Dec 1 2025
Pragmatic Approach To AI Search Visibility via @sejournal, @martinibuster

Bing published a blog post about how clicks from AI Search are improving conversion rates, explaining that the entire research part of the consumer journey has moved into conversational AI search, which means that content must follow that shift in order to stay relevant.

AI Repurposes Your Content

They write:

“Instead of sending users through multiple clicks and sources, the system embeds high-quality content within answers, summaries, and citations, highlighting key details like energy efficiency, noise level, and smart home compatibility. This creates clarity faster and builds confidence earlier in the journey, leading to stronger engagement with less friction.”

Bing sent me advance notice about their blog post and I read it multiple times. I had a hard time getting past the part about AI Search taking over the research phase of the consumer journey because it seemingly leaves informational publishers with zero clicks. Then I realized that’s not necessarily how it has to happen, as is explained further on.

Here’s what they say:

“It’s not that people are no longer clicking. They’re just clicking at later stages in the journey, and with far stronger intent.”

Search used to be the gateway to the Internet. Today the internet (lowercase) is seemingly the gateway to AI conversations. Nevertheless, people enjoy reading content and learning, so it’s not that the audience is going away.

While AI can synthesize content, it cannot delight, engage, and surprise on the same level that a human can. This is our strength and it’s up to us to keep that in mind moving forward in what is becoming a less confusing future.

Create High-Quality Content

Bing’s blog post says that the priority is to create high-quality content:

“The priority now is to understand user actions and guide people toward high-value outcomes, whether that is a subscription, an inquiry, a demo request, a purchase, or other meaningful engagement.”

But what’s the point in creating high-quality content for consumers if Bing is no longer “sending users through multiple clicks and sources” because AI Search is embedding that high-quality content in their answers?

The answer is that Bing is still linking out to sources. This provides an opportunity for brands to identify those sources to verify if they’re in there and if they’re missing they now know to do something about it. Informational sites need to review those sources and identify why they’re not in there, something that’s discussed below.

Conversion Signals In AI Search

Earlier this year at the Google Search Central Live event in New York City, a member of the audience told the assembled Googlers that their client’s clicks were declining due to AI Overviews and asked them, “what am I supposed to tell my clients?” The audience member expressed the frustration that many ecommerce stores, publishers, and SEOs are feeling.

Bing’s latest blog post attempts to answer that question by encouraging online publishers to focus on three signals.

  • Citations
  • Impressions
  • Placement in AI answers.

This is their explanation:

“…the most valuable signals are the ones connected to visibility. By tracking impressions, placement in AI answers, and citations, brands can see where content is being surfaced, trusted, and considered, even before a visit occurs. More importantly, these signals reveal where interest is forming and where optimization can create lift, helping teams double down on what works to improve visibility in the moments when decisions are being shaped.”

But what’s the point if people are no longer clicking except at the later stages of the consumer journey?  Bing makes it clear that the research stage happens “within one environment” but they are still linking out to websites. As will be shown a little further in this article, there are steps that publishers can take to ensure their articles are surfaced in the AI conversational environment.

They write:

“In fewer steps than ever, the customer reaches a confident decision, guided by intent-aligned, multi-source content that reflects brand and third-party perspectives. This behavior shift, where discovery, research, and decision happen continuously within one environment, is redefining how site owners understand conversion.

…As AI-powered search reshapes how people explore information, more of the journey now happens inside the experience itself.

…Users now spend more of the journey inside AI experiences, shaping visibility and engagement in new ways. As a result, engagement is shifting upstream (pre-click) within summaries, comparisons, and conversational refinements, rather than through multiple outbound clicks.”

The change in which discovery, research, and decision making all happen inside the AI Search explains why traditional click-focused metrics are losing relevance. The customer journey is happening within the conversational AI environment, so the signals that are beginning to matter most are the ones generated before a user ever reaches a website. Visibility now depends on how well a brand’s information contributes to the summaries, comparisons, and conversational refinements that form the new upstream engagement layer.

This is the reality of where we are at right now.

How To Adapt To The New Customer Journey

AI Search has enabled consumers to do deeper research and comparisons during the early and middle part of the buying cycle, a significant change in consumer behavior.

In a podcast from May of this year, Michael Bonfils (LinkedIn profile) touched on this change in consumer behavior and underlined the importance of obtaining the signals from the consideration stage of consumer purchases. Read: 30-Year SEO Pro Shows How To Adapt To Google’s Zero-Click Search

He observed:

“We have a funnel, …which is the awareness consideration phase …and then finally the purchase stage. The consideration stage is the critical side of our funnel. We’re not getting the data. How are we going to get the data?

But that’s very important information that I need because I need to know what that conversation is about. I need to know what two people are talking about… because my entire content strategy in the center of my funnel depends on that greatly.”

Michael suggested that the keyword paradigm is inappropriate for the reality of AI Search and that rather than optimize for keywords, marketers and business people should be optimizing for the range of questions and comparisons that AI Search will be surfacing.

He explained:

“So let’s take the whole question, and as many questions as possible, that come up to whatever your product is, that whole FAQ and the answers, the question, and the answers become the keyword that we all optimize on moving forward.

Because that’s going to be part of the conversation.”

Bing’s blog post confirmed this aspect of consumer research and purchases, confirming that the click is happening more often on the conversion part of the consumer journey.

Tracking AI Metrics

Bing recommends using their Webmaster Tools and Clarity services in order to gain more insights into how people are engaging in AI search.

They explain:

“Bing Webmaster Tools continues to evolve to help site owners, publishers, and SEOs understand how content is discovered and where it appears across traditional search results and emerging AI-driven experiences. Paired with Microsoft Clarity’s AI referral insights, these tools connect upstream visibility with on-site behavior, helping teams see how discovery inside summaries, answers, and comparisons translates into real engagement. As user journeys shift toward more conversational, zero-UI-style interactions, these combined signals give a clearer view of influence, readiness, and conversion potential.”

The Pragmatic Takeaway

The emphasis for brands is to show up in review sites, build relationships with them, and try as much as possible to get in front of consumers and build positive word of mouth.

For news and informational sites, Bing recommends providing high-quality content that engages readers and providing an experience that will encourage readers to return.

Bing writes:

“Rather than focusing on product-driven actions, success may depend on signals such as read depth, article completion, returning reader patterns, recirculation into related stories, and newsletter sign-ups or registrations.

AI search can surface authoritative reporting earlier in the journey, bringing in readers who are more inclined to engage deeply with coverage or return for follow-up stories. As these upstream interactions grow, publishers benefit from visibility into how their work appears across AI answers, summaries, and comparisons, even when user journeys are shorter or involve fewer clicks.”

I have been a part of the SEO community for over twenty-five years and I have never seen a more challenging period for publishers than what we’re faced with today. The challenge is to build a brand, generate brand loyalty, focus on the long-term.

