A surge of sophisticated phishing attacks is letting scammers take over full Google Ads Manager accounts (MCCs), giving them instant access to hundreds of client accounts and the power to burn through tens of thousands of dollars in hours without being noticed.
Driving the news. Agencies across LinkedIn, Reddit, and Google’s own forums are reporting a rise in MCC takeovers, even among teams using two-factor authentication. The attackers’ preferred weapon is a near-perfect phishing email that mimics Google’s account-access invitations.
Victims say hijackers add fake admin users, link their own MCCs, and begin launching fraudulent, high-budget campaigns.
In some cases, support tickets take days to escalate while money continues to drain.
One agency reported “tens of thousands” in ad spend racked up within 24 hours.
How it works. The scams look like standard client-access invites – same branding, format, and copy – but the link sends users to a Google Sites page posing as a Google login screen. Once credentials are entered, the attackers get full MCC access.
Why it’s getting worse. Advertisers say the phishing attempts are now almost indistinguishable from real Google messages. Several agencies admitted they would have clicked if not for small discrepancies in the sender domain or login URL.
The impact:
Budgets drained: fraudulent ads run immediately.
Malware exposure: ads often lead to harmful sites.
Account damage: invalid activity flags, disapprovals, and trust issues ripple for months.
Operational chaos: agencies lose access to every client account under the MCC.
What Google says. The Google Ads Community team posted a What to do if your account is compromised help doc, warning advertisers about rising credential theft during the holiday season, but hasn’t acknowledged the scale of the MCC takeover surge.
Why we care. These MCC hijacks aren’t just isolated security issues – they’re direct financial and operational threats that can wipe out budgets, compromise every client account, and take days for Google to contain. With attackers now bypassing 2FA through near-perfect phishing, even well-secured teams are suddenly vulnerable. If just one team member slips, an entire portfolio of accounts – spend, performance, and client trust – is instantly at risk.
What experts recommend. Marc Walker, founder and managing director of Low Digital Ltd, shared these recommendations to keep your accounts from being hijacked:
Always verify the URL: Google never uses Google Sites for login.
Confirm invites inside the MCC, not just via email.
Purge dormant users and inactive accounts to reduce attack surfaces.
Educate teams on phishing red flags, especially during high-volume holiday outreach.
Between the lines. If even one user in a large MCC falls for the scam, the attacker effectively acquires keys to an entire portfolio – and can drain budgets faster than Google’s support system can respond.
Bottom line. Google Ads hijacks are a serious operational threat for agencies and in-house teams. Until Google ships stronger MCC-level protections, vigilance remains the only real defense.
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http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-25 17:27:352025-11-25 17:27:35How To Optimize Content for LLMs: The Complete Guide for B2B Marketing Leaders
As shopping becomes more visually driven, imagery plays a central role in how people evaluate products.
Images and videos can unfurl complex stories in an instant, making them powerful tools for communication.
In ecommerce, they function as decision tools.
Generative search systems extract objects, embedded text, composition, and style to infer use cases and brand fit, then
LLMs surface the assets that best answer a shopper’s question.
Each visual becomes structured data that removes a purchase objection, increasing discoverability in multimodal search contexts where customers take a photo or upload a screenshot to ask about it.
Visual search is a shopping behavior
Shoppers use visual search to make decisions: snapping a photo, scanning a label, or comparing products to answer “Will this work for me?” in seconds.
For online stores, that means every photo must answer that task: in‑hand scale shots, on‑body size cues, real‑light color, micro‑demos, and side‑by‑sides that make trade‑offs obvious without reading a word.
These evolving behaviors map to specific intent categories.
General context
Multimodal search aligns with intuitive information-finding.
Users no longer rely on text-only fields. They combine images, spoken queries, and context to direct requests.
Quick capture and identify
By snapping a photo and asking for identification (e.g., “What plant is this?” or querying an error screen), users instantly solve recognition and troubleshooting tasks, speeding up resolution and product authentication.
Visual comparison
Showing a product and requesting “find a dupe” or asking about “room style” eliminates complex textual descriptions and enables rapid cross-category shopping and fit checking.
This shortens discovery time and supports quicker alternative product searches.
Information processing
Presenting ingredient lists (“make recipe”), manuals, or foreign text triggers on-the-fly data conversion.
Systems extract, translate, and operationalize information, eliminating the need for manual reentry or searching elsewhere for instructions.
Modification search
Displaying a product and asking for variations (“like this but in blue”) enables precise attribute searching, such as finding parts or compatible accessories, without needing to hunt down model or part numbers.
These user behaviors highlight the shift away from purely language-based navigation.
Multimodal AI now enables instant identification, decision support, and creative exploration, reducing friction across both ecommerce and information journeys.
You can view a comprehensive table of multimodal visual search types here.
Prioritize high-contrast color schemes. Black text on white backgrounds is the gold standard.
Critical details (e.g., ingredients, instructions, warnings) should be presented in clean, sans-serif fonts (e.g., Helvetica, Arial, Lato, Open Sans) and set against solid backgrounds, free from distracting patterns.
This means treating physical product labeling like a landing page, as Cetaphil does.
AI does not isolate your product. It scans every adjacent object in an image to build a contextual database.
Props, backgrounds, and other elements help AI infer price point, lifestyle relevance, and target customers.
Each object placed alongside a product sends a signal – luxury cues, sport gear, utilitarian tools – all recalibrating the brand’s digital persona for machines.
A distinctive logo within each visual scene ensures rapid recognition, making products easier to identify in visual and multimodal AI search “in the wild.”
Tight control of these adjacency signals is now part of brand architecture.
Deliberate curation ensures AI models correctly map a brand’s value, context, and ideal customer, increasing the likelihood of appearing in relevant, high-value conversational queries.
Run a co-occurrence audit for brand context
Establish a workflow that assesses, corrects, and operationalizes brand context for multimodal AI search.
Run this audit in AI Mode, ChatGPT search, ChatGPT, and another LLM model of your choice.
Gather the top five lifestyle or product photos and input them into a multimodal LLM, such as Gemini, or an object detection API, like the Google Vision API.
Use the prompt:
“List every single object you can identify in this image. Based on these objects, describe the person who owns them.”
This generates a machine-produced inventory and persona analysis.
Identify narrative disconnects, such as a budget product mispositioned as a luxury or an aspirational item, undermined by mismatched background cues.
From these results, develop explicit guidelines that include props, context elements, and on-brand and off-brand objects for marketing, photography, and creative teams.
Enforce these standards to ensure every asset analyzed by AI – and subsequently ranked or recommended – consistently reinforces product context, brand value, and the desired customer profile.
This alignment ensures consistent machine perception with strategic goals and strengthens presence in next-generation search and recommendation environments.
Brand control across the four visual layers
The brand control quadrant provides a practical framework for managing brand visibility through the lens of machine interpretation.
It covers four layers, some owned by the brand and others influenced by it.
Known brand
This includes owned visuals, such as official logos, branded imagery, and design guides, which brands assume are controlled and understood by both human audiences and AI.
Image strategy
Curate a visual knowledge graph.
List and assess adjacent objects in brand-connected images.
