I’ve delved deep into the world of SaaS SEO agencies, reviewing 63 firms to bring you the best in the business. My ratings are based on crucial elements such as industry experience and the caliber of their leadership, among other key aspects:
Notable Clients (30%): The firms’ track record with leading SaaS companies is a primary indicator of their SEO campaign success.
Leadership Experience (20%): I scored each agency based on the SaaS marketing expertise of its executive team.
Median Employee Tenure (15%): Long-tenured employees suggest the firm invests in skill development crucial for sophisticated campaigns.
Average Review Score (10%): Customer satisfaction as reflected in reviews is a strong marker of the agency’s effectiveness.
GEO Offering (10%): Agencies advancing into AI platform rankings beyond Google get extra points.
Year Established (5%): A history of effective strategy adaptation over years signals reliability.
Founder Status (5%): Agencies still led by their founders showcase stability and vision.
Media References (5%): Frequency of citation in authoritative sources underlines their thought leadership.
For each agency, I also highlight their Main Focus, reflecting their specific SEO approaches. Here’s my research distilled into a ranked list:
Highlighting First Page Sage: This top-rated agency in the US, serving giants like Salesforce and Verisign, offers a personalized approach, emphasizing content excellence for long-term ROI. For SaaS companies aiming to prioritize lead generation, their expertise is unmatched.
Next up, I introduce other trailblazers like REQ—known for its seamless blend of traditional advertising and SEO—and Clay Agency, which excels at optimizing user experience to boost conversions. No stone is left unturned in our evaluation!
Explore the sense of collaboration and strategic depth brought by each agency, helping you identify the right partner to elevate your SaaS brand in 2025.
In my quest to pinpoint the top manufacturing SEO agencies of 2025, I embarked on a comprehensive research journey, analyzing and ranking over 50 firms. Using a systematic algorithm, I focused on several key criteria:
Leadership Experience Score & Founder Status (15%): I evaluated the leadership teams of each agency, scoring them from 1-5 based on their industry track record, especially in manufacturing SEO. I also checked if the founding team remains active in a leadership capacity.
Year Founded (15%): The longevity of an agency speaks to its ability to adapt to changes and thrive even during economic downturns, making it a vital consideration.
Average Review Score (15%): I considered client reviews, with a focus on feedback from those in the manufacturing sector, carefully weighing their importance accordingly.
Median Employee Tenure (10%): Employee retention often reflects an agency’s commitment to ongoing staff development and client service excellence.
GEO Offering (10%): With AI like ChatGPT reshaping the landscape, I noted agencies offering geo-targeted services on these platforms.
Media References (5%): Media coverage served as a minor indicator of agency success, judging their visibility and reputability.
Notable Clients (20%): Working with high-profile manufacturing clients significantly demonstrates an agency’s SEO prowess and capability.
Approach to SEO (10%): I preferred agencies focusing on ROI and lead generation, a testament to their strategic thinking and client focus.
After evaluating 54 agencies, I proudly present the top 8 manufacturing SEO agencies of 2025:
In choosing the right SEO partner, I took into account several further considerations to help you assess if an agency aligns with your company’s needs. Here’s what to anticipate:
Factor
Minimum
Average
Excellent
Scope of Work
Limited to a single specialty.
Combines technical with basic SEO content.
Offers a comprehensive, custom SEO strategy.
Content Strategy
N/A — rarely provides content.
Uses standard templates.
Custom strategies with industry-specific knowledge.
Team Composition
Provides learning resources instead of a team.
General SEO team possibly lacking relevant experience.
Dedicated strategists and industry-specific writers.
Campaign Updates
Sporadic email updates.
Weekly meetings with email updates.
Frequent meetings with emergency support.
Measuring Results
Single, initial report; little ongoing updates.
Standard metrics apply to all clients.
Focus on client-specific metrics and ROI.
When discussing with potential agencies, consider asking these crucial questions to get a better understanding of their specialty:
Specialty
Many agencies claim to do it all, but each has a particular strength. For instance, First Page Sage excels in crafting thought leadership and tailored strategic SEO plans. Ensure agencies provide clear answers about their specialties.
Follow-up questions:
What does your agency specialize in?
What are your core services?