Read Bing’s blog post:

How AI Search Is Changing the Way Conversions are Measured 

Featured Image by Shutterstock/ImageFlow

Ecommerce MGMT 0 Comments
Dec 1 2025
Best Text Message Strategy for 2026

About 84% of American consumers have opted in to text communication from at least one business, according to a May 2025 SimpleTexting survey. Fifty-two percent of the survey’s 1,000 respondents subscribe to texts from four or more brands.

If the survey is accurate, the question is no longer whether to use text messaging — SMS, MMS, RCS — but how to use it effectively.

Female hands holding a smartphone

Roughly eight in 10 U.S. consumers have agreed to receive commercial text messages, according to a recent survey.

Maturity Model

Transactional messages get shoppers hooked. Shoppers like receiving a straightforward text when an order is shipped. Delivery updates are even better.

Yet the channel is much more than notifications alone.

A maturity model is a framework that describes how a capability, process, or system evolves. It breaks development into stages, usually progressing from simple, ad hoc to sophisticated, optimized, and scalable.

The model can help merchants progress from a text with a tracking number to a comprehensive strategy of marketing, customer service, and operations.

To develop an effective strategy, ecommerce marketers can view their business through four text-message stages.

  • Starter: Basic transactional broadcasts that pass information and answer questions, such as, “Where’s my order?”
  • Growth: Merchants introduce triggered and segmented messages tied to shopper behavior. Texting becomes a revenue channel.
  • Full-stack: The business integrates text messaging across the entire customer lifecycle, supporting onboarding, retention, upsells, replenishment cycles, and loyalty activations.
  • AI-orchestrated and automated: The output is the same as the full-stack stage, except that artificial intelligence has transformed the channel into an automated, coordinated marketing component.

Identify the Stage

Every ecommerce business sits somewhere on the maturity spectrum, knowingly or not. The key is understanding which stage aligns with the business’s operational reality rather than its ambitions.

Here is a guide:

  • Text messaging that solely informs shoppers is in the Starter stage.
  • A strategy of influencing purchase decisions is in Growth.
  • A Full-stack stage helps retain and maximize customer lifetime value.
  • An automated and predictive text-messaging process is approaching AI orchestration.

A merchant’s text stage typically depends on operational factors.

  • Order volume. High order volume justifies automated and segmented messaging. Conversely, basic transactional texts suit small stores.
  • Repeat customers. Sellers with many returning shoppers benefit the most from lifecycle marketing and personalized reminders.
  • Catalog complexity. Behavioral triggers can help stores with extensive products or variants.
  • Data discipline. Segmentation and personalization require clean, unified buyer profiles. Without meaningful data, advanced text strategies fall apart.
  • Capacity. Even the best tools require maintenance. A one-person business may not be ready for full-stack text marketing, much less AI-led automation.

Leveling Up

Moving through the maturity model is not a race. It is a progression based on operational readiness and customer expectations. The best programs grow intentionally, not explosively.

Starter to Growth. Merchants graduate from Starter when transactional messages run smoothly, and the business begins to feel the limitations of one-way communication.

How to grow:

  • Add abandoned-cart reminders.
  • Introduce a short welcome series.
  • Segment messages by at least one variable.
  • Improve text-communication opt-in placement and incentives.
  • Test a few event-triggered messages.

Growth to Full-stack. The shift from Growth to Full-stack occurs when merchants recognize that texts should align with email, loyalty, and the larger customer journey, not behavior alone.

How to advance:

  • Clean and consolidate customer data across platforms.
  • Develop message sequences for onboarding, replenishment, and retention.
  • Use customer preferences to manage frequency and message types.
  • Coordinate message timing with email marketing instead of duplicating it.
  • Introduce dynamic content or personalized recommendations.

Full-stack to AI-orchestrated and automated. The final stage adds intelligence. AI adjusts timing, sequencing, content, and discounts in response to real-time signals.

How to transition:

  • Adopt tools that support real-time decisioning and predictive segmentation.
  • Let AI generate or tailor message content within brand guidelines.
  • Use machine learning to optimize message send times and frequency.
  • Allow algorithms to manage lifecycle triggers.

This final stage is quickly emerging, as the precision of AI blends with multichannel marketing. Hence merchants who succeed with texting will send the right messages, not necessarily the most.

Ecommerce MGMT 0 Comments
Nov 29 2025
What we still don’t know about weight-loss drugs

<div data-chronoton-summary="

  • Mixed research results Despite promising applications, recent studies delivered disappointments: GLP-1 drugs failed to slow Alzheimer’s progression in a major trial.
  • Pregnancy concerns People who stop taking GLP-1s before pregnancy may experience excessive weight gain and potentially higher risks of complications. Conflicting studies have created confusion about pre-pregnancy use, while postpartum usage is increasing without understanding potential impacts.
  • Long-term questions When people stop taking GLP-1s, most regain significant weight and see worsening heart health. Scientists still don’t know if indefinite use is necessary or safe, nor understand long-term effects on children or healthy-weight people using them for weight loss.

” data-chronoton-post-id=”1128511″ data-chronoton-expand-collapse=”1″ data-chronoton-analytics-enabled=”1″>

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

Weight-loss drugs have been back in the news this week. First, we heard that Eli Lilly, the company behind the drugs Mounjaro and Zepbound, became the first healthcare company in the world to achieve a trillion-dollar valuation.

Those two drugs, which are prescribed for diabetes and obesity respectively, are generating billions of dollars in revenue for the company. Other GLP-1 agonist drugs—a class that includes Mounjaro and Zepbound, which have the same active ingredient—have also been approved to reduce the risk of heart attack and stroke in overweight people. Many hope these apparent wonder drugs will also treat neurological disorders and potentially substance use disorders, too.

But this week we also learned that, disappointingly, GLP-1 drugs don’t seem to help people with Alzheimer’s disease. And that people who stop taking the drugs when they become pregnant can experience potentially dangerous levels of weight gain during their pregnancies. On top of that, some researchers worry that people are using the drugs postpartum to lose pregnancy weight without understanding potential risks.

All of this news should serve as a reminder that there’s a lot we still don’t know about these drugs. This week, let’s look at the enduring questions surrounding GLP-1 agonist drugs.

First a quick recap. Glucagon-like peptide-1 is a hormone made in the gut that helps regulate blood sugar levels. But we’ve learned that it also appears to have effects across the body. Receptors that GLP-1 can bind to have been found in multiple organs and throughout the brain, says Daniel Drucker, an endocrinologist at the University of Toronto who has been studying the hormone for decades.

GLP-1 agonist drugs essentially mimic the hormone’s action. Quite a few have been developed, including semaglutide, tirzepatide, liraglutide, and exenatide, which have brand names like Ozempic, Saxenda and Wegovy. Some of them are recommended for some people with diabetes.

But because these drugs also seem to suppress appetite, they have become hugely popular weight loss aids. And studies have found that many people who take them for diabetes or weight loss experience surprising side effects; that their mental health improves, for example, or that they feel less inclined to smoke or consume alcohol. Research has also found that the drugs seem to increase the growth of brain cells in lab animals.

