Ecommerce MGMT, Author at Ecommerce Management Education

Mar 25 2026

How Zero-Party & First-Party Data Can Fuel Your Intent-Based SEO Strategy via @sejournal, @rio_seo

There’s an interesting paradox currently occurring in the realm of marketing. Marketers have more tools and data at their fingertips, yet despite this influx of information, marketing leaders also somehow have less clarity than ever before.

Over the past decade, Google’s algorithms and privacy regulations have significantly shifted traditional SEO best practices. SEO has evolved from a precise science to more of a trust discipline, where marketers must infuse credibility and authority into their content to improve visibility.

The new opportunity at hand isn’t scraping more consumer behavior but rather listening to it in a new manner. By diving deeper into zero-party data, information customers willingly share, and first-party data, behavior observed directly on your own channels, chief marketing officers can shape their SEO strategies around real human intent.

Search success will be contingent on whether brands understand their audience well enough to create relevant, authentic, and trustworthy content at every step of the customer journey, not just when an algorithm prompts them to.

The Connection Between Zero-Party Data And SEO

Zero-party data is marketing’s cleanest and clearest source of truth. It uncovers the information customers want you to have. It unveils their preferences, motivations, and needs through methods like surveys, quizzes, chatbots, and more.

First-party data shows what users do. Zero-party data shows you why they did what they did. When paired together, both forms of data bridge the gap between analytics and empathy.

For example, a retail brand might ask site visitors in a post-purchase survey, “What is most likely to motivate you to make a purchase?” The choices the site visitor can choose between are price, sustainability, or convenience. Now, consider if nearly half of those respondents chose “sustainability.”

This insight shouldn’t fall into a void, but rather should be acted upon quickly. It’s not a trend but rather a clear signal. The content and SEO teams can now focus on creating content around “eco-friendly shopping” and other relevant sustainability topics, while communications teams can align messaging around the same topic. In turn, seamless collaboration and alignment take place.

Moving Beyond Keywords To Conversations

Traditional SEO honed in on what people typed into the search bar. Zero-party data reveals what people mean when they’re searching for a business, product, or service. Algorithms are increasingly rewarding intent satisfaction when evaluating content. When your content addresses and is built on declared motivations, like why someone is looking for your specific solution, you’re aligned with the future of search.

How To Turn Customer Data Into Search Strategy

The issue isn’t that CMOs aren’t collecting data; it’s the struggle with turning it into action that drives meaningful change.

An intent-based SEO strategy has three phases, which we will discuss next (capture, interpret, and activate).

Phase 1: Capture

Customers aren’t going to hand over information if they don’t see a clear value in doing so. To encourage this, marketers must highlight a mutual benefit in the information exchange. A few methods include:

Gated research studies.
Short post-purchase surveys.
Interactive quizzes or calculators.
Preference centers so customers only receive communication around specified topics that matter most to them.
Incentives such as coupons and exclusive promotions for newsletter subscribers.

Each of the aforementioned information exchanges becomes a declared-intent breadcrumb. Users have granted your business permission to act on their feedback and are much more actionable than cookie trails alone.

Phase 2: Interpret

Collecting information from myriad channels can make it difficult to determine where they should focus their attention first. To dissect and pull out the insights that matter most from unstructured and structured feedback, CMOs should invest in qualitative analysis tools. Tools like text analytics, for example, can make it easy for CMOs and CX teams alike to mine for common themes.

Customer Data Platforms (CDPs), can also help you create audiences and segments to deliver more personalized content that resonates with customers. This might look like a retail marketing manager only receiving newsletters, ebooks, or blogs that are related to the retail industry and trends.

These types of thematic content pillars can help inform supporting search queries, schema markup, content priorities, and more.

Phase 3: Activate

In this phase, you’ll set your plans into action. First, connect declared intent to keyword intent. For example, if customers talk about “security peace of mind,” this gives you clear insight into what they’re interested in learning more about and how your company can help. You could create content that explicitly speaks to “how we secure your personal data.”

On the other hand, if they’re talking about “easy to implement,” it may be beneficial for you to provide explainer-type content, such as a short video or an FAQ page (with FAQ schema), to address “how to integrate [product name]” searches.

Zero-party data helps move the needle with SEO efforts; from a guessing game to an action engine, producing content that doesn’t just satisfy search algorithms, but also the people behind the search, too.

Leadership Enablement: Aligning Teams, Culture, And Technology

To build an insight-to-action culture, CMOs should encourage teams to share qualitative learnings regularly, whether through a cadence of weekly meetings, via email, or a combination of the two. Customer experience teams should make Voice of Customer insights loud and clear to help inform SEO and content briefs.

It’s also important to highlight and reward cross-functional wins to showcase how working together helps drive growth. This might look like an SEO strategy that was informed by CX feedback or a case study that solves a pressing challenge clients typically face, informed by online reputation feedback.

Operationalize The Feedback Loop

CMOs can install a regular “intent feedback loop” to operationalize the data your company receives and act upon that data. This might look like:

Gather declared data (surveys, chatbot transcripts, online reviews, call center logs).
Identify what motivates consumers most (customers often talk about time savings, value for money, trust issues, emotions).
Update content briefs and keyword maps (primary and secondary keywords, content requirements, search intent to ensure you’re staying up to speed).
Measure whether your content is landing with your intended audience on an emotional and intellectual level. Engagement, recall, and action are key determinants of content success, not just how it ranks.

This type of feedback framework helps organizations embed customers’ preferences and desires directly into the content published, helping your business create the content that actually connects with your target audience.

The Metrics To Add

Measuring what matters most is integral to assess the impact of zero-party data analysis efforts. Alongside other SEO metrics, the following can gain a holistic view of your SEO performance:

Resonance Metrics

Engagement quality is a true testament of attention. Meanwhile, volume, while great to have, is somewhat meaningless if you have an abundance of unqualified leads. Instead, look at:

Average engagement time: How long people stick around to view your content.
Return visits: People who come back to consume more of your content.
Scroll depth: Visitors should scroll down to read the entirety of your content because they find it to be that interesting.

Relevance Metrics

Marketers must track growth in high-intent and branded queries, as these are most often the terms that someone who is on the verge of buying will use when searching for your business. If you’re showing up for phrases customers typically use when at the decision-making stage, such as “State Farm compared vs. Geico car insurance,” this indicates deeper resonance.

