Ecommerce Management Advisors
Ecommerce Management Advisors
Google adds an Achievements report in Search Console so you can track click-based milestones, see in-progress goals, and jump to Insights and tips.
New research from OpenAI and Harvard finds that “Seeking Information” messages now account for 24% of ChatGPT conversations, up from 14% a year earlier.
This is an NBER working paper (not peer-reviewed), based on consumer ChatGPT plans only, and the study used privacy-preserving methods where no human read user messages.
The working paper analyzes a representative sample of about 1.1 million conversations from May 2024 through June 2025.
By July, ChatGPT reached more than 700 million weekly active users, sending roughly 2.5 billion messages per day, or about 18 billion per week.
The three dominant topics are Practical Guidance, Seeking Information, and Writing, which together account for about 77% of usage.
Practical Guidance remains around 29%. Writing declined from 36% to 24% over the past year. Seeking Information grew from 14% to 24%.
The authors write that Seeking Information “appears to be a very close substitute for web search.”
The paper classifies intent as Asking, Doing, or Expressing.
About 49% of messages are Asking, 40% are Doing, and 11% are Expressing.
Asking messages “are consistently rated as having higher quality” than the other categories, based on an automated classifier and user feedback.
Non-work usage rose from 53% in June 2024 to 73% in June 2025.
At work, Writing is the top use case, representing about 40% of work-related messages. Education is a major use: 10% of all messages involve tutoring or teaching.
Only 4.2% of messages are about computer programming, and 1.9% concern relationships or personal reflection.
The study documents rapid global adoption.
Early gender gaps have narrowed, with the share of users having typically feminine names rising from 37% in January 2024 to 52% in July 2025.
Growth in the lowest-income countries has been more than four times that of the highest-income countries.
If a quarter of conversations are information-seeking, some queries that would have gone to search may go toward conversational tools.
Consider responding to this shift with content that answers questions, while adding expertise that a chatbot can’t replicate. Writing and editing account for a large share of work-related use, which aligns with how teams are already folding AI into content workflows.
ChatGPT is becoming a major destination for finding information online.
In addition to the shift toward finding info, it’s worth highlighting that 70% of ChatGPT use is personal, not professional. This means consumer habits are changing broadly.
As this technology grows, it’ll be vital to track how your audience uses AI tools and adjust your content strategy to meet them where they are.
Featured Image: Photo Agency/Shutterstock
Google appears to have disabled or is testing the removal of the &num=100 URL parameter that shows 100 results per page.
Reports of the change began around September 10, and quickly spread through the SEO community as rank-tracking tools showed disruptions.
Google hasn’t yet issued a public statement.
The &num=100 parameter has long been used to retrieve 100 results in one request.
Over the weekend, practitioners noticed that forcing 100 results often no longer works, and in earlier tests it worked only intermittently, which suggested a rollout or experiment.
@tehseoowner reported on X:
Google has seemingly removed the ability to do &num=100, can anyone confirm?
If you use the parameter, only 2 pages show. This “2 pages” filter has been tested for a year, but usually if you clicked to page 2 they have the rest the top 100. Now it shows nothing after page 2.
— SEOwner (@tehseowner) September 11, 2025
Keyword Insights wrote:
‼️ Google has killed the n=100 SERP parameter. Instead of 1 request for 100 SERP results, it now takes 10 requests (10x the cost). This impacts Keyword Insights’ rankings module. We’re reviewing options and will update the platform soon.
— Keyword Insights (@keywordinsights) September 14, 2025
Clark and others documented tools showing missing rankings or error states as the change landed.
Some platforms’ search engine results page (SERP) screenshots and daily sensors briefly stalled or displayed data gaps.
Multiple SEO professionals saw sharp declines in desktop impressions in Google Search Console starting September 10, with average position increasing accordingly.
Clark’s analysis connects the timing of those drops to the &num=100 change. He proposes that earlier desktop impression spikes were partly inflated by bots from SEO and AI analytics tools loading pages with 100 results, which would register many more impressions than a normal 10-result page.
This is a community theory at this stage, not a confirmed Google explanation.
Over the past year, many teams reported rising impressions without matching clicks and associated that pattern with AI Overviews.
Clark argues the &num=100 change, and the resulting tool disruptions, offer an alternate explanation for at least part of that decoupling, especially on desktop where most rank tracking happens.
This remains an interpretation until Google comments or provides new reporting filters.