Build and reinforce an “Object Bible” to reduce narrative drift and ensure lifestyle signals consistently support the intended brand persona and value.
Latent brand
These are images and contexts AI captures “in the wild,” including:
User photos.
Social sightings.
Street-style shots.
These third-party visuals can generate unintended inferences about price, persona, or positioning.
An extreme example is Helly Hansen, whose “HH” logo was co-opted by far-right and neo-Nazi groups, creating unintended associations through user-posted images.
Shadow brand
This quadrant consists of outdated brand assets and materials presumed private that can be indexed and learned by LLMs if made public, even unintentionally.
Audit all public and semi-public digital archives for outdated or conflicting imagery.
Remove or update diagrams, screenshots, or historic visuals.
Funnel only current, strategy-aligned visual data to guide AI inferences and search representations.
AI-narrated brand
AI builds composite narratives about a brand by synthesizing visual and emotional cues from all layers.
This outcome can include competitor contamination or tone mismatches.
Image strategy
Test the image’s meaning and emotional tone using tools like Google Cloud Vision to confirm that its inherent aesthetics and mood align with the intended product messaging.
When mismatches appear, correct them at the asset level to recalibrate the narrative.
Factoring for sentiment: Aligning visual tone and emotional context
Images do more than provide information.
They command attention and evoke emotion in split seconds, shaping perceptions and influencing behavior.
In AI-driven multimodal search, this emotional resonance becomes a direct, machine-readable signal.
Emotional context is interpreted and sentiment scored.
The affective quality of each image is evaluated by LLMs, which synthesize sentiment, tone, and contextual nuance alongside textual descriptions to match content to user emotion and intent.
To capitalize on this, brands must intentionally design and rigorously audit the emotional tone of their imagery.
Tools like Microsoft Azure Computer Vision or Google Cloud Vision’s API allow teams to:
Score images for emotional cues at scale.
Assess facial expressions and assign probabilities to emotions, enabling precise calibration of imagery to intended product feelings such as “calm” for a yoga mat line, “joy” for a party dress, or “confidence” for business shoes.
Align emotional content with marketing goals.
Ensure that imagery sets the right expectations and appeals to the target audience.
Start by identifying the baseline emotion in your brand imagery, then actively test for consistency using AI tools.
Ensuring your brand narrative matches AI perception
Prioritize authentic, high-quality product images, ensure every asset is machine-readable, and rigorously curate visual context and sentiment.
Treat packaging and on-site visuals as digital landing pages. Run regular audits for object adjacency, emotional tone, and technical discoverability.
AI systems will shape your brand narrative whether you guide them or not, so make sure every visual aligns with the story you intend to tell.
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Google Posts now supports scheduling and multi-location publishing within Google Business Profiles. This should make it easier for you to manage your Google Posts for your business(es) and client(s).
Scheduling. When you add a new Google Post within Google Business Profiles, there is a new option to “schedule this post.” You can then select a date and time for when you want the post to be scheduled.
Lisa Landsman from Google said on LinkedIn, “plan your entire week or month in advance! You can now schedule your Google Posts to go live automatically at the perfect time.”
Multi-location publishing. Also, if you manage multiple locations for a business and you want to quickly copy those Google Posts to some or all of those locations, you can now. Lisa Landsman explained, “Easily create a single post and apply it instantly to multiple business locations in one click..”
What it looks like. Here is a GIF of this in action:
Why we care. Businesses are busy and you don’t always have time to drop what you are doing to create a Google Post about a new event or message. But now, when you have time, you can pre-schedule these Google Posts at your convenience. Also, you can quickly copy them to other locations you manage.
As Google’s Lisa Landsman wrote, “We know the upcoming holiday season is a crucial, and hectic, time for your business. It’s also your biggest opportunity to get your events, offers, and updates in front of potential customers who are actively searching.”
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In the good ol’ days of blogging, traffic was the main goal, and it was relatively easy to get.
Now, especially for ecommerce blogs, it’s getting harder to stay visible.
The number of Google searches that end with a click is slowly decreasing, while the number of searches that end with no clicks has increased.
While the number changes are small, they’re continuing to move in the direction of no-click searches. AI Overviews give people the answers they need at a glance, and website traffic is taking a toll as a result.
Aside from these trends in Google search, ecommerce blogs also face an uphill battle against big players like Amazon or Walmart.
With all of this in mind, you might be wondering: is it still worth the effort to build an ecommerce blog?
Here’s a real world example that shows why it still matters:
Pet care brand Petlibro has been around since 2020, but they didn’t start posting on their blog until 2022. Semrush’s Domain Overview suggests their organic growth has been pretty substantial since then.
Their website is ranking organically for over 25,000 keywords and stands in the first result for almost 1,500 of those.
And not only that: Petlibro is being mentioned and cited by AI search engines — more than 700 times.
AI search references Petlibro’s blog articles and mentions the brand directly in its response.
Their blog isn’t a separate entity to their ecommerce site. It’s a strategic tool that helps their brand get seen both in Google and in AI search — and get more conversions in the process.
Here’s the point: blogging is still valuable, especially for ecommerce brands, even in the era of AI search.
The difference between today and ten years ago is that the main goal isn’t traffic: it’s delivering clear, distinctive value for the reader.
Basically, you need to build something that AI can’t.
We’re going to dive deeper into ecommerce blog examples that are currently seeing big results and show you how to apply their strategies to your own brand.
What Makes an Ecommerce Blog Successful?
The more you study top ecommerce blogs, the more patterns start to emerge.
Before we explore each of the following examples in depth, keep an eye out for these key aspects of successful ecommerce blogs:
They know exactly who they’re talking to: All the top ecommerce blog examples we’ll discuss have a very clear target audience. And the content speaks directly to those people.
They understand intent: People search for certain terms just to gain information. Others search to learn about products, and others search because they’re ready to buy. The best ecommerce blogs know the difference between those different search intents. Then, they can create content that matches the intent of the search.
They present information in a way that’s easy to read and understand: There’s no specific format that guarantees success. But each example uses blog design essentials to make the information understandable. Their content also includes strong introductions and content that’s unique and interesting.
They integrate their store directly with their blog: The most successful ecommerce blogs are focused on conversions over traffic, and use smart integrations to showcase their products on the blog.
They prepare content to do well in the age of AI search: These blogs show up consistently in AI search by producing the kind of material AI loves to reference and mention. You’ll see how they create content that’s well structured, authoritative, and unique.
Now let’s see seven ecommerce blogs that exemplify these principles.
The goal of any ecommerce blog is to do more than just build traffic. You also want to build authority, win visibility in both Google and AI search, and nudge readers closer to buying.
The following examples cover a range of categories and company sizes. While they may not all have tens of thousands of visits per month, they’re all using their blog as a conversion tool and a way to get seen both in Google and in AI search.
And they all have something to teach you about staying visible, memorable, and findable as an ecommerce blog.
Note: We got the numbers for each of these from Semrush’s SEO Toolkit. Traffic numbers aren’t going to be 100% accurate (only the brands themselves will have the most up-to-date numbers). But it’s still useful for understanding broad trends.