So far, so promising. But there are a few outstanding gray areas.

Are they good for our brains?

Novo Nordisk, a competitor of Eli Lilly, manufactures GLP-1 drugs Wegovy and Saxenda. The company recently trialed an oral semaglutide in people with Alzheimer’s disease who had mild cognitive impairment or mild dementia. The placebo-controlled trial included 3808 volunteers.

Unfortunately, the company found that the drug did not appear to delay the progression of Alzheimer’s disease in the volunteers who took it.

The news came as a huge disappointment to the research community. “It was kind of crushing,” says Drucker. That’s despite the fact that, deep down, he wasn’t expecting a “clear win.” Alzheimer’s disease has proven notoriously difficult to treat, and by the time people get a diagnosis, a lot of damage has already taken place.

But he is one of many that isn’t giving up hope entirely. After all, research suggests that GLP-1 reduces inflammation in the brain and improves the health of neurons, and that it appears to improve the way brain regions communicate with each other. This all implies that GLP-1 drugs should benefit the brain, says Drucker. There’s still a chance that the drugs might help stave off Alzheimer’s in those who are still cognitively healthy.

Are they safe before, during or after pregnancy?

Other research published this week raises questions about the effects of GLP-1s taken around the time of pregnancy. At the moment, people are advised to plan to stop taking the medicines two months before they become pregnant. That’s partly because some animal studies suggest the drugs can harm the development of a fetus, but mainly because scientists haven’t studied the impact on pregnancy in humans.

Among the broader population, research suggests that many people who take GLP-1s for weight loss regain much of their lost weight once they stop taking those drugs. So perhaps it’s not surprising that a study published in JAMA earlier this week saw a similar effect in pregnant people.

The study found that people who had been taking those drugs gained around 3.3kg more than others who had not. And those who had been taking the drugs also appeared to have a slightly higher risk of gestational diabetes, blood pressure disorders and even preterm birth.

It sounds pretty worrying. But a different study published in August had the opposite finding—it noted a reduction in the risk of those outcomes among women who had taken the drugs before becoming pregnant.

If you’re wondering how to make sense of all this, you’re not the only one. No one really knows how these drugs should be used before pregnancy—or during it for that matter.

Another study out this week found that people (in Denmark) are increasingly taking GLP-1s postpartum to lose weight gained during pregnancy. Drucker tells me that, anecdotally, he gets asked about this potential use a lot.

But there’s a lot going on in a postpartum body. It’s a time of huge physical and hormonal change that can include bonding, breastfeeding and even a rewiring of the brain. We have no idea if, or how, GLP-1s might affect any of those.

How—and when—can people safely stop using them?

Yet another study out this week—you can tell GLP-1s are one of the hottest topics in medicine right now—looked at what happens when people stop taking tirzepatide (marketed as Zepbound) for their obesity.

The trial participants all took the drug for 36 weeks, at which point half continued with the drug, and half were switched to a placebo for another 52 weeks. During that first 36 weeks, the weight and heart health of the participants improved.

But by the end of the study, most of those that had switched to a placebo had regained more than 25% of the weight they had originally lost. One in four had regained more than 75% of that weight, and 9% ended up at a higher weight than when they’d started the study. Their heart health also worsened.

Does that mean that people need to take these drugs forever? Scientists don’t have the answer to that one, either. Or if taking the drugs indefinitely is safe. The answer might depend on the individual, their age or health status, or what they are using the drug for.

There are other gray areas. GLP-1s look promising for substance use disorders, but we don’t yet know how effective they might be. We don’t know the long-term effects these drugs have on children who take them. And we don’t know the long-term consequences these drugs might have for healthy-weight people who take them for weight loss.

Earlier this year, Drucker accepted a Breakthrough Prize in Life Sciences at a glitzy event in California. “All of these Hollywood celebrities were coming up to me and saying ‘thank you so much,’” he says. “A lot of these people don’t need to be on these medicines.”

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

Ecommerce MGMT 0 Comments
Nov 29 2025
The Download: the mysteries surrounding weight-loss drugs, and the economic effects of AI

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

What we still don’t know about weight-loss drugs

Weight-loss drugs have been back in the news this week. First, we heard that Eli Lilly, the company behind Mounjaro and Zepbound, became the first healthcare company in the world to achieve a trillion-dollar valuation.

But we also learned that, disappointingly, GLP-1 drugs don’t seem to help people with Alzheimer’s disease. And that people who stop taking the drugs when they become pregnant can experience potentially dangerous levels of weight gain. On top of that, some researchers worry that people are using the drugs postpartum to lose pregnancy weight without understanding potential risks.

All of this news should serve as a reminder that there’s a lot we still don’t know about these drugs. So let’s look at the enduring questions surrounding GLP-1 agonist drugs.

—Jessica Hamzelou

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

If you’re interested in weight loss drugs and how they affect us, take a look at:

+ GLP-1 agonists like Wegovy, Ozempic, and Mounjaro might benefit heart and brain health—but research suggests they might also cause pregnancy complications and harm some users. Read the full story.

+ We’ve never understood how hunger works. That might be about to change. Read the full story.

+ Weight-loss injections have taken over the internet. But what does this mean for people IRL?

+ This vibrating weight-loss pill seems to work—in pigs. Read the full story.

What we know about how AI is affecting the economy

There’s a lot at stake when it comes to understanding how AI is changing the economy right now. Should we be pessimistic? Optimistic? Or is the situation too nuanced for that?

Hopefully, we can point you towards some answers. Mat Honan, our editor in chief, will hold a special subscriber-only Roundtables conversation with our editor at large David Rotman, and Richard Waters, Financial Times columnist, exploring what’s happening across different markets. Register here to join us at 1pm ET on Tuesday December 9.

The event is part of the Financial Times and MIT Technology Review “The State of AI” partnership, exploring the global impact of artificial intelligence. Over the past month, we’ve been running discussions between our journalists—sign up here to receive future editions every Monday.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Tech billionaires are gearing up to fight AI regulation 
By amassing multi-million dollar war chests ahead of the 2026 US midterm elections. (WSJ $)
+ Donald Trump’s “Manhattan Project” for AI is certainly ambitious. (The Information $)

2 The EU wants to hold social media platforms liable for financial scams
New rules will force tech firms to compensate banks if they fail to remove reported scams. (Politico)

3 China is worried about a humanoid robot bubble
Because more than 150 companies there are building very similar machines. (Bloomberg $)
+ It could learn some lessons from the current AI bubble. (CNN)+ Why the humanoid workforce is running late. (MIT Technology Review)

4 A Myanmar scam compound was blown up
But its residents will simply find new bases for their operations. (NYT $)
+ Experts suspect the destruction may have been for show. (Wired $)
+ Inside a romance scam compound—and how people get tricked into being there. (MIT Technology Review)

5 Navies across the world are investing in submarine drones 
They cost a fraction of what it takes to run a traditional manned sub. (The Guardian)
+ How underwater drones could shape a potential Taiwan-China conflict. (MIT Technology Review)

6 What to expect from China’s seemingly unstoppable innovation drive
Its extremely permissive regulators play a big role. (Economist $)
+ Is China about to win the AI race? (MIT Technology Review)

7 The UK is waging a war on VPNs
Good luck trying to persuade people to stop using them. (The Verge)

8 We’re learning more about Jeff Bezos’ mysterious clock project
He’s backed the Clock of the Long Now for years—and construction is amping up. (FT $)
+ How aging clocks can help us understand why we age—and if we can reverse it. (MIT Technology Review)

9 Have we finally seen the first hints of dark matter?
These researchers seem to think so. (New Scientist $)

10 A helpful robot is helping archaeologists reconstruct Pompeii
Reassembling ancient frescos is fiddly and time-consuming, but less so if you’re a dextrous machine. (Reuters)

Quote of the day

“We do fail… a lot.”

