I’ve recently stumbled upon a fascinating test by Google in their Shopping results. They’re experimenting with something called “Sponsored Shops,” which could totally change how we see competition in Shopping ads.
These “Sponsored Shops” spotlight entire stores rather than just individual products, meaning brands might need to rethink their strategy to gain visibility.
Imagine seeing a block in Shopping results that brings together several products from a single retailer, complete with store name, product ratings, and brand presence. It’s like a mini-storefront right there in the search results!
Why this matters to me. If this change spreads, it means the competition won’t just be about single products anymore. As a brand, I might need to ensure that my entire product feed is strong and diverse to capture these new ad placements.
Besides, this format has the potential to redirect traffic flow from individual product pages to broader store pages. For someone managing campaigns, it could mean prioritizing brand presence over just targeting specific product bids.
The bigger picture. It looks like Google’s trying to move Shopping ads slightly higher up the sales funnel. With one placement, I can emphasize a wide range of offerings and bolster my store’s identity.
Why this is notable for us. This approach can significantly boost exposure per impression by allowing multiple products to be showcased together. It’s an excellent way for us to strengthen brand presence in search results.
As a user, I find it makes discovery a lot simpler. I can easily browse a variety of items from one retailer without leaving the results page.
Reading between the lines. If this new format catches on, it’ll likely reward those, like me, who have invested in stronger product feeds and have great seller ratings. Merchants that depend solely on individual product listings might find themselves at a disadvantage.
What I’m curious about. I wonder how different parts of the ad unit will perform in terms of clicks. Stephanie Pratt, a Marketing Operating Lead, even pointed out the potential for consumer confusion between clicking on brand names versus individual products.
“It’ll be interesting to see the split of clicks on each part of the ad unit, and how much is on the brand name vs product and if that will confuse some consumers
The bottom line for us. If “Sponsored Shops” goes beyond its testing phase, Google Shopping might lean more towards store-level competition. This could mean a shift in strategy for me—from product-centric optimization to enhancing brand presence across the platform.
Where I first encountered this. This intriguing development was spotted by PPC Specialist Arpan Banerjee, who shared it on LinkedIn.
When I hear the terms “incremental” and “incrementality” in affiliate marketing, I sometimes wonder if they truly reflect their intended meaning. Often, they don’t indicate an actual increase in sales, new customers, or revenue. Many affiliate marketers seem to focus only on the affiliate channel, overlooking the broader company impact.
I’ve learned to question whether sales would occur without an affiliate program to assess true incrementality. This helps me determine if a partner genuinely brings new customers and revenue or just diverts those already heading towards checkout.
High-intent traffic is frequently mistaken for incremental value. But just because someone is ready to make a purchase doesn’t mean this touchpoint wouldn’t exist without affiliates. For instance, a coupon site might target consumers already at checkout, simply searching for brand discounts on Google.
Closing an affiliate program today might mean touchpoints still occur without extra costs like commissions and fees. Sure, this traffic involves high intent—it’s consumers in the checkout line. Nonetheless, I might be losing money if the touchpoint provides low or no value.
Note: Not all coupon or deal sites are detrimental. Some might genuinely add value, so I always ensure to test if sales remain consistent without the program before deciding.
The more customers heading to my checkout, the more top-ranking affiliates on Google earn. They depend on intercepting my traffic, which is why they’re sometimes labeled as parasitic. This is where incrementality becomes crucial.
Do touchpoints that consistently occur without your program constitute incremental sales? It’s vital for me to define incremental sales and value clearly.
Incremental sales are those driven by partners, which wouldn’t occur without them. Incremental value arises when affiliates enhance customer value through means your company couldn’t achieve, like increasing cart size or building trust for more conversions.
As a brand, I can offer discounts without an affiliate program. Even without the program, I could submit deals to sites that rank for my brand + coupons, achieving similar sales without incurring network fees, commissions, or salary costs.
