The 19 Ranking Surfaces Every Brand Should Optimize For

Search did not get replaced. It multiplied.

Here is the short version, because you deserve the answer before the argument. Search did not die when ChatGPT arrived. It did not shrink when TikTok became a search engine for people under 30. It multiplied. The single blue-links page you spent a decade optimizing is now one surface among nineteen, and your customers are moving fluidly between all of them without telling you.

I have spent 13 years building and scaling commerce across Shopify Plus, WordPress, Webflow, WooCommerce, Wix, and Magento, in seats ranging from CMO to Creative Director to VP of Sales to Founder and Chief Technologist. In every one of those seats the pattern repeated. A brand would pour money into Google rankings, hit page one, and still watch qualified demand slip away. Not because the SEO failed. Because the customer had already asked ChatGPT, watched a YouTube review, checked Amazon, said a question to a speaker in their kitchen, and tapped a Pinterest image, all before they ever typed a query into Google.

That fragmentation is why I built the framework I call Search Everywhere Optimization. The name is literal. Discovery now happens everywhere, so optimization has to happen everywhere too. Most teams hear that and panic, because it sounds like nineteen separate jobs, nineteen budgets, nineteen specialists. It is not. That is the whole point of the framework and the reason it works.

The insight underneath Search Everywhere Optimization is that these nineteen surfaces are not nineteen unrelated systems. They are nineteen consumers of the same underlying thing: a clear, structured, trustworthy description of who you are, what you sell, and why you can be believed. Google reads it. So does an AI model summarizing your category. So does a voice assistant, a shopping feed, a knowledge panel, a marketplace algorithm, and a Reddit thread that an LLM later cites. When you get that core body of truth right, you do not optimize nineteen times. You publish once, in a form every surface can parse, and you show up in many places at once.

I call that core asset your structured truth. It is the combination of your on-page content, your schema markup, your product data, your entity definitions, your reviews, and your identity signals, all saying the same accurate thing in machine-readable form. A brand with strong structured truth is legible to every engine that matters. A brand without it is a rumor that each system half-remembers differently.

Consider what a single well-built product page can do. With correct Product and Offer schema, it feeds Google Shopping and merchant listings. With a natural-question FAQ block and clean headings, it earns citations in AI Overviews and answer engines. With Speakable markup and conversational phrasing, it answers voice queries. With proper ImageObject data and descriptive alt text, it surfaces in visual search. With a linked organization entity, it strengthens your knowledge panel. One page. Six or seven surfaces. That is reach you build once and keep.

The mistake I see constantly is treating this as a channel problem. Teams say we need an SEO person and a social person and maybe an AI person, and they staff three silos that never share a source of truth. Then each silo publishes its own slightly different version of the brand, and the engines get confused, and the brand shows up weakly everywhere instead of strongly somewhere. Search Everywhere Optimization inverts that. Build the truth once. Structure it so machines can read it. Then distribute it to the surfaces where your audience actually is.

This article is the map. I will lay out all nineteen surfaces, group them into six clusters so you can reason about them without losing your mind, give you concrete tactics for each, hand you a 90-day roadmap, and show you what it looked like when I ran this playbook in production. By the end you will not think of search as a channel. You will think of it as a portfolio, and you will know how to manage it.

Why this matters now

The behavior changed first, and the strategy is still catching up. That gap is your opportunity, and it is closing.

Start with the AI answer shift. When someone asks a question and gets a synthesized answer at the top of the results, a large share of those searches now end without a single click to a website. The answer was enough. That is not a rounding error, it is a structural change in how attention flows. If your content is not the source the answer was built from, you were not just ranked lower. You were absent from the moment the decision got made. The user never saw your brand at all.

Now layer in the other shifts happening at the same time. Younger buyers open TikTok or Instagram and search there first, treating them as discovery engines rather than entertainment feeds. Voice assistants field questions in kitchens and cars where no screen is involved and only one answer gets read aloud. Shoppers begin product research inside Amazon, Walmart, or Etsy, skipping the open web entirely. People point their phone camera at an object and search by image. They ask an assistant to compare two products and it returns a verdict, not a list. Every one of these is a discovery surface, and every one of them has its own ranking logic.

Here is the uncomfortable truth that gets executives to pay attention. A brand can hold its Google rankings perfectly and still vanish. I have watched it happen. Traffic from classic search stays flat on the dashboard, everyone assumes discovery is healthy, and meanwhile the brand is invisible in AI Overviews, absent from voice results, missing from marketplace search, and never mentioned in the Reddit and YouTube content that AI models increasingly cite. The dashboard says fine. The business says declining. Because the dashboard was measuring one surface out of nineteen.

This is why I insist that discovery is a portfolio, not a channel. A portfolio is something you diversify on purpose. You do not put your entire retirement into one stock because it did well last decade, and you should not put your entire discovery strategy into one surface because it did well last decade. The engines are not correlated. Google can be strong while your AI presence is zero. Your marketplace rank can be excellent while your knowledge panel is wrong. Managing discovery as a portfolio means you know your exposure to each surface, you know which ones matter most for your business, and you invest across them deliberately instead of over-indexing on the one you happen to understand.

The timing argument is simple. The behavioral shift already happened. Your customers are already discovering brands across all nineteen surfaces. What has not happened yet, in most companies, is the strategic response. The org chart still has an SEO person and a social person and nobody who owns the whole portfolio. The measurement still tracks organic sessions and calls it a day. The content still gets written for one surface and never structured for the rest.