Content Creation
Their approach to content creation reveals much about their understanding and view of your industry. Identify whether they involve your team’s experts and their methodology for content planning, especially if content creation is not a primary service.
Follow-up questions:
How involved will my team’s experts be?
How do you organize your content plan?
How many people will be assigned to my account?
What is the writer’s background?
Determining Results
Determining success is crucial and should be transparent. Any agency reluctant to discuss or adapt its preferred metrics is a red flag.
Follow-up questions:
How do you measure campaign success?
How frequently do we receive progress reports?
How do you attribute marketing results?
Can you provide client testimonials, specifically from my industry?
Learning More About Choosing an SEO Agency
For more insights on selecting a manufacturing SEO agency, feel free to reach out to us.
I’ve always been fascinated by how Google Search has driven innovation by rewarding high-quality content with visibility and traffic. In the last article, I explored the risks of Google AI over-personalizing results and reinforcing filter bubbles.
This time, I’m examining a different concern. If Google’s new AI results lean toward uniformity, favoring big brands and consensus views, it might stifle creativity and innovation, while speeding up the web’s commodification.
Some might think this worry is naive, as the internet is largely commodified. Historically, however, small websites believed they had a shot at ranking and driving traffic. The internet has been perceived as a vast digital marketplace of ideas. But with AI models seeking consensus, appearing in AI search when you diverge from mainstream could become challenging.
To gain traffic via Google, these companies now resort to buying ads or leveraging platforms like TikTok and Instagram. Most choose the latter, abandoning efforts to rank in Google entirely. Not all sites losing visibility lacked editorial quality—some offered high-value, human-focused content.
The core issue is that if these companies vanish, the diversity of information indexed by Google—and now utilized in AI search—becomes limited. Prodding smaller publishers to migrate to social platforms could further diminish web diversity. If independent creators face consistent exclusion from rankings, their drive to share unique perspectives might dwindle.
Social media could serve as a counterbalance in Google’s strategy, which is somewhat promising. Google recently decided to rank YouTube Shorts within Discover, and has a ‘Short Video’ tab on many results. It’s also showing increased interest in posts from Reddit and LinkedIn. Maybe, in Google’s perspective, unique opinions should emerge from independent creators, while mainstream views stem from larger brands. Only time will reveal the truth.
The impact of advertising
Ads in AI Overviews are already appearing, giving us a glimpse into Google’s monetization plans for AI. Meanwhile, we can analyze how Google has altered ads and ecommerce to accommodate AI.
The move to Performance Max (PMAX) bidding in Google Ads has perplexed many advertisers. Its opaque system limits control and data visibility, potentially making advertisers complacent as Google assures better returns with reduced effort. However, what happens if advertisers wish to understand their audience deeply?
When Google manages PMAX bidding without disclosing what works, it learns about your customers using your resources without sharing insights. This deprives you of applying these learnings across other advertising channels. In some sectors, Google might learn enough to bypass you with customers, similar to Google Travel integrating Flights, Hotels, and more. Truly, AI is a double-edged sword.
Google’s aggressiveness in promoting its ad options strikes me distinctly. I encountered an ad via a full-screen takeover on an organic SERP—a rarity for Google whose full-screen takeovers usually signal terms changes or opt-ins.
Recent Terms and Conditions underline Google’s user data sharing across Alphabet properties to personalize advertising. This sharing combines with modeled data to fine-tune targeting on both micro and macro levels.
It seems Google will continue this path unless opposed. Google’s vast market share limits alternatives for searchers, publishers, and advertisers, offering them few escape options. This enables Google to prioritize monetized AI results over organic traffic, though adjusted ad labeling might blur distinctions further.
The updated Terms and Conditions, shown to EU users, emphasize Google’s data use across platforms. Including Google Ad services in the update illustrates their reach through our ad data, indicating how advertisers fund Google’s platform enhancements, despite limited data access.
So what can we do to protect the health of the internet?
I’m captivated by AI’s potential, often diving in with reckless excitement. I confess to leaning towards “AI doomism,” believing negative scenarios are more probable due to our tendencies and lack of oversight.
Once technology manifests, it cannot be undone, particularly online, where it is ever rememberable. Human memory is flawed, but the internet remembers, so the AI genie is now out of the bottle.