If partner-exclusive deals drive sales through unique platforms, it demonstrates incremental value. That’s something unattainable without them, making the affiliate an asset.
Here are some content types and programs adding real incremental value.
Product and brand comparisons
Product and brand comparisons represent two key areas where affiliates can drive value. The affiliate decides which brand or retailer secures the sale, influencing customer choices. For smaller brands, appearing in comparisons with major players can establish credibility and drive incremental revenue.
Affiliates who present unbiased comparisons and reviews cultivated trust, adding value and potentially broadening my customer base.
Tip: Utilizing non-affiliates for brand comparisons can be a more cost-effective strategy.
For instance, I might pay a one-time fee for an independent comparison versus ongoing affiliate commissions, potentially saving money long term.
Moreover, for a smaller brand, being included in comparative reviews can be a significant opportunity to weave into larger brand traffic and attract their customer base.
Types of partners that can offer this value include:
Review and comparison websites.
Listicle sites (SEO and PPC).
YouTubers.
Communities and forums with user-generated content and shopping guides.
When it comes to creators, both those who review and those who don’t, they possess unique content styles that can enhance incrementality.
Some creators add significant value simply through brand mentions and their trusted recommendations—whether they produce detailed reviews or provide other engaging content.
Ultimately, I’ve found that detailed data analysis and testing help me navigate what incrementality means for my business. This involves discerning between true incremental partners and those who merely capitalize on existing customer journeys.
When I think about how often I scroll through LinkedIn, I’m excited to share that the platform is launching a cutting-edge AI-powered feed ranking system. It’s designed to analyze what we post, read, and engage with, thanks to large language models and advanced GPUs. This innovation aims to provide more personalized content updates for its vast user base of 1.3 billion.
Why this matters to me. Understanding LinkedIn’s content surfacing process can be a game-changer for anyone wanting their posts—or their brand’s—to gain visibility. The focus is on what’s relevant and engaging within our network. As LinkedIn Tweaked their system, posts that show expertise and contribute to trending professional topics have a better chance to go viral, regardless of our existing connections.
What’s under the hood. LinkedIn has revamped its feed recommendation mechanism using large language models and sophisticated transformer models, all powered by GPU infrastructure. The overhaul targets two key functions: the retrieval and ranking of relevant posts in our feeds.
Unified retrieval system. One of the most intriguing aspects for me is how LinkedIn has consolidated its discovery processes into a single model powered by LLMs (large language models). Previously, posts could come from various sources such as network activity and trending topics. Now, LinkedIn uses LLM-generated embeddings to interpret post content and align it with our professional interests.
For instance, by engaging with posts about small modular reactors, I might see content linked to renewable energy or other related fields, even if they use different terminology.
Ranked by your interests. Once posts are retrieved, LinkedIn ranks them utilizing a transformer-based sequential model. Instead of looking at posts individually, the model examines patterns in my past interactions, including likes, comments, and the time I spent viewing content.
This helps LinkedIn adapt to my evolving professional interests and recommend content that aligns with these shifts.
System performance and architecture. Powered by a GPU infrastructure that processes millions of posts, this system keeps our feeds fresh.
LinkedIn reports that this system can refresh content embeddings in mere minutes and retrieve suitable candidates in under 50 milliseconds.
Enhancing feed quality and authenticity. LinkedIn has also announced updates aimed at boosting content quality:
Addressing automated engagement. They’ve started cracking down on tools that automate comments or use engagement pods to fake discussions. LinkedIn clarifies these violate platform policies and devalue genuine interactions.
Cutting down on engagement bait and generic content. The platform will deprioritize content designed solely to provoke comments or clicks—such as posts begging for comments to inflate reach, irrelevant video-text pairings, and regurgitated thought-leadership content.
Helping newcomers customize their feeds faster. New users can now utilize the “Interest Picker” during signup to select topics of interest, whether it be leadership, career growth, or job-seeking skills, ensuring relevance from day one.