That lag is precisely why acting now pays disproportionately. Competitors are still arguing over AI search being real. Meanwhile the surfaces that reward structured truth are relatively uncrowded, because most brands have not shown up yet. Being early to a marketplace's search algorithm, to answer-engine citations, to voice, to a well-built knowledge entity, is like being early to Google in 2004. The cost of entry is low and the durability is high, because once you are the cited source, you tend to stay the cited source.

I am not telling you to abandon Google. Classic search still drives enormous volume and still anchors the whole system. I am telling you that treating Google as the entire game is a bet that stopped being safe. The brands that win the next five years will be the ones that saw discovery fragment and responded by building for all nineteen surfaces at once, rather than defending the one hill they already held while the customers walked to the other eighteen.

The map: 19 surfaces in six clusters

Nineteen surfaces is a lot to hold in your head. So I do not ask you to. I group them into six clusters, and each cluster shares an audience intent and a ranking logic, which means the tactics inside a cluster reinforce each other. Learn the clusters and the whole system becomes manageable.

Here are the six, with the surfaces in each.

• Cluster 1, Classic search: SEO (Search Engine Optimization), CWV (Core Web Vitals), and E-E-A-T (Experience, Expertise, Authoritativeness, Trust). This is the foundation. Everything else stands on it.
• Cluster 2, AI answer engines: AEO (Answer Engine Optimization), GEO (Generative Engine Optimization), and AAO (Agent Action Optimization). This is where synthesized answers and autonomous agents decide who gets cited and who gets acted on.
• Cluster 3, Voice and visual: VSO (Voice Search Optimization) and VxSO (Visual Search Optimization). Screenless and camera-first discovery.
• Cluster 4, Commerce: MSO (Marketplace Optimization), PFO (Product Feed Optimization), and ASO (App Store Optimization). Where buying intent lives.
• Cluster 5, Social and community: SSO (Social Search Optimization), VDO (Video Search Optimization), FEO (Feed/Discover Optimization), and CSO (Community Search Optimization). Discovery inside social platforms, video, algorithmic feeds, and forums.
• Cluster 6, Local, global, and identity: LSO (Local Search Optimization), GLOBO (Global/Locale Optimization), KGO (Knowledge Graph Optimization), and Web3 (Decentralized Identity). Where you are, who you are, and how machines verify it.

A full side-by-side comparison table accompanies this article, mapping each surface to its primary engine, the signals it reads, and the first move to make. Use that table as your reference sheet. Here I want to explain why the cluster structure matters more than any individual surface.

The reason is shared work. Because these surfaces consume the same structured truth, the effort you spend on one cluster often pays off in another. Write a genuinely useful, well-structured article and you serve SEO in Cluster 1, AEO and GEO in Cluster 2, VSO in Cluster 3, and CSO in Cluster 5 all at once, because a clear answer with clean markup is exactly what each of those engines rewards. Build correct product data and you serve PFO and MSO in Cluster 4, VxSO in Cluster 3, and Shopping surfaces that feed AI shopping answers in Cluster 2. Define your organization as a clean entity and you serve KGO in Cluster 6, E-E-A-T in Cluster 1, and the citation logic of every AI model that checks who you are before quoting you.

That overlap is the entire economic case for Search Everywhere Optimization. If each surface required unique, non-transferable work, nineteen surfaces would be nineteen times the cost and no sane team would attempt it. But they do not. The core assets, structured content, schema markup, clean product feeds, and a linked identity, are reused across surfaces. You are not doing nineteen jobs. You are doing perhaps five foundational bodies of work and pointing them at nineteen destinations.

So how should you think about priority? Not every cluster matters equally to every business. A local service company lives and dies on Cluster 6, specifically LSO, and can treat Cluster 4 as optional. A consumer product brand must win Cluster 4 and Cluster 5 or it will lose to competitors who do. A B2B software company should obsess over Clusters 1 and 2, because that is where its buyers research. The map does not tell you to do everything equally. It tells you the full terrain so you can choose your terrain on purpose.

Read the clusters in order in the sections that follow, because I have sequenced them from foundation to frontier. Classic search first, since it underpins everything. Then AI answer engines, because that is the fastest-moving front. Then voice and visual, commerce, social and community, and finally local, global, and identity, which is where you cement who you are in the eyes of every machine. By the end you will have a tactic for all nineteen and a way to decide which ones to fund first.

Cluster 1: Classic search (SEO, Core Web Vitals, E-E-A-T)

Classic search is the foundation, and I mean that structurally, not sentimentally. Every other surface reads signals that originate here. If your on-page content is thin, your site is slow, and your authority is unproven, you will underperform everywhere, because the AI models, voice assistants, and knowledge systems are all sampling the same web your SEO lives on. Fix this cluster first or the rest leaks.

SEO ranks on three things that have not changed as much as people claim: relevance, authority, and technical accessibility. Relevance means the page actually answers the query and covers the topic completely, not just densely. Authority means credible sites link to you and credible entities associate with you. Technical accessibility means Google can crawl, render, and index the page without friction. The tactics that move these are unglamorous and they work.

• Build topic clusters, not orphan posts. Pick a pillar topic, write the definitive page on it, then write supporting pages on every subtopic and link them tightly. Internal links tell Google which page is the authority on what. This is the single most valuable structural move most sites are still not making.
• Match search intent precisely. Before writing, look at what already ranks for the query. If the top results are comparison tables, a personal essay will not rank no matter how good it is. Give the engine the format it has decided the query deserves, then out-execute on depth.
• Write for completeness. The pages that hold rankings answer the main question and the follow-up questions a real person would ask next. Coverage of the full question space beats keyword repetition every time.
• Fix technical basics: clean URL structure, logical headings, XML sitemaps, no crawl traps, no orphaned pages, fast server responses, and mobile-first rendering that actually works.