—Defense company Anduril explains its move-fast-and-break-things ethos to the Wall Street Journal in response to reports its systems have been marred by issues in Ukraine.

One more thing

How to build a better AI benchmark

It’s not easy being one of Silicon Valley’s favorite benchmarks.

SWE-Bench (pronounced “swee bench”) launched in November 2024 as a way to evaluate an AI model’s coding skill. It has since quickly become one of the most popular tests in AI. A SWE-Bench score has become a mainstay of major model releases from OpenAI, Anthropic, and Google—and outside of foundation models, the fine-tuners at AI firms are in constant competition to see who can rise above the pack.

Despite all the fervor, this isn’t exactly a truthful assessment of which model is “better.” Entrants have begun to game the system—which is pushing many others to wonder whether there’s a better way to actually measure AI achievement. Read the full story.

—Russell Brandom

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ Aww, these sharks appear to be playing with pool toys.
+ Strange things are happening over on Easter Island (even weirder than you can imagine) 🗿
+ Very cool—archaeologists have uncovered a Roman tomb that’s been sealed shut for 1,700 years.
+ This Japanese mass media collage is making my eyes swim, in a good way.

Ecommerce MGMT 0 Comments
Nov 29 2025
Will Google’s AI Mode Dominate ChatGPT?

Jeff Oxford is my go-to interview for ecommerce SEO. The founder of Oregon-based 180 Marketing, an agency, Jeff first appeared on the podcast in 2022 when he addressed SEO’s “four buckets.” I invited him back late last year to explain AI’s impact on search traffic and how merchants can adapt.

In this our latest interview, he shared optimization tactics for ChatGPT, with a caveat: Google’s AI Mode could eventually dominate.

The entire audio of our conversation is embedded below. The transcript is edited for length and clarity.

Eric Bandholz: Welcome back. Please introduce yourself.

Jeff Oxford: I’m the founder of 180 Marketing, an agency focusing exclusively on ecommerce SEO. A big part of that lately has been helping brands navigate AI-driven search.

We work with seven- and eight-figure ecommerce companies, helping them grow organic traffic and conversions through the fundamentals — search, content, link building — and now layering in what I call “AI SEO.” Basically, optimizing so you show up in places like ChatGPT and other large language models.

I’ve worked in ecommerce SEO for about 15 years. I ran my own ecommerce sites before then, but I learned I’m better at marketing than operations. So I shifted into ecommerce SEO. Over the past year, I’ve focused heavily on ChatGPT and AI-driven SEO because it’s changing how people discover products.

There’s confusion around what to call this new discipline. Entrepreneurs often say “AI SEO.” The SEO community prefers “GEO,” which stands for Generative Engine Optimization. I’ve also heard “AEO” for Answer Engine Optimization and “LLMO” for Large Language Model Optimization. I prefer the simplicity of AI SEO. My team focuses on where traditional SEO and AI-powered optimization overlap so brands can benefit from both.

Premium ecommerce brands face an uphill battle with Google. Higher prices often lead to higher bounce rates, and Google responds by pushing those sites off page one, regardless of quality. ChatGPT, however, focuses on semantic relevance and draws from multiple sources. Some merchants are now seeing more conversions from ChatGPT than from traditional Google search.

Bandholz: Is ChatGPT the Google of AI SEO?

Oxford: Yes. We work with many ecommerce sites, giving us a broad data set. When we review analytics for AI-driven referrals, about 90% come from ChatGPT. Perplexity is usually second, followed by Claude and Gemini.

But tracking performance is much harder than with Google. Traditional SEO is simple to measure — Shopify or Google Analytics clearly shows organic search traffic and revenue. ChatGPT works differently. Users ask a question, get recommendations, and may or may not click through directly.

Often, they copy the product or brand name and search it on Google. That behavior means ChatGPT rarely appears in analytics as a referral source. Instead, its influence shows up as branded search traffic, which makes attribution tricky.

Bandholz: Are companies moving toward direct sales inside ChatGPT?

Oxford: Shopify and OpenAI announced a collaboration for direct checkout through ChatGPT, but I haven’t seen it widely implemented. Shopify merchants will eventually allow customers to purchase directly inside ChatGPT. Stripe merchants will have similar options through new tools that let developers enable in-chat transactions.

However, I’m unaware of any tracking tools — no equivalent of Google Search Console or Bing Webmaster Tools. Unless ChatGPT introduces advertising, there’s little incentive to build detailed analytics. If ads become part of the platform, I could see them adding conversion pixels and performance tracking, but that’s speculative.

Looking ahead, I suspect Google’s AI Mode may ultimately dominate. ChatGPT accounts for roughly 90% of AI-driven search referrals, but Google is positioning AI Mode as the future. It began as a beta feature, moved into the main interface, and now appears as an “AI” tab alongside images and videos and in the Chrome search bar. If user engagement remains strong, Google could eventually make AI Mode the default over traditional search results.

Despite ChatGPT’s growth, Google search traffic hasn’t declined. Studies show that Google search volume has increased slightly. ChatGPT holds only 1–2% of the search market share — less than DuckDuckGo. Google still commands the vast majority of actual information-seeking queries.

Bandholz: How do I get Beardbrand ranking in ChatGPT?

Oxford: All AI search tools run on LLMs. Just as traditional SEO focuses on Google, we focus on ChatGPT because it holds the largest share of AI-driven discovery. Improvements made for ChatGPT usually help across the other platforms.

The process starts with prompt research, similar to keyword research. Target prompts tied to high-volume transactional keywords — such as “best beard oil” or “where to buy beard oil.” Informational prompts like “what is beard oil” are too top-of-funnel to convert. Once you identify the core prompts, optimize your site around them.

Begin with your About page. The first sentence should clearly state that Beardbrand is a leading provider of beard oil. Maintain your brand voice afterward, but clarity in the opening line helps LLMs understand your core identity.

Next, optimize category and product pages with conversational FAQs, detailed specification tables, clear unique selling points, and defined use cases or target audiences. These elements help LLMs parse and match your products to user prompts.

