Better Metrics for AI Search Visibility

The rise of AI-generated search and discovery is pushing merchants to measure their products’ visibility on those platforms. Many search optimizers are attempting to apply traditional metrics such as traffic from genAI and rankings in the answers. Both fall short.

Traffic. Focusing on traffic obscures the purpose of AI answers: to satisfy a need on-site, not to generate clicks.

AI-generated solutions do not typically include links to branded websites. Google’s AI Overviews, for example, sometimes links product names to organic search listings.

Thus visibility does not equate to traffic. A merchant’s products could appear in an AI answer and receive no clicks.

Brand names cited in Google’s AI Overviews often link to organic search listings, such as this example for North Face hiking boots.

Rankings. AI answers often include lists. Many sellers are trying to track those lists to rank at or near the top. Yet tracking such rankings is impossible.

AI answers are unpredictable. A recent study by Sparktoro found that AI platforms recommend different brands and different orders every time the same person asks the same question.

Better AI Metrics

Here are better metrics to measure AI visibility.

Product or brand positioning in LLM training data

Training data is fundamental to AI visibility because large language models default to what they know. Even when they query Google and elsewhere, LLMs often use their training data to guide the search terms.

It’s therefore essential to track what LLMs retain about your brand and competitors and, importantly, what is incorrect or outdated. Then focus on providing missing or corrected data on your site and across all owned channels.

Manual prompting in ChatGPT, Claude, and Gemini (at least) will help identify the gaps. The prompts can be:

• “What do you know about [MY PRODUCT]?”
• “Compare [MY PRODUCT] vs [MY COMPETITOR’S PRODUCT].”

Profound, Peec AI, and other AI visibility trackers can set up these prompts to monitor product positioning over time.

When using such visibility tools, keep in mind:

• AI tracking tools enter prompts via LLMs’ APIs. Humans often see different results due to personalization and differences among AI models. API results are better for checking training data because LLMs likely return results from that data (versus live searches) to save resources.

• The tools’ visibility scores depend entirely on the prompt. In the tools, separate branded prompts in a folder, as they will likely score 100%. Also, focus on non-branded prompts that reflect a product’s value proposition. Prompts irrelevant to an item’s key features will likely score 0%.

Most cited sources

LLM platforms increasingly conduct live searches when responding to prompts. They may query Google or Bing — yes, organic search drives AI visibility — or crawl other sources such as Reddit.

Citations, such as articles or videos, from those live searches influence the AI responses. But the citations vary widely because LLMs fan out across different (often unrelated) queries. So, trying to get included in every cited source is not realistic.

However, prompts often produce the same, influential sources repeatedly. These are worth exploring to include your brand or product. AI visibility trackers can collect the most cited URLs for your brand, product, or industry.

Brand mentions and branded search volume

Use Search Console or other traditional analytics tools to track:

• Queries that contain your brand name or a version of it.
• Number of clicks from those queries.
• Impressions from those queries. The more AI answers include a brand name, the more humans will search for it.

In Search Console, create a filter in the “Performance” section to view data for branded queries.

Create a filter in Search Console’s “Performance” section to view data for branded queries.

What The Latest Web Almanac Report Reveals About Bots, CMS Influence & llms.txt via @sejournal, @theshelleywalsh

The Web Almanac is an annual report that translates the HTTP Archive dataset into practical insight, combining large-scale measurement with expert interpretation from industry experts.

To get insights into what the 2025 report can tell us about what is actually happening in SEO, I spoke with one of the authors of the SEO chapter update, Chris Green, a well-known industry expert with over 15 years of experience.

Chris shared with me some surprises about the adoption of llms.txt files and how CMS systems are shaping SEO far more than we realize. Little-known facts that the data surfaced in the research, and surprising insights that usually would go unnoticed.

You can watch the full interview with Chris on the IMHO recording at the end, or continue reading the article summary.

“I think the data [in the Web Almanac] helped to show me that there’s still a lot broken. The web is really messy. Really messy.”

Bot Management Is No Longer ‘Google, Or Not Google?’

Although bot management has been binary for some time – allow/disallow Google – it’s becoming a new challenge. Something that Eoghan Henn had picked up previously, and Chris found in his research.

