I’m excited to share that Google has announced some transformative updates to its search capabilities. These updates include the introduction of information agents and enhanced agentic experiences that will elevate how we interact with search. Google’s AI will continuously scan the web, ensuring we receive the most current information, much like a personal assistant would.
In a recent announcement, Google revealed new search agents, focusing on information agents and additional agentic functionalities within Google Search. These information agents are designed to monitor the web for changes to our tasks, seamlessly supporting us on our journey through various challenges and questions.
Liz Reid, the head of Google Search, stated, “We’re entering the era of Search agents, where you can easily create, customize, and manage multiple AI agents for your many tasks, right in Search.” This new era provides a unique opportunity to tailor search experiences to our specific needs.
Information Agents. These agents are designed to keep us informed about our questions and tasks. Google’s agents will intelligently sift through the internet—exploring blogs, news sites, social posts, and accessing the freshest real-time data on finance, shopping, and sports, to ensure we receive the most relevant updates on our inquiries.
The information agents will then compile an “intelligent, synthesized update” that not only provides the necessary information but also enables us to take action.
The Example. Envision yourself apartment hunting. You can simply input all your specific requirements, and your agent will continuously scan listings, alerting you whenever a match surfaces. Similarly, if you’re keen on not missing any sneaker collaborations from your favorite athletes, your agent will notify you about new releases.
Availability. These exciting capabilities are set to roll out this summer, initially available to Google AI Pro & Ultra subscribers.
Agentic Experiences. Google is also extending its agentic booking capabilities within Google Search to encompass new tasks like finding local experiences and services. Imagine effortlessly booking a private karaoke room for an exact time and with specific food options, all handled by Google Search.
Google will provide the most current pricing and availability information, along with direct links for purchase, streamlining experiences across various services, including home, repair, beauty, and pet care. These features are expected in the U.S. this summer.
Personal Intelligence Expanding. In addition, Google has revealed plans to broaden its Personal Intelligence feature within AI Mode, now reaching around 200 countries and territories, supporting 98 languages.
Today, I’m excited to share that Google has announced the launch of its latest AI model, Gemini 3.5 Flash. This powerful update is now the default engine for Google’s AI Mode, transforming how we experience search every day.
At the recent Google I/O, I learned about Gemini 3.5 Flash directly from Google’s head of Search, Liz Reid. She described this model as Google’s “newest Flash model delivering sustained frontier performance for agents and coding.” It’s thrilling to know that this technology is now impacting users worldwide.
What really excites me is that 3.5 Flash doesn’t just enhance AI Mode in Google Search; it also powers the Gemini app for everyone, regardless of whether they are paid users or not. It’s great to see Google making such advancements widely accessible.
Developers, you’re in for a treat! 3.5 Flash is now integrated into Google Antigravity, Gemini API for Google AI Studio, Android Studio, and more. For those in enterprise, it’s now part of the Enterprise Agent Platform and Gemini Enterprise.
Koray Kavukcuoglu, CTO of Google DeepMind and Chief AI Architect, shared that Gemini 3.5 Flash rivals the intelligence of large flagship models while providing the speed we expect from the Flash series. It outshines previous models, making remarkable strides in agentic and coding performance benchmarks. I’m truly impressed by its capabilities in multimodal understanding too.
Why should I care? Well, with Gemini 3.5, Google Search’s AI Mode is smarter and more efficient than ever. I’m eager to explore how AI Mode’s responses evolve, especially for the queries that matter most to my site.
The rapid changes in search technology mean it’s crucial to stay informed and adaptable. This update reaffirms the importance of keeping pace with Google’s innovations.
I’ve often pondered the impact of AI on our work as SEO professionals. As AI takes over repetitive tasks, those of us who can strategically guide its use will find our skills even more valuable.
By now, you’ve likely heard the dire predictions:
Verizon’s CEO, Dan Schulman, cautioned that AI might push U.S. unemployment rates to 20%-30% in the next few years.
Anthropic’s CEO, Dario Amodei, warned of AI wiping out a significant portion of entry-level white-collar jobs.