For blog posts, include expert quotes, statistics, citations, and simple language. Update old pieces. Recency heavily influences whether ChatGPT cites a page. However, maintain a hyper-focused site — remove outdated or off-topic content to improve your likelihood of being referenced in AI search results.

Bandholz: What else should we know?

Oxford: The biggest takeaway is that AI SEO relies heavily on brand mentions, similar to how traditional SEO relies on link building. In AI search, these mentions — often called citations — strongly correlate with whether ChatGPT recommends your products. Your first step is finding “best beard oil” articles across the web, especially those ChatGPT frequently cites. Then work to get your products included.

Send samples, offer substantial affiliate commissions, and accept break-even on those sales if it increases your presence in authoritative lists. These citations can meaningfully influence ChatGPT’s product recommendations.

Digital public relations also helps. Create data or stories journalists want to reference — for example, statistics about beard trends, grooming habits, or consumer preferences. Unique data tends to get picked up, generating high-value brand mentions.

Another helpful tool is Qwoted. It’s similar to Haro but with a paid model that filters out spam, so journalists actively use it. Reporters from Forbes, Inc., HuffPost, and even The Wall Street Journal post requests for expert quotes. Ecommerce founders can easily respond to topics such as tariffs, AI adoption, and hiring. These quotes often generate both brand mentions and backlinks, helping both AI SEO and traditional SEO. Paid plans start around $100 per month, and a single top-tier mention usually justifies the cost.

Bandholz: Where can people hire you, follow you, find you?

Oxford: Our website is 180marketing.com. I’m on LinkedIn.

Ecommerce MGMT 0 Comments
Nov 28 2025
Google’s Mueller Says Sites In A ‘Bad State’ May Need To Start Over via @sejournal, @MattGSouthern

Google’s John Mueller says sites with low-quality AI content should rethink their purpose rather than manually rewrite pages. Starting fresh may be faster than recovering.

  • Manually rewriting AI content doesn’t automatically restore a site’s value or authenticity
  • Mueller recommends treating recovery as starting over with no content, not as a page-by-page editing task
  • Recovering from a “bad state” may take longer than launching on a new domain
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Nov 28 2025
SEO Pulse: ChatGPT Gets Shopping & What Drives AI Citations via @sejournal, @MattGSouthern

Welcome to the week’s Pulse: updates affect how product discovery works, what drives visibility in ChatGPT, and how background assets impact Core Web Vitals.

OpenAI launched shopping research in ChatGPT, SE Ranking published the largest study yet on ChatGPT citation factors, and Google’s John Mueller clarified that background video loading won’t hurt SEO if content loads first.

Here’s what matters for you and your work.

ChatGPT Launches Shopping Research For All Users

OpenAI rolled out shopping research in ChatGPT on November 24, making personalized buyer’s guides available to all logged-in users across Free, Go, Plus, and Pro plans.

The feature works differently from standard ChatGPT responses. Users describe what they need, answer clarifying questions about budget and preferences, and receive a detailed buyer’s guide after a few minutes of research.

Key Facts: Powered by GPT-5 mini. Nearly unlimited usage through the holidays. Merchants can request inclusion through OpenAI’s allowlisting process.

Why SEOs Should Pay Attention

Shopping research pulls more of the product comparison journey inside ChatGPT before users click through to merchant sites. This changes where product discovery happens in the funnel.

Traditional search sent users to comparison sites, retailer pages, and review platforms to build their own shortlist. Shopping research does that work inside the chat interface, asking clarifying questions and surfacing product recommendations based on constraints like budget, features, and intended use.

Crystal Carter, Head of AI Search & SEO Communications at Wix, highlighted the personalization implications in a LinkedIn post:

Make sure your brand affinities, and communities are clearly stated on YOUR website, in your support documentations, FAQs, and make moves to get it cited on other websites, because for some customers, these considerations are make or break, and they will build it into their models.

Her testing showed ChatGPT delivering different restaurant recommendations to users with different profile preferences, pulling from Google Business Profiles and other sources to match stated affinities.

For retailers and affiliate publishers, visibility now depends partly on how products and pages appear in OpenAI’s shopping system. The allowlisting process means merchants need to opt in rather than relying solely on organic crawling.

Read our full coverage: ChatGPT Adds Shopping Research For Product Discovery

Study Reveals Top 20 Factors Driving ChatGPT Citations

SE Ranking analyzed 129,000 unique domains across 216,524 pages in 20 niches to identify which factors correlate with ChatGPT citations.

Referring domains ranked as the single strongest predictor. Sites with up to 2,500 referring domains averaged 1.6 to 1.8 citations, while those with over 350,000 referring domains averaged 8.4 citations.

Key Facts: Domain traffic matters only above 190,000 monthly visitors. Content over 2,900 words averaged 5.1 citations versus 3.2 for articles under 800 words. Pages with 19 or more data points averaged 5.4 citations.

Why SEOs Should Pay Attention

The study suggests that traditional SEO fundamentals still align with AI citation likelihood, but the thresholds matter more than gradual improvements. A site with 20,000 monthly visitors performs similarly to one with 200 monthly visitors, but crossing 190,000 visitors doubles citation rates.

This creates different optimization priorities than traditional search. Building from zero to moderate traffic won’t improve ChatGPT visibility, but scaling from moderate to high traffic will. The same pattern holds for referring domains, where the jump happens at 32,000 domains.

Manidurga BLL, an IT student analyzing the research, broke down the implications in a LinkedIn post with video:

The AI revolution isn’t just changing how we search. It’s rewriting the entire playbook for digital authority. For us tech students and future developers, this means rethinking content strategy from day one. Building domain authority isn’t just about Google anymore. It’s about teaching AI systems to trust and cite your work.

The post includes a detailed video walkthrough of the study findings, highlighting that heavy Quora and Reddit presence correlates with 7 to 8 citations, while review platform listings average 4 to 6 citations.

The research also found that .gov and .edu domains don’t automatically outperform commercial sites despite common assumptions. What matters is content quality and domain authority, not domain extension.

Read our full coverage: New Data Reveals The Top 20 Factors Influencing ChatGPT Citations

Mueller: Background Video Loading Unlikely To Affect SEO

Google Search Advocate John Mueller says large video files loading in the background are unlikely to have a noticeable SEO impact if page content loads first.

A site owner on Reddit asked whether a 100MB video would hurt SEO if the page prioritizes loading a hero image and content before the video continues loading in the background. Mueller responded that he doesn’t expect a noticeable SEO effect.

Key Facts: Using preload=”none” on video elements prevents browsers from downloading video data until needed. Core Web Vitals metrics should verify implementation meets performance thresholds.

Why SEOs Should Pay Attention

The question addresses a common concern for sites using large hero videos or animated backgrounds. Site owners have avoided background video because of performance worries, but Mueller’s guidance clarifies that proper implementation won’t create SEO problems.