Relationship Metrics

Loyalty metrics, while not a metric SEOs track, can correlate with how well your SEO program is working. Reframing SEO performance as a reflection of customer understanding helps CMOs dig a layer deeper, past solely tactics, and understand deeper-rooted customer emotions that could be preventing your business from scaling. Look at:

Zero-party response rate: The percentage of users who are willing to share their personal information and experiences.
Repeat engagement: Consumers who continue to engage with your business and see value in doing so.
Customer lifetime value: How valuable a customer is to your business over time (how much they purchase, do they churn quickly)
Retention rate: Customers who continue to do business with you that you’ve worked hard to acquire and keep.

The Future Belongs To Human-Declared Intent

We may be in the age of AI, but the future is human. Yes, AI can generate a keyword-optimized blog in a matter of seconds, but human touch is where the real value is. And human-informed data will be your business’s ultimate differentiator.

Zero- and first-party data reveal pertinent insights that elevate organizations when this data is acted upon. It unlocks insights into why people search and not just what they search for. It also uncovers where in the sales journey customers are getting stuck and blockers for purchasing.

Moving forward, to fuel your SEO efforts:

Ask customers what matters most to them.
Listen to what they have to say.
Create content that addresses those asks.
Optimize it for human needs, not just engagement and clicks.
Measure customer experience metrics, not just SEO.

When marketing leaders take consumer feedback to heart, they bridge the gap between traffic and trust, building stronger relationships that lead to more purchases, repeat customers, and improved brand experiences.

More Resources:

Featured Image: Anton Vierietin/Shutterstock

Ecommerce MGMT 0 Comments

Mar 25 2026

Is Your Website Ready for AI Search? A Practical Audit for CMOs via @sejournal, @lorenbaker

AI-driven discovery is reshaping how brands earn visibility and conversions. Most CMS stacks weren’t built for this shift.

Is your CMS structured for AI-powered search and answer engines?
Can your content be interpreted, reused, and surfaced by machine-driven systems?
Is your current tech stack quietly limiting performance in search?

Discoverability depends on structured data, flexible architecture, and systems that adapt quickly.

Watch the on-demand webinar to see how to evaluate whether your Drupal site, or other CMS, is built for what’s next.

How To Audit Your CMS for AI-Driven Search & Conversion Performance

In this practical, marketer-focused on-demand session, we’ll walk through how CMOs and marketing leaders can assess whether their current CMS and digital stack support modern search behavior or restrict it.

You’ll leave with a clear understanding of what AI readiness means at the platform level, and how to identify risk areas before they impact growth.

You’ll Learn:

Where enterprise Drupal implementations most often fall short in AI-driven discovery
How AI search changes SEO strategy, content modeling, and conversion optimization
What defines an AI-ready CMS stack, including structured content, composable architecture, and open-source flexibility

Check out the slides below or watch the full presentation, on demand!

Ecommerce MGMT 0 Comments

Mar 25 2026

The Download: tracing AI-fueled delusions, and OpenAI admits Microsoft risks

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.

The hardest question to answer about AI-fueled delusions

What actually happens when people spiral into delusion with AI? To find out, Stanford researchers analyzed transcripts from chatbot users who experienced these spirals.

Their findings suggest that chatbots have a unique ability to turn a benign, delusion-like thought into a dangerous obsession. But the research struggles to answer a vital question: does AI cause delusions or merely amplify them? Read the full story to understand the answer’s enormous implications.

—James O’Donnell

This story is from The Algorithm, our weekly newsletter giving you the inside track on all things AI. Sign up to receive it in your inbox every Monday.

The next era of space exploration

Our footprint in the solar system is rapidly expanding. Programs to build permanent Moon bases and find life on Mars have transitioned from science fiction to active space agency missions. The scientists behind them will not only shed new light on the cosmos, but also reveal where humanity is headed.

To examine what the future holds in store, MIT Technology Review features editor Amanda Silverman will sit down on Wednesday with award-winning science journalist and author Robin George Andrews for an exclusive subscriber-only Roundtable conversation about “The Next Era of Space Exploration.” Register here to join the session at 16:00 GMT / 12:00 PM ET / 9:00 AM PT.

The must-reads

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

1 OpenAI has admitted its close ties with Microsoft are a business risk
It highlighted the dangers in a pre-IPO document. (CNBC)
+ OpenAI is wooing private equity firms with a sweeter deal than Anthropic’s. (Reuters $)
+ It’s also building a fully automated researcher. (MIT Technology Review)
+ And wants to muscle in on Google’s search dominance. (Telegraph $)

2 The US just banned all new foreign-made consumer routers
Citing national security concerns. (BBC)
+ The EU has been urged to tighten rules for big tech-built smart TVs. (Guardian)

3 Elon Musk’s “Terafab” chip factory faces a harsh reality check
In the form of chip production shortages. (Bloomberg)
+ Future AI chips could be built on glass. (MIT Technology Review)

4 Mark Zuckerberg is building an AI CEO to help him run Meta
He wants everyone to have their own personal AI agent. (WSJ $)
+ But don’t let the hype about agents get ahead of reality. (MIT Technology Review)

5 Palantir has become a “poisonous” flashpoint on the campaign trail
Candidates are facing scrutiny over their ties to the company. (FT $)
+ Palantir’s access to sensitive UK data is also causing concern. (Guardian)

6 Mistral’s CEO has called for AI companies to pay a content levy in Europe
It would apply to all commercial models on the continent. (FT $)
+ Siemens’ CEO says Europe risks “disaster” from prioritizing AI independence. (FT $)

7 Hong Kong police can now demand device passwords under a new law
Refusing to comply could lead to a year in jail. (Guardian)

8 Russia’s aspiring SpaceX rival has put its first internet satellites into orbit
It plans to create a low-Earth orbit network. (Bloomberg $)

9 A biotech startup wants to replace animal testing with nonsentient “organ sacks”
The genetically engineered system is backed by billionaire Tim Draper (Wired $)
+ Several new technologies are promising alternatives to lab animals. (MIT Technology Review)

10 AI agents in a video game spontaneously created their own religion
They reinterpreted a mission in the MMORPG. (Gizmodo)
+ They’re not the first agents to get religious. (MIT Technology Review)

Quote of the day

“I think we’ve achieved AGI.”

—Nvidia CEO Jensen Huang tells the Lex Fridman Podcast that artificial general intelligence is already here (at least by one generous definition).