Core Web Vitals is the speed and stability layer, and it is a ranking signal precisely because it correlates with how long humans stay. The three metrics to know: Largest Contentful Paint, which measures how fast the main content loads and should land under about 2.5 seconds. Interaction to Next Paint, which measures responsiveness to taps and clicks. Cumulative Layout Shift, which measures how much the page jumps around while loading, where lower is better and layout that jumps as ads and images pop in is the usual culprit.

Tactically, CWV is won by compressing and correctly sizing images, serving modern formats, deferring non-critical JavaScript, reserving space for images and embeds so nothing shifts, and using a CDN. I have taken sites from failing CWV to passing and watched both rankings and conversion improve, because the same speed that pleases the algorithm pleases the buyer. Nobody converts on a page that stutters.

E-E-A-T is the trust layer, and it is where most brands leave the most on the table. It stands for Experience, Expertise, Authoritativeness, and Trust, and Google uses it to decide if you are credible, especially on topics that affect health, money, or safety. The first E, Experience, is the newest and the most actionable. It rewards content that demonstrates the author actually did the thing, not just researched it.

• Put real authors on your content, with real bios, real credentials, and links to their other work and profiles. Anonymous content underperforms on trust-sensitive topics.
• Show first-hand experience explicitly. Original photos, specific numbers, tests you actually ran, mistakes you actually made. Generic content that could have been written by anyone signals nothing.
• Earn authority through citations and links from respected sources in your field, and through being mentioned alongside recognized entities. Authority is conferred, not claimed.
• Build trust signals into the site itself: clear contact information, transparent policies, secure connections, accurate business details, and genuine reviews that you do not censor.

Here is why this cluster is the foundation for everything downstream. When an AI model decides which sources to cite, it favors content that reads as expert, experienced, and trustworthy, because that is what its training and its ranking signals reward. When a voice assistant picks the one answer to read aloud, it picks from authoritative sources. When a knowledge system builds your entity, it draws on the same authority signals. Win Cluster 1 and you have not just won Google. You have built the credibility layer that every other surface checks before it trusts you.

Cluster 2: AI answer engines (AEO, GEO, AAO)

This is the cluster everyone is anxious about and almost nobody is systematically optimizing for. That combination is exactly why it rewards effort right now. Three surfaces live here, and they represent a progression: getting answered, getting cited, and getting acted on.

AEO, Answer Engine Optimization, is about being the source that direct-answer systems pull from. Think Google's AI Overviews, featured snippets, and the answer boxes that resolve a question without a click. The engine is extracting a concise, confident answer from somewhere, and your job is to be the somewhere. The pattern that wins is answer-first structure. State the answer plainly in the first sentence or two under a heading that matches the question, then expand with the detail. Engines lift the clean, self-contained answer near the top far more readily than a conclusion buried in paragraph nine.

• Format questions as headings, phrased the way people ask them, and answer immediately below in 40 to 60 words before elaborating.
• Use lists and tables for anything comparative or sequential. Answer engines parse structured formats more reliably than prose and reproduce them more readily.
• Add FAQPage and, where relevant, HowTo schema so the question-answer relationship is explicit in machine-readable form, not just implied by layout.
• Keep answers factually tight and self-contained, because an extracted snippet has to make sense with no surrounding context.

GEO, Generative Engine Optimization, is the larger and stranger game. This is about getting cited and represented inside the answers that ChatGPT, Gemini, Claude, Perplexity, and similar systems generate. These models do not just rank pages, they synthesize. They read across many sources and produce a blended answer, and the brands that get named in that answer win a kind of visibility that did not exist five years ago. The mechanics are different from classic SEO in ways that matter.

• Get mentioned across the wider web, not just on your own site. These models weigh what third parties say about you: reviews, roundups, forum threads, comparison articles, and reputable publications. A brand that only talks about itself is nearly invisible to generative synthesis. Digital PR and earned mentions are now GEO tactics.
• Be quotable and specific. Models reproduce clear claims, concrete statistics, and named methods more readily than vague marketing language. If you want to be cited, publish things worth citing: original data, defined frameworks, specific numbers.
• Maintain consistency across the web so the model encounters the same facts about you repeatedly. Contradictory information makes a model hedge or omit you. Repetition of consistent, structured truth makes it confident.
• Structure your content so a machine can extract a clean claim without ambiguity. The same answer-first, well-marked-up content that wins AEO also feeds GEO.

Test this directly, and this is where most teams fail. Actually ask ChatGPT, Gemini, Claude, and Perplexity the questions your customers ask. Recommend a tool for X. Compare A and B. What is the best option for Y. See if you appear, how you are described, and how accurate the description is. That is your GEO scoreboard, and there is no dashboard that will hand it to you. You have to run the queries.

AAO, Agent Action Optimization, is the frontier, and it is arriving faster than the org charts expect. As AI agents move from answering questions to taking actions, booking, buying, comparing, filling forms, the question shifts from can the agent describe you to can the agent transact with you. An agent that cannot parse your pricing, cannot read your availability, cannot complete your checkout, will route the user to a competitor it can operate. Optimizing for agents means making your critical actions machine-legible.