1. Garmin
Industry: Consumer electronics
Organic blog traffic: 61.8K
Backlinks: 77.7K
Keywords: 46.1K
In the world of smartwatches and specialty sports gear, Garmin truly stands out. Their blog has grown consistently since mid 2022.
So, what makes this ecom blog stand out?
First off, the articles are a healthy mix of informational and commercial content.
For example, this article on finding your V02 max ranks for 4.6k keywords, and ranks #1 for 95 of those. It even shows up in the AI overview for a couple of difficult keywords.
The article is a deep-dive into a complex topic their audience is interested in. And while someone searching “good v02 max” may not be immediately interested in buying a watch, Garmin still includes plenty of ways to explore their products from this blog post.
For instance, readers can see CTAs to some of their most relevant watches in the sidebar, and they also see links to product categories in the text.
But Garmin also knows how to focus their blog on buying intent, which is why they also rank for terms like “Garmin aviation watch.”
From this single keyword, Garmin’s article on aviation watches gets 3.7k monthly organic traffic by ranking for 63 keywords. (I guess pilots really like their watches.)
But more than just creating content for search, Garmin has cracked the code on creating content that gets mentioned by AI.
Just look at Garmin’s incredible AI visibility score, with over 52k mentions:
AI search loves to highlight product information directly from the brand. Which is why Garmin’s clear, detailed support documentation appears so often in AI search results.
But their blog posts are also cited by AI to respond to product-related questions, like which smartwatch has the best battery life.
Something else that Garmin has done well is combine their content efforts on their owned channels with mentions across the web. Whether it’s tech review sites, YouTube videos, fitness blogs, or Google reviews, Garmin’s products are mentioned positively in a lot of places.
The result?
Semrush’s AI Visibility Index found that Garmin ranked #4 in AI Share of Voice for consumer electronics brands. They sit right at the top with heavy hitters like Apple and Google.
Key Lessons from Garmin’s Blog
Garmin is a multi-billion dollar company, well-known in its space. But importantly, they dominate their category. When you own a category (like smartwatches), it’s much easier for AI to surface your content and products to users.
Another company doing this is Patagonia. They dominate the category of ethical fashion, and have gained 21.96% of the AI Share of Voice (for Fashion & Apparel).
Another lesson from Garmin’s blog is the importance of providing clear information about your products.
AI search results tend to cite brands as authorities on their own products. But if you don’t answer the questions searchers have about your products? AI will usually attempt to base its answers on someone else’s article (whether that information is correct or not).
Finally, remember that your blog isn’t a solo marketing effort. When you partner with content creators outside your owned channels, you can expand your visibility in AI.
The more positive mentions your brand gets, the more likely you are to see yourself in AI answers and overviews.
We’ve already introduced you to Petlibro above: showing the power of blogging for ecommerce brands. Not only do they show up in search results, Petlibro’s blog posts are also being cited and mentioned by AI.
Take this post for example:
This informational post answers the question of how often to change the filters in a cat fountain. It’s not too long, but it answers the question clearly and gives just the right amount of detail.
So, along with ranking for 44 different keywords, it’s also showing up inside the answers given by ChatGPT and other AI search tools.
Another post, explaining why cats bring you toys, ranks in the top 10 for 14 keywords, and appears in the AI overview in Google.
But Petlibro doesn’t just post informational articles. They do a great job of striking the balance of intent, focusing on content that matches what the searcher is looking for.
For example, this blog article about choosing the perfect cat tree gets more than 500 visits per month and ranks for 127 keywords. Best of all, most of these keywords have commercial or transactional intent.
Key Lessons from Petlibro’s Blog
First off, Petlibro shows it’s important to develop a healthy mix of informational and transactional content.
Going after keywords at the top of the funnel works to build your authority. But content that helps point people to the right products when they’re already in the mood to buy brings more immediate results.
Next, for your brand to be visible in both Google and AI, you need to answer the questions people are asking. You can start by doing research on forums, but also try tools like Semrush’s AI SEO toolkit for prompt research.
This can give you an idea of the prompts people are using in AI platforms, and which websites AI is currently referencing or mentioning directly.
For example, let’s try searching for “home security camera systems.”
In the Prompt Research report, you can see AI volume for that topic, how difficult it is to gain visibility, the intent of the questions in this topic, and more details about the prompts used and the brands mentioned.
This gives you a great starting point to see what people are asking about within your topic. Then, you can create content that answers those questions.
3. Great Jones Goods
Industry: Cookware
Organic blog traffic: 11.6K
Backlinks: 1.7K
Keywords: 4.9K
Great Jones Goods’ blog stands out with fantastic visuals and content that is tailored to their audience.
Honestly, just looking at this blog is making me want to get into the kitchen and bake something.
Their blog has two main sections: recipes and personal profiles.
You gotta love these recipe posts. Just take this one for arroz con gandules:
Each recipe has a different author. So each post has a very personal feel.
It’s just like your favorite recipe blog, but without so many layers of fluff.
The posts also mention the cookware the author used (subtly highlighting their own products).
And each recipe is also accompanied by beautiful step-by-step visuals.
This all looks great: but what about the results?
Great Jones Goods isn’t getting millions in traffic. But their content does show up in all the right places.
For example, their profiles of chefs and well-known people rank in search results:
And their recipe posts also show up in AI overviews:
Their blog is consistent and targeted at their specific audience. Instead of being “sales-y,” they focus on being part of the community that they want to sell to.
Key Lessons from Great Jones Goods’ Blog
Beautiful, descriptive visuals are a key component of high-quality blog content. Plus, it’s a great way to make your blog stand out as different. When you’re creating content for your blog, ask yourself: how can I create something that AI can’t?
Great Jones does this by including step-by-step imagery and real-world examples of their products in use. That’s something shoppers love to see, and AI can’t replicate.
Another key takeaway from this ecommerce blog example is to include your community in your content. Great Jones does this with in-depth personal profiles that talk about the joy of cooking — something their target audience shares.
People crave connection with other humans, now more than ever. You can use your blog to become part of that community.
Try including people that the community already knows and loves. This will help your blog be more personal, as well as give you new ways to promote your blog.
When your brand is dedicated to a mission, you can use your blog to promote and grow that mission. And that’s exactly what the period underwear brand Thinx has done with their “Periodical” section.
First, they chose an incredibly appropriate name for their blog. Next, they filled it with articles all about menstrual health for women and teens.
The articles are generally on the short side, but answer key questions their audience is asking. And with that, they’re able to rank for difficult keywords like “when do you ovulate,” “period blood clots,” or “period nausea.”
Just this one article on ovulation ranks for 1.3k keywords, most of which are either hard or very hard to rank for per Semrush data.
They also build educational resources around the message: Get BodyWise.
Thinx takes body literacy seriously. In fact, they have a dedicated resource page aside from their blog that is built to provide candid, accessible information for people who bleed.
This even includes a series of educational videos from Dr. Saru Bala on women’s health.
Everything they do on the blog supports their mission to make period products and education more accessible to everyone who needs it.
And while their content doesn’t heavily promote their products (possibly on purpose), they do list a handful of relevant products at the end of each blog post. Just the right mix of promotional and educational.
Key Lessons from Thinx Periodical Blog
Your company mission statement isn’t just something that lives quietly on your About page.