I’ve learned that few searches actually lead to clicks, and discovery now occurs across AI, social media, and search engines. To keep our ecommerce brand visible, we need to make smart organic content investments.
The landscape of organic content is changing, shifting from a mindset of ‘publish more’ to ‘prove more.’ AI summaries and shopping features directly answer user questions in search results, which means visibility alone isn’t enough to resolve buyer uncertainties.
As an ecommerce brand, our goal is to achieve organic visibility that garners recognition and trust amid the SERP noise. It’s crucial to invest in organic assets that achieve three things:
– Reduce buyer uncertainty.
– Are easily readable by machines.
– Work across multiple discovery platforms.
The forces shaping organic content’s ROI in 2026
I’m observing three key forces influencing how content performs in searches today.
AI discovery is normal now
Generative AI is a regular feature in organic search results, providing direct answers to broad questions through tools like Google’s AI Overviews. These systems often use citations from web content to form their answers.
Over the past decade, I’ve delved into hundreds of resumes, conducted numerous interviews, and steered several technical assessments for SEO candidates.
Throughout this journey, I’ve come across many outstanding professionals. However, I’ve also observed a recurring pattern of interview mistakes that can hinder even the most capable candidates.
Here are 11 common pitfalls I’ve noticed in SEO interviews, along with tips on how you can easily dodge them.
1. Projecting Arrogance Instead of Confidence
Confidence is essential! While imposter syndrome is prevalent in SEO, it’s crucial to exhibit genuine trust in your abilities and experience. However, there’s a thin line between showing confidence and coming off as arrogant.
It’s important to discuss your achievements such as:
Complex projects you’ve navigated
Remarkable results you achieved
Stakeholder buy-in you garnered
Clearly articulate what you accomplished and how, while showcasing your theoretical knowledge. Engage in discussions and respect differing opinions—assuming they’ll agree with you can border on arrogance.
SEO isn’t one-size-fits-all. You might have experiences leading to different conclusions from your interviewer, and that’s okay—it’s part of SEO’s diverse nature.
When interviewing, I search for team-oriented individuals who are confident in their knowledge yet open to new insights and collaborative growth. Avoiding arrogance helps you come across as teachable and receptive to feedback.
2. Offering Vague Project Details
Interview time is your moment to shine, showcasing your work. A common mistake is assuming interviewers will fill in the blanks when discussing projects. Be specific about project significance, using the STAR method:
Situation: The issue or opportunity
Task: Your role and the goal
Action: Steps taken
Result: Outcomes and learnings
Utilizing this technique aids in conveying clarity and context. Select examples with outcomes you’re proud of or can explain why they fell short.
3. Dodging the Question
Some candidates avoid directly answering questions due to uncertainty or discomfort, opting to address topics they’re more familiar with. However, if an interviewer asks about navigating a complex website migration, they genuinely want to hear about it.
Pay attention to their queries, explaining if you need a moment to think. If unfamiliar with a situation, acknowledge this but discuss what you might do instead. Honesty trumps fabricated tales.
4. Misreading Your Audience
Building rapport with interviewers is key, requiring an understanding of your audience. Answer their questions clearly, align your language with theirs, and be mindful of their SEO knowledge level.
Avoid overloading non-SEO stakeholders with jargon they might not grasp, while avoiding superficial complexity when addressing SEO experts.
5. Disrespecting the Site’s Progress
When interviewing, never assume negligence on the company’s part concerning their SEO. Acknowledge issues respectfully, understanding there could be constraints they’re navigating.
Inquire about challenges instead, which can provide insights into potential hurdles if you join their team.
6. Unprepared for Common Questions
Interviews can be daunting, and memories may falter. To combat this, come prepared with relevant projects or challenges that align with core SEO areas.