• Expose clean, structured data for the things agents need: prices, availability, specifications, and options, ideally via schema and, where possible, well-documented APIs.
• Simplify critical paths so an automated actor can complete them without hitting brittle, human-only friction like unlabeled buttons or CAPTCHA walls on routine steps.
• Keep your structured truth accurate and current, because an agent acting on stale data will fail loudly and route around you next time.

The through-line across all three: AI answer engines reward brands that publish clear, structured, consistent, verifiable truth and that are talked about credibly elsewhere. That is not a trick. It is the same discipline as good SEO, aimed at machines that synthesize rather than rank.

Cluster 3: Voice and visual (VSO, VxSO)

Voice and visual search share a trait that changes how you optimize: they often remove the screen and the text box from the equation. In voice, the query is spoken and the answer is spoken, and usually only one answer gets read. In visual, the query is an image and the input is a camera, not a keyboard. Both reward brands that have made their content legible in formats beyond typed keywords.

VSO, Voice Search Optimization, is about being the single answer an assistant reads aloud. When someone asks a smart speaker or their phone a question by voice, there is no page of ten links. There is one response, and it is usually pulled from a concise, authoritative source. The stakes are winner-take-all in a way screen search is not. Second place is silence.

Voice queries also differ in shape. People speak in full, natural questions, longer and more conversational than what they type. They type best running shoes and they say what are the best running shoes for flat feet if I run on pavement. So the optimization follows the language.

• Target natural-language, question-form queries. Build FAQ content around the actual spoken questions people ask, phrased conversationally, with direct answers immediately below each question.
• Write concise, self-contained answers. A voice assistant will read roughly a sentence or two. Lead with the answer in plain language, then let the page expand for readers who continue on screen.
• Add Speakable schema where supported, marking the specific sections of a page that are suited to being read aloud. It tells assistants exactly which passage to voice.
• Win featured snippets and answer boxes, because voice assistants frequently source spoken answers from that same extracted content. AEO work and VSO work reinforce each other directly.
• Nail local voice intent. A large share of voice search is local and immediate, near me, open now, so accurate local data feeds voice results too, tying this cluster to LSO.

VxSO, Visual Search Optimization, is about being found when the query is a picture. Google Lens, Pinterest Lens, and in-app camera search let people point at an object, a room, an outfit, a plant, a product, and find matches or information. For commerce this is enormous, because it captures intent at the exact moment someone sees something they want but cannot name. The optimization is about making your images machine-readable and richly described.

• Write genuinely descriptive alt text on every meaningful image. Not keyword stuffing, actual description of what the image shows, including attributes a searcher would use: color, material, style, context. This serves accessibility and visual search at once.
• Add ImageObject schema and, for products, ensure product images are tied to structured product data so the visual match can connect to price, availability, and specs.
• Use high-quality, well-lit, clearly composed images. Visual search matches on what the machine can actually see. Cluttered, tiny, or ambiguous images match poorly.
• Provide multiple angles and contexts for products, because a shopper's camera photo may be taken from any angle in any setting. More reference images means more chances to match.
• Invest in Pinterest specifically if your category is visual, home, fashion, food, design, beauty. Pinterest functions as a visual search engine with strong commercial intent, and rich pins tied to your product data extend your reach there.

Notice the overlap that makes this cluster efficient. The natural-question FAQ content you build for voice is the same content that wins AEO in Cluster 2. The descriptive image data you build for visual search is the same product data that powers PFO and MSO in Cluster 4. You are not opening two new workstreams. You are extending assets you are already building into formats that serve screenless and camera-first discovery. That is Search Everywhere Optimization working as designed: one body of structured truth, many surfaces served.

Cluster 4: Commerce (MSO, PFO, ASO)

This cluster is where intent turns into revenue, and it operates on different engines than the open web. A shopper on Amazon is not on Google. An app installer in the App Store is not reading your blog. If you sell products or run an app, these three surfaces can matter more than your website, and they are governed by algorithms that reward conversion signals as much as relevance.

MSO, Marketplace Optimization, is search inside the marketplaces: Amazon, Walmart, Etsy, eBay, and their peers. These platforms run their own search engines, and a majority of product searches in many categories start on a marketplace, not on Google. The ranking logic is distinct because the marketplace optimizes for its own revenue, which means it favors listings that sell. Relevance gets you considered. Conversion and sales velocity get you ranked.

• Optimize titles, bullets, and descriptions for the marketplace's search, using the terms and structure that platform's buyers use. Amazon's algorithm and Etsy's algorithm reward different things, so treat each on its own terms.
• Drive conversion on the listing itself: strong images, clear benefits, complete specifications, competitive pricing, and answered questions. A listing that converts climbs, which brings more traffic, which compounds.
• Earn and maintain reviews, because ratings are both a ranking factor and the single biggest trust driver at the point of purchase. Systematic, policy-compliant review generation is not optional here.
• Keep inventory in stock and fulfillment fast, because marketplaces suppress listings that cannot deliver. Operational health is an MSO signal, not just a logistics concern.

PFO, Product Feed Optimization, is the data layer behind Shopping ads, Google Shopping's free listings, Merchant Center, and increasingly the shopping answers that AI systems generate. Your product feed is a structured file describing every product: title, description, price, availability, images, GTIN, brand, category, and attributes. The quality of that feed determines where and if your products surface. Most feeds are mediocre, which is good news, because a great feed stands out.