It should be a living, breathing part of your business ethos.
It should come through in your marketing.
When your blog has a core mission behind it, the content you create has a clear direction. You’re not just chasing keywords: you’re building educational resources that truly benefit your audience.
The result?
Thinx builds brand affinity naturally over time, increasing the chances that folks will choose Thinx over a competitor when they’re ready to buy.
5. King Arthur Baking
Industry: Cooking ingredients
Organic blog traffic: 730K
Backlinks: 133K
Keywords: 338K
King Arthur Baking’s blog ranks in the top 10 for some of the most difficult keywords in baking. That includes terms like “baguette,” “pizza,” or “types of cinnamon.”
So, how did they get here?
King Arthur Baking didn’t limit themselves to written content. They created a content ecosystem that also included multimedia content.
Currently, the King Arthur YouTube channel has over 330K subscribers. They post recipes, along with video versions of their podcast episodes.
These videos work seamlessly inside their blog posts.
For example, check out their blog post on chocolate chip cookies.
The video from their YouTube video is part of the image gallery at the top.
But it’s also spliced together with the step-by-step recipe instructions below.
Doing this increases their chances of ranking for difficult keywords. And in some cases, they even rank more than once in the search results.
Key Lessons from King Arthur’s Bakery Blog
Google and AI won’t rank what they can’t understand, so giving clear structure and formatting to your blog is an essential first step to rank better.
For example, King Arthur uses schema markup for their recipes. This helps them rank in rich results on Google.
Another lesson from King Arthur is using multimedia when it makes sense. Try creating videos that show your products in action, or clearly answer a question that your audience is asking. These can help you increase time on page and appear in more search results.
Finally, know when to push your products. King Arthur does a great job of subtly adding their products to content.
For example, their blog posts include “featured products,” a CTA to “Shop this recipe,” and “Recommended for you” products at the end of each post.
6. Keychron
Industry: Electronics
Organic blog traffic: 62.1K
Backlinks: 7.1K
Keywords: 25.8K
For a seriously niche blog and product, Keychron has a pretty hefty presence online. Their blog has had steady traffic growth since around 2020. And they rank for all kinds of keywords about keyboards.
For example, this article about hall effect switches gets over 1,700 visits per month.
The post ranks #1 for that main keyword. But it also appears in search results, AI overviews, and image packs for 137 other keywords.
Their blog posts do a great job of using visuals to explain topics about the tech. And they get to gently promote their own products when appropriate.
Of course, this kind of top-of-the-funnel content is likely to drive less traffic as more people rely on AI Overviews and other AI tools for quick answers to their questions.
But it can still drive some traffic. And careful linking and CTA placement can turn that traffic into conversions.
Key Lessons from Keychron’s Blog
One key takeaway from Keychron’s blog?
Don’t be afraid to go niche.
Your audience may have very deep knowledge of a topic (like keyboards), or they may be generalists looking for an overall view of the topic. It’s up to you to know who your audience is, and develop content for them.
Topics like “Best Keyboards for World of Warcraft” may seem niche, but it fits Keychron’s highly specific audience (and does a great job of showcasing their products).
The root domain didn’t take as much of a hit. But the blog experienced a spike and a sudden drop around early 2021.
Thankfully, Huckberry didn’t let that stop them.
They still had another card up their sleeve: their YouTube channel.
While the channel was created back in 2016, there was no consistency, and hardly any views.
But sometime after traffic dipped on the blog, we see a change in the posting pattern on YouTube. Suddenly, they’re posting consistently.
They share video series, interviews, and more (some of which get hundreds of thousands of views).
And over time, Huckberry became the go-to place for adventure content for men.
They started sharing videos about culinary travel and adventure stories with members of the community. Plus, they posted gear reviews that linked back to their products.
That multimedia strategy helped Huckberry’s blog gain consistent growth again. Plus, their YouTube channel took off — today, it boasts over 375K subscribers.
That video strategy made them adapt the way they present content on their blog as well.
Many posts include videos with gear reviews and style help. The videos are funny, personable, and mention the brand’s products without sounding like a sales pitch — it really sounds like two friends shooting the breeze.
The posts themselves also do a beautiful job of incorporating products:
Almost all their posts follow classic blog post templates, but maintain the vibe of a cool online magazine.
Key Lessons from Huckberry’s Blog
Huckberry’s key lesson is this: don’t give up after a traffic dip.
Blog traffic can dip for many different reasons, but it doesn’t mean your blog is a lost cause. When you see a dip, dig into the data.
Have you lost ranking on major keywords? Are clicks down? Run through a basic SEO checklist to make sure you’ve got your bases covered.
Then, go back to the question we’ve talked about before: What can you create that AI can’t replicate? Define how your blog is differentiated from what AI answers can deliver, and what value you can bring to your audience.
Your Ecommerce Blog Can Succeed — If You Trust the Process
You can’t build a successful ecommerce blog overnight. But the brands above prove it’s worth the effort.
When you do it right, your blog becomes more than a traffic source. It’s a growth engine that boosts visibility, builds trust, and strengthens your brand in both Google and AI search.
Keep answering your customers’ questions, stay focused on your niche, and build consistency over time.
But remember: your blog is just one piece of your overall strategy.
To go deeper into building a comprehensive marketing strategy for your ecommerce brand, check out our full ecommerce marketing guide.
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Imagine telling someone that www.mysite.com/blog/myarticle and www.mysite.com/myarticle are actually the same page. To you, they’re the same, but to Google, even a small difference in the URL makes them separate pages. That is where the canonical tag steps in. In this guide, we will walk you through what a canonical URL is, how URL canonicalization works, when to use it, and which mistakes to avoid so that search engines always understand your preferred page version.
A canonical URL is the main version of a webpage that you want search engines to index, avoiding duplicate content issues
The canonical tag, placed in the HTML head, signals which URL is the preferred version to search engines
Using canonical URLs helps consolidate link equity, improves crawl efficiency, and enhances user experience
Implement canonical tags in scenarios like duplicate content, URL versions, and syndicated content to inform search engines which URL to prioritize
Yoast SEO can automate canonical URL handling, reducing manual errors and ensuring consistency across your site
What is a canonical URL?
A canonical URL is the main, preferred, or official version of a webpage that you want search engines like Google to crawl and index. It helps search engines determine which version of a page to treat as the primary one when multiple URLs lead to similar or duplicate content. As a result, it avoids duplicate content and protects your SEO ranking signals.
All of the following URLs can show the same page, but you should set only one as the canonical URL:
https://www.mysite.com/product/shoes
https://mysite.com/product/shoes?ref=instagram
https://m.mysite.com/product/shoes
https://www.mysite.com/product/shoes?color=black
What is a canonical tag?
A canonical tag (also called a rel="canonical" tag) is a small HTML snippet placed inside the section of a webpage to tell search engines which URL is the canonical or master version. It acts like a clear label saying, “Index this page, not the others.” This prevents duplicate content issues, consolidates ranking signals, and supports proper canonicalization across your site.
Here’s an example of a canonical tag in action:
This tag should be placed on any alternate or duplicate versions that point back to the main page you want indexed.
How does URL canonicalization work?