According to Ford’s CEO, Jim Farley, AI could replace half of white-collar workers in the U.S.
SEO, a field I’ve been passionate about for years, is certainly in the crosshairs. But does this mean our careers are at risk? Not necessarily.
The landscape is evolving, yes. But if you’ve been in SEO as long as I have, you’re no stranger to adaptation.
Our roles have always demanded that we wear many hats, from being technical analysts to creative strategists. AI won’t replace this expertise—it’ll replace superficial approaches to SEO.
Success will belong to those who understand search behavior deeply, link it to business outcomes, and make insightful decisions.
The version of SEO many remember is already outdated. I’ve practiced SEO since before it even had a name, and every so often, someone claims that “SEO is dead.” While the field has changed drastically, it’s far from deceased.
SEO, as interpreted today, requires understanding how people search for your offerings and knowing how to meet their needs across various platforms. This journey is only just beginning for those of us in the know.
In a time where everyone can leverage AI tools, the real differentiator is how adeptly we employ these tools to achieve our visions.
Even now, some people believe that writing SEO prompts in AI means they can call themselves experts. But SEO isn’t just about title tags or decoding search engines; it’s about understanding user psychology and combining technical systems with strategic execution.
With AI, we’re entering a new phase requiring new skills. We’ll work more efficiently by incorporating AI into essential SEO tasks. The depth of our conversations with AI will be key to our differentiation.
Here’s a look at how I’ve begun integrating AI into my workflow for greater productivity and insight:
AI can help with the basics—like generating metadata—where precision takes precedence over creativity. We can use AI for better recommendations and design, allowing developers to work with better-prepared resources.
AI is also instrumental in drawing insights from GSC, GA4, and tools like Semrush to gather actionable user data and preferences.
Another frontier is using AI to prototype and improve upon web design layouts, thereby streamlining collaboration with designers and developers.
AI’s presence in analytics is similarly transformative. Though GA4 initially posed a setback for established workflows, AI allows us to develop new, more insightful reports.
Ultimately, my career’s foundation isn’t just in managing tasks that AI could handle. It’s in understanding customers, reading data for insights, and connecting these insights back to real-world results.
Like many others in our field, I’ve witnessed great companies start with grassroots efforts, which have only grown with time. As AI continues to evolve, its role isn’t one of replacement—but of empowerment.
SEO isn’t fading—it’s transforming, waiting for us to lead it into a new era.
I’ve always found SEO reporting to be a bit of a hassle. It used to mean spending hours exporting data from Google Search Console (GSC), tidying it up in spreadsheets, and then trying to make sense of it all in Data Studio.
Now, with AI tools like Claude Code, my workflow has completely changed. I can instantly create customized data visuals and reports in a fraction of the time it used to take.
Let me walk you through the journey of transforming GSC data into tailored reports, streamlining the entire process.
Using Claude Code is different from the standard Claude experience. While the regular Claude.ai acts like a chatbot, Claude Code functions as an AI coding assistant right on my computer. It’s capable of reading GSC CSV files, analyzing large datasets, and transforming raw data into clear, visual reports.
Initially, setting up Claude Code can be daunting, especially if you aren’t familiar with technical tasks. But don’t worry, the setup is a one-time effort. Once it’s up and running, generating reports takes just minutes.
The real magic happens after you connect Claude to GSC. Whether you’re in an enterprise environment or you’re an independent SEO consultant, having Claude Code set up is invaluable.
Starting your journey with Claude Code begins by creating an account on Claude.ai. Even without a paid subscription, I find the platform extremely helpful for generating SEO reports.
A crucial step in using Claude Code is installing Node.js on your machine. For this tutorial, I used a Mac, but it’s compatible with other operating systems too. Once Node.js is installed, I am able to install Claude Code and verify my setup through simple terminal commands.
After setting everything up, I navigated a series of prompts in Claude, choosing how to access GSC data and defining key parameters for my reporting.