So, how do we prepare for AI’s future and craft frameworks, guidelines, and rules preserving internet health while fostering AI innovation? How do we allow diverse content discoveries without stifling AI progress?
I believe in collaboration between digital marketing and publishing industries, which are already uniting to protect copyright interests. Operating separately won’t generate internet-protecting measures on either side.
Until solid AI regulations are created and enforced, setting collective, collaborative internet protection standards surpasses individual interests. Like unionized workers defend against exploitation by powerful companies, we need collective bargaining and protection.
Some EU movements aim for broader digital and AI regulation, but digital marketing and SEO might benefit from self-developed, community-enforced standards, moving beyond “black hat” or “white hat” labels, especially for AI. It’s a dialogue worth pursuing.
I’ve got great news—Google Search Console has officially rolled out custom annotations for performance reports! After extensive testing, this amazing feature is finally live.
Now, I can easily annotate my reports directly within Search Console. This means I’ll never forget essential events like coding changes, algorithm updates, or any website bugs that might crop up.
What are custom annotations? According to Google, custom annotations are “Notes you create yourself to mark important events specific to your property, such as when you launch a new feature, or fix a bug on your website.”
Google began testing this feature in May 2025, and it’s thrilling to see it live now.
What do they look like? Take a look at this screenshot of a custom annotation in Search Console:
How does it work? Adding custom annotations to my performance reports is a breeze. Here’s how I do it:
Right-click the chart on the specific date I want to annotate.
Select a date using the date picker.
Type my note in the text field (up to 120 characters).
Click Add.
I can add up to 200 annotations on a single property, which is fantastic!
To delete annotations, here’s what I do:
Click the annotation marker on the chart to see the note.
Select DELETE in the annotation pop-up window.
Select Cancel or Delete on the following screen to cancel/confirm.
Note that I can’t edit annotations, and any annotations older than 500 days will be automatically deleted.
Why do I care? Annotations are an excellent way to keep track of changes on my website as I review these performance reports. As Google mentioned, “Annotations in Search Console help you understand changes in your data by providing context on your charts.”
Here are additional reasons Google encourages using annotations:
Infrastructure changes like updating a template or a site migration
SEO efforts like implementing a new plugin or hiring an agency
Changing content to focus on different user intents
External events that affect your business, such as holidays
It’s important to remember that annotations are visible to anyone who has access to those properties, so I make sure to post cautiously.
I recently discovered something intriguing from court filings related to Google’s antitrust case, revealing FastSearch, a system unfamiliar to many search marketers. At the heart of Google’s AI Overviews, FastSearch prioritizes speed over the deeper analysis we’ve come to expect from traditional search results.
This leads me to wonder: what does FastSearch really focus on?
FastSearch is Google’s internal mechanism designed for grounding Gemini models and producing AI Overviews. While the traditional Google Search analyzes vast amounts of web data using numerous ranking signals, FastSearch prefers a more targeted approach, emphasizing speed.
FastSearch uses RankEmbed signals, which generate condensed, ranked web results that models can use to yield grounded responses faster than the traditional Search processes. However, this comes with a tradeoff in quality.
Marie Haynes highlighted this revelation after examining the legal decisions regarding Google’s monopoly case.
FastSearch achieves faster results by making three compromises.
Smaller document pool: Instead of scanning Google’s entire index, it accesses a focused subset of pages to cut down processing time.
Simplified ranking signals: It mainly uses RankEmbed signals to emphasize semantic connections over traditional authority indicators such as backlinks.
Acceptable accuracy threshold: While FastSearch results are less detailed compared to fully ranked outcomes, they are deemed satisfactory for grounding AI responses.
The court documents also describe RankEmbed as a high-level signal capable of identifying patterns in extensive data sets. This focus on semantics means content with clear topical relevance might perform better than pages relying on high domain authority but lacking relevance.
Traditional SEO strength doesn’t automatically ensure visibility in AI Overviews.
Google integrates FastSearch into its Vertex AI platform. This means Google’s business users can leverage the technology for AI without receiving direct FastSearch results, safeguarding Google’s intellectual property.
For content strategy, FastSearch highlights the importance of clarity, topical depth, structure for extraction, and maintaining traditional SEO fundamentals. These strategies are critical for AI visibility.