Canonicalization is the process of selecting the representative or canonical URL of a piece of content. From a group of identical or nearly identical URLs, this is the version that search engines treat as the main page for indexing and ranking.
Once you understand that, canonicalization becomes much easier to visualize. Think of it as a three-step workflow.
How the canonicalization process works
Here’s how the canonicalization works:
Search engines detect duplicate or similar URLs
Google groups URLs that return the same (or almost the same) content. These could come from:
URL parameters
HTTP vs. HTTPS versions
Desktop vs. mobile URLs
Filtered or sorted pages
Regional versions
Accidental duplicates like staging URLs
You signal which URL is canonical
You can guide search engines using canonical signals like:
The rel="canonical" tag
301 redirects
Internal links pointing to one preferred version
Consistent hreflang usage
XML sitemaps listing the preferred URL
HTTPS over HTTP
The strongest and clearest hint is the canonical tag placed in the head of the page.
Google selects one canonical URL
Google uses your signals, along with its own evaluation, to determine the primary URL. While Google typically follows canonical tags, it may override them if it detects stronger signals such as redirects, internal linking patterns, or user behaviour.
Once Google settles on the canonical URL, search engines will:
Consolidate link equity into the canonical page
Index the canonical URL
Treat all non-canonical URLs as duplicates
Reduce crawl waste
Avoid showing similar pages in search results
Canonical tags are a hint, not a directive. Google may still distribute link equity differently if it deems the canonical tag unreliable.
Reasons why canonicalization happens
Canonicalization becomes necessary when different URLs lead to the same content. Some common reasons are:
Region variants
For example, you have one product page for the USA and one for the UK, like: https://example.com/product/shoes-us and https://example.com/product/shoes-uk.
If the content is almost identical, use one canonical link or a clear regional setup to avoid confusion.
Pro tip: For regional variants, combine canonical tags with hreflang to specify language/region targeting.
Device variants
When you serve separate URLs for mobile and desktop, such as: https://m.example.com/product/shoes and https://www.example.com/product/shoes.
Canonical tags help search engines understand which URL is the primary version.
Protocol variants
Sorting and filtering often create many URLs that show similar content, like:
https://example.com/shoes?sort=price or https://example.com/shoes?color=black&size=7
A single canonical URL, such as https://example.com/shoes, tells search engines which page should carry the main ranking signals.
Maybe a staging or demo version of the site is left crawlable, or both https://example.com/page and https://example.com/page/ return the same content
Canonical tags and proper URL canonicalization help avoid these unintentional duplicates.
Some duplicate content on a site is normal. The goal of canonicalization in SEO is not to eliminate every duplicate, but to show search engines which URL you want them to treat as the primary one.
In practical aspects
In practice, canonicalization comes down to a few key things:
Placement
The canonical tag is placed in the head of the HTML, for example:
link rel="canonical" href="https://www.example.com/preferred-page" /
Each page should have at most one canonical tag, and it should point to the clean, preferred canonical URL.
Identification
Search engines examine several signals to determine the canonical version of a page. The rel="canonical" tag is important, but they also consider 301 redirects, internal links, sitemaps, hreflang, and whether the page is served on HTTPS. When these signals are consistent, it is easier for Google to pick the right canonicalized URL.
Crawling and indexing
Once search engines understand which URL is canonical, they primarily crawl and index that version, folding duplicates into it. Link equity and other signals are consolidated to the canonical page, which improves stability in rankings and makes your canonical tag SEO setup more effective.
The main rule for canonicalization is simple: if multiple URLs display the same content, choose one, make it your canonical URL, and clearly signal that choice with a proper canonical tag.
Why do canonical tags matter for SEO?
Google’s John Mueller puts it simply: ‘I recommend doing this kind of self-referential rel=canonical because it really makes it clear for us which page you want to have indexed or what this URL should be when it’s indexed.’
And that’s exactly why canonical tags matter; they tell search engines which version of a page is the real one. This keeps your SEO signals clean and prevents your site from competing with itself.
They’re important because they:
Avoid duplicate content issues: Canonical tags inform Google which URL should be indexed, preventing similar or duplicate pages from confusing crawlers or diluting rankings
Consolidate link equity: Canonicalization works similarly to internal linking; both are techniques used to direct authority to the page that matters most. Instead of splitting ranking signals across duplicate URLs, all information is consolidated into a single canonical URL
Improve crawl efficiency: Search engines don’t waste time crawling unnecessary duplicate pages, which helps them discover your important content faster
Enhance user experience: Users land on the correct, up-to-date version of your page, not a filtered, parameterized, or accidental duplicate
When to use canonical tags?
Canonical tags are useful in various everyday SEO scenarios. Here are the most common scenarios where you’ll want to use a rel=canonical tag to signal your preferred URL.
URL versions
If your page loads under multiple URL formats, with or without “www,” HTTP vs. HTTPS, and with or without a trailing slash, search engines may index each version separately. A canonical tag helps you standardize the preferred version so Google doesn’t treat them as separate pages.
Duplicate content
Ecommerce sites, blogs with tag archives, and category-driven pages often generate duplicate or near-duplicate content by design. If the same product or article appears under multiple URLs (filters, parameters, tracking codes, etc.), canonical tags help Google understand which canonical URL is the authoritative one. This prevents cannibalization and protects your canonical SEO setup.
If your content is republished on partner sites or aggregators, always use a canonical tag that points back to your original version. This ensures your page retains the ranking signals, not the syndicated copy, and search engines know exactly where the content was originally published.
If syndication partners don’t honor your canonical tag, consider using noindex or negotiating link attribution.
Paginated pages
Long lists or multi-page articles often create a chain of URLs like /page/2/, /page/3/, and so on. These pages contribute to the same topic but shouldn’t be indexed individually. Adding canonical tags to the paginated sequence (typically pointing to page 1 or a “view-all” version) helps consolidate indexing and keeps rankings focused on the primary page.
Pro tip: For paginated content, use self-referencing canonicals (each page points to itself) unless you have a ‘view-all’ page that loads quickly and is crawlable.
When you change domains, restructure URLs, or move from HTTP to HTTPS, using consistent canonical tags helps reinforce which pages replace the old ones. It signals to search engines which canonicalized URL should inherit ranking power. During migrations, canonical tags act as a safety net to prevent duplicate versions from competing with each other.
Implementing canonical URLs and canonical tags
URL canonicalization is all about giving search engines a clear signal about which version of a page is the preferred or canonical URL. You can implement it in several simple steps.
Using the rel=”canonical” tag
The most common way (as shown multiple times in this blog post) to set a canonical URL is by adding a rel="canonical" tag in the head section of your page. It looks like this:
link rel="canonical" href="https://www.example.com/preferred-url"/
This tag tells search engines which URL should carry all ranking signals and appear in search results. Ensure that every duplicate or alternate version links to the same preferred URL, and that the canonical tag is consistent throughout the site.
You can also use rel="canonical" in HTTP headers for non-HTML content such as PDFs. This is helpful when you cannot place a tag in the page itself.
Pro tip: While supported for PDFs, Google may not always honor canonical HTTP headers. Use them in conjunction with other signals (e.g., sitemaps).