We began our conversation by talking about how robots files are now being used to express intent about AI crawler access.

Chris responded to say that, firstly, there is a need to be conscious of the different crawlers, what their intention is, and fundamentally what blocking them might do, i.e., blocking some bots has bigger implications than others.

Second to that, requires the platform providers to actually listen to those rules and treat those files as appropriate. That isn’t always happening, and the ethics around robots and AI crawlers is an area that SEOs need to know about and understand more.

Chris explained that although the Almanac report showed the symptom of robots.txt usage, SEOs need to get ahead and understand how to control the bots.

“It’s not only understanding what the impact of each [bot/crawler] is, but also how to communicate that with the business. If you’ve got a team who want to cut as much bot crawling as possible because they want to save money, that might desperately impact your AI visibility.”

Equally, you might have an editorial team that doesn’t want to get all of their work scraped and regurgitated. So, we, as SEOs, need to understand that dynamic, how to control it technically, but how to put that argument forward in the business as well.” Chris explained.

As more platforms and crawlers are introduced, SEO teams will have to consider all implications, and collaborate with other teams to ensure the right balance of access is applied to the site.

Llms.txt Is Being Applied Despite No Official Platform Adoption 

The first surprising finding of the report was that adoption for the proposed llms.txt standard is around 2% of sites in the dataset.

Llms.txt has been a heated topic in the industry, with many SEOs dismissing the value of the file. Some tools, such as Yoast, have included the standard, but as yet, there has been no demonstration of actual uptake by AI providers.

Chris admitted that 2% was a higher adoption than he expected. But much of that growth appears to be driven by SEO tools that have added llms.txt as a default or optional feature.

Chris is skeptical of its long-term impact. As he explained, Google has repeatedly stated it does not plan to use llms.txt, and without clear commitment from the major AI providers, especially OpenAI, it risks remaining a niche, symbolic gesture rather than a functional standard.

That said, Chris has experienced log-file data suggesting some AI crawlers are already fetching these files, and in limited cases, they may even be referenced as sources. Green views this less as a competitive advantage and more as a potential parity mechanism, something that may help certain sites be understood, but not dramatically elevate them.

“Google has time and again said they don’t plan to use llms.txt which they reiterated in Zurich at Search Central last year. I think, fundamentally, Google doesn’t need it as they do have crawling and rendering nailed. So, I think it hinges on whether OpenAI say they will or won’t use it and I think they have other problems than trying to set up a new standard.”

Different, But Reassuringly The Same Where It Matters

I went on to ask Chris about how SEOs can balance the difference between search engine visibility and machine visibility.

He thinks there is “a significant overlap between what SEO was before we started worrying about this and where we are at the start of 2026.”

Despite this overlap, Chris was clear that if anyone thinks optimizing for search and machines is the same, then they are not aware of the two different systems, the different weightings, the fact that interpretation, retrieval, and generation are completely different.

Although there are different systems and different capabilities in play, he doesn’t think SEO has fundamentally changed. His belief is that SEO and AI optimization are “kind of the same, reassuringly the same in the places that matter, but you will need to approach it differently” because it diverges in how outputs are delivered and consumed.

Chris did say that SEOs will move more towards feeds, feed management, feed optimization.

“Google’s universal commerce protocol where you could potentially transact directly from search results or from a Gemini window obviously changes a lot. It’s just another move to push the website out of the loop. But the information, what we’re actually optimizing still needs to be optimized. It’s just in a different place.”

CMS Platforms Shape The Web More Than SEOs Realize

Perhaps the biggest surprise from Web Almanac 2025 was the scale of influence exerted by CMS platforms and tooling providers.

Chris said that he hadn’t realized just how big that impact is. “Platforms like Shopify, Wix, etc. are shaping the actual state of tech SEO probably more profoundly than I think a lot of people truly give it credit for.”

Chris went on to explain that “as well-intentioned as individual SEOs are, I think our overall impact on the web is minimal outside of CMS platforms providers. I would say if you are really determined to have an impact outside of your specific clients, you need to be nudging WordPress or Wix or Shopify or some of the big software providers within those ecosystems.”

This creates opportunity: Websites that do implement technical standards correctly could achieve significant differentiation when most sites lag behind best practices.