Connecting Claude to GSC involves interacting with the Search Console API, albeit a bit technical. But Claude guides me through each step, ensuring a smooth setup.
The exciting part comes after the connection is established. I can now rapidly create focused reports, such as identifying top-performing pages or tracking keyword trends over time, tailor-made for my needs.
Overall, Claude Code redefines how I manage SEO reporting. It offers the perfect balance of speed, flexibility, and control. Once the groundwork is laid, it makes my reporting both dynamic and precise, adapting to the demands of my stakeholders with ease.
An analysis of 200 GPT-5.2 responses revealed that enhanced reasoning increases the citation of sources, deepens research, and boosts early-stage funnel visibility.
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I’ve explored how AI provides a conversational experience through large language models (LLMs) and chatbots. However, I’ve noticed that no one has thoroughly examined the evolution of citations and mentions within these conversations.
By examining data from the Semrush AI Visibility Toolkit, I reviewed 20 buyer journeys across four industries, comparing the high and low reasoning of ChatGPT5.2.
In this analysis, you’ll discover:
How high reasoning cites a vastly different web with only 25.6% domain overlap and which source types gain or lose prominence.
The renewed importance of TOFU content: Brands cited at the Problem stage tend to persist through to the Selection stage under high reasoning.
How to differentiate your prompt tracking by reasoning modes, ensuring your AI visibility reports reflect two distinct systems instead of an average.
Methodology
Data collection utilized the Semrush AI Visibility Toolkit to capture prompts, citations, and fan-out queries generated by ChatGPT for each response.
We executed 100 prompts twice through GPT-5.2, once with minimal reasoning and once with high reasoning, totaling 200 responses.
Prompts covered 20 buyer journeys across four sectors (B2B SaaS, Finance, Consumer Tech, Health/Lifestyle), each consisting of 5 stages: Problem, Exploration, Comparison, Validation, Selection.
The citation rate represents the proportion of prompts where the response cited at least one external source.
The average citation quantifies the sources per cited response.
Fan-out queries are sub-queries the model generates internally for research before responding, surfaced via the Semrush API.
High Reasoning in GPT 5.2 Leads to More Citations and Searches
Activating high reasoning elevates the citation rate from 50% to 68%, nearly doubles the average sources per response (from 2.6 to 4.5), and multiplies fan-out queries by 4.6 times. High reasoning also draws from 173 unique domains versus 127 with minimal reasoning, with 99 domains appearing exclusively under high reasoning.
*Citation Rate signifies the share of prompts where at least one external source is cited.
This grounding is essential. When the model thinks more critically, it increasingly depends on web-based research, significantly impacting brand visibility, although user activation of reasoning remains uncertain.
Query intent provides a clearer indication than user demographics. Even free-tier users can access reasoning, albeit at limited rates, and ChatGPT automatically routes challenging prompts to Thinking mode. The critical question isn’t about affordability but about which prompts trigger reasoning automatically.
Complex comparisons, evaluation frameworks, compliance inquiries, and intricate shopping setups are most likely to invoke reasoning across all users. It’s crucial to categorize your audience by query type rather than paywall status.
High Reasoning Launches More Fan-out Queries in Later Stages
Users navigate problem-solving and purchasing decisions through stages, often within the same conversation. The distinction between minimal and high reasoning is not static; it varies based on the user’s journey stage.
For instance, consider a buyer evaluating CRM software:
Problem: “How do I know if my sales team needs a CRM?”
Exploration: “What types of CRM software exist for B2B SaaS?”
Comparison: “HubSpot vs. Salesforce vs. Pipedrive for a 50-person sales team.”
Validation: “Is HubSpot worth the price for mid-market B2B?”
Selection: “How do I get started with HubSpot Sales Hub?”
The following patterns are consistent across all 20 buyer journeys:
The citation rate increases as users progress through the funnel in both reasoning modes, but early-stage gaps close faster in high reasoning: +35pp at the Problem stage, only +5pp at Validation.