Also, ensure the canonical tag is as close to the top of the head section as possible so that search engines can see it early. Each page should have only one canonical tag, and it should always point to a clean, accessible URL. Avoid mixing signals. The canonical URL, your internal links, and your sitemap entries should all match.
Setting a preferred domain in Google Search Console
Google lets you choose whether you prefer your URLs to appear with or without www. Setting this preference helps reinforce your canonical signals and prevents search engines from treating www and non-www versions as different URLs.
To set your preferred domain, open your property in Google Search Console, go to Settings, and choose the version you want to treat as your primary domain.
Redirects (301 redirects)
A 301 redirect is one of the strongest signals you can send. It permanently informs browsers and search engines that one URL has been redirected to another and that the new URL should be considered the canonical URL.
Use 301 redirects when:
You merge duplicate URLs
You change your site structure
You migrate to HTTPS
You want to consolidate link equity from outdated pages
Of course, redirects replace the old URL, while canonical tags suggest a preference without removing the duplicate.
With Yoast SEO Premium, you can manage redirects effortlessly right inside your WordPress dashboard. The built-in redirect manager feature of the SEO plugin helps you avoid unnecessary 404s and prevents visitors from landing on dead ends, keeping your site structure clean and your user experience smooth.
A smarter analysis in Yoast SEO Premium
Yoast SEO Premium has a smart content analysis that helps you take your content to the next level!
There are a few more ways to support your canonical setup.
XML sitemaps: Always include only canonical URLs in your sitemap. This helps search engines understand which URLs you want indexed
Hreflang annotations: For multi-language or multi-region sites, hreflang tags help search engines serve the correct regional version while still respecting your canonical preference
Link HTTP headers: For files like PDFs or other non-HTML content, using a rel="canonical" HTTP header helps you specify the preferred URL server-side
Each of these methods reinforces your canonical signals. When you use them together, search engines have a much clearer understanding of your canonicalized URLs.
Implementing canonicalization in WordPress with Yoast
Manually adding a rel="canonical" tag to the head of every duplicate page can be fiddly and error prone. You need to edit templates or theme files, keep tags consistent with your sitemap and internal linking, and remember special cases, such as PDFs or paginated series. Modifying site code and HTML is risky when you have numerous pages or multiple editors working on the site.
Yoast SEO makes this easier and safer. The plugin automatically generates sensible canonical URL tags for all your pages and templates, eliminating the need for manual theme file edits or code additions. You can still override that choice on a page-by-page basis in the Yoast SEO sidebar: open the post or page, go to Advanced, and paste the full canonical URL in the Canonical URL field, then save.
Automatic coverage: Yoast automatically adds canonical tags to pages and archives by default, which helps prevent many common duplicate content issues
Manual override: For special cases, use the Yoast sidebar > Advanced > Canonical URL field to set a custom canonical. This accepts full URLs and updates when you save the post
Edge cases handled: Yoast will not output a canonical tag on pages set to noindex, and it follows best practices for paginated series and archives
Developer options: If you need custom behavior, you can filter the canonical output programmatically using the wpseo_canonical filter or use Yoast’s developer API
Cross-domain and non-HTML: Yoast supports cross-site canonicals, and you can use rel=”canonical” in HTTP headers for non-HTML files when needed
Both Yoast SEO and Yoast SEO Premium include canonical URL handling, and the Premium version adds extra automation and controls to streamline larger sites.
Canonical URLs may seem like a small technical detail, but they play a huge role in helping search engines understand your site. When Google finds multiple URLs displaying the same content, it must select one version to index. If you do not guide that choice, Google will make the decision on its own, and that choice is not always the version you intended. That can lead to split ranking signals, wasted crawl activity, and frustrating drops in visibility.
Using canonical URLs gives you back that control. It tells search engines which page is the primary version, which ones are duplicates, and where all authority signals should be directed. From filtering URLs to regional variants to accidental duplicates that slip through the cracks, canonicals keep everything tidy and predictable.
The good news is that canonicalization does not have to be complicated. A simple rel=”canonical” tag, consistent URL handling, smart redirects, and clean sitemap signals are enough to prevent most issues. And if you are working in WordPress, Yoast SEO takes care of almost all of this automatically, so you can focus on creating content instead of wrestling with code.
At the end of the day, canonical URLs are about clarity. Show search engines the version that matters, remove the noise, and keep your authority consolidated in one place. When your signals are clear, your rankings have a solid foundation to grow.
We’re happy to announce we’re providing an additional tool to analyze the performance
of your website by query type in the Search Console Performance Report:
the branded queries filter. This new feature is designed to help analyze the queries driving traffic
to your site by automatically differentiating between branded and non-branded queries.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-20 05:00:002025-11-20 05:00:00Introducing the Branded queries filter in Search Console
A new column called “Original Conversion Value” has started appearing inside Google Ads, giving advertisers a long-requested way to see the true, unadjusted value of their conversions.
How it works. Google’s new formula strips everything back:
Conversion Value
– Rule Adjustments (value rules)
– Lifecycle Goal Adjustments (e.g., NCA bonuses)
= Original Conversion Value
Why we care. For years, marketers have struggled to isolate real conversion value from Google’s layers of adjustments — including Conversion Value Rules and Lifecycle Goals (like New Customer Acquisition goals). Original Conversion value makes it easier to diagnose performance, compare data across campaigns, and spot when automated bidding is boosting value rather than actual conversions.
In short: clearer insights, cleaner ROAS, and more confident decision-making.
Between the lines:
Value adjustments are useful for steering Smart Bidding.
But they also inflate numbers, complicating reporting and performance analysis.
Agencies and in-house teams have long asked Google for a cleaner view.
What’s next. “Original Conversion Value” could quickly become a go-to column for:
Revenue reporting
Post-campaign analysis
Troubleshooting inflated ROAS
Auditing automated bid strategies
First seen. This update was first picked up by Google Ads Specialist Thomas Eccel when he shared spotting the new column on LinkedIn
The bottom line. It’s a small update with big clarity. Google Ads is giving marketers something rare: a simpler, more transparent look at the value their ads actually drive.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/11/Screenshot-2025-11-18-at-16.40.16-l0XhcV.webp?fit=549%2C451&ssl=1451549http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-18 17:01:322025-11-18 17:01:32Google Ads quietly rolls out a new conversion metric
Google announced the release of its latest AI model update, Gemini 3. “And now we’re introducing Gemini 3, our most intelligent model, that combines all of Gemini’s capabilities together so you can bring any idea to life,” Google’s CEO, Sundar Pichai wrote.
Gemini 3 is now being used in AI Mode in Search with more complex reasoning and new dynamic experiences. “This is the first time we are shipping Gemini in Search on day one,” Sundar Pichai said.
AI Mode with Gemini 3. Google shared how AI Mode in Search is now using Gemini 3 to enable new generative UI experiences like immersive visual layouts and interactive tools and simulations, all generated completely on the fly based on your query.
Here is a video of showing how RNA polymerase works with generative UI in AI Mode in Search.
“In Search, Gemini 3 with generative layouts will make it easy to get a rich understanding of anything on your mind. It has state-of-the-art reasoning, deep multimodal understanding and advanced agentic capabilities. That allows the model to shine when you ask it to explain advanced concepts or ideas – it reasons and can code interactive visuals in real-time. It can tackle your toughest questions like advanced science.”