One of the more interesting insights from this conversation was that so much on the web is broken and how little impact we [SEOs] really have.

Chris explained that “a lot of SEOs believe that Google owes us because we maintain the internet for them. We do the dirty work, but I also don’t think we have as much impact perhaps at an industry level as maybe some like to believe. I think the data in the Web Almanac kind of helped show me that there’s still a lot broken. The web is really messy. Really messy.”

AI Agents Won’t Replace SEOs, But They Will Replace Bad Processes

Our conversation concluded with AI agents and automation. Chris started by saying, “Agents are easily misunderstood because we use the term differently.”

He emphasized that agents are not replacements for expertise, but accelerators of process. Most SEO workflows involve repetitive data gathering and pattern recognition, areas well-suited to automation. The value of human expertise lies in designing processes, applying judgment, and contextualizing outputs.

Early-stage agents could automate 60-80% of the work, similar to a highly capable intern. “It’s going to take your knowledge and your expertise to make that applicable to your given context. And I don’t just mean the context of web marketing or the context of ecommerce. I mean the context of the business that you’re specifically working for,” he said.

Chris would argue that a lot of SEOs don’t spend enough time customizing what they do to the client specifically. He thinks there’s an opportunity to build an 80% automated process and then add your real value when your human intervention optimizes the last 20% business logic.

SEOs who engage with agents, refine workflows, and evolve alongside automation are far more likely to remain indispensable than those who resist change altogether.

However, when experimenting with automation, Chris warned we should avoid automating broken processes.

“You need to understand the process that you’re trying to optimize. If the process isn’t very good, you’ve just created a machine to produce mediocrity at scale, which frankly doesn’t help anyone.”

Chris thinks that this will give SEOs an edge as AI is more widely adopted. “I suggest the people that engage with it and make those processes better and show how they can be continually evolved, they’ll be the ones that have greater longevity.”

SEOs Can Succeed By Engaging With The Complexity

The Web Almanac 2025 doesn’t suggest that SEO is being replaced, but it does show that its role is expanding in ways many teams haven’t fully adapted to yet. Core principles like crawlability and technical hygiene still matter, but they now exist within a more complex ecosystem shaped by AI crawlers, feeds, closed systems, and platform-level decisions.

Where technical standards are poorly implemented at scale, those who understand the systems that shape them can still gain a meaningful advantage.

Automation works best when it accelerates well-designed processes and fails when it simply scales inefficiency. SEOs who focus on process design, judgment, and business context will remain essential as automation becomes more common.

In an increasingly messy and machine-driven web, the SEOs who succeed will be those willing to engage with that complexity rather than ignore it.

SEO in 2026 isn’t about choosing between search and AI; it’s about understanding how multiple systems consume content and where optimization now happens.

Watch the full video interview with Chris Green here:

Thank you to Chris Green for offering his insights and being my guest on IMHO.

More Resources: 

Featured Image: Shelley Walsh/Search Engine Journal

Information Retrieval Part 1: Disambiguation

TL;DR

1. Disambiguation is the process of resolving ambiguity and uncertainty in data. It’s crucial in modern-day SEO and information retrieval.
2. Search engines and LLMs reward content that is easy to “understand,” not content that is necessarily best.
3. The clearer and better structured your content, the harder it is to replace.
4. You have to reinforce how your brand and products are understood. When grounding is required, models favor sources they recognize from training data

The internet has changed. Channels have begun to homogenize. Google is trying to become something of a destination, and the individual content creator is more powerful than ever.

Oh, and we don’t need to click on anything.

But what makes for great content hasn’t changed. AI and LLMs haven’t changed what people want to consume. They’ve changed what we need to click on. Which I don’t necessarily hate.

As long as you’ve been creating well-structured, engaging, educational/entertaining content for years. All this chat of chunking is a bit smoke and mirrors for me.

However, it is absolutely not all rubbish. Concepts like ambiguity are a more destructive force than ever. If you permit a quick double negative, you cannot not be clear.

The clearer you are. The more concise. The more structured on and off-page. The better chance you stand. There’s no place for ambiguous phrases, paragraphs, and definitions.

This is known as disambiguation.

What Is Disambigation?