Fan-out queries peak during the Comparison stage, with high reasoning triggering 24 sub-queries per response compared to 5.5 in minimal reasoning. For Selection, these numbers are 15.4 and 2.6, respectively.
Average citations per response culminate during the Comparison stage (9.8 high, 5.8 minimal) and narrow during the Selection stage (4.7 high, 2.6 minimal). The citation pattern resembles an hourglass throughout the funnel.
Aggregately, minimal reasoning triggers 245 search queries over 100 prompts, while high reasoning triggers 1,130. In high reasoning, the model conducts thorough investigations for each prompt, with most research occurring during the Comparison and Selection phases.
What does fan-out look like?
A B2B SaaS prompt that requires high reasoning, like comparing Salesforce, HubSpot, and Pipedrive for a 50-person sales team, breaks down into different queries regarding API rate limits, compliance standards, support tools, pricing tiers, and more. Each aspect requires specific retrieval. The brand that succeeds here will be the one with clean, accessible documentation for each sub-query, not merely ranking for the initial prompt.
The Selection stage features a remarkable variance in per-response queries: between 0 and 40 fan-out queries with the same five-stage cohort. This variance is driven mainly by the specificity of prompts.
Bounded prompts (like “should I finance through the dealer at 0% APR or use a bank?” or “draft an RFP to 3 SEO agencies”) run zero queries since the answer’s structure is predefined. On the other hand, open-ended tasks (“shopping list for a $3,000 home gym” or “which travel card system matches our grocery spending?”) prompt 28 to 40 queries. With no single query type dominating the Selection stage, the model’s research intensity correlates with the degrees of freedom left by the prompt.
For marketers: Capturing early-funnel visibility is highly dependent on reasoning mode. If buyers engage with ChatGPT in reasoning mode, your Problem-stage and Exploration-stage content become more relevant. Otherwise, visibility might only surface during the Comparison stage.
How Reasoning Alters Brand Representation in Conversations
A session with an LLM is more conversational than transactional. Does an initially cited brand endure till the concluding stage? If yes, early-funnel visibility multiplies. If no, each step is an independent battleground.
For minimal reasoning, persistence from the Problem stage to the Selection stage rarely happens. With high reasoning, however, continuous brand presence was recorded in 4 journeys across all 5 stages.
Within individual responses, high reasoning strongly relies on specific sources, with 51 out of 100 high-reasoning responses citing the same domain multiple times versus 26 in minimal reasoning. When committed, high reasoning cites a source repeatedly.
Analyzing brand names mentioned in the text provides a broader perspective. With a relaxed test criterion, persistence was noticeable in 3 high-reasoning sessions and 2 in minimal reasoning: HubSpot through CRM Selection, American Express in Business Credit Cards, and prominent mentions of Sony and Canon in Mirrorless Cameras. Consumer Tech again emerges, albeit without citation persistence, showing dominance through continuous conversation presence.
High reasoning establishes a consistent perception of the solution landscape throughout a session. Crucially, TOFU prompts possess enormous value. A brand appearing at the Problem stage is likely to be present at the Selection stage. Top-of-funnel content transcends mere brand awareness for AI visibility—it’s a predictor of where the model’s reasoning lands at decision-making points.
There are two more significant insights:
All four persistent journeys occur within Finance, indicating persistence thrives on authoritative-source content like regulatory pages and official brand sites, echoing the +28pp lift in Finance.
For marketers focusing on account-based strategies or market creation, visibility in reasoning mode is paramount as it’s the sole mode turning early funnel efforts into selection-stage citations.
Reasoning Mode: A Distinct Search Paradigm
The champions under minimal reasoning differ from those under high reasoning: Three out of four cited domains diverge. The diversity in source types and citation stages is unmistakable.
I’m particularly intrigued by these findings:
Firstly, measurement. It’s imperative to differentiate low and high reasoning in our prompt trackers to avoid oversimplification, as their functions are distinct.
This endeavor may seem costlier, but it significantly enhances prompt tracking accuracy.