More Gemini 3. Google added that Gemini 3 has:
State-of-the-art reasoning
Deep multimodal understanding
Powerful vibe coding so you can go from prompt to app in one shot
Improved agentic capabilities, so it can get things done on your behalf, at your direction
Availability. Gemini 3 is now rolling out, yes, in AI Mode but here also:
For everyone in the Gemini app and for Google AI Pro and Ultra subscribers in AI Mode in Search
For developers in the Gemini API in AI Studio, our new agentic development platform, Google Antigravity; and Gemini CLI
For enterprises in Vertex AI and Gemini Enterprise
Why we care. Gemini 3 is currently powering AI Mode, the future of Google Search. It will continue to power more and more search features within Google, as well as other areas within Google’s platforms.
Being on top of these changes and how they impact search and your site and maybe Google Ads is important.
Ask the same question in 11 AI search engines, and you’ll get 11 different answers.
Sometimes wildly different.
Some engines focus on visuals and shoppable results. Others go deep into research. A few just try to get you an answer, fast.
Each platform prioritizes and presents it differently.
And those differences matter.
Not just for users, but for brands trying to get discovered in AI search.
So, I tested popular and lesser-known AI engines on accuracy, depth, user experience, and other factors.
Only four made the cut.
In this guide, you’ll learn which AI search engines came out on top, including pros, cons, and pricing. I’ll also share which engines didn’t make my list, and why.
Along the way, you’ll get a few tips on using these insights to improve your AI visibility.
Start with a quick overview of my findings below. Or jump straight to the #1 AI search engine on my list: ChatGPT.
What Are the Best AI Search Engines?
Tool
Best for
Pros
Cons
Price
ChatGPT
Comprehensive research and shoppable product comparisons
Visual layout with tables and images; remembers context across follow-ups; direct purchase links
Overwhelming results for broad queries; accuracy issues; overly agreeable
Free or $20+/month
Google AI Mode
Quick product searches with real buyer reviews
Fast product results with pricing and reviews; integrates Google ecosystem
Vague on informational queries; no comparison tables; unavailable in some regions
Free
Sigma Chat (Formerly Bagoodex)
Research deep dives that build on previous questions
Weak product presentation; no pricing or buy links; poor visuals
Free or $10+/month
Microsoft Copilot
Fast answers in clean, skimmable formats
Clean categorization; fast responses; easy to skim
Surface-level depth; no product links; weak for shopping
Free
How I Tested 11 AI Search Engines
To keep things consistent, I ran the same set of prompts across 11 AI search tools.
Note: For this article, I defined “AI search engine” as any generative AI platform that can understand queries, pull information from sources, and deliver answers in natural language.
This included big names like ChatGPT, AI Mode, and Perplexity.
And newer players like Arc, Andi, and Sigma Chat.
I focused on one topic (running shoes) and tested a range of prompts across different search intents.
This showed how well each engine handled the full customer journey, from research to shopping.
This included:
“Best running shoes”: Assesses top-level recommendations and how each engine handles broad prompts
“Best running shoes for beginner marathon training”: Evaluates personalization and context handling as the prompt narrows
“How long do running shoes last?”: Gauges accuracy on general product knowledge and durability expectations
“Of the trainers you’ve recommended, which ones will last the longest?”: Tests the accuracy of product details and the engine’s ability to remember details from previous prompts
“Can I wear any of these running shoes recommended for hiking?”: Assesses how each AI handles reasoning, real-world nuance, and potential safety considerations
I evaluated each tool on five factors:
Accuracy: Did it understand the intent and get the facts right?
Depth: Did it add helpful context or just summarize existing content?
Transparency: Did it credit or link to its sources?
User experience: Was the output fast, skimmable, and well-organized?
Adaptability: Could it handle follow-up questions naturally or refine vague prompts?
After testing all 11 AI search engines, these four stood out as the best for different reasons.
1. ChatGPT
Best for comprehensive research and shoppable product comparisons
This is true whether you’re comparing products, researching topics, or looking for a step-by-step tutorial.
It also remembers context across follow-up questions.
I started with a broad prompt and added specifics as the conversation progressed. ChatGPT remembered key details without making me repeat myself.
For shopping queries, the visual presentation stood out.
When I searched for running shoes, for example, ChatGPT returned products with images, prices, reviews, and short descriptions.
It also included links to retailers and external articles. This made verifying product details and purchasing easy.
The summary tables were particularly useful.
After inquiring about shoe lifespan, ChatGPT delivered a clean comparison table with products and their expected mileage.
For brands: ChatGPT’s visual layout isn’t just useful for shoppers. If you’re trying to get your brand referenced by AI search engines, it also reveals what these models prioritize. Use tables, clear specs, and organized categories on your product pages to help both shoppers and AI find your information faster.
ChatGPT is also evolving quickly.
Features like Instant Checkout (currently limited to select Etsy sellers in the United States) let users complete purchases directly inside the chat.
Great for shoppers — and even greater for the brands featured in ChatGPT’s recommendations.
Where ChatGPT Falls Short
When I tested ChatGPT, I got what most people want from AI search: answers that feel confident and complete.
But not every response was perfect.
Broad prompts, such as “Best running shoes,” resulted in lengthy lists of brands, product categories, and features.
The information took real effort to digest.
Specific prompts worked much better.
I also noticed minor inaccuracies in some instances, like when I asked about shoe lifespan.
After fact-checking the replies, some details didn’t match the manufacturer’s specifications.
For example, ChatGPT said the Brooks Ghost running shoe has a lifespan of 450 to 500 miles. But the actual range is 300 to 500 miles.
This also highlights a larger problem.
ChatGPT pulls information from multiple sources, such as blog posts and brand sites.
But it also relies on forums like Quora and Reddit, where users share personal experiences.
It then aggregates the information into its responses. This can lead to inaccurate and misleading information.
For brands: Provide clear answers to common user questions on your site. Otherwise, AI search engines may turn to other, potentially inaccurate sources for this information. Add tables with specifications, be explicit about ranges and measurements, and use structured data so AI can extract and cite your product information correctly.
ChatGPT also tends to be overly agreeable.
Whatever you prompt, ChatGPT will lean toward flattery and agreement — even when it involves safety.
For example, when I asked, “Can I wear any of these running shoes recommended for hiking?”
ChatGPT’s response was:
“Good question 👍 — you can hike in road running shoes, but whether it’s a good idea depends on the terrain and how far you’re going.”
Not the worst.
But not as good as other AI search engines in this aspect, like AI Mode, which was more cautious.
AI Mode said:
“It is not recommended to use the road running shoes previously mentioned for hiking…they lack the key features that provide the necessary grip, protection, and stability for off-road trails. Using them for hiking could lead to injury.”
Overall, ChatGPT is fast, detailed, and helpful.
But it can be too generous with information — and too polite to push back.
It pulls product listings, prices, and reviews directly into the search interface. This makes it ideal for shoppers who want to quickly compare products before purchasing.
What AI Mode Does Well
AI Mode shines when you have clear buying intent.