FastSearch’s emergence shouldn’t lead us to neglect SEO fundamentals. According to Google’s Danny Sullivan, solid SEO is key for effective generative engine optimization. This means understanding user searches, creating valuable content, and making it accessible to search systems both remain vital.
In summary, optimizing your content approach involves conducting semantic audits, tracking AI performance separately, testing content structures, and keeping traditional SEO practices. FastSearch offers insights into Google’s future, spotlighting the need for transparent and helpful content that makes a significant impact on users.
As someone deeply immersed in the digital world, I’ve witnessed how AI is fundamentally changing how we search for information online. It’s quite a challenge to ensure that our content stays both visible and impactful as these AI platforms evolve.
While traditional SEO tactics are still important, I’m learning that embracing AI SEO is essential for thriving. By tailoring our content for AI systems, we can distinguish our brands among AI-generated responses, especially within large language models like Google’s Gemini, Microsoft Copilot, and ChatGPT.
It’s surprising to see that ninety percent of businesses are worried about losing SEO visibility in this AI-dominated search era, as revealed in a recent survey. Many plan to increase their SEO budgets; however, prioritizing strategies remains a common challenge.
To navigate this rapidly changing field, I’ve found five key factors that can drastically improve our AI search visibility. These factors are fundamental industry “pillars” of technical AI SEO that will be vital in making our content stand out in this new, AI-driven search ecosystem.
Content retrievability is another crucial aspect. It’s all about making sure AI systems can easily find, extract, and attribute information from our content. If AI can’t access or accurately pull our content, it won’t appear in AI-generated answers, meaning we miss out on engagement opportunities.
Structuring pages with clear headings, concise bullet points, and optimized multimedia content is key. It’s remarkable how a study showed that schema-marked pages have a 40% higher click-through rate than those without it.
To improve content alignment, it’s all about speaking the language of AI. AI systems favor content with clear, direct answers that align with people’s conversational queries.
Including summaries at the start of pages and using a conversational tone can greatly increase AI’s use of our content in response generation. Interestingly, 88.1% of queries triggering AI Overviews are informational in nature.
We need to focus on competitive differentiation by offering unique insights or perspectives that stand out from competitors. AI systems prioritize relevance and value, seeking content that truly stands out to users.
Harnessing unique data or case studies, and offering fresh perspectives can set our content apart. It’s noteworthy that 22% of B2B marketers attribute success to understanding their audience deeply.
Authority signals build trust with AI systems by proving the credibility and reliability of our content. AI trusts content that’s backed by authoritative sources.
This means including consistent source citations and gaining backlinks. Pages with more backlinks rank higher, as noted in a Backlinko study.
Entity mapping allows AI systems to grasp the relationships between key entities in our content. By clearly identifying and linking these entities, we help AI build a more nuanced understanding of the content’s context.
Explicitly naming and linking key entities and creating a semantically related internal linking strategy enhances AI’s ability to surface our content in relevant searches. A recent experiment demonstrated that sites with comprehensive schema markup significantly outperformed their counterparts.
In conclusion, AI visibility requires more than just traditional SEO. Optimizing for content retrievability, alignment, differentiation, authority, and entity mapping will ensure our brand remains not just visible, but authoritative in AI-driven search results.
As AI continues to transform search landscapes, partnering with a knowledgeable SEO agency becomes increasingly crucial. Agencies blending traditional SEO strategies with cutting-edge AI optimization will be invaluable for leading in this ever-evolving field.
I can’t help but feel restless as I ponder the evolving landscapes of SEO and AI search. Treating ChatGPT like Google seems like a recipe for failure in today’s world of RAG, reranking, and probabilistic systems.
As someone engulfed in SEO for years, I’ve tried to relate each new technology to the tools I know well.
Remember the buzz around “mobile SEO” when mobile search surged or when “voice search optimization” became the new must-know with voice assistants?
In my journey, I once thought I had Google all figured out. That belief shattered after examining how ChatGPT selects citations, analyzing Perplexity’s ranking process, and digging into Google’s AI Overview criteria.
I’m not claiming that SEO is obsolete or that we’ve encountered a total paradigm shift. I want to share the lingering questions that suggest we might need to fundamentally alter our methods of understanding.