Secondly, the relevance of funnel stages. In the latest AI Mode user behavior study, it was observed that users heavily rely on shortlists, much like they do with Google’s top results. It initially appeared that focusing on BOFU prompts to generate shortlists was most strategic.
Nonetheless, TOFU prompts carry substantial benefits due to their persistence potential. Brands entering the buyer journey early can remain present throughout. Mapping buyer journeys and tracking persistence offer the best insights.
This post originally appeared on the author’s website and is reproduced here with permission.
I often get asked in 2026, “How do we measure this?” when it comes to AI visibility.
People want to know if their brand is appearing in ChatGPT or if Perplexity is recommending them. They also wonder if their work on AI grounding last quarter made any impact.
The truth is, the solution doesn’t exist yet. Anyone offering a straightforward dashboard for tracking your brand’s presence in AI spaces across search, assistive, and agent modes is just making an educated guess.
Tracking queries we assume users might ask, or adapting search keywords as a best guess, won’t cut it. These prebuilt lists often miss the mark as they choose easily mapped or ideal scenarios that don’t reflect reality.
The visibility question itself is valid, but the precise answer everyone seeks simply isn’t feasible.
Brands looking for perfect AI-era visibility KPIs are chasing a mirage. Instead, we need a methodology inspired by economic measurement of complex systems—this is where my Funnel Query Pathway comes in.
This unique approach serves as strategy, measurement, and analysis, unlike traditional metrics that were reliable when search rankings were predictable and measurable.
Now, we must rethink our approach in a complex AI landscape, asking new questions and measuring different signals.
I studied economics at Liverpool John Moores University, which gives me a unique perspective on measurement challenges where traditional tools fail at larger scales.
As with macroeconomics dealing with vast, unobservable systems, AI visibility is too opaque and personalized for old tools. We need macro principles to guide AI-era brand measurement.
AI systems have similar structural complexities as macroeconomics:
Opacity hinders visibility into the system’s state, with AI algorithms operating like a black box. Personalization means users receive unique outputs from the same inputs, influencing the visibility paths.
With expanding possibilities across apps, systems, and devices, AI environments now introduce variables that weren’t present in traditional search models.
The Funnel Query Pathway methodology focuses on these macro aspects, shifting away from keyword mapping to a broader approach focused on cohorts and intent at the node level.
AI-era acquisition begins at the conversion moment projected upward, contrary to traditional funnel methods.
Hey there! I’m thrilled to share how we can make our product images work harder for us by optimizing them for visual search AI. Whether it’s through Google Lens, using alt text, or implementing structured data, these strategies are key to ensuring our products are more discoverable and fuel our eCommerce growth.
Imagine our potential customers finding our products just by snapping a photo! It’s amazing, right? With the power of visual search, we can tap into a whole new audience and boost our visibility.
So, let’s delve into the intricacies of visual search AI and uncover how these techniques can propel our products to new heights.
In 2025, I’ve noticed that while the costs of Google Ads continue to climb, there’s been a silver lining. Advertisers like myself have been improving conversion efficiency, which means growth is still within reach.
I’ve observed that although we’re paying more per click, the data from WordStream by LocaliQ shows we’re getting better at converting those clicks. The benchmark report, analyzing over 16,000 campaigns, highlights an increase in average CPC to $5.42, up from $4.66 last year, with 87% of industries seeing a rise.
Despite this jump in CPC, the average conversion rate has improved to 8.18%. This indicates we’re becoming more efficient, even as traffic costs rise.
Why advertisers should care. The benchmarks clearly point out that inexpensive traffic is fading fast. For us advertisers, this means absolute reliance on volume is not sustainable anymore.
To maintain profitability, I’ve realized that focusing on stronger targeting, creative enhancements, better landing pages, and smarter automation is vital.
The report suggests advertisers who adapt well to automation and intent-driven targeting are improving conversion efficiency, despite the rising costs.
By the numbers. Here’s what stands out:
$5.26 — Average Google Ads CPC in 2025, increased from $4.66 in 2024.
87% — Percentage of industries experiencing CPC hikes annually.