Disambiguation is the process of resolving ambiguity and uncertainty in data. Ambiguity is a problem in the modern-day internet. The deeper down the rabbit hole we go, the less diligence is paid towards accuracy and truth. The more clarity your surrounding context provides, the better.

It is a critical component of modern-day SEO, AI, natural language processing (NLP), and information retrieval.

This is an obvious and overused example, but consider a term like apple. The intent and understanding behind it are vague. We don’t know whether people mean the company, the fruit, the daughter of a batshit, brain-dead celebrity.

Years ago, this type of ambiguous search would’ve yielded a more diverse set of results. But thanks to personalization and trillions of stored interactions, Google knows what we all want. Scaled user engagement signals and an improved understanding of intent and keywords, phrases, and context are fundamental here.

Yes, I could’ve thought of a better example, but I couldn’t be bothered. You see my point.

Why Should I Care?

Modern-day information retrieval requires clarity. The context you provide really matters when it comes to a confidence score systems require when pulling the “correct” answer.

And this context is not just present in the content.

There is a significant debate about the value of structured data in modern-day search and information retrieval. Using structured data like sameAs to signify exactly who this author is and tying all of your company’s social accounts and sub-brands together can only be a good thing.

The argument isn’t that this has no value. It makes sense.

• It’s whether Google needs it for accurate information parsing anymore.
• And whether it has value to LLMs outside of well-structured HTML.

Ambiguity and information retrieval have become incredibly hot topics in data science. Vectorization – representing documents and queries as vectors – helps machines understand the relationships between terms.

It allows models to effectively predict what words should be present in the surrounding context. It’s why answering the most relevant questions and predicting user intent and ‘what’s next’ has been so valuable for a long time in search.

See Google’s Word2Vec for more information.

Google Has Been Doing This For A Long Time

Do you remember what Google’s early, and official, mission statement regarding information was?

Their former motto was “don’t be evil.” Which I think in more recent times they may have let slide somewhat. Or conveniently hidden it.

Organizing the world’s information has become so much more effective thanks to advances in information retrieval. Originally, Google thrived on straightforward keyword matching. Then they moved to tokenization.

Their ability to break sentences into words and match short-tail queries was revolutionary. But as queries advanced and intent became less obvious, they had to evolve.

The advent of Google’s Knowledge Graph was transformational. A database of entities that helped create consistency. It created stability and improved accuracy in an ever-changing web.

Now queries are rewritten at scale. Ranking is probabilistic instead of deterministic, and in some cases, fan-out processes are applied to create an all-encompassing answer. It’s about matching the user’s intent at the time. It’s personalized. Contextual signals are applied to give the individual the best result for them.

Which means we lose predictability depending on temperature settings, context, and inference path. There’s a lot more passage-level retrieval going on.

Thanks to Dan Petrovic, we know that Google doesn’t use your full page content when grounding its Gemini-powered AI systems. Each query has a fixed grounding budget of approximately 2,000 words total, distributed across sources by relevance rank.

The higher you rank in search, the more budget you are allotted. Think of this context window limit like crawl budget. Larger windows enable longer interactions, but cause performance degradation. So they have to strike a balance.

Hummingbird, BERT, RankBrain – Foundational Semantic Understanding

These older algorithm shifts were pivotal in making Google’s systems treat language and meaning differently.

• Hummingbird (2013) helped Google identify entities and things quickly, with greater precision. This was a step toward semantic interpretation and entity recognition. Think of keywords at a page level. Not query level.
• RankBrain (2015): To combat the ever-increasing and never-before-seen queries, Google introduced machine learning to interpret unknown queries and relate them to known concepts and entities.

RankBrain was built on the success of Hummingbird’s semantic search. By mastering NLP systems, Google began mapping words to mathematical patterns (vectorization) to better serve new and ever-evolving queries.

These vectors help Google ‘guess’ the intent of queries it has never seen before by finding their nearest mathematical neighbors.

The Knowledge Graph Updates

In July 2023, Google rolled out a major Knowledge Graph update. I think people in SEO called it the Killer Whale Update, but I can’t remember who coined the phrase. Or why. Apologies. It was designed to accelerate the growth of the graph and reduce its dependence on third-party sources like Wikipedia.

As somebody who has spent a long time messing around with entities, I can really understand why. It’s a giant, expensive time-suck.