These questions have emerged from months of intense analysis of AI search systems, documented observations of ChatGPT’s behavior, and reverse-engineering Perplexity’s ranking factors.
The Questions That Won’t Let Me Sleep
The questions reflecting on AI’s complexities have dismantled much of what I once confidently believed about search optimization.
When Math Doesn’t Add Up
While I grasp PageRank and link equity, encountering Reciprocal Rank Fusion in ChatGPT’s code led to moments of realization where I comprehended my gaps:
Why does RRF prefer consistency over singular excellence in query results? Is securing the #4 spot across multiple queries superior to achieving #1 once?
How do vector embeddings alter semantic distance from conventional keyword matching? Are we striving for semantic intent or mere words?
Why does temperature=0.7 cause unpredictable rankings? Are repeated tests now mandatory?
How do cross-encoder rerankers approach query-document pairs versus PageRank? Is now the time to shift towards real-time relevance?
These questions echo traditional SEO concepts but seem rooted in entirely different mathematical frameworks when juxtaposed with LLMs. Or are they?
When Scale Feels Unbreachable
While Google indexes trillions, ChatGPT retrieves a measly 38-65 results. This stark 99.999% reduction leads to pressing inquiries that linger:
Why does ChatGPT retrieve so few results compared to Google’s billions? Is this a short-term anomaly or a foundational shift?
How do token limits imbuing rigid confines differ from traditional search’s freedom? When did search results shrink in their dimensionality?
Does the k=60 constant in RRF conceal a ceiling on visibility? Has position 61 supplanted the secondary page?
Are these mere modern-day constraints? Or do they signal a novel information retrieval ideology?
The Questions that Continue to Haunt Me
Here are 101 questions that persist, gnawing at what I believed I knew about SEO in the AI era:
Is OpenAI employing CTR for citation rankings?
Does AI perceive our page layout as Google does or focus just on text?
Should our writing gear towards shorter paragraphs for AI to digest content adeptly?
Can interaction metrics like scroll depth or mouse movement influence AI ranking signals?
What is the effect of low bounce rates on our citation potential?
Could session data like reading order prompt AI model rerankings?
How might a nascent brand integrate into offline training data to earn visibility?
What strategies optimize a web/product page for probabilistic systems?
Why do citations transform inexplicably?
Is running multiple tests necessary to gauge variance?
How can Google’s “blue links” aid in acquiring specific answers to long-form questions?
Do LLMs mirror the same reranking algorithms?
Does web_search act as a binary switch or a probabilistic trigger?
Should our focus pivot to accolades or citations?
Is reranking deterministic or stochastic?
Do Google and LLMs utilize identical embedding models, and if so, what’s the corpus variance?
Which pages garner maximal requests by LLMs and maximum visits by users?
Should we monitor drift post-model updates?
Why does EEAT manipulate seamlessly in LLMs contrary to traditional Google search?
Who among us amplified traffic tenfold post-Google algorithm revelation?
Why does the answer structure morph even within a mere day’s interval?
Could post-click engagement amplify our odds of inclusion?
Is session memory gearing citation bias towards preliminary sources?
Why inherently are LLMs more prone to bias than Google?
Does offering a downloadable dataset escalate citation potential?
Why does content in Turkish retain anachronistic data despite contemporary queries?
Are vector embeddings capturing semantic difference distinctly from keyword associations?
Should we master LLMs’ “temperature” value henceforth?
How can a modest website emerge in ChatGPT or Perplexity answers?
What events unfold if our entire site optimizes solely for LLM targeting?
Might AI agents evaluate images alongside pages at an instant, or simply focus on surrounding text?
How could we ascertain AI tools leveraging our content?
Could AI models quote a lone sentence from our blog posts?
How do we ensure AI comprehends our business purpose?
What differentiates pages showing in Perplexity or ChatGPT but absent from Google?
Does AI preference newer content over steadfast, older references?
Once retrieved, how might AI rerank content?
Could LLMs retain our brand voice enveloping their outputs?
Is there a mechanism enabling AI summaries with direct links to our pages?
Can we monitor when our content is quoted without linked acknowledgment?