For senior technical SEO roles, you might want to prepare examples like:
Complex issues with crawling or indexing
Large SEO projects needing stakeholder buy-in
Handling organic traffic drops
Leading a website migration
For SEO account manager roles, examples might include:
Explaining performance changes to stakeholders
Presenting SEO strategies to diverse audiences
Onboarding new clients after a successful pitch
Having detailed examples ready, using the STAR method, can help you adapt your responses effectively.
7. Lacking Substance in Responses
A common mistake is speaking before thinking, often leading to rambling. It’s okay to take your time. Listen carefully and structure your responses for clarity.
If the question is unclear, ask for clarification instead of trying to muddle through. Transparency about unfamiliar scenarios could open doors to learning opportunities with interviewers.
8. Bribery or Threats
This should be obvious, but don’t resort to bribing or making threats. Whether it’s promises of backlinks or ‘exclusive’ strategies, honesty is essential in demonstrating your competency.
Similarly, avoid suggesting potential negative actions against businesses—it reflects poorly on your professional integrity and may disqualify you for future opportunities.
9. Overzealous Networking
Enthusiasm for standing out sometimes leads to excessive contact within a company. Be mindful of how often and with whom you’re reaching out.
While follow-ups are valuable, avoid overwhelming busy professionals outside of the formal process.
10. Misrepresenting Your Role
Being honest about your involvement in projects is crucial. Exaggerating contributions will surface in detailed questioning and highlight limited knowledge or expertise.
Speak truthfully about your impact and learnings from team collaborations, distinguishing between your contributions and those of the group.
11. Blaming ‘Google Lies’
It’s a frequent error to attribute discrepancies to Google’s supposed deceit. Relying on such rationale can reveal a lack of technical understanding.
Instead, think creatively and rationally about possible explanations, showcasing a thoughtful approach to problem-solving in the SEO realm.
Ace Your SEO Interview
By steering clear of these common missteps, you position yourself as a confident, well-prepared, and collaborative candidate. With the right approach, you can leave a memorable impression and secure your next SEO role.
In a bold move, I’m witnessing firsthand how SerpApi is requesting a federal court to dismiss Reddit’s lawsuit. This legal battle centers around the alleged scraping of Reddit content from Google Search. From my perspective, SerpApi argues that Reddit is using copyright law to exert control over user posts and public search results.
Reddit’s initial complaint was amended in February, but I noticed that SerpApi remains firm. They argue that Reddit has not adequately demonstrated copyright ownership, technical circumvention, or tangible harm resulting from these actions.
SerpApi’s argument. From a blog post by SerpApi CEO Julien Khaleghy, I gather that the lawsuit is flawed for several reasons:
Reddit, interestingly enough, does not own the majority of the content in question, as user agreements clearly state that content ownership resides with the users themselves. It’s fascinating to see that Reddit only has a non-exclusive license to these posts.
The snippets Reddit presented, including dates and short fragments, don’t appear to be copyrightable at all from what I’ve read in the claims.
SerpApi’s stance is that they accessed Google Search pages, not directly interfacing with Reddit’s platform, which I believe weakens Reddit’s argument substantially.
DMCA concerns. In what I find a compelling argument, Khaleghy asserts that Reddit’s claim of a Digital Millennium Copyright Act (DMCA) violation lacks merit. SerpApi contends that their actions parallel what any user might see when conducting a Google search. Khaleghy strongly points out that:
There’s no evidence of encryption breaches or authentication bypass by SerpApi.
Accessing publicly available web pages doesn’t constitute “circumvention” under existing DMCA guidelines.
Reddit seems to be attempting to enforce copyright claims over content that doesn’t belong to them, which is an intriguing angle to this case.
Moreover, Reddit’s privacy policy acknowledges that public posts may surface in search results, supporting SerpApi’s use of the data.
Backstory. It’s clear to me that legal conflicts surrounding search scraping and AI data have gained high stakes lately:
Oct. 22: I came across information about Reddit filing lawsuits against SerpApi, Perplexity, Oxylabs, and AWMProxy, claiming they scraped large amounts of Reddit content through Google Search, referring to a decoy post created solely for Google’s crawler.