• Write feed titles the way shoppers search, front-loading the attributes that matter: brand, product type, key specs, size, color. The feed title is a ranking and matching field, not a place for cleverness.
• Fill every attribute the feed spec allows, including GTINs, and keep category mappings correct. Missing or wrong attributes cause products to be excluded from relevant results silently.
• Keep prices and availability accurate and fresh, because mismatches between feed and landing page get products disapproved and erode trust with the platform.
• Segment and label your feed so you can prioritize your best products and diagnose performance. A feed you cannot analyze is a feed you cannot improve.
• Recognize that clean feed data increasingly powers AI shopping answers too. The same structured product truth that wins Shopping is what an AI assistant reads when it recommends products.

ASO, App Store Optimization, is search inside the Apple App Store and Google Play, which are their own massive discovery engines with their own ranking logic. If you have an app, a large share of installs come from store search and browse, not from ads. ASO is SEO for the stores.

• Optimize the app title and subtitle with the terms people search, and fill the keyword field or description with relevant terms per each store's rules.
• Treat the screenshots and preview video as conversion assets, because the store ranks partly on install conversion rate. A listing that converts browsers into installers ranks higher.
• Drive ratings and reviews deliberately and respond to them, since rating volume and score are strong ranking and trust signals.
• Watch retention and engagement, because both stores increasingly reward apps that keep users, not just apps that get installed.

The unifying idea across commerce is that these engines reward proof of value. Marketplaces rank what sells. Feeds surface what is described completely and accurately. Stores promote what converts and retains. Optimizing this cluster is less about tricking an algorithm and more about presenting complete, accurate, compelling product truth and then earning the conversion and review signals that prove it. Build the structured product data once and it feeds all three surfaces plus visual search and AI shopping answers.

Cluster 5: Social and community (SSO, VDO, FEO, CSO)

Social platforms stopped being places you broadcast to and became places people search. That shift is the whole reason this cluster exists. Four surfaces here, and together they cover how discovery works inside social apps, video platforms, algorithmic feeds, and the forums that AI models increasingly treat as ground truth.

SSO, Social Search Optimization, is being found when people search inside TikTok, Instagram, Pinterest, and similar platforms. A striking share of younger users open TikTok or Instagram to look things up the way older users open Google: restaurants, products, how-tos, opinions. These in-app search engines index captions, on-screen text, audio transcripts, hashtags, and engagement. Optimizing for them looks like SEO wearing social clothes.

• Put your keywords in the places the platform's search reads: spoken words in the video, on-screen text, the caption, and relevant hashtags. Say the query out loud in the content, because these platforms transcribe audio.
• Answer real questions in your content. How-to, comparison, and recommendation formats perform in social search because they match search intent, not just entertainment intent.
• Optimize the first frame and first seconds, since they double as the thumbnail and the hook that drives the engagement the algorithm reads as a quality signal.

VDO, Video Search Optimization, is being found in video search, primarily YouTube, which is both the second-largest search engine and a primary research destination. YouTube ranks on relevance, watch time, and engagement, and it also feeds video results into Google. Video is also one of the most-cited formats in AI answers about how to do things.

• Optimize titles, descriptions, and chapters for search intent, and include a full, accurate transcript, because YouTube and downstream engines read it. Chapters help both viewers and machines find the relevant segment.
• Design for watch time and retention, since those are the dominant ranking signals. The first 30 seconds decide if the video ranks.
• Target question and how-to queries explicitly, because those are what people search video for and what AI systems cite video to answer.

FEO, Feed/Discover Optimization, is about earning placement in algorithmic discovery feeds, chiefly Google Discover, but the logic extends to other personalized feeds. Discover does not respond to a query at all. It pushes content to users based on interest, so it is a genuinely different game: you are not answering a search, you are being selected for a feed.

• Publish content people want to engage with on topics with sustained interest, with compelling, honest headlines and high-quality large images, since Discover leans heavily on visual presentation.
• Maintain strong E-E-A-T and site quality, because Discover favors trusted sources and suppresses clickbait and low-quality sites.
• Cover timely and evergreen-interest topics in your space, because Discover rewards freshness and sustained topical relevance together.

CSO, Community Search Optimization, is presence in the community platforms that both humans and AI models mine for real opinions: Reddit, Quora, specialized forums, and Q&A sites. This surface got dramatically more important once AI models started citing Reddit and similar communities heavily in their answers. When a model wants a real human opinion, it often reaches for a forum thread. If your brand is discussed accurately there, you get pulled into answers. If it is absent or misrepresented, you do not.

• Participate authentically in the communities where your category is discussed, adding genuine value rather than spamming links. Communities punish and downrank promotional behavior, and so do the algorithms.
• Earn honest mentions and answer real questions helpfully, because a helpful, upvoted answer becomes a durable citation that both the community's own search and external AI systems draw from.
• Monitor how your brand is represented in these communities and correct the record where it is wrong, transparently and as yourself, because misinformation in a high-authority forum propagates into AI answers.
• Encourage genuine customer discussion and reviews in these spaces, since authentic community sentiment is exactly what generative engines reach for when asked what real people think.

The connective tissue across this cluster is that social and community content is now training data and citation source, not just distribution. A helpful YouTube video, a well-optimized TikTok, a genuinely useful Reddit answer, these do double duty: they reach humans searching the platform today, and they become sources that AI systems cite tomorrow. That is why I treat Cluster 5 as part of search, not separate from it. The line between social content and search content dissolved, and the brands that still run social as pure broadcast are missing half its value.

Cluster 6: Local, global, and identity (LSO, GLOBO, KGO, Web3)

This cluster answers three machine questions that underpin trust across every other surface: where are you, who are you speaking to, and who are you, verifiably. Get these right and every other engine has an easier time believing and placing you. Get them wrong and you undermine work done everywhere else.