Can we identify prompts or themes fostering additional citations?
What shifts when monthly client SEO reports rebrand as “AI Visibility AEO/GEO Reports”?
Is there a facility to estimate brand mentions within AI results akin to search volume metrics?
Could Cloudflare logs reveal AI bot exposure to our domain?
Could model updates reset pre-existing reranking preferences while retaining partial memory?
Why are traditional queries often more definitive sans AI hallucinations?
Who in the system dictates final citation preferences?
Can human feedback loops reshape LLM source rankings?
When might an AI initiate midpoint searches amid answers, and why are multiple continuous AI searches within a single chat window observed?
Does being once-cited predispose future citation allocation? Can LLM ranks sustain visibility likened to Google’s top 10?
Do frequent citations autonomously elevate a domain’s retrieval priority?
Are user clicks on linked sources embedded in feedback signals?
Do Google and LLMs employ identical deduplication protocols?
Might citation velocity be traced akin to SEO link velocity?
Will LLMs someday curate a lasting “citation graph” paralleling Google’s link constructs?
Do LLMs correlate brands entwined in related subjects or question clusters?
How long elapses before repeated interactions etch into durable brand memory within LLMs?
Why doesn’t Google reveal 404s while LLM responses do?
Why fabricate citations while Google directs only to accessible URLs?
Do LLM retraining phases present reset opportunities post-visibility slump?
How should we construct recovery roadmaps against AI model misinformation?
Why might some LLMs cite while others disregard?
Are ChatGPT and Perplexity leveraging identical web data repositories?
Do OpenAI and Anthropic gauge trust and freshness identically?
Do source-specific limits apply to maximum AI citations per response?
How shall we verify citation following content evolution?
What’s the simplest route to trace prompt-level visibility over extended periods?
How can we persuade LLMs to regard our assertions as factual?
Does a topic-aligned video linked to the page fortify cross-format grounding?
Could identical questions lead to divergent brand suggestions for differing users?
Might LLMs register previous brand engagements?
Can previous click histories skew subsequent LLM endorsements?
How do retrieval and reasoning converge on citation attributions?
Why does ChatGPT retrieve 38-65 outputs while Google spans billions?
How do cross-encoder rerankers diverge from PageRank in query-document evaluations?
How does a backlink-void site surpass authorities within LLM result sets?
Why impose token barriers absent in conventional search?
Why does LLM temperature determination yield erratic rankings?
Does OpenAI allocate a dedicated crawl budget to web properties?
Do Knowledge Graph recognition and LLM token embedding methods diverge?
How is crawl-index-serve distinct from retrieve-rerank-generate dynamics?
Do temperature settings in LLMs generate inconsistent rankings?
Why is tokenization integral?
How does a knowledge cutoff induce unintentional blind spots versus real-time crawling dynamics?
When Trust Turns Probabilistic
I grapple with how Google reliably links to tangible URLs while AI systems, astoundingly, can fabricate information:
Why might LLMs fabricate citations while Google anchors existing URLs?
How do hallucination rates of 3-27% stand against Google’s 404 incidence?
Why do similar queries yield conflicting “facts” in AI over search indices?
How does obsolete data prevail in Turkish content despite contemporary inquiries?
Are we orienting ourselves around systems liable to mislead users? How does one manage that eventuality?
Where We Stand
I’m not suggesting AI search optimization/AEO/GEO is utterly unlike SEO. Yet, I confront 100+ unanswered questions challenging my foundational SEO acumen at this moment.
Perhaps solutions await folks with more advanced insights. For now, I remain entwined in seeking answers but know these queries will persist, with brand new ones arising on the horizon.
The mechanisms generating these queries aren’t vanishing. We must engage, scrutinize, and potentially innovate approaches to fathom and leverage them.
The victors in this novel expanse won’t inevitably own the totality of wisdom. But they will bravely ask, probe, and identify workable solutions amid ambiguity.
As an advertiser, I need to be vigilant about the phone numbers I include in my Google Ads. Recently, Google has announced stricter rules, and any number linked to fraud or past policy breaches will soon be disallowed.
Google is updating its Destination requirements policy to ensure all phone numbers used in ads are free from any ties to fraudulent activities or previous policy violations. This is part of an ongoing effort to prevent misleading advertising tactics.