Clark wrote about the shift after observing significant drops in desktop impressions across multiple accounts starting on September 10.
He wrote:
“… I’m seeing a noticeable decline in desktop impressions, resulting in a sharp increase in average position.
“This is across many accounts that I have access to and seems to have started around September 10th when the change first begun.”
Keyword Insights said:
“Google has killed the n=100 SERP parameter. Instead of 1 request for 100 SERP results, it now takes 10 requests (10x the cost). This impacts Keyword Insights’ rankings module. We’re reviewing options and will update the platform soon.”
Ryan Jones suggests:
“All of the AI tools scraping Google are going to result in the shutdown of most SEO tools. People are scraping so much, so aggressively for AI that Google is fighting back, and breaking all the SEO rank checkers and SERP scrapers in the process.”
Take a closer look at recent Search Console trends.
If you noticed a spike in desktop impressions in late 2024 or early 2025 without clicks, some of those impressions may have been driven by bots. Use the week-over-week changes since September 10 as a new baseline and note any substantial changes in your reporting.
Check with your rank-tracking provider. Some tools are still working with pagination or alternative methods, while others have had gaps and are now fixing them.
Google has been reached out to for comment, but hasn’t confirmed whether this is a temporary test or a permanent shift.
Tool vendors are already adapting, and the community is reevaluating how much of the ‘great decoupling’ story stemmed from methodology rather than user behavior.
We’ll update if Google provides any guidance or if reporting changes show up in Search Console.
Featured Image: Roman Samborskyi/Shutterstock
Unless you have been living under a rock, you would have seen or experienced the evolution of marketing in recent years; often centered around the marketing leader and the chief marketing officer (CMO) role.
The CMO role has come under fire for performance, for the lack of big bang delivery, for not moving away from vanity metrics, and often being overly defensive at the leadership table.
In coaching CMOs and equivalent titles, there are several recurring themes, one of which stands out in almost all coachees: Your job as a CMO is being a company executive first and then being a department leader.
You are in the C-Suite to represent the business needs, and business needs will trump your department and team needs, often going against how you are wired.
The business needs and the department needs shouldn’t be different. However, they are often at odds, especially when you, as the leader, haven’t placed the right guardrails; what often occurs is that you have followed poorly thought-through goals, key performance indicators (KPIs), and enabled disconnected objectives and key results (OKRs).
In other scenarios, the CMO role is being removed and not replaced, and the CMO title is removed. Repeatedly being replaced with VP, director, or “head of” titles, often resulting in the marketing leader not being in the C-Suite and regularly reporting one to two steps removed from the CEO.
There are often reasons why there is a rebrand or title change within the C-Suite:
The truth: The difference between marketing and growth setups is either negligible or a huge gulf.
Many confident marketing leaders would set up their teams in a very similar way; they would similarly set goals, but the department would work and operate in small ways.
Like the best marketing organizations, the best growth departments are hyper-connected. They are intertwined cross-functionally, and they are pushing numbers constantly, reporting on the most important metrics and being able to tell the story of how it’s all connected. Reporting which KPI connects to which goal, how each goal connects up to the business objective, and how the brand brings performance.
There are specific skill sets that differentiate successful CGOs from traditional CMOs – areas that often come up and stand apart marketing and growth. These include data fluency and the ability to crunch data themselves, adopting an experimentation-first mindset, being able to test, learn, and iterate as second nature, and everything CGOs do has revenue attribution baked in.
Many CGOs are taking ownership of the entire customer lifecycle, and are happy to jump into product analysis and request missing product feature builds. There are many CMOs who struggle with the shift from leads and marketing qualified leads (MQLs) to customer lifetime values (CLVs).
Often, CGOs have a greater understanding of tech stacks and the investment required in technical tools, and are more than comfortable working directly with product and engineering teams. Often the Achilles’ heel of CMOs.
Growth leaders will often have sophisticated attribution models and real-time performance dashboards, focusing on performance across the board and being on top of numbers. Many CMOs can struggle with getting into the weeds of data and being able to talk confidently with the executive committee members.
CGOs will often have direct relationships with investors and board members, whereas “traditional CMOs” are regularly disconnected and have limited relationships with important management and investors.
In coaching CGOs, there are unique pressures that emerge in their sessions. The business requires its growth department to be accountable for every number and drive business performance through (almost all) marketing activities. No easy task.