LSO, Local Search Optimization, is being found in local and near me searches and on the map. For any business with a physical location or a service area, this is not a nice-to-have, it is often the primary discovery engine, and it runs largely through Google Business Profile and the map pack. Local ranking weighs relevance, distance, and prominence.

• Claim and fully complete your Google Business Profile: correct category, hours, services, attributes, photos, and description. Completeness and accuracy are direct ranking factors.
• Keep NAP, name, address, phone, perfectly consistent across your site and every directory and citation, because inconsistency confuses the systems that verify your legitimacy.
• Earn and respond to reviews continuously, since review quantity, quality, recency, and your responses all feed local ranking and local trust.
• Publish location-specific and service-specific content, and build local citations, so the systems associate you with your places and services concretely.

GLOBO, Global/Locale Optimization, is serving the right content to the right language and region. If you operate across countries or languages, this determines if the correct version of your site reaches the correct audience and if search engines understand your international structure. Done wrong, it produces duplicate content, wrong-language results, and cannibalized rankings.

• Implement hreflang correctly to tell engines which language and regional version to serve, and keep the mappings complete and reciprocal.
• Truly localize, do not just translate. Adapt currency, units, examples, cultural references, and search terms to each market, because people in different markets search with different words for the same thing.
• Structure international sites clearly, with a consistent, crawlable pattern for regions and languages, so engines can map your global footprint without confusion.
• Localize your structured data and business details per market, because local relevance signals apply internationally too.

KGO, Knowledge Graph Optimization, is establishing your brand as a recognized entity in the knowledge graphs that power knowledge panels, and that AI systems consult to understand who you are. When Google shows a panel about your company, or an AI confidently states what your brand is, it is drawing on entity data. Being a well-defined entity is one of the most valuable identity investments you can make, because it feeds trust across every AI and search surface.

• Define your organization with clear, consistent structured data, Organization schema with sameAs links to your official profiles, so machines can connect your identity across the web.
• Establish and maintain accurate presence on the sources that feed knowledge graphs, including Wikidata where appropriate, and ensure the facts about your brand are consistent everywhere they appear.
• Build entity associations by being mentioned alongside recognized entities, people, brands, categories, and topics, so the graph understands your relationships and relevance.
• Keep your core facts, founding, leadership, products, location, consistent across every source, because contradictions weaken the entity and make AI systems hedge when describing you.

Web3, Decentralized Identity, is the emerging frontier of verifiable, self-owned identity, including systems like ENS (Ethereum Name Service). I am pragmatic about this one. For most brands today it is a small, forward-looking investment, not a priority. But the direction is real: portable, verifiable identity that a brand controls, rather than identity scattered across platforms that can misrepresent it. For brands in relevant spaces, or brands that want to secure their name early, establishing decentralized identifiers is a low-cost hedge.

• Secure your brand's decentralized names, such as ENS domains, the way you secured your social handles, to prevent impersonation and reserve future optionality.
• Maintain verifiable links between your decentralized identity and your official web presence, so the connection is provable rather than claimed.
• Treat this as insurance and positioning for now, scaled to how relevant Web3 is to your actual audience, rather than a channel you must monetize today.

The theme across Cluster 6 is verification. Local systems verify where you are. Global systems verify who you are speaking to. Knowledge graphs verify who you are. Decentralized identity verifies that the claim is yours. Every AI answer, voice result, and search ranking leans on these verifications, even when the user never sees them. Solidify your identity here and you make every other surface trust you more.

The five behaviors that win across all 19

After enough time optimizing across every one of these surfaces, a pattern becomes undeniable. The engines are different, but the behaviors that win are the same five, over and over. Master these and you are not optimizing nineteen surfaces separately. You are practicing five disciplines that pay off across all of them. This is the operating core of Search Everywhere Optimization.

The first behavior is structured truth through schema. Give machines your information in a format they can parse without guessing. Schema markup, Organization, Product, FAQPage, HowTo, Article, ImageObject, LocalBusiness, and their siblings, turns your content from prose a machine has to interpret into data a machine can read directly. This is the most valuable technical work in the entire framework, because every cluster consumes it.

• Implement the schema types that match your content, and validate them so they actually parse. Broken markup helps nothing.
• Keep the structured data consistent with the visible content, because mismatches get penalized and erode trust.
• Extend schema to products, reviews, FAQs, how-tos, and your organization, so each surface finds the data it needs. One correct Product schema feeds Shopping, marketplaces, visual search, and AI shopping answers at once.

The second behavior is direct answers. Every modern discovery surface, answer engines, voice, featured snippets, AI synthesis, rewards content that answers the question immediately and clearly. The old SEO habit of burying the answer under 800 words of preamble is now actively harmful, because the extraction systems grab the clean answer near the top and skip the rest.

• Lead with the answer. State it plainly in the first sentence or two under a heading that matches the question, then expand.
• Structure for extraction with clear headings, short self-contained answers, lists, and tables, so any engine can lift a clean, correct response.
• Phrase headings as the questions people actually ask, so the match between query and answer is obvious to the machine.

The third behavior is unique content that demonstrates experience. Generic content, the kind an AI could generate about any brand, ranks nowhere well anymore and gets cited nowhere. What wins is content that only you could produce: original data, first-hand experience, specific numbers, defined frameworks, genuine expertise. This is the E-E-A-T behavior, and it is also the GEO behavior, because models cite what is specific and quotable.