Oct. 29: SerpApi’s response, branding Reddit’s allegations as inflammatory, was a critical move, showcasing their resolve to defend access to public search data.
Dec. 19: Further intensifying the narrative, Google filed a lawsuit against SerpApi, accusing them of bypassing bot protections to scrape licensed search functionalities.
Feb. 23: SerpApi retaliated by requesting the court to dismiss the lawsuit filed by Google, arguing that Google is inappropriately leveraging the DMCA to limit access to public search results.
Importance. This case captivates me as it explores whether companies can legally extract information from Google’s search results without infringing on copyright laws or the DMCA, potentially impacting SEO tools and AI data training significantly.
Looking forward. I eagerly await the court’s decision on whether Reddit’s amended complaint holds up. A dismissal with prejudice would put an end to Reddit’s claims against SerpApi in this instance, which could send ripples through the industry.
As I delve into the world of AI-driven search, it’s clear that advice around AI is becoming way too simple. What really sets you apart are knowledge graphs, expert entities, and how you influence trusted datasets.
Recently, I came across a Harvard Business Review article that resonates with the shifts we’re noticing in SEO. AI Overviews and Google’s AI-enhanced search features are not only creating what’s known as a zero-click environment but they’re also redefining user journeys and behaviors.
User journeys that were once multi-touch are now compressed into a single, synthesized answer. The metaphor of the “Search” monolith crumbling visually captures this transformation.
In this dramatic shift, brands like mine lose many traditional touchpoints, requiring a change in marketing strategy. HBR brilliantly highlights how algorithms are reshaping first impressions. However, while pointing in the right direction, the article’s tactical advice feels too generic and superficial.
Much of the advice sounds strategic yet lacks deep operational insight. This gap is crucial for sustainable visibility and long-term success.
The challenge is deeper than what appears as simple advice to navigate at an executive level. Real structural change is essential to adapt to the evolving search landscape.
The Problem with Flock Tactics
The HBR article brings forward schema, authorship signals, and branded concepts but these suggestions risk becoming “flock tactics.” They spread because they’re easy to grasp, yet they lose their edge once widely adopted.
Schema
Schema is highly debated in LLM and AI optimization. Although Microsoft Bing uses schema for its LLMs, Google’s models have a more complex relationship with third-party LLMs.
Incorporating schema in AI and SEO activities is useful, but presenting it as a fundamental tactic neglects its diminishing returns when everyone implements it.
Another oversight is the importance of external knowledge systems such as Wikidata. LLMs often rely on these authorities more than on any single website.
There’s a significant gap in understanding how models process structured versus unstructured data signals.
E-E-A-T — Shallow Authorship Signals
Using real experts’ credentials aligns with E-E-A-T but often becomes superficial, focusing on bios and headshots without actually strengthening expertise.
There’s a profound difference between mere display of bios and nurturing an expert entity recognized in academia or industry.
Only genuine expertise creates the signals that AI models trust.
Vanity Concepts
Creating branded concepts like “The Acme Index” sounds appealing but is difficult to successfully execute. External adoption is key for them to gain traction.
These concepts must be embraced by reputable sources, which is a hurdle many brands fail to overcome.
The Structural Blind Spots
Beyond tactics, there are deeper structural issues in perceiving AI solely as an external shift rather than an opportunity to innovate internally.
Internalizing AI Infrastructure
The potential to integrate AI deeply into operations, through AI assistants or domain-specific agents, is often overlooked.
In controlled environments, fundamentals like site architecture and data structures remain crucial for success, even if they need to be reimagined for AI.
After conducting a thorough comparison of over 35 SEO agencies focusing on AI startups, I’ve ranked them based on five crucial factors. Each agency was evaluated to identify their capacity in rapidly evolving markets.
The criteria used in this assessment include:
Notable Clients (35%): Agencies were assessed based on their clientele, specifically those in AI and software startups, highlighting their proficiency in adaptable markets.