One More Thing

Beyond gene-edited babies: the possible paths for tinkering with human evolution

In 2018, a Chinese scientist created the world’s first gene-edited babies, a milestone that fell between a medical breakthrough and the start of a slippery slope toward human enhancement.

He achieved the feat with CRISPR, which was sweeping across biology labs because it was so easy to use. For his actions, He was sentenced to three years in prison, and his work was roundly excoriated. Yet even his biggest critics saw the basic idea as inevitable.

In the years since, CRISPR has continued getting easier and easier to administer. What does that mean for the future of our species? Read the full story to find out why.

—Antonio Regalado

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.)

+ This candle-powered Game Boy is a romantic approach to gaming during a blackout.
+ Apparently, Monopoly would be more fun if we actually followed the rules.
+ Watching rubber bands explode these everyday objects is strangely hypnotic.
+This spellbinding site simulates what Earth looked like hundreds of millions of years ago.

Ecommerce MGMT 0 Comments

App Biotechnology and health

Mar 25 2026

This scientist rewarmed and studied pieces of his friend’s cryopreserved brain

L. Stephen Coles’s brain sits cushioned in a vat at a storage facility in Arizona. It has been held there at a temperature of around −146 degrees °C for over a decade, largely undisturbed.

That is, apart from the time, a little over a year ago, when scientists slowly lifted the brain to take photos of it. Years before, the team had removed tiny pieces of it to send to Coles’s friend. Coles, a researcher who studied aging, was interested in cryogenics—the long-term storage of human bodies and brains in the hope that they might one day be brought back to life. Before he died, he asked cryobiologist Greg Fahy to study the effects of the preservation procedure on his brain. Coles was especially curious about whether his cooled brain would crack, says Fahy.

Coles’s brain was preserved shortly after he died in 2014, but Fahy has only recently got around to analyzing those samples. He says that Coles’s brain is “astonishingly well preserved.”

“We can see every detail [in the structure of the brain biopsies],” says Fahy, who is chief scientific officer at biotech companies Intervene Immune and 21st Century Medicine (where he is also executive director). He hopes this means that Coles’s brain still stands a chance of reanimation at some point in the future.

Other cryobiologists are less optimistic. “This brain is not alive,” says John Bischof, who works on ways to cryopreserve human organs at the University of Minnesota.

Still, Fahy’s research could help provide a tool to neuroscientists looking for new ways to study the brain. And while human reanimation after cryopreservation may be the stuff of science fiction, using the technology to preserve organs for transplantation is within reach.

Banking a brain

Coles, a gerontologist who spent the latter part of his career studying human longevity, opted to have his brain cryogenically preserved when he died of pancreatic cancer.

After he was declared dead, Coles’s body was kept at a low temperature while he was transferred to Alcor, a cryonics facility in Arizona. His head was removed from his body, and a team perfused his brain with “cryoprotective” chemicals that would prevent it from freezing. They then removed it from his skull and cooled it to −146 °C.

Coles had another request. As a scientist, he wanted his cryopreserved brain to be studied. Hundreds of people have opted to have their brains—with or without the rest of their bodies—stored at cryonic facilities (the remains of 259 individuals are currently stored as either whole bodies or heads at Alcor). But scientists know very little about what has happened to those brains, and there’s no evidence to suggest they could be revived. Coles had met Fahy through their shared interest in longevity, and he asked him to investigate.

“He thought that if he had himself cryopreserved, we could learn from his brain whether cracking was going to happen or not,” says Fahy. That’s what typically happens when organs are put into liquid nitrogen at −196 °C, he says. The extreme cooling creates “tension in the system,” he says. “If you tap it, it’ll just shatter.” This cracking is less likely at the slightly warmer temperatures used for preservation.

Fahy was involved from the time the samples were taken.

“We had Greg Fahy on the phone coordinating the whole thing, [including] where the biopsies were taken,” says Nick Llewellyn, who oversees research at Alcor. (Llewellyn was not at Alcor at the time but has discussed the procedure with his colleagues.) The biopsied samples were stored in liquid nitrogen and earmarked for Fahy. The rest of the brain was cooled and kept in a temperature-controlled storage container at Alcor.

Bouncing back

It wasn’t until years later that Fahy got around to studying those biopsies. He was interested in how the cryoprotectant—which is toxic—might have affected the brain cells. Previous research has shown that flooding tissues with cryoprotectant can distort the structure of cells, essentially squashing them.

It’s one of the many challenges facing cryobiologists interested in storing human tissues at very low temperatures. While the vitrification of eggs and embryos—which cools them to −196 °C and essentially turns them to glass—has become relatively routine (thanks in part to Fahy’s own work on mouse embryos back in the 1980s), preserving whole organs this way is much harder. It is difficult to cool bigger objects in a uniform way, and they are prone to damaging ice crystal formation, even when cryoprotectants are used, as well as cracking.

Fahy found that when he rewarmed and rehydrated Coles’s brain cells, their structure seemed to bounce back to some degree. Fahy demonstrated the effect over a Zoom call: “It looks like this,” he said with his hands as if in prayer, “and it goes back to this,” he added, connecting his forefingers and thumbs to create a triangle shape.

The structure of the tissue looks pretty intact, too, to him at least, though he admits a purist expecting a pristine structure would be disappointed. He and his colleagues have been able to see remarkable details in the cells and their component parts. “There’s nothing we don’t see,” says Fahy, who has shared his results, which have not yet been peer reviewed, at the preprint server bioRxiv. “It seems that [by taking the cryogenic approach] you can preserve everything.”

As for the cracking, “from what I was told, no cracks were observed [by the team that initially preserved the brain],” says Fahy. The team at Alcor took photographs of the brain when they took the biopsies, but the images were later lost due to a server malfunction, he says. In the more recent photos, the brain is covered in a layer of frost, which makes it impossible to see if there are any cracks, he adds. Attempts to remove the frost might damage the brain, so the team has decided to leave it alone, he says.

Back to life?

Fahy and his colleagues used chemicals to “fix” Coles’s brain samples once they had been rewarmed. That process is typically used to stop fresh tissue samples from decaying, but it also effectively kills them.

But he thinks his results suggest that it might be possible to cryopreserve small pieces of brain tissue and reanimate them to learn more about how they work. Functional recovery seems to be possible in mice—a few weeks ago a team in Germany showed that they were able to revive brain slices that had been stored at −196 °C. Those brain samples showed electrical activity after being cooled and rewarmed.

If cryobiologists can achieve the same feat with human brain samples, those samples could provide neuroscientists with new insights into how living brains work.