The timeline:
Policy update effective: December 10, 2025
Enforcement ramp-up: Over roughly 8 weeks after rollout
What’s changing. Any phone number identified as fraudulent or having a history of policy violations will be rejected under the new Destination requirements policy, resulting in ad disapprovals.
Why it matters to me. This update is crucial because it targets individuals who might misuse legitimate-looking phone numbers to deceive users or bypass policy enforcement. It’s a reminder for me to thoroughly review and verify all contact information across my campaigns to avoid disruptions in ad delivery, delays in approvals, or impacts on my campaign performance.
Steps for advertisers. If I’m affected by these changes, I’ll receive a disapproval notice and can consult Google’s help center for steps to rectify any disapproved ads or assets.
First seen. This significant update was initially shared by Anthony Higman, founder of ADSQUIRE, on X.
Reading between the lines. This policy update is part of Google’s broader strategy to enhance ad verification and destination standards amid growing attention on scams and maintaining consumer trust. It’s clear that the responsibility for ad content now goes beyond just the landing page.
I’m excited to share that Google is enhancing our ability to understand Display campaign performance with the rollout of asset-level reporting. This new feature will let us see how each creative performs, which will undoubtedly help us make smarter optimization decisions.
Why it Matters to Us. Previously, our insights were limited to an overall view of ad performance. Now, we can dive deeper, analyzing each asset—be it images, headlines, or descriptions—to understand what’s truly driving engagement.
How We Can Use This. Google Ads is introducing a new Assets tab where I’ll be able to:
Examine the performance of each creative asset.
Track when assets were last updated, giving insight into iteration history.
Decide which assets to keep, update, or remove based on performance data.
The Details. To help us get started, Google has published a support page titled “About asset reporting in Display,” which includes links on:
Get started
How it works
Asset reporting for your Display campaigns
Assessing asset performance
Looking Deeper. This update draws parallels with Performance Max reporting features, highlighting Google’s ongoing efforts to merge insights across different campaign formats and increase transparency in automated advertising.
What’s Next. Although the feature isn’t live yet, I discovered its mention in Google’s support center, first pointed out by PPC News Feed founder Hana Kobzová, indicating that a broader release is on the horizon.
I recently came across Google’s latest announcement about their AI tool called Opal. It’s causing quite a stir among SEOs and content creators, including myself, who are wondering about its implications.
Google’s blog post described Opal as a tool for creating ‘optimized’ content in a ‘scalable way.’ This has left many of us questioning whether this approach aligns with Google’s own search guidelines, particularly those relating to scaled content abuse.
What Google Shared. According to Google’s blog, Opal is particularly useful for creators and marketers aiming to produce consistent and scalable custom content. It can generate optimized blog posts, social media captions, and even video ad scripts from a single product concept.
The Policy Concerns. This leads us to Google’s scaled content abuse policy, which warns against generating numerous pages primarily to manipulate search rankings. The practice usually involves creating unoriginal content that offers little value to users.
Google’s examples include using generative AI tools to churn out many pages without adding user value.
Does This Breach Google’s Guidelines? The pressing question is whether promoting Opal contradicts Google’s established rules. As long as the main goal isn’t to game the search rankings, but rather to genuinely assist users, Google states using such AI tools is acceptable.
Interestingly, Reddit’s use of AI to translate pages on a large scale was something Google seemed fine with, as noted in a related discussion.
Community Backlash. Many within the SEO community argue that Google’s stance appears contradictory, sparking considerable debate. I gathered several reactions shared by SEOs, highlighting these concerns.
Some voices suggest Google is now promoting AI tools that could very well create ‘spam’ content, while traditionally, it has opposed such practices.
Our Role and Responsibility. This situation prompts us to consider how ‘AI slop’ might damage the web. Google’s algorithms are, fortunately, designed to reward content that genuinely aids users, emphasizing that AI isn’t inherently negative.
When leveraging AI tools like Opal, it’s crucial to use them as aids rather than letting them fully automate without oversight. Responsibly integrating AI will ensure content remains valuable and user-focused.
As of now, we’re still awaiting further comments from Google to shed more light on this topic. I will make sure to update the story when we receive their statement.