Leadership Experience Score (25%): A score from 1-5 that evaluates the leadership, focusing on their history in marketing and tech startups.
Average Reviews (25%): Agency performance was rated from 1-5, weighted more by reviews from AI firms.
Company Size and Year Founded (15%): While not as critical, company size and longevity are indicative of sustainable growth and enduring success.
The top agencies are displayed below, noting their rankings, headquarters, and SEO specializations.
Tech-focused marketing services centered on modern marketing channels like SEO, short-form video, and social media
First Page Sage
At First Page Sage, we’re leading the field with innovative SEO and generative engine optimization strategies tailored for AI companies. Our robust content production helps AI firms solidify their authority, with proven success on Google and AI platforms like ChatGPT.
“First Page Sage provides top quality content marketing with competent teams possessing specialized industry knowledge. Clients report measurable organic results within year one that significantly increased online leads.”
Clay Agency
Specializing in the technical side of SEO, Clay Agency excels in branding and UX/UI design, making them perfect for AI companies aiming to unveil products or services interactively and refresh their image in the AI realm.
“The Clay Agency worked as an extension of our own team, delivering an interface that clients are extremely proud of. Their tech-savvy teams are familiar with market trends, creatively tackling technical challenges.”
Marketing Eye
At Marketing Eye, we focus on technical SEO for tech firms, including website auditing and keyword analysis. Besides technical services, we also manage content and social media campaigns, particularly in the retail sector, while also supporting various tech companies.
One of the more established names here, our lean team thrives on blending marketing expertise with computing acumen, ensuring continued prominence in the field.
“Marketing Eye provides superior service, delivering measurable growth. Their teams are competent and professional but might require additional training.”
RNO1
RNO1 specializes in digital branding and product design for tech, AI, and commerce brands, offering technical SEO, market research, and services like AR/VR and Web3, distinguishing them from others.
Notable Clients: Prive, TakeUp, Fluxa
Leadership Experience: 3.5
Company Size: 51-100
Year Founded: 2018
Headquarters: Seattle, WA
Average Reviews: 4.2
Main Focus: Market research and UX/UI design for SaaS Companies
“RNO1 offers a redesigned website praised by users, but their teams sometimes rely too much on online management over direct communication.”
REQ
With REQ‘s expertise in branding, PR, and reputation management, we’re ideal for companies launching new products. While primarily focusing on branding and PR, our SEO services complement traditional marketing strategies effectively.
Notable Clients: Katabat, Verint, ActiveNav
Leadership Experience: 3.8
Company Size: 51-100
Year Founded: 2008
Headquarters: Washington, DC
Average Reviews: 4.3
Main Focus: Branding and UX focused SEO for tech companies
“REQ provides an excellent SEO analytics department that improves client reporting visibility and dramatically raises CTR, though improvements are needed in web development and response speed.”
Optimizely
Optimizely focuses on optimizing web pages through A/B testing, multivariate testing, and personalization, perfect for companies with solid content needing enhanced technical support.
Notable Clients: Google Cloud, Salesforce, New Era
Leadership Experience: 3.8
Company Size: 500+
Year Founded: 2010
Headquarters: New York, NY
Average Reviews: 4.0
Main Focus: A/B Testing, Mobile optimization, Conversion Rate Optimization
“Optimizely offers an intuitive UI that integrates easily, though lacking in extensive server-side testing capabilities.”
Directive Consulting
Directive Consulting excels in PPC and tech-focused marketing, offering performance-based campaigns that blend paid services with SEO to enhance visibility.
Notable Clients: Amazon, Snap Inc
Leadership Experience: 4.0
Company Size: 50-249
Year Founded: 2014
Headquarters: Irvine, CA
Average Reviews: 4.8
Main Focus: Tech-focused marketing services centered on modern marketing channels like SEO, short-form video, and social media
Seeing the shifts in Google’s search traffic firsthand, I’ve noticed publishers losing organic search traffic, yet there’s a silver lining with breaking news traffic soaring by 103%, and Google Discover clicks surging.