The key is load sequencing. If a page shows its hero image, text, and navigation immediately while a 100MB video loads in the background, users get a fast experience and search engines see content quickly.

The Reddit thread included debate about the guidance, with one commenter noting Mueller’s response contradicts concerns about network contention competing with critical resources. WebLinkr, an r/SEO moderator, defended Mueller’s position and noted web developers sometimes overstretch the impact of page speed factors on SEO.

This changes the calculation for sites considering background video. The decision now focuses on user experience and bandwidth costs rather than SEO penalties.

Technical implementation still matters. Using preload=”none” on video elements prevents the browser from downloading video data speculatively, saving bandwidth for users who never play the video.

Read our full coverage: Mueller: Background Video Loading Unlikely To Affect SEO

Theme Of The Week: Discovery Moves Upstream

Each story this week shows discovery happening earlier in the journey.

ChatGPT shopping research handles product comparison before users reach merchant sites. The SE Ranking study reveals what builds citation authority at scale rather than incremental gains. Mueller’s video guidance removes a technical barrier that kept sites from using rich media.

Taken together, this week is about where decisions really form, before anyone ever types a query into Google.

Top Stories Of The Week:

More Resources:

Featured Image: Pixel-Shot/Shutterstock

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Nov 28 2025
The Impact AI Is Having On The Marketing Ecosystem

I’m not someone who’s drunk much of the AI Kool Aid. I have sipped it. Swilled it around my mouth like you would an 1869 Château Lafite Rothschild.

But I’ve seen enough cult documentaries to know you should spit it back into the glass.

Do I love the opportunities it’s provided me in a work sense? Absolutely. Do I think it’s fundamentally shifted the marketing ecosystem? No. I think it’s expedited what’s been happening for some time.

  • Reddit’s resurgence is search-dominated.
  • The booming creator economy shows people trust people.
  • Word of mouth still travels.
  • Content still goes viral.
  • People don’t click unless they have to.
When you take a step back, Reddit’s traffic surge is absurd (Image Credit: Harry Clarkson-Bennett)

LLMs provide a good proxy as to how you’re seen online. And they really lean into review platforms and strong brands. Associating your brand with your core topics, removing ambiguity, and strengthening your product positioning is never a bad thing.

It’s not just about search anymore. In reality, it never should have been. It’s about connecting. Generating value from the different types of media.

TL;DR

  1. The search customer journey spans TikTok, YouTube, Instagram, and everything in between.
  2. Last-click attribution is outdated: BOFU platforms get the credit, but creators, communities, and discovery platforms do the heavy lifting.
  3. AI hasn’t broken anything, it’s just exposing how messy, multi-platform, and people-driven it’s always been.
  4. Brands win by understanding their audience, investing in creators, and building experiences that cut through an enshittified internet.
Image Credit: Harry Clarkson-Bennett

The Customer Journey Has Changed

True. But it’s been changing for a long time. Paid channels are becoming more expensive, owned channels like search send fewer clicks (mainly a Google-driven mechanic), and earned channels are looking more like the golden ticket to corporate stooges.

The majority of brands use last click attribution (gross, get away from me). A method that overvalues search. For the last decade or more, there have been discovery platforms that are more valuable than search – TikTok, YouTube, Instagram. Pick one.

I like time decay or a position-based/first and last touch model in the “new” world (Image Credit: Harry Clarkson-Bennett)

We tend to use search for finding products, brands, or stories we know exist. And for comparison, related searches. But as AI Mode rolls ever closer and Google looks to greedily take on middle of the funnel queries, Google’s role as a discovery platform will change. Theoretically, at least.

Like every big tech company, enough is never enough, and they don’t want to send you clicks. Unless you pay for them, of course.

And it isn’t just Facebook. These companies are disastrously greedy (Image Credit: Harry Clarkson-Bennett)

Search Is No Longer A Single Platform Journey

The Rise at Seven SEO and Data teams analysed 1.5 billion searches across five channels of the most-searched keywords on the internet and found that:

  • A buying journey can take anywhere from two days to 10 weeks, with up to 97 interactions along the way.
  • Google only accounts for just 34.5% of total search share.
  • YouTube (24%), TikTok (16.7%), and Instagram (20.9%) make up more than 60%.
  • The average consumer now uses 3.6 platforms before making a purchase.

But Google isn’t really a discovery platform. Maybe a bit. Google Shopping. Some comparison searches. But it’s not what anyone is there for.

Someone sees a product on Instagram or TikTok. They read a review of it on Reddit (probably through Google, albeit with a branded search) and watch videos of it on TikTok or YouTube.

They might even buy direct or via Amazon. At best, they perform a branded search in Google.

Now, tell me, last click attribution makes sense.

I think it’s worth noting here that so many of these other platforms are driven by a clickless algorithm. Google requires a click. A fundamental search. The others have homepages that stare directly into your soul.

I don’t think any of this is new. And I suspect it’s been a while since search was a single platform journey. But it depends on what you define as search, I suppose.

Google’s Messy Middle  is about right. We have been living through an era of marketing desperately tied to trying to track every penny. Something that has been a near-impossible job for some time. At some point, you just have to sit down, try to know your audience better than anyone else, and have at it.

We need to influence clicks via search before that happens. Brands have to focus their time on the right channels for their audience. Not just search. That’s why knowing your audience and using an attribution model that doesn’t just value the BOFU click matters.

But Has AI Been The Catalyst?

Probably a bit. Behavior has been changing long before LLMs hit the public arena. It’s changed because people have better options. More visually decisive. More authentic. The creator economy has boomed because people trust people.

  • When I’m doomscrolling on the bog or on the tube (praise be to the 5G gods), I might get served a new product.
  • If I want real opinions or reviews about said product, I might go to Reddit (albeit through Google) to see what someone thinks. Well, I wouldn’t because I’m an adult with a wife and a mortgage, but you see my point.
  • I might subscribe to specific Substacks or creators who use and speak about the product.
  • My favorite LLM might give me product ideas (which I would check very carefully).
  • Hell, I might even see something IRL on the tube.

A lot of this ends with a Google search. Maybe all of it. Google is a navigational engine. Hence, the last click attribution issue. I suspect the last click isn’t the most important session in the majority of cases.

Unless you’re young, lazy, or both, AI just won’t cut the mustard. Hell, Google’s kingpin tells you not to blindly trust AI. Even the guys fundamentally selling us this stuff are telling us it has some pretty serious flaws.

You’re a naysayer if you ask Sam about the company valuation, spiralling costs, or insane problems. (Image Credit: Harry Clarkson-Bennett)

It’s one of the reasons user journeys are so much more complex and elongated.

  1. We have more effective platforms and opinions than ever.
  2. We have more shitty platforms and opinions than ever.

Cutting through the noise is everything. For people and brands. So you have to learn how to build brands and products that are bold and get the right people talking and sharing.

90% of marketers say creator content yields stronger engagement and 83% link it to more conversions. And 61% of consumers trust recommendations from creators more than they trust brand advertising.

