency of ad appearances could be affected.
What’s changing? As of this month, Google is rolling out an expanded policy affecting more search scenarios, which it plans to continue implementing through 2028.
This updated policy allows Google to limit ads on searches they believe might lead to poor user experiences.
How Google decides: User feedback is becoming crucial. Advertisers with frequent complaints about misleading content or practices could face limits on where their ads appear.
Additionally, if an ad makes it challenging to recognize who the advertiser is, Google might also impose restrictions.
Why we care: It’s not just about policy compliance anymore. Google is placing more emphasis on advertiser trust signals and branding clarity. Advertisers who don’t make their brand identity clear or have negative feedback histories might see reduced reach.
This shift underscores the importance of brand transparency in Search ads. Advertisers should reevaluate their ad copy and branding to ensure it’s evident who they are and their ad’s purpose.
What advertisers should do: To align with this update, advertisers are encouraged to enhance brand visibility in ads and landing pages, avoid overly generic messages, and clarify any brand affiliations.
Including a domain headline in the first position of responsive search ads can also help in making the advertiser’s identity more apparent.
The bottom line:Google’s updated policy prioritizes advertiser trustworthiness and clarity, potentially limiting visibility for those creating confusion with their identity or practices.
First spotted: Anthony Higman, Founder of Adsquire, first noticed this update. He expressed his concerns on LinkedIn.
I’m excited to share that Google Analytics is introducing significant updates aimed at streamlining our data analysis efforts. The introduction of cleaner source attribution and enhanced filtering controls is set to make evaluating cross-channel performance much simpler.
With these updates, I’m finding it easier to manage fragmented traffic source reports, enhance cross-channel performance analysis, and minimize noise in the analytics data we rely on.
What’s New. The new Source Group reporting dimension consolidates different traffic source variations into one cohesive category.
For example, instead of seeing scattered source names like “facebook,” “fb,” and others, all Facebook-related traffic can now be grouped under a single identifiable value.
At the same time, Google’s improvements to the Source Platform field ensure classifications align consistently across advertising channels, providing us with clearer data insights.
Why We Care. This cleaner source classification allows me to perform more accurate attribution analysis and cross-channel reporting. Instead of dealing with traffic fragmented by inconsistent labels, I can better understand which platforms truly drive conversions and where our budgets are yielding the best performance.
Including AI traffic sources like ChatGPT and Perplexity in this analysis offers a standardized way to measure these emerging channels alongside traditional ones. New hostname filters further refine data quality by making sure that only approved domain traffic enters our reporting.
The Big Picture. As we manage campaigns across multiple platforms, inconsistent source naming complicates attribution and budget analysis. This new reporting structure is a breath of fresh air, simplifying these comparisons and enhancing our strategic decision-making.
Between the Lines. This update extends source standardization beyond Google’s properties to platforms like TikTok, Pinterest, and Amazon, while also including support for emerging AI-driven traffic sources such as ChatGPT and Perplexity.
Also New. Google has added hostname filters in the Admin section, allowing us to exclude events from unapproved domains before reporting, enhancing data accuracy.
This feature helps prevent unwanted traffic from skewing our analysis, ensuring that our data remains precise and actionable.
What Advertisers Get. The updates provide standardized source reporting, retroactive access to historical source group data, cleaner attribution analysis, and more control over which domains contribute to reporting.
I’m excited to share Microsoft Ads’ latest tool—Product Explorer. It’s a remarkable addition that helps advertisers like us quickly spot catalog issues that might be hindering ad performance.
The introduction of Product Explorer represents Microsoft’s effort to create a central hub where advertisers can effortlessly monitor product catalog health and performance. Navah Hopkins, the Microsoft Product Liaison, highlighted its potential to revolutionize how we handle large product feeds.
Managing these expansive feeds often means struggling to pinpoint which items are ready to serve, which are capturing impressions, or which are missing vital data. Product Explorer steps in to make this task significantly more manageable.
What’s new? Now, I can explore my entire product catalog through a searchable interface. This tool allows for filtering by SKU, title, GTIN, and product ID, helping to quickly identify active products that are delivering performance results.
What it does. Product Explorer is designed to highlight eligibility issues and metadata gaps, along with other elements that might prevent products from serving. Plus, it offers recommended actions and the option to export filtered product lists for deeper analysis.
Why we care. As advertisers, having diagnostics and performance reporting combined in one interface means we can move more products into a servable state while identifying underperforming inventory more efficiently.
From searchable catalog reporting to gaining product-level performance insights covering the last 30 days, this tool offers issue detection and actionable recommendations to enhance feed quality.
The big picture. As retail advertising becomes more automated, focusing on feed quality is increasingly essential. Accurate visibility into catalog issues can significantly impact the reach and performance of our campaigns.
Availability. According to Navah Hopkins, the tool is live and ready for use in our accounts.