The growth leader must evolve the former marketing approach into a fresh growth approach, which requires a new culture of performance, tactical refresh, a dedicated approach within teams in the department. That has to transform traditional disciplines following historical goals and tactics into the new growth approach. It’s no mean feat, especially in long-serving teams and traditional businesses.
Having built growth departments, holding both CMO and CGO titles, many long-term impacts are overlooked:
The shift from CMO to CGO isn’t just about changing titles or acting differently; it’s about fundamentally reimagining how marketing drives business growth.
For marketing leaders reading this, the question isn’t whether this evolution will happen, but how quickly you can adapt to lead the charge for departmental and business success.
Something I share in coaching is, if you’re a current CMO (or equivalent), you should step back and ask yourself the following questions:
If the answer is yes, you’re already on the right path. If not, it’s time to evolve before the decision is made above you or for you.
If this fills you with dread, then I can only be direct: You will have to learn to change your approach or get used to feeling the heat of business evolution.
For organizations considering this transition, remember that the best CGOs don’t just inherit marketing teams; they proactively transform them.
They build a culture where every team member understands their direct impact on business growth, where specialists learn to think and operate as generalists, and where the entire department becomes a revenue-generating engine rather than being considered a cost center.
Smart marketing leaders can also lead this transformation, but being able to prove they can evolve themselves and the people around them to this new way of working is critically important. A word to wise: Do not put yourself forward without knowing you are will be an essential leader in this new operating model and when it struggles you will be the leader they look to get the new system back on track.
The companies that get this transition right will see marketing finally claim its rightful seat (back) at the strategic table.
Those that don’t risk relegating their marketing function to tactical execution will see many of their competitors pull ahead with integrated growth strategies.
The choice now is yours: Evolve your marketing leadership to meet the demands of modern business, or watch as your competitors rewrite the rules of growth, while you’re struggling with metrics and influencing your business cross-functionally.
The future belongs to leaders who can bridge the gap between marketing’s art and growth’s science. The title will change and revert, but the question is: Will you be one of the modern marketing leaders, or could you be left behind?
More Resources:
Featured Image: Anton Vierietin/Shutterstock
The judge overseeing the legal battle between WP Engine versus Automattic and Matt Mullenweg issued a ruling that fully dismissed two of WP Engine’s claims, allowed several to proceed, and gave WP Engine the chance to amend others.
Those two counts survived the motion to dismiss. That means WP Engine can try to prove that Automattic/Mullenweg interfered with its contracts and business opportunities. This shows that the judge didn’t throw out WP Engine’s entire “you’re sabotaging our business” approach. If WP Engine wins on these counts they could be eligible to receive damages.
In total, the judge’s order allowed nine claims to proceed and one to partially survive.
These are the remaining claims that survived and are allowed to proceed:
This is the claim that partially survived:
Promissory Estoppel (Count 6)
This is based on specific promises, such as free plugin hosting on wordpress.org, which the court found definite enough to proceed, while broader statements like “everyone is welcome” were too vague to support the claim.
The judge dismissed two of the claims with “leave to amend,” which means the court found an issue with how WP Engine pleaded their claims. The claims were not legally sufficient, but the judge gave WP Engine the option to update its complaint to fix the problems. If WP Engine amends successfully, those claims can return to the case.
1. Antitrust claims of monopolization, attempted monopolization, and illegal tying (Sherman Act & Cartwright Act).
On the antitrust claims, the Court found WP Engine failed to define a relevant market, stating:
“…consumers entering the WordPress ecosystem by electing a WordPress web content management system would know they were locked-in to WordPress aftermarkets. Mullenweg’s purported deception and extortionate acts did not change that fundamental operating principle of the WordPress marketplace.”
2. CFAA extortion claim (Count 3): WP Engine alleged Automattic threatened to block wordpress.org access and demanded licensing fees.
Regarding the extortion claims, WP Engine alleged that Automattic and Mullenweg violated the Computer Fraud and Abuse Act (CFAA) by threatening to block WP Engine’s access to wordpress.org and demanding licensing fees.
The Court dismissed this claim with leave to amend, finding the allegations did not sufficiently establish “extortion” under CFAA standards. The judge noted that merely threatening to block access, even coupled with demands for licensing, did not meet the statutory requirements as pled. However, WP Engine has been given time to amend the complaint (“with leave to amend”).