Brain cryopreservation “can capture a little bit more of the complexities of the brain,” says Shannon Tessier, a cryobiologist at Massachusetts General Hospital who is developing technologies to preserve hearts, livers, and kidneys for transplantation. “[Being] able to use human brains from deceased individuals [could] add another layer to the research tool kit,” she says.

And Fahy’s paper shows “what happens when we try and vitrify a one-liter, dense, massive goop,” says Matthew Powell-Palm, a cryobiologist at Texas A&M University. “We now have a strong indication that quite large [tissues and organs] can be vitrified by perfusion [without forming too much ice],” he says.

All of the scientists I spoke to, including Fahy, are also working on ways to cool and preserve organs for transplantation. These are in short supply partly because once an organ is removed from a donor, it usually must be transplanted into its recipient within a matter of hours.

Cryopreservation could buy enough time to make use of more organs, find better organ-donor matches, and potentially even prepare recipients’ immune systems and save them from a lifetime of immunosuppressant drugs, says Bischof, who has also been developing new technologies for organ cryopreservation.

Bischof, Fahy, and others have made huge strides in their attempts so far, and they have managed to remove, cryopreserve, and transplant organs in rabbits and rats, for example. “We’re at the cusp of human-scale organ cryopreservation,” says Bischof.

But when it comes to preserving brains, donation isn’t the aim. Coles had hoped to be reanimated—a far more ambitious goal that hinges on the ability to restore brain function.

Brain reanimation

Fahy acknowledges that while the structure of Coles’s brain samples did bounce back, there is no evidence to suggest the cells could be brought back to life and regain electrical activity and a functioning metabolism. “Restoring it to function … that’s a whole other story,” he says.

But he thinks that successful cryopreservation of the brain “is the gateway to human suspended animation, which [could allow] us to get to the stars someday.” Figuring out human preservation would also allow people to avoid death through what he calls “medical time travel”—journeying to an unspecified time in the future when science will have found a cure for whatever was due to kill that person. “That would be an ultimate goal to pursue,” he says.

“I put the chances [of brain reanimation] at pretty low,” says Alcor’s own Llewellyn. “The kind of technology we need is practically unfathomable.”

The brains already in storage at Alcor and other facilities have been preserved in ways that “have not been validated to work for reanimation,” says Tessier. An expectation that they’ll one day be brought back to life in some form is “quite a jump of faith and hope that’s not based on science,” she says.

As Powell-Palm puts it: “There are so many ways in which those neurons could be toast.”

Ecommerce MGMT 0 Comments

Subscriber eBooks Subscriber-Only Stories

Mar 25 2026

Exclusive eBook: Are we ready to hand AI agents the keys?

We’re starting to give AI agents real autonomy, but are we prepared for what could happen next?

This subscriber-only eBook explores this and angles from experts, such as “If we continue on the current path … we are basically playing Russian roulette with humanity.”

by Grace Huckins June 12, 2025

ACCESS EBOOK

Related Stories:

Access all subscriber-only eBooks:

ACCESS ALL EBOOKS

Ecommerce MGMT 0 Comments

Mar 25 2026

How Foreign Brands Test the U.S. Market

You have a product. You’ve done the research. The U.S. market feels like the obvious next step, but you haven’t launched there yet. You’ve wondered, “What if it doesn’t work?”

That voice is right to ask. Most products fail not because the item is bad, but because of inadequate preparation and misjudged demand.

I’m the founder of OT Growth Labs, a Los Angeles-based agency helping international brands launch and scale in the U.S. Since 2008 I’ve served worldwide in executive ecommerce marketing roles for leading consumer companies.

The U.S. is the world’s largest consumer market. But for brands coming from Europe, Asia, or Latin America, it’s often where products die quietly. Consumers are different, compliance is different, and your domestic playbook won’t travel.

So before spending big money, test the demand in two ways:

Virtual testing measures interest before inventory exists.
Physical testing sells a real product in small quantities.

Virtual Testing

Screenshot of a person looking at a computer screen.

Virtual product demonstrations are low-cost, fast to launch, and require no inventory.

Virtual testing gauges whether consumers want your product — before you make it. It’s ideal for early-stage brands, limited budgets, or high-risk products. It won’t replace physical sales, but it’s a smart first filter.

Start with a landing page.

Explain your product thoroughly and the problem it solves. Disclose packaging, format, ingredients, claims, and label design. Give visitors an action step, such as joining a waitlist, requesting early access, or opting in to receive launch notifications.

Drive traffic through ads, social media, and influencers. It’s an encouraging signal if visitors sign up.

Brands with a platform or app already generating traffic can avoid a separate landing page by upselling to existing users. It saves time and money.

Don’t test a single concept. Run two or three variations and compare results. In my experience, the version that wins in the U.S. is rarely the one that worked at home. U.S. consumers respond to numbers and to bold, specific language: “clinically tested,” “formulated by veterinarians,” “organic.” They want proof up front.

Virtual testing:

Pros. Low cost, fast to launch, no inventory.
Cons. Measures interest only, not product or purchase intent.

Physical Testing

Selling a physical test product offers real data, reviews, and market validation.

The most reliable way to validate demand is to sell a product. Ship a small batch to the U.S. from your current manufacturer, or produce in the U.S. with a minimum run.

The latter option, manufacturing in the U.S., is longer and more expensive, but often worth it in my experience. “Made in the U.S.A.” on the label is frequently a strong selling point.

Physical testing answers questions that a landing page cannot: Does the product perform? Does the packaging hold up? Is the formula good? Is the price right? What do customers say?

Sales will tell you more than months of research, as will reviews, which are critical. An overwhelming percentage of U.S. consumers rely on reviews before buying.

Brands in adjacent categories often use physical testing as a learning loop. They launch a small batch, collect reviews, improve the formula or positioning, and then scale. The final version wins because of findings from the tests.

Physical testing:

Pros: Real sales data, reviews, market validation.
Cons: Expensive and slow. A small batch can take a year from start to shelf. It requires compliance prep, label and design creation, and formula testing. Finding a manufacturer willing to run small batches is a challenge.

Test, Then Scale

Entering the U.S. market is getting harder. Tariffs are rising, and regulations are tightening. Imports valued at less than $800 are no longer exempt from duties — a direct hit on international companies shipping small quantities.

Foreign brands succeed in the U.S. through testing and information-gathering, not just superior products.