7.52% — Across-the-board average conversion rate in 2025.
$70.11 — Average cost per lead in Google Ads, 2025.
Highest CPCs. Industries like Attorneys & Legal Services led with $8.58, while areas like Finance & Insurance, and Home Improvement consistently hovered in the $7+ range.
Lowest CPCs. The Arts & Entertainment and Travel & Hospitality sectors fell in the $2–$3 range, benefitting from reduced competition.
Highest conversion rates (strong intent / local services)
Automotive Repair led with 14.67%, followed by other high-intent services like home services ranging from 12–14%.
Lowest conversion rates (complex or high-consideration journeys)
Finance & Insurance was at the bottom with 2.55%, and B2B, legal, and high-ticket items were between 3–5%.
The cost-per-lead is stabilizing, thankfully. Although the average CPL rose modestly by 5.13% to $70.11 in 2025, it’s a relief after years of sharper increases. Legal services remain costly, while auto repair is more cost-effective.
Automation is changing performance benchmarks. I’ve seen how Google Ads has embraced AI-driven optimization. As conversion rates rise, smarter bidding systems and improved intent matching are effectively connecting advertisers with high-quality users.
While automation like Smart Bidding and Performance Max is shaping campaigns, I know that not every account is thriving. Some have zero conversions, and failure to optimize or poorly set up tracking continues to waste spend.
Interestingly, accounts using negative keywords experience conversion rates up to three times higher, underscoring how foundational practices are essential even in an AI era.
Between the lines. The benchmarks present a mixed message. Costs are rising, yet Google’s automation aids efficiency for those optimizing their campaigns effectively.
The biggest challenge now isn’t finding cheap clicks—it’s enhancing conversion quality and maximizing value from expensive traffic.
Bottom line.Google Ads is more costly than ever, but by embracing automation, focusing on conversion quality, and improving account efficiency, growth is still possible.
As someone deeply involved in technical SEO, I’ve realized that our projects thrive when I effectively communicate their value to both executives and developers.
What sets a great SEO professional apart from the rest is their knack for managing stakeholders. This skill is crucial in technical SEO, where projects often involve numerous teams, making it challenging to convey the importance of our work.
At the core of stakeholder management is the perceived value of our work. In technical SEO, this can be especially tricky. People outside the SEO realm might not immediately grasp the significance of optimizing a site’s internal linking or implementing schema markup.
The most successful technical SEO projects aren’t merely seen as SEO enhancements; they are viewed as vital to business outcomes like revenue growth, better conversion rates, and operational efficiency. By strengthening this connection, I find it easier to gain stakeholder support and showcase long-term value.
Why Aligning Technical SEO Work with Business Impact Is Essential
For most executives and development teams, technical SEO isn’t at the forefront. That’s why I ensure our technical SEO recommendations are directly linked to measurable business goals.
Take, for instance, a scenario where a company modifies its website’s CMS. The SEO implications of such a change are often overlooked on a project manager’s long list of priorities. It’s not until I clearly demonstrate the risks and their potential impact that SEO is properly emphasized.
Technical SEO initiatives can be inherently complex. They require a strong grasp of the company’s systems and teams, coupled with excellent communication and management skills.
Even though I might see this work as pivotal to the site’s SEO health, others might not appreciate its value if I’m talking in terms of crawl budget or index management. Drawing parallels to core business goals helps make our work more comprehensible and valuable.
Aligning technical SEO initiatives with business performance and goals is the best way for me to secure buy-in and highlight their impact.
Business Outcomes That Drive SEO Buy-In
Understanding the metrics and business goals is crucial for demonstrating how technical SEO can impact performance. Most organizations set corporate goals like expanding reach, boosting revenue, or entering new markets.
Revenue
For many businesses, whether a charity or a multinational, the bottom line is revenue. Connecting technical SEO efforts to revenue growth is a surefire way for me to secure support and illustrate its value.
Conversion
I can also show the value of technical SEO by linking it to conversion optimization. Studies indicate that a one-second delay in page load speeds can slash conversions by up to 7%.