It instantly surfaces product options with images, prices, star ratings, and quick links to retailers. And it’s all in a clean, scrollable layout.
When I searched “best running shoes,” it showed a curated carousel of options with price comparisons across multiple sites.
I especially liked how it paired Google Reviews with its recommendations — a small detail that makes decision-making faster and builds trust.
For me, that worked perfectly.
Getting straight to the products moved me faster toward a decision.
But some users may prefer more background or context for researching and weighing options. ChatGPT’s research-style answers still win in this regard.
For brands: AI Mode pulls heavily from Google Reviews and structured product data. Focus on getting detailed, positive reviews and keeping your product schema markup up to date. These signals can influence whether your products appear in AI-generated results.
Where AI Mode Falls Short
AI Mode is not yet available in all countries, although it’s rolling out quickly.
And unlike ChatGPT, it didn’t provide any comparison tables for any of my prompts. Just products and bullet points.
This meant more scrolling and clicking to find and digest the information.
This was evident when I asked which of the recommended shoes would last the longest.
AI Mode’s response was vague and unhelpful. It said the Brooks Ghost shoe was “exceptionally long-lasting.”
It didn’t provide any of the specifics that would make me want to purchase this shoe. Like mileage range and how it differed between the options.
If you’re early in the evaluation phase, AI Mode can feel limiting.
But it delivers when you want a shortlist of top contenders.
Pricing
AI Mode is available for free within Google Search, depending on your region.
Best for research deep dives that build on previous questions
Sigma Chat’s iterative search and in-depth replies are excellent if you love to research.
Ask a question, get an answer, then drill deeper into related topics — and it remembers the full thread.
Note: Bagoodex launched in 2024 and has since rebranded as Sigma Chat. For this review, I tested it against the standard modes of other tools. ChatGPT’s Thinking mode and Perplexity’s Research mode are designed for deep research and may perform differently.
What Sigma Chat Does Well
Sigma Chat stood out for its ability to build on previous context.
When I asked follow-up questions, it remembered what I’d already searched and adjusted its answers accordingly.
No need to repeat myself or reframe the entire query.
For example, after I asked which of the recommended shoes would last the longest, it specifically referenced “marathons.”
(Even though I hadn’t mentioned this criterion again after the initial prompt.)
Sigma Chat’s follow-up suggestions also stood out for their potential to aid deep research.
Instead of ending with one answer, it nudged me toward related questions I hadn’t considered:
This makes it particularly helpful for any kind of research, whether you’re comparing products, building content outlines, or researching niches.
For brands: Sigma Chat rewards depth and topic clustering. To increase visibility in AI tools like this, build content hubs around your main topics — link related pages together and cover every sub-question your audience might ask. The more complete your coverage, the easier it is for AI to surface your site in deep research queries.
Another interesting feature of this AI search engine?
It suggests prompts tailored to content creation. This is especially helpful if you’re using it for marketing purposes.
After providing search results for the best running shoes for a marathon, it offered unexpected options like:
“Write a blog post about this topic”
“Create an image on this topic”
I tested the blog prompt, and it generated a quick draft titled “Marathon Training on a Budget: Choosing Durable Running Shoes.”
It wasn’t something you’d publish as-is, but it was a decent starting point.
If you’re prone to writer’s block or need to quickly draft comparison content around competitor products, it’s a particularly helpful feature.
From there, it suggested additional prompts like “Add a call to action” and “Shorten for social media.”
This makes it easy for marketers to generate content for multiple platforms at once.
Microsoft Copilot has the cleanest layout of any AI search engine I tested.
It’s fast, structured, and organized. Perfect for people who want distraction-free takeaways.
What Microsoft Copilot Does Well
When you ask Copilot a question, it responds instantly with skimmable categories, bullet points, and emojis.
For example, when I searched “best running shoes,” it broke recommendations into helpful categories:
“Best overall”
“Best stability shoe”
“Best daily trainer”
When I narrowed the query to “best running shoes for beginner marathon training,” Copilot further refined the results.
It added details about who each shoe was best for, making the advice more actionable — a nice touch for a tool focused on clarity.
Even for informational queries like “can I wear these for hiking,” Copilot delivered a simple breakdown.
And added specific scenarios where running shoes would and wouldn’t be ideal for hiking.
When you want fast, direct answers without having to sift through a bunch of content, Copilot is a great option.
For brands: Pay close attention to how Copilot structures its answers — categories, comparisons, “best for” labels. Use similar formatting on your own pages to help AI tools extract and present your content more effectively.
Where Microsoft Copilot Falls Short
Copilot’s polished format comes at a cost: depth and shoppability.
Its responses are tidy but often too surface-level — especially for commercial searches like “best running shoes.”
When I tested this prompt, it didn’t link directly to any product pages or show pricing.
So, I couldn’t easily comparison shop, verify information, or choose a merchant and purchase immediately.
Instead, it summarized content from other “best” listicles and linked those sources.
Like Sigma Chat, unless Microsoft improves its shoppability, it’s unlikely consumers will use it for this purpose.
Instead, Copilot works better as a light research tool — especially when you want fast information with minimal reading.
AI Search Engines That Didn’t Make the Cut (and Why)
All of these AI search engines had their pros and cons.
But overall, they fell short for different reasons.
Claude
I really liked Claude, but the output was very similar to ChatGPT.
This isn’t a problem, but I didn’t want to list tools that were similar in functionality.
I wanted to provide only the best.
Compared to ChatGPT, Claude lacked product links and visuals:
The wall of text made the information challenging to process.
I did like the categorization, but ChatGPT does this too — with tables that are easier to skim.
Perplexity
Like Claude, Perplexity came somewhat close to ChatGPT in overall performance.
When asked a prompt with buying intent, it provided a short summary along with product images, pricing, and star ratings.
No tables to help me quickly compare features and options, though.
The summary was also fairly generic.
And didn’t feel all that tailored to my prompt, even when I used the more specific “marathon” wording.
Brave
Brave, a privacy-focused AI search engine, felt too much like traditional search.
It features long lists of articles without any clear hierarchy or comparison features.
While this might be helpful for browsing links, it doesn’t summarize much or help you make quick decisions.
Andi
Andi, a minimal AI search tool, offered few results, sometimes just one (e.g., a single Reddit thread).
It’s a bit like the “I’m Feeling Lucky” button on Google. Simple to use but extremely limiting for in-depth research or shopping.
Arc
Arc, a mobile- and browser-based AI search engine, requires a download to use.
This is inconvenient compared to browser-based AI search.
When so many other options exist, it’s hard to justify using this AI engine for this reason alone.
You
You is a solid AI search engine that has been around for multiple years.
But it was slow to respond and didn’t link to products in commercial searches.
Ultimately, I found it less useful than the other AI tools overall.
What This Means for Your AI Search Visibility
After testing 11 AI search engines, one thing became clear.
No matter how their formatting or preferences differ, the goal remains the same: to serve clear, credible, and well-structured content.
If your pages do that — with comprehensive coverage, positive reviews, and clean markup — you’ll be positioned to perform well across all AI search engines and LLMs.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-18 14:52:452025-11-18 14:52:45I Tested 11 AI Search Engines: Only These 4 Made the Cut