Start small, the market will tell you the rest.

Ecommerce MGMT 0 Comments

Mar 24 2026

Google Begins Rolling Out The March 2026 Spam Update via @sejournal, @MattGSouthern

Google started rolling out the March 2026 spam update today, according to the Google Search Status Dashboard.

The update is global and in all languages, with a rollout that may take a few days.

What’s New

The Search Status Dashboard listed the update as an incident affecting ranking at 12:00 PM PT on March 24, with the release note posted at 12:18 PM PDT.

Google’s description reads:

“Released the March 2026 spam update, which applies globally and to all languages. The rollout may take a few days to complete.”

Google hasn’t published a blog post or announced new spam policies with this rollout. So far, it seems to be a standard spam update, not a broader policy change like the March 2024 update, which added categories such as content abuse, expired domain abuse, and site reputation abuse.

How Spam Updates Work

Google describes spam updates as improvements to spam-prevention systems like SpamBrain, targeting sites violating spam policies, which can lead to lower rankings or removal from search results.

Spam updates differ from core updates, which re-assess content quality. Spam updates enforce policies against violations like cloaking, link spam, and content abuse.

Sites affected by a spam update can recover, but recovery takes time. Google states improvements may only appear once automated systems detect compliance over months.

Context

This is Google’s first spam update since the August 2025 spam update, which ran from August 26 to September 22 and took nearly 27 days to complete. That update was characterized by SISTRIX as penalty-only, with affected spammy domains losing visibility but no broad ranking changes.

Google’s estimated timeline of “a few days” for the March 2026 update suggests a shorter rollout than recent spam updates, though timelines can stretch. The December 2024 spam update completed in seven days. The August 2025 update took nearly four weeks.

The March 2026 spam update comes about three weeks after the February Discover update finished rolling out.

Why This Matters

Ranking changes during spam update rollouts can happen quickly. Monitoring Search Console data over the next few days will help distinguish spam-related drops from normal fluctuation.

Google hasn’t announced new spam policy categories with this update, so the existing spam policies remain the relevant framework for evaluating any impact.

Looking Ahead

Google will update the Search Status Dashboard when the rollout is complete. Search Engine Journal will report on the completion and any observed effects.

Featured Image: Hurunaga Yuuka/Shutterstock

Ecommerce MGMT 0 Comments

Mar 24 2026

Google Adds AI & Bot Labels To Forum, Q&A Structured Data via @sejournal, @MattGSouthern

Google updated its Discussion Forum and Q&A Page structured data documentation, adding several new supported properties to both markup types.

The most notable addition is digitalSourceType, a property that lets forum and Q&A sites indicate when content was created by a trained AI model or another automated system.

Content Source Labeling Comes To Forum Markup

The new digitalSourceType property uses IPTC digital source enumeration values to indicate how content was created. Google supports two values:

TrainedAlgorithmicMediaDigitalSource for content created by a trained model, such as an LLM.
AlgorithmicMediaDigitalSource for content created by a simpler algorithmic process, such as an automatic reply bot.

The property is listed as recommended, not required, for both the DiscussionForumPosting and Comment types in the Discussion Forum docs, and for Question, Answer, and Comment types in the Q&A Page docs.

Google already uses similar IPTC source type values in its image metadata documentation to identify how images were created. The update extends that concept to text-based forum and Q&A content.

New Comment Count Property

Google added commentCount as a recommended property across both documentation pages. It lets sites declare the total number of comments on a post or answer, even when not all comments appear in the markup.

The Q&A Page documentation includes a new formula: answerCount + commentCount should equal the total number of replies of any type. This gives Google a clearer picture of thread activity on pages where comments are paginated or truncated.

Expanded Shared Content Support

The Discussion Forum documentation expanded its sharedContent property. Previously, sharedContent accepted a generic CreativeWork type. The updated docs now explicitly list four supported subtypes:

WebPage for shared links.
ImageObject for posts where an image is the primary content.
VideoObject for posts where a video is the primary content.
DiscussionForumPosting or Comment for quoted or reposted content from other threads.

The addition of DiscussionForumPosting and Comment as accepted types is new. Google’s updated documentation includes a code example showing how to mark up a referenced comment with its URL, author, date, and text.

The image property description was also updated across both docs with a note about link preview images. Google now recommends placing link preview images inside the sharedContent field’s attached WebPage rather than in the post’s image field.

Why This Matters

For sites that publish a mix of human and machine-generated content, the digitalSourceType addition provides a structured way to communicate that to Google. The new properties are optional, and no existing implementations will break.

Google has not said how it will use the digitalSourceType data in its ranking or display systems. The documentation only describes it as a way to indicate content origin.

Looking Ahead

The update does not include changes to required properties, so existing forum and Q&A structured data implementations remain valid. Sites that want to adopt the new properties can add them incrementally.

Ecommerce MGMT 0 Comments

Mar 24 2026

The Agency Playbook for Surviving the Agentic AI Era

Search is moving from queries typed into a box to conversations held with systems that understand intent, context, and outcomes. People no longer look for pages. They look for solutions, guidance, and confidence that they are making the right choice.

Agentic AI pushes this shift further. Instead of waiting for instructions, agents act on goals. They discover information, compare options, trigger workflows, and adjust based on feedback. For digital leaders, this means visibility is no longer only a ranking problem. It becomes a problem of influence inside AI systems.

SEO now touches product, data, knowledge management, and experience design. This playbook explains how to prepare for that shift, build capability, and lead change.

Search Is Becoming AI-Mediated

AI systems have become the layer between users and the web. They read content on behalf of users, make selections instead of requiring users to browse, and influence decisions in ways that search pages once did.

This shift changes how people interact with information. Users now ask broader, more complex questions, expecting systems to understand nuance and intent. The traditional act of navigating through links is giving way to direct answers and immediate actions.

Content can no longer be designed solely for human readers. It must also be structured in ways that AI systems can interpret accurately and confidently. In this environment, trust and evidence carry more weight than keywords or search optimization tactics.

Winning in search today means becoming part of the models that shape decisions, not just appearing in the results.

What Agentic AI Means For SEO And Digital

Agentic AI is changing how people discover and choose brands. Discovery now depends on how well models learn from your content, the paths users take on your site, and the external signals that establish credibility. These systems decide when your brand is relevant, based on what they understand and trust.

During evaluation, AI compares your product, price, quality, reviews, and suitability for a given user against other options. It looks for proof, tests claims, and weighs real signals over marketing language.