Two of WP Engine’s claims were fully dismissed:
Count 4
Count 4 was dismissed because the California Penal Code allows government prosecutors to bring criminal charges for attempted extortion, but it does not give private parties like WP Engine the right to sue under that statute. The dismissal was not about whether Automattic’s conduct could be considered extortion but about whether WP Engine had the legal authority to use that law in a civil case.
Count 16
The court dismissed Count 16 because trademark misuse is only recognized as a defense, not as a lawsuit that can be filed on its own. WP Engine may still raise trademark misuse later if Automattic tries to enforce trademarks against it.
The exact wording is:
“With no authority from WPEngine that authorizes pleading declaratory judgment of trademark misuse as a standalone cause of action rather than an affirmative defense, the Court GRANTS Defendants’ motion to dismiss Count 16, without prejudice to WPEngine asserting it as an affirmative defense if appropriate later in this litigation.”
Automattic CEO and WordPress co-founder posted an upbeat blog post about the court ruling that offered a simplified summary of the court order, which is fine, but simplification can leave out details. He’s right that the decision narrows the case and that the attempted extortion claim is out for good.
He wrote:
“…the court dismissed several of WP Engine and Silver Lake’s most serious claims — antitrust, monopolization, and extortion have been knocked out!”
The attempted extortion under California Penal Code (Count 4) was indeed “knocked out.” But the Computer Fraud and Abuse Act (CFAA) extortion claim (Count 3) was dismissed with leave to amend, meaning WP Engine has the opportunity to try again.
The antitrust and monopolization claims (Counts 12–15) were also dismissed but with leave to amend, meaning they too are not permanently gone.
His post is technically correct.
But the simplification leaves out what the judge allowed to move forward:
Automattic’s motion to dismiss Count 1 (intentional interference with contractual relations) and Count 2 (intentional interference with prospective economic relations) were denied, and both will move forward, potentially making WP Engine eligible to receive damages if they win on these counts.
Then there are the others that are moving forward:
Featured Image by Shutterstock/Kaspars Grinvalds
When I last wrote about Google AI Mode, my focus was on the big differentiators: conversational prompts, memory-driven personalization, and the crucial pivot from keywords to context.
As we see with the Q2 ad platform financial results below, this shift is rapidly reshaping performance advertising. While AI Mode means Google has to rethink how it makes money, it forces us advertisers to rethink something even more fundamental: our entire strategy.
In the article about AI Mode, I laid out how prompts are different from keywords, why “synthetic keywords” are really just a temporary band-aid, and how fewer clicks might just challenge the age-old cost-per-click (CPC) revenue model.
This follow-up is about what these changes truly mean for us as advertisers, and why holding onto that keyword-era mindset could cost us our competitive edge.
The biggest shift since we first got keyword-targeted online advertising is now in full swing. People aren’t searching with those relatively concise keywords anymore, the ones we optimized for how Google used to weigh certain words in a query.
Large language models (LLMs) have pretty much removed the shackles from the search bar. Now, users can fire off prompts with hundreds of words, and add even more context.
Think about the 400,000 token context window of GPT-5, which is like tens of thousands of words. Thankfully, most people don’t need that much space to explain what they want, but they are speaking in full sentences now, stutters and all.
Google’s internal ads in AI Mode document shares that early testers of AI Mode are asking queries that are two to three times as long as traditional searches on Google.
And thanks to LLMs’ multi-modal capabilities, users are searching with images (Google reports 20 billion Lens searches per month), drawing sketches, and even sending video. They’re finding what they need in entirely new ways.
Increasingly, users aren’t just looking for a list of what might be relevant. They expect a guided answer from the AI, one that summarizes options based on their personal preferences. People are asking AI to help them decide, not just to find.
And that fundamental change in user behavior is now reshaping the very platforms where these searches happen, starting with Google.
All of this definitely poses a threat to Google’s primary revenue stream. But as I mentioned in a LinkedIn post, the traffic didn’t vanish; it just moved.
Users didn’t ditch Google; they simply stopped using it the way they did when keywords were king. Plus, we’re seeing new players emerge, and search itself has fragmented:
This creates a fresh challenge for us advertisers: How do we design campaigns that actually perform when intent originates in these wildly new ways?
The Q2 earnings calls were packed with GenAI details. Some of the most jaw-dropping figures involved the expected infrastructure investments.
Microsoft announced plans to spend an eye-watering $30 billion on capital expenditures in the coming quarter, and Alphabet estimated an $85 billion budget for the next year. I guess we’ll all be clicking a lot of ads to help pay for that. So, where will those ads come from when keywords are slowly being replaced by prompts?