• Publish original research, real results, and first-hand experience with concrete specifics. Vague claims signal nothing to humans or machines.
• Put credentialed, real authors on your content, because authorship is a trust signal across search and AI systems alike.
• Say something worth quoting. If your content contains no claim, statistic, or method distinctive enough to cite, it will not be cited.

The fourth behavior is speed and technical health. Fast, stable, accessible experiences win across the board, because the same performance that satisfies Core Web Vitals satisfies the human who converts and the crawler that indexes. Speed is not just a Cluster 1 concern, it is a universal one.

• Meet Core Web Vitals: fast loading, responsive interaction, stable layout. Compress images, defer non-critical scripts, reserve space to prevent shifts.
• Ensure clean crawlability and rendering, so every engine can access and understand your content.
• Make critical actions machine-legible and frictionless, which serves both users and the agents in Cluster 2.

The fifth behavior is linked identity. Connect your brand across the web into one coherent, verifiable entity, so every system understands you are the same trustworthy brand everywhere. This is the KGO behavior extended into a discipline, and it is what makes AI systems confident enough to name you.

• Use Organization schema with sameAs links tying together your site, profiles, and listings.
• Keep your core facts consistent across every platform, directory, and mention, because consistency builds entity strength and contradictions weaken it.
• Build associations with recognized entities in your space, so your relationships and relevance are legible to knowledge graphs and AI models.

Here is the payoff of thinking in behaviors rather than surfaces. Nineteen surfaces sound like nineteen strategies, which sounds impossible. Five behaviors sound like a practice a real team can adopt. Structure your truth, answer directly, be genuinely unique, be fast and healthy, and link your identity. Do those five consistently and you will show up across surfaces you never explicitly optimized for, because you finally built the thing all of them were looking for.

A 90-day Search Everywhere roadmap

Strategy without sequence produces paralysis. So here is the exact order I would run a Search Everywhere program in the first 90 days, built so each phase makes the next easier. Adjust the emphasis to your business, a local service company weights local heavily, a product brand weights commerce, but keep the sequence, because the foundation has to come first.

Weeks 1 and 2, foundation. Do not chase AI citations before your house is in order. Start with the structured truth and technical base that every surface reads.

• Audit and implement core schema: Organization with sameAs links, Product for commerce, Article and FAQPage for content, LocalBusiness if you have locations. Validate everything.
• Fix Core Web Vitals failures and obvious technical issues: image compression, layout stability, crawlability, mobile rendering.
• Establish or correct your identity basics: consistent NAP everywhere, complete Google Business Profile, accurate core facts across your main profiles.
• Baseline your measurement: confirm GA4 and Search Console are clean, and run a first round of AI-citation checks so you know your starting position.

Weeks 3 and 4, AI search. With the foundation set, move to the fastest-moving front while competitors are still hesitating.

• Restructure your most important content answer-first: question-form headings, direct answers up top, lists and tables where they fit, FAQ blocks with FAQPage schema.
• Run and record your GEO baseline directly. Ask ChatGPT, Gemini, Claude, and Perplexity the questions your customers ask, note where you appear and how you are described, and log the gaps.
• Identify the third-party surfaces where you are absent or misrepresented, roundups, comparisons, key forums, and start the outreach and participation to fix that.

Weeks 5 through 8, depth and E-E-A-T. Now you build the substance that makes citations and rankings durable rather than fleeting.

• Build out topic clusters on your priority themes: a definitive pillar plus supporting pages, tightly interlinked, with real depth and completeness.
• Add genuine experience signals: real authors with real bios, original data, first-hand specifics, photos, tested claims. Replace generic content with content only you could write.
• Earn authority: digital PR, guest expertise, and honest mentions from respected sources, plus authentic participation in the communities where your category is discussed.
• Strengthen your knowledge entity: Wikidata and knowledge-graph sources where appropriate, consistent facts, and associations with recognized entities.

Weeks 9 through 12, expand by fit. Only now, with foundation, AI, and depth in place, do you extend into the surfaces that match your specific business. This is where you choose your terrain.

• If you sell products, invest in commerce: optimize marketplace listings, clean and complete your product feed, and if you have an app, do a full ASO pass.
• If discovery for your audience happens on social and video, build there: optimize content for in-app search on TikTok, Instagram, and YouTube, with transcripts, on-screen text, and question-format content, and pursue Discover-worthy publishing.
• If you serve local or global markets, deepen those: expand local content and citations, or implement hreflang and true localization for your key markets.
• Extend voice and visual: natural-question FAQ and Speakable where supported, descriptive image data and ImageObject schema, Pinterest if your category is visual.

Two principles govern the whole 90 days. First, sequence beats simultaneity. Teams that try to do all six clusters at once do all of them shallowly and see nothing move. Teams that build the foundation, then AI, then depth, then expand, see compounding results because each phase strengthens the next. Second, expansion is a choice, not an obligation. The last phase says expand by fit, not expand into everything. A B2B software company probably should not spend week 12 on Etsy. A local dentist probably should not spend it on ENS. The map shows you all nineteen surfaces so you can pick the six or eight that matter for you and go deep, rather than spreading thin across all of them.

At day 90 you will not be finished, because Search Everywhere Optimization is ongoing, not a project. But you will have a validated foundation, a live AI-search presence, real content depth, and momentum on the surfaces that fit your business, plus a measurement baseline that tells you what is working. That is a position almost none of your competitors will have, because almost none of them are running this deliberately.

The most common mistakes

I have made some of these myself and cleaned up after many more. Each one is common, each one is costly, and each one has a concrete fix. Read this section as a pre-mortem: if you are about to run a Search Everywhere program, avoid these and you will save months.