When supporting decisions, AI doesn’t just provide information. It actively guides users toward what it considers the best fit. Your brand might be brought forward or quietly passed over, depending on how well it matches user needs.

In this landscape, SEO is no longer just about publishing content. It’s about shaping how AI systems perceive your brand and when they choose to recommend it.

New Operating Model For SEO

The future of search brings marketing, product, and data teams into a shared effort. Success depends on how well these areas work together to shape how AI systems perceive and present your brand.

The key is building structured knowledge that AI can easily process and apply. Instead of designing for clicks and views, focus on creating journeys that help users complete tasks through the systems guiding them. It’s also critical to train these systems with the right brand messages, supported by clear evidence and consistent proof points.

Ongoing visibility requires monitoring how models reference your brand, how they rank it, and how they reason about its relevance. This means continuously refining the signals you send, improving your content, updating product data, and reinforcing trust in every interaction.

The goal remains clear and hasn’t really changed from our technical goals for SEO. Make it easy for AI agents to understand, trust, and ultimately recommend your brand.

Maturity Model

Level	Name	Description	Key indicators
0	Manual SEO	Basic optimization and manual workflows	Keyword focus, isolated content execution, minimal data alignment
1	Assisted SEO	AI supports research and content creation	AI‑assisted briefs, content suggestions, faster execution, manual oversight
2	Integrated AI workflows	Core SEO tasks automated and structured	Content pipelines, structured data adoption, automated QA, analytics integration
3	Agent‑driven operations	Agents monitor, trigger, and refine SEO	Automated reporting, performance triggers, self‑adjusting content modules
4	Autonomous acquisition systems	Self‑improving systems tied to revenue	Continuous testing, adaptive journeys, revenue‑linked triggers, real‑time optimization

The goal is not automation alone. It is intelligence and improvement at scale.

Technical And Data Foundations

To prepare for agentic SEO, organizations need more than traditional content systems built for publishing. They need strong foundations that help AI systems understand, evaluate, and act with confidence.

This starts with clarity, which means crafting messaging that is consistent, accurate, and easy for machines to interpret. Structure is also essential, requiring content, data, and signals to be organized in ways that align with how AI systems process and reason through information.

Key components of this are:

Structured data that turns content into machine‑readable knowledge.
Knowledge graphs that explain relationships between products, categories, and needs.
Taxonomy and naming standards to ensure consistency across pages, feeds, and assets.
APIs and automation for publishing and optimization, so agents can trigger updates.
Clean product and service data, including specifications, pricing, and availability.
Evaluation systems to audit AI outputs and detect hallucinations or misalignment.
Identity and trust signals, including reviews, authority, certifications, and product proof.

This calls for a shift from simply building web pages to creating a well-organized information architecture. The goal is to structure information in a way that AI systems can easily navigate, understand, and apply.

In practice, this means bringing together product data, content metadata, and customer intent into a single, connected system. It involves defining the key entities your business represents, such as products or services, and mapping how they relate to what users are trying to accomplish. Content feeds and structured data should reflect the actual state of the business rather than just marketing language.

Equally important is creating feedback loops that show how AI systems interpret and reference your brand. These insights help you see where your content is being used, how it is being understood, and whether it is guiding users toward your brand. With this information, you can keep refining what you share to improve how systems recognize and recommend you.

Instead of asking, “How do we rank for this query?” leaders will ask, “How do systems understand us, trust us, and act on our information?”

KPI And Measurement Model

Traditional key performance indicators still hold value, but they no longer capture the full picture. Rankings and session metrics continue to provide insight, yet they now exist within a broader framework shaped by how AI systems retrieve, interpret, and act on information. Ranking reports will sit alongside AI retrieval dashboards, and session counts will be evaluated alongside metrics focused on task completion and user outcomes.

In my opinion, you should also be looking to monitor:

Share of voice in AI assistants.
Retrieval and inclusion rate in AI answers.
Brand alignment and brand safety in model outputs.
Presence in multi‑step reasoning chains.
Task completion and conversion paths from AI systems.
Cost per automated workflow and cost per agent‑driven action.
Model education, data freshness, and trust scores.

As measurement evolves, the focus moves from tracking visitor numbers to understanding how AI systems shape decisions. To navigate this shift, leaders should design metrics that reflect influence within these systems. Visibility will measure whether the brand is appearing in AI-generated responses and assistant-led interactions.

Accuracy will assess whether the brand is being represented correctly and safely across touchpoints. Trust will reflect whether AI systems choose your content and signals over others when making recommendations. Action will capture whether AI-driven experiences result in tangible outcomes like leads, bookings, or purchases. Efficiency will show whether AI agents are reducing manual effort, improving speed, and delivering better user experiences.

Success will no longer be defined by visibility alone but by a brand’s ability to perform across discovery, decision support, and operational impact.

Talent And Capability Model

Agentic SEO is not a standalone skill set, it draws from a mix of disciplines that span marketing, data, and product. Success in this space requires a collaborative approach, where expertise is integrated rather than siloed.

Future-facing teams bring together SEO and content strategy, data and automation engineering, product and user experience thinking, as well as governance and prompt development. Legal and compliance awareness also play a critical role, ensuring that outputs remain responsible and aligned with brand and regulatory standards.

These teams operate in cross-functional pods, organized around delivering customer outcomes rather than managing individual channels. This structure allows them to move faster, adapt to change, and create more cohesive experiences across AI-driven platforms.

Modern SEO teams include several key roles. The SEO strategist focuses on how AI systems search, retrieve, and rank content. The data engineer manages the integrity of structured content, metadata, and live data feeds. The automation specialist builds the workflows and agents that connect information to user actions. The AI evaluator audits model outputs to ensure accuracy, brand alignment, and safety. The product partner bridges SEO efforts with real user journeys, making sure that discovery leads to meaningful interaction and conversion.

As this approach matures, teams will spend less time producing content manually and more time designing the systems, signals, and experiences that guide AI behavior and improve how users discover and engage with the brand.

The First 90 days

Days 1 To 30: Foundation And Alignment

Audit content, data, and search performance.
Map where AI already touches customer journeys.
Identify gaps in structure, trust signals, and data quality.
Set goals for AI visibility and agent‑driven workflows.

Days 31 To 60: Build And Test Pilots

Launch structured data and knowledge base improvements.
Test AI‑assisted content and QA pipelines.
Introduce early agent monitoring for SEO signals.
Create evaluation benchmarks for AI accuracy and brand safety.