It explicitly expanded and restructured how entities are recognized and classified in the Knowledge Graph. Particularly, person entities with clear roles such as author or writer.

• The number of entities in the Knowledge Vault increased by 7.23% in one day to over 54 billion.
• In July 2023, the number of Person entities tripled in just four days.

All of this is an effort to combat AI slop, provide clarity, and minimize misinformation. To reduce ambiguity and to serve content where a living, breathing expert is at the heart of it.

Worth checking whether you have a presence in the Knowledge Graph here. If you do and can claim a Knowledge Panel, do it. Cement your presence. If not, build your brand and connectedness on the internet.

What About LLMs & AI Search?

There are two main ways LLMs retrieve information:

• By accessing their vast, static training data.
• Using RAG (a type of grounding) to access external, up-to-date sources of information.

RAG is why traditional Google Search is still so important. The latest models no longer train on real-time data and lag a little behind. Before the primary model dives in to respond to your desperate need for companionship, a classifier determines whether real-time information retrieval is necessary.

They cannot know everything and have to employ RAG to make up for their lack of up-to-date information (or verifiable facts through their training data) when retrieving certain answers. Essentially trying to make sure they aren’t chatting rubbish.

Hallucinating if you’re feeling fancy.

So, each model needs its own form of disambiguation. Primarily, this is achieved via:

• Context-aware query matching. Seeing words as tokens and even reformatting queries into more structured formats to try and achieve the most accurate result. This type of query transformation leads to fan-out and embeddings for more complex queries.
• RAG architectures. Accessing external knowledge when an accuracy threshold isn’t reached.
• Conversational agents. LLMs can be prompted to decide whether to directly answer a query or to ask the user for clarification if they don’t meet the same confidence threshold.

Remember, if your content isn’t accessible to search retrieval systems it can’t be used as part of a grounding response. There’s no separation here.

What Should You Do About It?

If you have wanted to do well in search over the last decade, this should’ve been a core part of your thinking. Helpful content rewards clarity.

Allegedly. It also rewards nerfing smaller sites out of existence.

Doesn’t mean you can’t be both. Great content entertains, educates, inspires, and enhances.

Use Your Words

You need to learn how to write. Short, snappy sentences. Help people and machines connect the dots. If you understand the topic, you should know what people want or need to read next almost better than they do.

• Use verifiable claims.
• Cite your sources.
• Showcase your expertise through your understanding.
• Stand out. Be different. Add information to the corpus to force a mention and/or citation.

Structure The Page Effectively

Write in clear, straightforward paragraphs with a logical heading structure. You really don’t have to call it chunking if you don’t want to. Just make it easy for people and machines to consume your content.

• Answer the question. Answer it early.
• Use summaries or hooks.
• Tables of contents.
• Tables, lists, and actual structured data. Not schema. But also schema.

Make it easy for users to see what they’re getting and whether this page is right for them.

Intent

Lots of intent is static. Commercial queries always demand some level of comparison. Transactional queries demand some kind of buying or sales process.

But intent changes and millions of new queries crop up every day.

So, you need to monitor the intent of a term or phrase. News is probably a perfect example. Stories break. Develop. What was true yesterday may not be true today. The courts of public opinion damn and praise in equal measure.

Google monitors the consensus. Tracks changes to documents. Monitors authority and – crucially here – relevance.

You can use something like Also Asked to monitor intent changes over time.

The Technical Layer

For years, structured data has helped resolve ambiguity. But we don’t have real clarity over its impact on AI search. Cleaner, well-structured pages are always easier to parse, and entity recognition really matters.

• sameAs properties connect the dots with your brand and social accounts.
• It helps you explicitly state who your author is and, crucially, isn’t.
• Internal linking helps bots navigate across connected sections of your website and build some form of topical authority.
• Keep content up to date, with consistent date framing – on page, structured data, and sitemaps

If you like messing around with the Knowledge Graph (who the hell doesn’t?), you can find confidence scores for your brand.

According to Google’s very own guidelines, structured data provides explicit clues about a page’s content, helping search engines understand it better.

Yes, yes, it displays rich results etc. But it removes ambiguity.

Entity Matching

I think this ties everything together. Your brand, your products, your authors, your social accounts.