Google’s AI Overviews might be cutting into traditional search clicks, but I believe publishers can still find significant growth through breaking news and Google Discover according to recent insights from Define Media Group.
Organic search clicks have dropped 42% since AI Overviews began expanding in Google Search, based on Define Media Group’s analysis of Google Search Console data from 64 sites. It’s quite revealing!
Why we care. AI-generated answers are dramatically reshaping how search traffic is distributed. While evergreen content loses clicks, real-time news coverage and Discover distribution are becoming more potent channels for us publishers.
By the numbers. In Google Search, Discover, and Google News, breaking news traffic has grown 103% from November 2024 to early 2026 within the company’s dataset. However, losses have mainly hit informational and evergreen content.
Here are some figures to consider: organic search traffic averaged 1.7 billion clicks per quarter from Q1 2023 through Q1 2024. Post AI Overviews launch, traffic took a 16% plunge immediately and couldn’t recover. As Google expanded AI Overviews in May 2025, these declines accelerated. By Q4 2025, search traffic had fallen 42% from the pre-AI Overviews baseline.
Discover’s role: Google Discover, which has grown by 30% across the portfolio, is becoming a primary growth engine for breaking news distribution, rising steadily even as web search traffic dips. It’s the first time Discover and web search have driven almost equal traffic.
Interestingly, the report highlights a significant increase in Discover traffic following the December 2025 Google core update, although some gains eased after the February 2026 Discover core update. Yet, according to Chartbeat data, Discover was the main driver of Google referrals to news sites last summer.
Why is this happening? AI Overviews appear less frequently for news queries compared to other topics. Reports show that AI Overviews appeared for only about 15% of news queries, which is nearly three times less often than in categories like health and science.
It seems news queries frequently trigger the Top Stories carousel, linking directly to publisher articles, especially for major events such as international conflicts. Define Media Group suggests that Google may avoid AI summaries for breaking news due to rapid changes and high accuracy needs.
I recently discovered a fantastic update from Google Search Console that’s now available for all eligible sites. This new feature shows exactly how much traffic comes from branded versus non-branded search queries, and I couldn’t wait to explore its potential.
Google’s branded queries filter, which was announced on November 20, allows us to separate branded and non-branded search traffic in the Performance report. This is a game-changer for anyone who’s struggled with manual regex filters or keyword lists to achieve similar results.
Why I care. As someone deeply invested in understanding brand demand versus discovery traffic, this new native segmentation in Search Console makes life so much easier. Finally, I can accurately measure and compare these insights.
What Google announced. Today, Google confirmed through a LinkedIn post that this branded queries filter is accessible to us all. It helps analyze the queries driving traffic by autofiltering between branded and non-branded ones.
Exploring the details. This filter can be found in the Search results Performance report and allows queries to be segmented into two main groups:
Branded: These queries include our brand name, its variations, any misspellings, and brand-related products and services.
Non-branded: This group covers all other types of queries.
When applying the filter, Search Console restricts metrics like impressions, clicks, CTR, and average position, focusing solely on the selected group. The filter works across all search types including Web, Image, Video, and News.
Notable insights. Google also enriched the Insights report with a new card that breaks down clicks between branded and non-branded traffic, providing a clearer picture of brand recognition.
As Google explained, this feature helps us measure the traffic from users already familiar with our brand compared to those discovering it for the first time.
Understanding Google’s classification. Google employs an AI-driven system to classify queries as branded. This system can adeptly recognize brand names in various languages, handle misspellings or variations, and detect queries that mention unique brand products or services.
There might be occasional misclassifications due to the contextual nature of brand detection, and Google clarifies that this filter doesn’t impact search rankings.
Keeping an eye out. With today’s announcement, this feature is supposedly available for all eligible sites. However, some sites might not qualify yet due to specific query and impression volume requirements.