Google shared some numbers to illustrate the scale of this shift. AI Overviews already reach 2 billion users a month. AI Mode itself is up to 100 million. The real question is, how is AI actually enabling better ads, and thus improving monetization?
Google reports:
Microsoft Ads tells a similar story. In Q2 2025, it reported:
So, what’s an advertiser to do with all this?
As shared recently in a conversation with Kasim Aslam, these ecosystems are becoming intent originators. That old “search bar” is now a conversation, a screenshot, or even a voice command.
If your campaigns are still relying on waiting for someone to type a query, you’re showing up to the party late. Smart advertisers don’t just respond to intent; they predict it and position for it.
But how? Well, take a look at the Google products that are driving results for advertisers: They’re the newest AI-first offerings. Performance Max, for example, is keywordless advertising driven by feeds, creative, and audiences.
Another vital step for adapting to this shift is AI Max, which I’d call the most unrestrictive form of keyword advertising.
It blends elements of Dynamic Search Ads (DSAs), automatically created assets, and super broad keywords. This allows your ads to show up no matter how people search, even if they’re using those sprawling, multi-part prompts.
Sure, advertisers can still use today’s best practices, like reviewing search term reports and automatically created assets, then adding negatives or exclusions for the irrelevant ones. But let’s be honest, that’s a short-term, old-model approach.
As AI gains memory and contextual understanding, ads will be shown based on scenarios and user intent that isn’t even explicitly expressed.
Relying solely on negatives won’t cut it. The future demands that advertisers focus on getting involved earlier in the decision-making process and making sure the AI has all the right information to advocate for their brand.
In the AI Mode era, prompts aren’t just simple queries; they’re rich, multi-turn conversations packed with context.
As I outlined in my last article, these interactions can pull in past sessions, images, and deeply personal preferences. No keyword list in the world can capture that level of nuance.
Tinuiti’s Q2 benchmark report shows Performance Max accounts for 59% of Shopping ad spend and delivers 18% higher click-through rates. This is a clear illustration that the platform is taking control of targeting.
And when structured feeds plus dynamic creative drive a 27% lift in conversions according to Google data, it’s because the creative itself is doing the targeting.
Those journeys happen out of sight, which is the biggest threat to advertisers whose strategies aren’t evolving.
One of my key takeaways from the AI Mode discussion was the risk of “zero-click” journeys. If the assistant delivers what a user needs inside the conversation, your brand might never get a visit.
According to Adobe Analytics, AI-powered referrals to U.S. retail sites grew 1,200% between July 2024 and February 2025. Traffic from these sources now doubles every 60 days.
These users:
Even more importantly, 53% of users say they plan to rely on AI tools for shopping going forward.
In short, users are starting their journeys before they reach a traditional search engine, and they’re more engaged when they do. And winning in this environment means rethinking our levers for influence.
As I argued before, platforms aren’t killing keyword advertising; they’re evolving it. The advertisers winning now are leaning into the new levers:
The future isn’t about bidding on a query. It’s about supplying the AI with the best “raw ingredients” so you win the recommendation at the exact moment of decision.
That mindset shift is the real competitive advantage in the AI-first era.
My previous AI Mode post was about the mechanics of the shift. This one is about the mindset change required to survive it.
Keywords aren’t vanishing, but their role is shrinking fast. In an AI-driven, context-first search landscape, the brands that thrive will stop obsessing over what the user types and start shaping what the AI recommends.
If you can win that moment, you won’t just get found. You’ll get chosen.
More Resources:
Featured Image: Smile Studio AP/Shutterstock
We’ve all asked a chatbot about a company’s services and seen it respond inaccurately, right? These errors aren’t just annoying; they can seriously hurt a business. AI misrepresentation is real. LLMs could provide users with outdated information, or a virtual assistant might provide false information in your name. Your brand could be at stake. Find out how AI misrepresents brands and what you can do to prevent them.
AI misrepresentation occurs when chatbots and large language models distort a brand’s message or identity. This could happen when these AI systems find and use outdated or incomplete data. As a result, they show incorrect information, which leads to errors and confusion.
It’s not hard to imagine a virtual assistant providing incorrect product details because it was trained on old data. It might seem like a minor issue, but incidents like this can quickly lead to reputation issues.