Looking at core web vitals scores is important, but framing it as potential conversion loss grabs more attention from stakeholders.
Cost Reduction
I often notice that the potential for cost reduction is overlooked in SEO. Website visits incur hosting, infrastructure, and security costs that add up quickly with large sites.
Highlighting how technical SEO can reduce unnecessary expenses is key.
These four strategies help stakeholders better understand, support, and prioritize technical SEO projects.
1. Determine the Value of the Work
I never assume an SEO activity is worthwhile just because it’s a “best practice.” Every task I undertake ties directly to a business benefit and a core KPI.
Even if the immediate result is not new revenue, the activity should support revenue growth, conversion enhancements, or cost efficiency.
When I review and optimize internal site structures, I aim for improved rankings and increased organic traffic, translating to more conversions and revenue.
2. Identify How the Work Will Impact Company Goals
Once I understand the value of my technical SEO tasks, I align them with broader company or project goals to gain stakeholder approval.
For instance, if my goal is increased profitability in a certain region, and the task involves optimizing hreflang tags, I focus on how this supports the company’s goals, rather than technical specifics.
3. Communicate Effectively
Communicating SEO work’s impact is challenging, but breaking it down into ‘who, what, where, why, when, and how’ makes it understandable for stakeholders at all levels.
My goal is to make even the most technical aspects digestible by linking tasks back to business metrics everyone understands and values.
4. Prove the Impact Over Time
By consistently showing the positive results of technical SEO, I align our efforts with business objectives and make future conversations with stakeholders simpler.
After completing a project, I regularly review the outcomes to understand the impact, allowing for better future planning and adjustments.
Business Impact Matters More Than Technical Best Practices
Assumptions of what might enhance performance can sometimes miss the mark. Without revisiting previous implementations, I might not know what actually worked.
Just because something is hailed as “best practice” doesn’t confirm it will fit my site. Continually evaluating technical SEO outcomes helps reaffirm their business value.
When I search for products on Google, I’ve noticed significant changes to the results page. Now, product packs and scrollable carousels appear multiple times within a single results page, reshaping my shopping experience.
As part of my ongoing journey to boost ecommerce visibility, I constantly analyze data. Recently, I’ve tracked searches presenting up to 60 individual organic product listings on one page. These premium placements increasingly mark the beginning of the purchase journey for many users.
This transformation is gradual, and interestingly, I see many brands still adjusting their strategies. It’s crucial to revisit these changes because the opportunity for traffic through product packs is immense, with fierce competition. Today’s leading brands approach this differently.
Thanks to Nozzle, I’ve delved into data from over 63,000 merchants across a wide array of ecommerce keywords from January 2025 to January 2026. Here’s what I discovered that really caught my attention.
Defining Success: Appearances vs. Actual Traffic
I found that just appearing in product packs and actually capturing traffic are two distinct achievements, and the difference between them can be substantial as the data shows.
For instance, in this dataset:
eBay appears in product results for 874,621 keywords.
Home Depot has a similar presence, appearing for 831,699 keywords.
However, the estimated traffic paints a contrasting picture:
eBay garners about 3.2 million visits from these pack appearances.
Home Depot, meanwhile, generates nearly 28.8 million visits from a slightly smaller keyword range.
The secret? Quality position within the pack. Home Depot’s products consistently snag prime, visible, above-the-fold spots that attract shoppers’ clicks.
For eBay, many keywords involve long-tail marketplace terms that dilute overall impact. Understanding Google’s use of product packs to drive purchase decisions for common goods is crucial for brands aiming to compete effectively in this space.
For marketers: Dissecting product pack performance means wisely segmenting data, focusing on categories with significant search volumes to optimize visibility within the packs. That’s how to pinpoint where the genuine opportunities lie.
The Critical Gap: Distinguishing Product Pack Visibility
Product carousels scroll horizontally, increasing exposure for the first few slots, while listings tucked further back remain unseen. This distinction is crucial for assessing true reach.