The algorithms trust people because people do.

Channel-By-Channel Breakdown

Things don’t happen in a silo. Call me old-fashioned, but I think we’d all do well to work together as a marketing department. AIOs don’t just affect search. They have a knock-on effect on the entire ecosystem, and it’s important we understand the what and the why.

SEO

Where do we start? I’ll try and be brief. The most obvious and direct threat is zero-click search, which has been on the rise for some time. While AI hasn’t been the key driver of this, it has and will continue to reduce referral traffic.

  • AIOs have significantly reduced CTR, particularly for informational, TOFU queries.
  • AI Mode is there to steal middle of the funnel clicks to “help users make the right decision.”
  • LLMs offer something of an alternative to search. Although based on what people really use them for, I think they are complementary, rather than a replacement.

I think AI has done some very interesting things in the SEO space. Vibe engineering platforms like Cursor and prototyping platforms like Lovable have opened up new worlds.

If you can wade through the shit, you can do some brilliant things.

Then you have Profound’s Zero Click conference, where one of the speakers said he felt sorry for anyone working in SEO. According to this turdy savant, there’s very little crossover between SEO and insert favorite acronym before proceeding to discuss lots of SEO ideas from 2012.

People who just do not understand marketing, SEO, the internet, or people. These are the guys driving the enshittification of our day-to-day lives. (More on this later).

PPC

PPC and SEO are ugly cousins, really. We operate in the same space, we target the same traffic. So it stands to reason that AIOs and AI Mode significantly impact paid search.

If you can believe it, it’s broadly a negative.

I know. I, too, am stunned.

Thanks to Seer Interactive, we have near-conclusive data that proves how serious this impact has been. When an AIO is present, and you are not cited, clicks are down over 78%.

Even when there’s no AIO present, paid clicks are down 20%. This is disastrous. Customer acquisition becomes more expensive, and the blended CPAs are significantly more expensive.

This may show a real and significant shift in user behavior. Users are becoming so used to getting what they want from a TOFU search, they don’t even follow up when an AIO isn’t present.

Attention is slipping everywhere.

Social

We’ve seen the rapid rise of disinformation in search. Google’s been promoting fake content to millions of people on Discover and has been struggling to block them for some time. Gaming the system isn’t new. PBNs, expired domain abuse, link schemes. You name it, it’s working.

Some very good expired domains and PBN abuse here, post the 2025 SPAM update (Image Credit: Harry Clarkson-Bennett)

Thankfully, the Vote Leave Take Control team have put their talents to good use and can now tell me what casino site should I choose.

The scale is unprecedented. Bullshit flies everywhere.

And that’s where social comes in. Globally, the average person spends 2 hours and 24 minutes on social media every day. That’s a lot of time to be hit by fake news. Personalized fake news, too. So maybe it’s not a surprise that social use has been on the decline for the last couple of years.

According to this study by the FT, social media use has decreased by 10% and has been driven by (*shakes fist*) the youth. I think these platforms are a shell of what they once were. The connections they provided have been replaced by absolute bullshit.

They will do literally anything to get and hold your attention. Except help you stay in touch with people or watch something that isn’t AI-generated. The content quality bell curve we see in search is mirrored by the enshittification of social channels.

  • First, the platform attracts users with some bait, such as free access.
  • Then the activity is monetized, bringing in the business customers with no thought for the user experience.
  • Once everyone is “trapped,” the value is transferred to their executives and shareholders.

People with no understanding of marketing or people thinking that auto-generated comments will boost their profile on LinkedIn. Businesses using AI to cut corners to generate meaningless bullshit and throwing it at me. See Coca Cola advert for reference.

Nothing says happy holidays like being fired for an incompetent robot.

The lights are on, the wheels are turning, but nobody is home. Or cares. The Mark Zuckerburgs of the world are, hopefully, turning people off hyper addictive brain rot.

Impressive, I know. Thank god for Ryan Air.

Best social media strategy on the planet (Image Credit: Harry Clarkson-Bennett)

As email is an owned channel, there’s not an obvious issue with generative AI. However, the Litmus State of Email Report shows the top roadblocks and operational challenges encountered by teams.

Image Credit: Harry Clarkson-Bennett

AI makes all of these roadblocks worse. Crummy, personality-devoid content churned out at scale will lower engagement. And it doesn’t take a genius to figure out that execs would love to save on personnel.

Operationally, you’d think AI will help. But if producing high-quality content at scale and improving your core benchmarks are fundamental issues, I’m not sure AI is the answer.

Personalization, research, and distribution. Absolutely. Creating content that draws real people in and engages with them. Color me sceptical.

Paid Vs. Earned Vs. Owned

This is all about the funnel. If it becomes more expensive to acquire customers in their unaware/aware phase with paid campaigns, your owned and earned channels need to work harder. They need to work harder to increase your conversion rate.

  • Paid campaigns or projects are designed to do two things: reach a newer potential audience and retarget an existing, highly qualified one. But they’re becoming more expensive. Especially in a PPC sense.
  • Most sensible companies are trying to build their email databases off the back of search and organic social. Owned media is simultaneously under threat and incredibly valuable.
  • Earned media – public exposure through word of mouth and shared content – is arguably more important than ever. People really trust people’s opinions.
Never a truer word spoken (Image Credit: Harry Clarkson-Bennett)

What Should You Do?

As an SEO and a marketer, you should focus on creating real connections with people. Understanding your audience. Leveraging people that have influence over your audience. Build, work with, and promote brilliant creators and own your audience data internally.

Squeeze every last drop out of your content. Cut and share it in appropriate formats across multiple channels.

Email is almost certainly the most applicable channel for most brands. You actually own it. Then figure out the role your brand plays in that journey. Create a great user experience on and off-site. Make sure it’s well documented, and you own everything in your control. Speak to your PPC and social teams to understand the challenges they’re having.

  • Help Center.
  • FAQ and product pages.
  • ToV consistency and brand guidelines.
  • Reviews and complaints (On and off-site).
  • Technical site quality.
  • Content quality.
  • Large-scale, TOFU campaigns.

This isn’t just about marketing. Or LLMs. They are just a good proxy for how you are seen on the internet.

It’s about working together as a marketing department with a shared goal of creating and amplifying brilliant experiences to the right people. Maximising the value of your owned channels, to reduce the reliance on paid, and doing things that create brand advocates and cause your earned media to soar.

There’s an opportunity here to do great things!

But whatever you do, don’t forget about good quality SEO. It’s the primary purpose of our job and it still works.

More Resources:

This post was originally published on Leadership in SEO.

Featured Image: MR.DEEN/Shutterstock

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Nov 28 2025
Paid Ad Scheduling Across Time Zones That Actually Works via @sejournal, @brookeosmundson

Scheduling ads in Google or Microsoft Ads sounds simple until you realize how many hours you’re wasting showing them at the wrong time.