Many factors lead to these inaccuracies. Of course, the most important one is outdated information. AI systems use data that might not always reflect the latest changes in a business’s offerings or policy changes. When systems use that old data and return it to potential customers, it can lead to a serious disconnect between the two. Such incidents frustrate customers.
It’s not just outdated data; a lack of structured data on sites also plays a role. Search engines and AI technology like clear, easy-to-find, and understandable information that supports brands. Without solid data, an AI might misrepresent brands or fail to keep up with changes. Schema markup is one option to help systems understand content and ensure it is properly represented.
Next up is consistency in branding. If your brand messaging is all over the place, this could confuse AI systems. The clearer you are, the better. Inconsistent messaging confuses AI and your customers, so it’s important to be consistent with your brand message on various platforms and outlets.
There are various ways AI failures can impact brands. AI tools and large language models collect information from sources and present it to build a representation of your brand. That means they can misrepresent your brand when the information they use is outdated or plain wrong. These errors can lead to a real disconnect between reality and what users see in the LLMs. It could also be that your brand doesn’t appear in AI search engines or LLMs for the terms you need to appear.
At the other end, chatbots and virtual assistants talk to users directly. This is a different risk. If a chatbot gives inaccurate answers, this could lead to serious issues with users and the outside world. Since chatbots interact directly with users, inaccurate responses can quickly damage trust and harm a brand’s reputation.
AI misrepresenting brands is not some far-off theory because it has an impact now. We’ve collected some real-world cases that show brands being affected by AI errors.
All of these cases show how various types of AI technology, from chatbots to LLMs, can misrepresent and thus hurt brands. The stakes can be high, ranging from misleading customers to ruining reputations. It’s good to read these examples to get a sense of how widespread these issues are. It might help you avoid similar mistakes and set up better strategies to manage your brand.
All of these cases show what AI misrepresentation can do to your brand. There is a real need to properly manage and monitor AI systems. Each example shows that it can have a big impact, from huge financial loss to spoiled reputations. Stories like these show how important it is to monitor what AI says about your brand and what it does in your name.
It’s not easy to fix complex issues with your brand being misrepresented by AI chatbots or LLMs. If a chatbot tells a customer to do something nasty, you could be in big trouble. Legal protection should be a given, of course. Other than that, try these tips:
Find and start using tools that monitor your brand in AI and LLMs. These tools can help you study how AI describes your brand across various platforms. They can identify inconsistencies and offer suggestions for corrections, so your brand message remains consistent and accurate at all times.
One example is Yoast SEO AI Brand Insights, which is a great tool for monitoring brand mentions in AI search engines and large language models like ChatGPT. Enter your brand name, and it will automatically run an audit. After that, you’ll get information on brand sentiment, keyword usage, and competitor performance. Yoast’s AI Visibility Score combines mentions, citations, sentiment, and rankings to form a reliable overview of your brand’s visibility in AI.
Optimize your content for inclusion in LLMs. Performing well in search engines is not a guarantee that you will also perform well in large language models. Make sure that your content is easy to read and accessible for AI bots. Build up your citations and mentions online. We’ve collected more tips on how to optimize for LLMs, including using the proposed llms.txt standard.
If nothing else, get professional help. Like we said, if you are dealing with complex brand issues or widespread misrepresentation, you should consult with professionals. Brand consultants and SEO experts can help fix misrepresentations and strengthen your brand’s online presence. Your legal team should also be kept in the loop.
Last but not least, don’t forget to use SEO monitoring tools. It goes without saying, but you should be using SEO tools like Moz, Semrush, or Ahrefs to track how well your brand is performing in search results. These tools provide analytics on your brand’s visibility and can help identify areas where AI might need better information or where structured data might enhance search performance.
Businesses of all types should actively manage how their brand is represented in AI systems. Carefully implementing these strategies helps minimize the risks of misrepresentation. In addition, it keeps a brand’s online presence consistent and helps build a more reliable reputation online and offline.
AI misrepresentation is a real challenge for brands and businesses. It could harm your reputation and lead to serious financial and legal consequences. We’ve discussed a number of options brands have to fix how they appear in AI search engines and LLMs. Brands should start by proactively monitoring how they are represented in AI.
For one, that means regularly auditing your content to prevent errors from appearing in AI. Also, you should use tools like brand monitor platforms to manage and improve how your brand appears. If something goes wrong or you need instant help, consult with a specialist or outside experts. Last but not least, always make sure that your structured data is correct and aligns with the latest changes your brand has made.