Disparities among major retailers further illustrate this point:
REI has a massive catalog of 3.8 million products, yet 1.52 million of these require scrolling before they are visible.
Walmart finds itself in a similar spot, with 1.29 million of its 3.5 million unique products are relegated to non-visible placements.
Even industry titans often miss out on optimal visibility, skewing the perceived benefits of their presence. Analyzing visible versus non-visible appearances is essential for identifying where optimizing product data and feeds can yield substantial returns.
For CMOs: When using total product pack appearances as a metric, it’s wise to ask how many of those appearances are truly visible. Understanding this ratio better reflects the channel’s contribution to the business.
Does Discounting Drive Product Pack Visibility?
It’s a common belief that discounted items might secure better placement in Google’s product packs. However, data from the top 10 merchants doesn’t necessarily support this notion.
Amazon.com leads the pack with 49% of its catalog discounted, achieving a 72% visibility rate, placing it squarely mid-tier.
eBay, on the other hand, discounts only 8% of its products yet matches the highest visibility rate in the dataset at 81%.
Walmart Seller discounts 24% of its items, reaching 81% visibility, while Walmart itself discounts 27% but ranks lower at 62% visibility.
This irregularity indicates that discounting is just one of many factors. It doesn’t solely determine a product’s chance of securing a prominent spot. Feed quality, category relevance, reviews, and image standards wield greater influence.
For retail teams: If your strategy for product packs relies heavily on promotions, you might need to pivot. The current landscape favors strategies aligned with where purchasing decisions occur over sheer pricing tactics.
Specialist Brands Competing with Giants and Winning
A refreshing realization from this data is that product pack success isn’t exclusive to the retail giants. Specialist brands, leveraging focused expertise, compete exceptionally well against far larger competitors.
Camp Chef, for instance, appears in results for 155,299 keywords—just a small fraction of Walmart or eBay’s footprint—yet it pulls in an estimated 2.6 million visits, thanks to advantageous product placements.
Brands like Fellow, expanding into niches such as high-end coffee makers, find opportunities for growth through strong organic channels.
These brands achieve impressive product pack traffic against much larger rivals because they prioritize category relevance and high-quality product feeds over sheer scale.
For brands traditionally overshadowed in traditional SEO, product packs present a chance to compete on a more level field. Detailed product data, competitive prices, quality imagery, and favorable reviews can supersede a larger competitor for crucial category keywords.
For agencies: This channel awards dedication and quality over brute scale. Brands with depth in a category can translate that expertise into superior product pack performance, outpacing broader competitors.
Staying Informed on Product Pack Visibility Shifts
Examining the entire dataset, I noticed a consistent pattern: nearly all merchants experience shifts in product pack visibility throughout the year.
Brands holding strong positions during parts of the year sometimes see fluctuations as Google adjusts how it surfaces product results. Some grew steadily midyear only to recede in Q4, while others surged during promotions before reverting to previous levels.
This fluidity is typical of the channel. Google regularly updates its criteria for product pack placements, influenced by factors like feed quality, product availability, review counts, pricing, and images.
The brands thriving are those with sustained visibility into performance, staying agile and responsive to changes before they impact revenue.
With Google’s future announcements and AI integration like Gemini 3 looming, the foundational structure of product packs will shift, influenced by agentic commerce and the Universal Commerce Protocol.
As Google navigates balancing paid and organic visibility, a two-tiered search economy emerges. Securing AI Overview citations becomes vital for brand recognition, impacting both organic and paid product pack performances.
The Bigger Picture
Google’s product packs have morphed from merely supplementary to pivotal touchpoints in commercial searches.
The extensive Nozzle data analysis of over 63,000 merchants reveals that competition is already fierce in this domain. Leaders are distancing themselves, and the gap between attentive and indifferent brands manifests tangibly in traffic and revenue disparities.
The silver lining is that the essentials for success in this space are accessible to most brands: robust product data, strategic pricing, high-quality creative, and vigilant monitoring.
These require not a colossal budget but focus, the right tools, and asking the right strategic questions within the right organizational levels.