Days 61 To 90: Scale And Govern

Deploy automation in high‑impact workflows.
Formalize model governance and feedback loops.
Train cross‑functional teams on AI‑ready processes.
Build dashboards for AI visibility, trust, and conversion.

Future Outlook

Search will not disappear. It will merge into tasks, journeys, and decisions across devices and interfaces. Brands that train AI systems, structure knowledge, and build agent‑ready operations will lead.

The winners will not be those who automate content. They will be those who help users and systems make better decisions at speed and scale.

More Resources:

Featured Image: Collagery/Shutterstock

Ecommerce MGMT 0 Comments

Mar 24 2026

The Science Of How AI Picks Its Sources via @sejournal, @Kevin_Indig

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

In “The science of how AI pays attention,” I analyzed 1.2 million ChatGPT responses to understand exactly how AI reads a page. This is Part 2.

Where Part 1 told you where on a page AI looks, this one tells you which pages AI routinely considers.

The data clarifies:

Why ~30 domains own 67% of citations in any topic.
The page structure that earns citations across 50+ distinct queries vs. the one that gets cited once.
Whether the ski ramp from Part 1 is actually steeper or flatter in your vertical.

1. ~30 Domains Own 67% Of AI Citations Per Topic

Classic search is a winner-takes-all game. The top result gets disproportionately more clicks than the second. Is that also true for ChatGPT answers? Is the distribution of cited domains democratic or totalitarian?

Approach:

Compute the citation share per domain per vertical.
Calculate the cumulative share captured by the top 10% of domains.
Dataset: 21,482 ChatGPT citation rows, 670 unique domains, 2,344 unique URLs, 127 unique prompts.

Results: The top 10 domains take 46% of all citations in a topic. The top 30 take 67%.

AI citation is slightly less concentrated than traditional organic search, but still extreme:

Effectively, there are ~30 seats (domains) at the citation table for any given topic. Everything else is nearly invisible.
Example: storylane.io appears as a cited source across 102 distinct prompts (unique questions asked of ChatGPT), reprise.com across 98. Even though reprise.com has more total citations (1,369 vs. storylane.io’s 968), storylane.io shows up in answers to a broader range of different questions.

We confirmed these findings in product-comparison verticals (SaaS tools, financial advisors). However, you’ll see below that the pattern is weaker in healthcare and open web topics, where no single domain dominates. Notably, the education sector receives the most AI citations of any vertical we studied.

What The Industry Patterns Showed

The findings above are from product comparison verticals (SaaS, financial advisors), but the pattern is weaker in healthcare and open web topics, where no single domain dominates, and stronger in the education sector.

Education is winner-take-most: the top 10% of domains capture 59.5% of all citations.

If you are not already in the top 5-10 domains in education, achieving citation breadth is exceptionally hard.
tefl.org alone answers 102 unique prompts and holds 18.75% of all Education citations.

Crypto is the second most concentrated at 43.0% for the top 10%.

A small set of technical documentation and comparison sites (alchemy.com, quicknode.com, chainstack.com) dominate Solana RPC and infrastructure queries.
The technical nature of Solana queries means few credible sources exist; once a domain earns trust in this niche, it captures a large share.

Finance sits at 29.4% for top-10%.

Concentration is query-type specific: Financial advisor locator pages (forfiduciary.com at 139 unique prompts, smartasset.com at 168 unique prompts) dominate city-level advisor queries.
But the long tail of financial product queries keeps total concentration moderate.

Healthcare is the least concentrated at 13.0% for the top 10%.

No single domain dominates. New entrants have a realistic path to citation reach.
The citation surface is spread across hundreds of domains, each covering a small slice of telehealth, HIPAA compliance, and healthcare app queries.

CRM/SaaS and HR Tech are similarly diffuse (16.1% and 14.4% top-10%).

These are multi-product software categories where dozens of comparison sites, review platforms, and vendor pages split citations.
Monday.com leads CRM with only 2.88% of all citations (37 unique prompts). A genuinely open competitive field

Top Takeaways

1. Breadth of topic coverage matters more than domain authority. A single well-structured comparison page (learn.g2.com: 65 unique prompts, 495 citations) can still outperform the entire domain portfolio of a well-known brand. The goal is not to rank for one query, but to answer a cluster.

2. Concentration reflects category maturity. Fragmentation is an opportunity. Education and Crypto have narrow, well-defined query spaces where a few authoritative sources have locked in trust. Healthcare and CRM are broad, fragmented categories where no single domain dominates. That fragmentation is your opening.

3. Citation reach (the number of distinct prompts a domain answers) is a more useful strategic metric than raw citation count. In low-concentration verticals like Healthcare and CRM, a focused 30-50 page strategy can realistically compete for a seat at the table. In high-concentration verticals like Education and Crypto, the path is narrower: become the definitive resource on a specific sub-topic or accept that you’re fighting for scraps.

2. The Citation Advantage Starts At 10,000 Words

In classic Search, word count and page length are somewhat indicative of ranks, as long as the quality is high. I wondered, again, if that is also true for showing up in ChatGPT answers?

Approach

Measure raw text length of every cited page.
Group length into seven buckets.
For each bucket, calculate average citations per page.

Results: More words do indeed correlate with more citations, but there’s a ceiling.

The 5,000-to-10,000 jump is the largest single step – nearly 2x. Pages above 20,000 characters average 10.18 citations each vs. 2.39 for pages under 500 characters.

The length effect is vertical-specific: Finance inverts it entirely. High-cited Finance pages average 1,783 words vs. 2,084 for low-cited pages – a 0.86x lift. Authoritative compact sources, rate tables, and regulatory summaries outperform comprehensive guides there. The 10,000-character rule holds for SaaS and editorial content.

Finance peaks at 5,000-10,000 words (10.9 citations/page), then drops sharply at 10,000-20,000 (4.92 citations/page).

Finance also shows the steepest absolute gain: Pages under 500 words earn only 3.84 citations/page while 5,000-10,000 pages earn 10.9, which is a 2.8x multiplier from length optimization alone.
Very long Finance pages may dilute the citation-triggering content with redundant detail.

Education shows the clearest length-wins-everything pattern.

Citations per page climb steadily from 1.85 (under 500 words) to 6.05 (20K+ words) with no drop-off.

Crypto and Product Analytics behave similarly to Education.