A campaign that performs well in one market might fall flat in another, not because your targeting or creative is off, but because of when your ads appear.

Managing time zones is one of the easiest ways to improve efficiency and stop unnecessary spend. Yet, many PPC managers still rely on default settings or assume their ad platform will “figure it out.”

In reality, effective ad scheduling requires strategy, testing, and an understanding of how local behavior differs across regions.

This guide breaks down how to identify true peak hours, segment campaigns by region, and use automation tools to make scheduling work in your favor, no matter where your audience is.

Understanding Time Zone Challenges In PPC

When advertising across multiple regions, time zone discrepancies can create challenges that impact ad delivery, engagement, and conversions.

A common pitfall is assuming that a single campaign schedule will work universally. In reality, what works in one location might be completely ineffective in another.

For example, if your Google Ads account is set to Eastern Time but your target audience is primarily on the West Coast, your ads might be running during off-hours, leading to suboptimal performance.

International campaigns require even more diligence to consider local business hours and consumer behavior patterns.

Another factor is peak engagement hours. While lunchtime or evening hours may be prime time in one country, those same hours could be completely irrelevant in another.

Understanding these nuances is essential for optimizing your ad scheduling strategy.

Advanced Strategies For Scheduling Ads Across Time Zones

Successfully managing ad scheduling across time zones requires a thoughtful approach that goes beyond the basics.

While many advertisers set simple schedules and hope for the best, the real wins come from leveraging automation, data-driven insights, and strategic segmentation.

Whether you’re running campaigns domestically across U.S. time zones or managing international PPC efforts, applying advanced techniques can help ensure your ads are served at the right time for the right audience.

Segmenting Campaigns By Time Zone For Better Control

If you’re running campaigns across multiple time zones, one of the best ways to stay in control is by creating separate campaigns for different regions.

This lets you adjust ad schedules, budgets, and bidding strategies based on local peak performance times rather than forcing a single schedule to work for every location.

For example, an ecommerce brand serving customers in the U.S. and Europe might run separate campaigns for each region.

The U.S. campaign can focus on morning and evening hours when engagement peaks, while the European campaign targets prime shopping hours in local time zones.

While this approach adds complexity, the benefits far outweigh the extra management effort. Automating adjustments with rules and scripts can help streamline this process, ensuring each campaign is optimized without constant manual oversight.

Leveraging Automated Bidding Over Fixed Schedules

Manual ad scheduling has its place, but automated bid strategies like Target ROAS or Maximize Conversions allow you to optimize bids dynamically rather than setting fixed hours.

These AI-driven approaches adjust bids in real time, ensuring ads appear when conversion probability is highest, regardless of time zone differences.

For instance, if data shows that users in one region convert at a higher rate between 9 a.m. and 11 a.m. but another region performs better in the evening, automated bidding will allocate more budget when it matters most.

Instead of manually adjusting bids every few weeks, let machine learning do the heavy lifting.

Optimizing Scheduling Based On Market-Specific Peak Hours

Different markets have different user behaviors, so it’s crucial to base your scheduling decisions on actual performance data rather than assumptions.

Google Ads’ ad schedule reports and Microsoft Ads’ time-of-day insights can help you identify when users in each region are most active.

For example, if analytics reveal that North American users are most engaged in the evening while European users peak in the morning, your campaigns should reflect that.

Instead of blanketing all markets with a generic ad schedule, tailor your approach based on real-time engagement trends.

Using Labels To Manage And Adjust Scheduling

One often overlooked yet powerful feature in Google and Microsoft Ads is the use of labels.

Labels let you group campaigns, ad groups, or keywords into easily manageable categories, making it simpler to track and adjust schedules.

For example:

  • Tagging campaigns by region allows for easy bulk adjustments when shifting schedules due to seasonal changes or promotional events.
  • Labeling time-sensitive ads ensures that you can quickly pause or resume campaigns as needed without sifting through dozens of settings.
  • Using automation scripts with labels enables automatic bid adjustments or scheduling changes based on real-time performance.

By applying labels effectively, you can streamline scheduling changes without manually editing each campaign, saving time and reducing errors.

Automating Scheduling Adjustments With Scripts

If you’re managing multiple time zones, Google Ads scripts can be a game-changer.

Rather than manually adjusting schedules, scripts can dynamically modify bids based on real-time performance data.

For example, a script could be set up to boost bids by 20% during high-converting hours and reduce them by 10% when conversions drop. This keeps campaigns optimized while freeing up time to focus on strategy rather than daily bid adjustments.

Scripts also work well with labels. You can program scripts to modify bid strategies for campaigns tagged with specific labels, ensuring changes are applied only to relevant ads.

Adjusting For Daylight Saving Time Changes

Another scheduling headache is Daylight Saving Time (DST), which varies by country and can cause misalignment in ad schedules.

A campaign that ran perfectly last month might suddenly be off by an hour if a region switches to DST.

To avoid this, maintain a calendar of DST changes in key markets and adjust schedules proactively.

Another option is using automated rules or machine learning-based bid adjustments to handle these shifts without manual intervention.

Budget Allocation Based On Regional Performance Trends

Rather than splitting your budget evenly across all time zones, consider allocating more spend to the highest-performing regions based on historical data.

By analyzing performance reports, you can determine which locations deliver the best ROI and adjust budgets accordingly.

For instance, if your data shows that conversions peak in the late evening for Pacific time zone users but decline in the early morning for Eastern time users, shift more budget toward the stronger-performing time periods.

This approach ensures ad spend is being used effectively rather than wasted on time slots that don’t generate conversions.

Turning Time Zones Into An Advantage

Ad scheduling is just one of many levers that can make or break your campaign performance. When your ads align with local customer behavior, your budget works harder, and engagement improves.

Use data to pinpoint when conversions actually happen, then adjust delivery windows to match those trends.

Lean on automation to keep schedules consistent, especially across multiple markets, and review reports often enough to spot shifting patterns.

Treat time zone planning as part of your optimization routine, not a one-time setup. The more precisely your ads reflect when people are active, the stronger your results will be.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

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Nov 28 2025
Moving toward LessOps with VMware-to-cloud migrations

Today’s IT leaders face competing mandates to do more (“make us an ‘AI-first’ enterprise—yesterday”) with less (“no new hires for at least the next six months”).

VMware has become a focal point of these dueling directives. It remains central to enterprise IT, with 80% of organizations using VMware infrastructure products. But shifting licensing models are prompting teams to reconsider how they manage and scale these workloads, often on tighter budgets.

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For many organizations, the path forward involves adopting a LessOps model, an operational strategy that makes hybrid environments manageable without increasing headcount. This operational philosophy minimizes human intervention through extensive automation and selfservice capabilities while maintaining governance and compliance.

In practice, VMware-to-cloud migrations create a “two birds, one stone” opportunity. They present a practical moment to codify the automation and governance practices LessOps depends on—laying the groundwork for a leaner, more resilient IT operating model.

Download the full article.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

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