Taking these steps reduces the risks of misrepresentation and enhances your brand’s overall visibility and trustworthiness. AI is moving ever more into our lives, so it’s important to ensure your brand is represented accurately and authentically. Accuracy is very important.
Keep a close eye on your brand. Use the strategies we’ve discussed to protect it from AI misrepresentation. This will ensure that your message comes across loud and clear.
Google is about to give agencies and advertisers access to prime retail media placements on ecommerce sites such as Best Buy, Costco, and Target.
The deal, announced on September 10, 2025, connects Google’s Search Ads 360 (SA360) platform with a network of more than 200 enterprise-level retailers via Criteo’s advertising and commerce platform.
“With Criteo’s expansive network of retailer partners, we’re helping advertisers connect with customers at a critical moment in their shopping journey,” said Bill Reardon, general manager, enterprise platform at Google, via a press release.
Retail media is exploding. Adtelligent estimated that worldwide retail media spend would reach $145 to $165 billion in 2025, up from $59 billion in 2019.
In the United States, the market is valued at more than $60 billion and is growing at roughly 20% per year, according to various sources.
Advertisers use digital retail media primarily to promote products sold on the given retailer’s website. Thus many retail media advertisers are actually the store’s suppliers, boosting sales via the retail channel.
Digital retail media has taken off, in part, because it relies on first-party customer data and does not require intrusive cookies or complex privacy protocols.
A few “walled gardens,” i.e., closed platforms or ecosystems operated by a single company, dominate the market. Of these, Amazon is far and away the leader.
In Q2 2025, Amazon’s ad revenue reached $15.69 billion, a 23% year-over-year increase, hitting a record 9.36% of the company’s total revenue and marking it as the fastest-growing segment.
Amazon has leveraged its massive ecommerce marketplace and best-in-class shopping data. Together, these create a self-reinforcing advertising flywheel that drives conversions for advertisers and revenue for Amazon.
Amazon’s retail media flywheel works because the company controls the entire process, from initial customer acquisition to final purchase, collecting all the behavioral data along the way. It has first-party data that is real, recent, and relevant.
Compared to third-party data, Amazon and nearly any walled-garden advertising solution will be much more effective at targeting shoppers and producing sales.
These closed ecosystems also help with measurement. Since the ads are running and converting in a walled garden, advertising attribution is easy.
While Amazon is the undisputed leader in digital retail media, Google is the king of digital advertising generally. The company generated approximately $71.3 billion in advertising revenue during Q2 2025, representing a 10.4% year-over-year increase.
Some $54 billion of that Q2 revenue was specific to search advertising. A significant portion of that revenue passes through the company’s SA360 platform. That demand will now connect to Criteo and its retail media network.
This deal is a significant shift for the market. In the past, Google’s retail-related advertising products, such as AI-assisted PMax or Shopping Ads, have focused on driving traffic to retail websites. The idea was that someone would query on Google search, see an ad for a relevant product, and go to the retailer to buy it.
With Criteo’s help, Google can now offer a more complete way to advertise to consumers. Its platform not only guides advertisers to retail sites, but also shoppers on those sites to specific sponsored products.
In a sense, the Google and Criteo deal targets Amazon’s dominance in digital retail media and walled gardens generally.
Google gains a foothold in the digital retail media market, providing SA360 advertisers with the opportunity to extend search campaigns into high-intent shopping environments, all with unified measurement and attribution.
Brands that had been using retail media now have an alternative. Rather than concentrating advertising spend in a few dominant ecosystems, the Google-Criteo integration opens access to a broader range of retail ad placements.
It opens some access to some of those walled gardens, and, as several pundits have put it, fosters competition and “democratizes” retail media.
While Criteo and Google had been in discussions for some time, the deal’s announcement came just days after Google survived what could have been a devastating antitrust ruling. It was also less than two weeks before a second remedy hearing.
In August 2024, a U.S. District Court found Google guilty of maintaining an illegal monopoly over the general online search and text advertising markets.
Leading up to the September 2025 remedy hearing, some observers thought that Google would be required to divest some of its products, such as the Chrome browser.
Instead, the court, on September 2, 2025, ruled that Google change its behavior, including:
In a separate April 2025 case, a federal judge found Google guilty of illegally monopolizing key segments of the digital advertising market. The remedy hearing for this case is scheduled for September 22, 2025.
Google’s “democratizing” deal with Criteo could be an indication to the courts that the company aims to encourage competition.