Length consistently pays off, plateauing around the 10,000-20,000 tier (5.34 and 4.01, respectively). Both are technical verticals where comprehensiveness signals authority.

SaaS shows the weakest length effect: Citations per page range from 1.06 (1,000-2,000 words) to 2.77 (20,000+ words).

Even the longest CRM pages only get 2.77 citations per page on average.
In this vertical, length alone does not determine citations. Format, structure, and domain authority appear more important.

Healthcare shows a moderate length effect (1.74 to 3.92 citations/page).

But with one anomaly: 5,000-10,000 words (2.80) underperforms vs. 2,000-5,000 words (3.36).
Very long Healthcare pages may include too much clinical detail that dilutes citation-triggering content.

Top Takeaways

1. Universal finding: Very short pages (under 1,000 words) underperform in every vertical. The underperformance of thin content is consistent, but the reward for long content is vertical-specific.

2. Target your length based on industry, content type, and query intent, not a universal word count. For Finance verticals: Aim for 5,000-10,000 words. Education, Crypto, and Product Analytics: Go as long as possible. CRM/SaaS: Prioritize structure over word count.

3. 58% Of Cited URLs Are Cited Once

When we look at the citations within a topic, we often see many pages on a domain getting cited. So, how many citations can a single page get?

Approach

1. Count the number of unique prompts for each page.

Classify number of citations into: 1, 2-5, 6-10, 11+.
Inspect the top URLs per vertical for structural patterns.

Results: On average, 67% of cited URLs appear in only one prompt.

Think of it like a footprint game. Raw citation count tells you how popular a page is. Citation breadth tells you how strategically valuable it is. An evergreen page in AI citation is not one that gets cited a lot; it is one that keeps appearing across diverse queries.

The top 4.8% of URLs (cited 10+) are all category-level comparisons or guides answering “what is it,” “who uses it,” “how to choose,” and “pricing” in a single URL.

The citation pool isn’t a meritocracy of the best answer, but the degree varies sharply.

CRM/SaaS has the highest one-hit rate at 84.7%.
Finance produces the highest-reach evergreen pages: forfiduciary.com covers 119 unique prompts.
Crypto generates the most concentrated evergreen pages at 55.4% in the technical tier: chainstack.com/best-solana-rpc-providers-in-2026 (63 prompts), alchemy.com/overviews/solana-rpc (62 prompts), and rpcfast.com/blog/rpc-node-providers (61 prompts). All three are comparison pages covering the Solana RPC provider landscape from slightly different angles.
Education evergreen pages follow a different logic: tefl.org, internationalteflacademy.com, and gooverseas.com get cited broadly because they answer TEFL-adjacent queries (cost, location, certification type) from a single resource. One URL serves many query angles.

1. Evergreen pages share consistent structural patterns: Category-level guide format (best X for 2025/2026), broad topic coverage within a single page (what is X, how to choose X, top X vendors, pricing), and explicit year anchoring in URL or title. Pages that answer a class of questions earn citation breadth.

2. The top 5 evergreen pages in every vertical are either comparison roundups, authoritative guides, or directory/listing pages. No thin single-topic page reaches the 11+ prompt tier in any vertical.

3. A single evergreen page covering 10+ query intents is worth more in AI citation reach than 10 single-intent pages. The ROI of comprehensive content is front-loaded: one well-built page compounds citation reach over time. The long tail exists, but the top 5% of pages capture a disproportionate share of ongoing citation activity.

4. The Ski Ramp Is Steeper In Some Verticals

The science of how AI pays attention showed that ChatGPT cites 44.2% from the top 30% of any page. Does that trend hold across different verticals?

Approach: Re-run the same positional analysis across 7 verticals with 42,460 matched citations.

Results: The trend is real but varies by topic. One number holds everywhere: The bottom 10% of any page earns 2.4-4.4% of citations, roughly a quarter of what the peak band earns. The conclusion section is nearly invisible to AI, regardless of vertical.

What The Industry Patterns Showed

The true peak decile across all verticals is not the very opening. The 10-20% band is where AI reads hardest in every vertical. The first 10% is typically navigation, headlines, and intro fluff that AI skips.

Finance is the extreme case. 43.7% of citations land in the first 30% of the page. Finance pages front-load rate data, percentages, and key figures. AI grabs them and rarely reads past the halfway point.
Healthcare and HR Tech have the flattest ramps. Useful content is distributed more evenly across those pages.
Education peaks at the 30-40% decile rather than 10-20%, because educational content tends to bury the key answer slightly deeper after the intro.

Top Takeaways

1. Put your most citable claims and data in the first 30% of the page – no matter what industry you’re in. Summaries and conclusions rarely get cited.

2. For Finance brands: Front-load your thesis and statistics as much as possible.

What This Means For How You Build LLM Visibility

The domains that own citation share didn’t get there by writing better sentences. They built pages that hold true topical authority, addressing multiple queries in one place, and then repeated that authority across enough sub-topics to hold multiple seats at the table.

Getting cited across 30, 60, or 100 distinct prompts requires a targeted content architecture: pages built around query clusters and owning entire topics rather than individual keywords. Teams that keep the traditional “one keyword, one page” model will be structurally locked out of AI citation, even if their individual pages are beautifully written.

But as the data shows, there is no universal playbook. The tactics that work for a broad CRM platform could actively harm a Finance brand.

Methodology

We analyzed ~98,000 ChatGPT citation rows pulled from approximately 1.2 million ChatGPT responses from Gauge.

Because AI behaves differently depending on the topic, we isolated the data across 7 distinct, verified verticals to ensure the findings weren’t skewed by one specific industry.

Analyzed verticals:

B2B SaaS
Finance
Healthcare
Education
Crypto
HR Tech
Product Analytics

To reverse-engineer the citation selection, I ran the data through several layers of analysis:

Structural parsing: I measured the raw character length of every cited page and mapped heading hierarchies (H1s, H2s, H3s) to see how information architecture impacts visibility.
Positional mapping: I used Jaccard sliding-window similarity to pinpoint exactly where on the page the AI extracted its answers from, down to the specific decile.
Entity & Sentiment extraction: I ran the opening text of unique cited URLs through the Google Natural Language API to classify named entities (dates, prices, products) and used TextBlob to score sentiment, comparing the performance of corporate content against user-generated content (UGC).

Featured Image: Roman Samborskyi/Shutterstock; Paulo Bobita/Search Engine Journal

Ecommerce MGMT 0 Comments