What you say about your brand matters now more than ever.

• The company you keep (the phrases on a page).
• The linked accounts.
• The events you speak at.
• Your about us page(s).

All of it helps machines build up a clear picture of who you are. If you have strong social profiles, you want to make sure you’re leveraging that trust.

At a page level, title consistency, using relevant entities in your opening paragraph, linking to relevant tags and articles page, and using a rich, relevant author bio is a great start.

Really, just good, solid SEO. Don’t @ me.

PSA: Don’t be boring. You won’t survive.

More Resources:

This post was originally published on Leadership in SEO.

Featured Image: Roman Samborskyi/Shutterstock

What If User Satisfaction Is The Most Important Factor In SEO? via @sejournal, @marie_haynes

Let me see if I can convince you!

I’ve shared a bunch in this video and summarized my thoughts in the article below. Also, this is the second blog post I’ve written on this topic in the last week. There is much more information on user data and how Google uses it in my previous blog post.

Ranking Has 3 Components

We learned in the DOJ vs Google trial that Google’s ranking process involves three main components:

1. Traditional systems are used for initial ranking.
2. AI Systems (such as RankBrain, DeepRank, and RankEmbed BERT) re-rank the top 20-30 documents.
3. Those systems are fine-tuned by Quality Rater scores, and more importantly IMO, results from live user tests.

The DOJ vs. Google lawsuit talked extensively about how Google’s massive advantage stems from the large amounts of user data it uses. In its appeal, Google said that it does not want to comply with the judge’s mandate to hand over user data to competitors. It listed two ways it uses user data – in a system called Glue, a system which incorporates Navboost that looks at what users click on and engage with, and also in the RankEmbed model.

RankEmbed is fascinating. It embeds the user’s query into a vector space. Content that is likely to be relevant to that query will be found nearby. RankEmbed is fine-tuned by two things:

1. Ratings from the Quality Raters. They are given two sets of results – “Frozen” Google results and “Retrained” results – or, in other words, the results of the newly trained and refined AI-driven search algorithms. Their scores help Google’s systems understand whether the retrained algorithms are producing higher-quality search results.

2. Real-world live experiments where a small percentage of real searchers are shown results from the old vs. retrained algorithms. Their clicks and actions help fine-tune the system.

The ultimate goal of these systems is to continually improve on producing rankings that satisfy the searcher.

More Thinking On Live Tests – Users Tell Google The Types Of Pages That Are Helpful, Not The Actual Pages

I’ve realized that Google’s live user tests aren’t just about gathering data on specific pages. They are about training the system to recognize patterns. Google isn’t necessarily tracking every single user interaction to rank that one specific URL. Instead, it is using that data to teach its AI what “helpful” looks like. The system learns to identify the types of content that satisfy user intent, then predicts whether your site fits that successful mold.

It will continue to evolve its process in predicting which content is likely to be helpful. It definitely extends far beyond simple vector search. Google is continually finding new ways to understand user intent and how to meet it.

What This Means For SEO

If you’re ranking in the top few pages of search, you have convinced the traditional ranking systems to put you in the ranking auction.

Once there, a multitude of AI systems work to predict which of the top results truly is the best for the searcher. This is even more important now that Google is starting to use “Personal Intelligence” in Gemini and AI Mode. My top search results will be tailored specifically for what Google’s systems think I will find helpful.

Once you start understanding how AI systems do search, which is primarily vector search, it can be tempting to work to reverse engineer these. If you’re optimizing by using a deep understanding of what vector search rewards (including using cosine similarity), you’re working to look good to the AI systems. I’d caution against diving in too deeply here.

Given that the systems are fine-tuned to continually improve upon producing results that are the most satisfying for the searcher, looking good to AI is nowhere near as important as truly being the result that is the most helpful. I would argue that optimizing for vector search can do more harm than good unless you truly do have the type of content that users go on to find more helpful than the other options they have. Otherwise, there’s a good chance you’re training the AI systems to not favor you.

My Advice

My advice is to optimize loosely for vector search. What I mean by this is to not obsess over keywords and cosine similarity, but instead to understand what it is your audience wants and be sure that your pages meet the specific needs they have. Is using a knowledge of Google’s Query Fan-Out helpful here? To some degree, yes, as it is helpful to know what questions users generally tend to have surrounding a query. But, I think that my same fears apply here as well. If you look really good to the AI systems trying to find content to satisfy the query fan-out, but users don’t tend to agree, or if you’re lacking other characteristics associated with helpfulness compared to competitors, you might train Google’s systems to favor you less.

Make use of headings – not for the AI systems to see, but to help your readers understand that the things they are looking for are on your page.

Look at the pages that Google is ranking for queries that should lead to your page, and truly ask yourself what it is about these pages that searchers are finding helpful. Look at how well they answer specific questions, whether they use good imagery, tables, or other graphics, and how easy it is for the page to be skimmed and navigated. Work to figure out why this page was chosen as among the most likely to be helpful in satisfying the needs of searchers.

Instead of obsessing over keywords, work to improve the actual user experience. If you make your page more engaging, focusing more on metrics like scrolls and session duration, rankings should naturally improve.

And mostly, obsess over helpfulness. It can be helpful to have an external party look at your content and share why it may or may not be helpful.

I have found that even though I have this understanding that search is built to continually learn and improve upon showing searchers pages they are likely to find helpful, I still find myself fighting the urge to optimize for machines rather than users. It is a hard habit to break! Given that Google’s deep learning systems are working tirelessly on one goal – predicting which pages are likely to be helpful to the searcher – that should be our goal as well. As Google’s documentation on helpful content suggests, the type of content that people tend to find helpful is content that is original, insightful, and provides substantial value when compared to other pages in the search results.

More Resources:

This post was originally published on Marie Haynes Consulting.

Featured Image: Chayanit/Shutterstock

New Microsoft Retail AI Guide Echoes SEO

Microsoft published a playbook early this month to help retailers increase visibility in AI search, browsers, and assistants.

“A guide to AEO and GEO” (PDF) from the heads of Microsoft Shopping and Copilot, and Microsoft Advertising, includes and confirms actionable tips worth the read.

Microsoft’s new guide aims to help retailers increase AI visibility.

GEO vs. AEO

The rise of AI platforms has created a proliferation of ill-defined acronyms. The guide attempts to clarify two of them:

• GEO. Generative engine optimization. “Optimizes content for generative AI search environments (like LLM-powered engines) to make it discoverable, trustworthy, and authoritative.”

• AEO. Answer/Agentic Engine Optimization. “Optimizes content for AI agents and assistants (like Copilot or ChatGPT) so they can find, understand, and present answers effectively.”

Intent-driven product data

To me, the most useful part of the guide reinforces my article on optimizing product feeds for AI. Product feeds and on-page descriptions should clearly address use cases, such as shoes “best for day hikes above 40 degrees.”

The guide also recommends:

• Product page titles that are detailed and descriptive,
• Front-loading product descriptions with benefits: who it’s for, the problem it solves, and how it’s better,
•  Q&As,
• Comparison tables,
• Detailed alt text for product images,
• Complementary products that match the intent,
• Transcripts for videos.

Social proof

The guide emphasizes the importance of factual entities such as verified customer reviews, certifications, sustainability badges, and partnerships. It warns against using exaggerated or unverifiable claims, stating, “AI systems penalize low-trust language.”

It advises applying social proof consistently across your site and all channels, and verifying any subjective claims about your business or product. For example, if you assert a product is the best in a category, include why, such as “according to [XYZ’s] tests.”

Structured data

Per the guide, structured data markup, such as Schema.org, is key for AI visibility.

However, I’ve seen no evidence to support that recommendation. The guide does not explain how LLMs use Schema. To my knowledge, AI training data does not store Schema markup, and AI bots crawl text-only content.

Yet for live searches, Schema may be helpful because traditional search engines support it, and LLMs rely on those platforms.

Nonetheless, the guide recommends:

• Schema Types: Product, Offer, AggregateRating, Review, Brand, ItemList, and FAQ.

• Dynamic fields: price, availability, color, size, SKU, GTIN, and dateModified.

• ItemList markup for collections and category pages to clarify product groupings.

While helpful, Microsoft’s “A guide to AEO and GEO” doesn’t introduce anything new. The recommendations align with longstanding SEO tactics and reinforce the views of industry pros.