Mistake one: treating Google as the whole game. The most expensive error is assuming that healthy Google rankings mean healthy discovery. They do not, and I said earlier why: you can hold rankings and still vanish from AI answers, voice, marketplaces, and social. The fix is to measure your presence across clusters, not just organic sessions, and to fund the portfolio deliberately rather than defending one surface.

Mistake two: writing for keywords instead of answers. Content stuffed with keywords but structured as a wall of prose fails on every modern surface. Answer engines cannot extract from it, voice cannot read it, and readers bounce. The fix is answer-first structure: question-form headings, direct answers up top, lists and tables, and completeness over repetition.

Mistake three: skipping schema, or implementing it broken. Schema is the most valuable technical work in the framework, and most sites either omit it or ship markup that does not validate and does not match the visible content. The fix is to implement the schema types that match your content, validate them, and keep them consistent with what is on the page.

Mistake four: publishing generic content an AI could have written about anyone. If your content contains no original data, no first-hand experience, no specific numbers, and no distinctive method, it will rank poorly and be cited nowhere. The fix is to add genuine experience and specificity: real authors, real results, real photos, real claims worth quoting.

Mistake five: only talking about yourself. Generative engines weigh what third parties say about you, and a brand that only publishes on its own site is nearly invisible to AI synthesis and weak on authority. The fix is earned mentions: digital PR, honest reviews, roundups, and authentic community participation, so the wider web corroborates your structured truth.

Mistake six: inconsistent facts across the web. Different addresses, different founding dates, different product descriptions, and conflicting claims scattered across platforms make every verification system hedge. Knowledge graphs weaken, AI systems get cautious, local ranking suffers. The fix is a single source of truth and consistent facts everywhere, enforced deliberately.

Mistake seven: ignoring the surfaces where your buyers actually research. A product brand that never optimizes marketplaces, or a brand whose young audience searches TikTok while the brand runs TikTok as pure broadcast, is absent at the moment of decision. The fix is to map where your specific audience discovers, then invest there, using the cluster that fits.

Mistake eight: treating this as a one-time project. Search Everywhere Optimization is ongoing. Engines change, AI answers shift, new surfaces emerge, and your structured truth drifts stale if nobody maintains it. The fix is to run it as a continuous practice with regular AI-citation checks, feed audits, review generation, and content refreshes, not a launch you declare done.

One meta-mistake ties the rest together: siloing the work so no single source of truth exists. When the SEO team, the social team, and the commerce team each maintain their own version of the brand, the engines get contradictory signals and the brand shows up weakly everywhere. The fix is the founding principle of this whole framework: build one body of structured truth, own it centrally, and distribute it to every surface. Avoid that one mistake and most of the others become much harder to make.

Tools and how to measure it

You cannot manage a portfolio you cannot see, and the hardest part of Search Everywhere Optimization is that no single dashboard shows all nineteen surfaces. So you assemble your view from a few core tools plus one discipline most teams skip: checking AI citations by hand. Here is how I measure it.

Start with the tools that cover classic search and site health. Google Search Console is your primary window into how Google sees you: queries, impressions, clicks, position, indexing status, and Core Web Vitals reporting. It is free and it is non-negotiable. GA4 is your behavior layer: where traffic comes from, what people do, and how discovery converts, though you will need to configure it to distinguish channels meaningfully. For CWV specifically, use the field data in Search Console alongside lab tools to diagnose Largest Contentful Paint, Interaction to Next Paint, and Cumulative Layout Shift.

For deeper classic-search work, the established SEO platforms cover rank tracking, backlink analysis, technical audits, and content gap analysis. For schema, use a validator to confirm your structured data actually parses, and check it after every significant template change, because markup breaks silently.

Now the part almost nobody does, and the part that separates real Search Everywhere programs from the rest: test AI citations directly. There is no Search Console for ChatGPT. So you build your own scoreboard by hand. Take the questions your customers actually ask, the recommend a tool for X, the compare A and B, the best option for Y, and run them across ChatGPT, Gemini, Claude, and Perplexity on a regular cadence. Record three things for each: if you appear, how you are described, and how accurate the description is. That log is your GEO and AEO measurement, and its trend over time tells you if your AI presence is improving.

For the other surfaces, measure at the source, because each has its own analytics.

• Commerce: use the marketplace's own seller analytics for MSO, Merchant Center diagnostics and performance for PFO, and App Store Connect and Play Console for ASO. Watch listing conversion, feed disapproval rates, and install conversion.
• Social and community: use each platform's native search and content analytics for SSO and VDO, watch retention and watch time on video, monitor Discover traffic in Search Console for FEO, and track brand mentions and sentiment in communities for CSO.
• Local and identity: use Google Business Profile insights for LSO, watch international performance segmented by market for GLOBO, and monitor if your knowledge panel appears and is accurate for KGO.

Now the KPIs, organized by cluster so you know what good looks like where.

• Classic search: organic sessions and conversions, rankings for priority terms, CWV pass rate, and indexed-page health.
• AI answer engines: citation rate and accuracy across the major models on your priority questions, and featured-snippet and AI Overview presence. This is the hand-built scoreboard.
• Voice and visual: featured-snippet and answer-box wins for question queries, and visual-search and Pinterest referral traffic where relevant.
• Commerce: marketplace search rank and listing conversion, feed coverage and disapproval rate, Shopping performance, and app store rank and install conversion.
• Social and community: in-app search visibility and engagement, video watch time and search rankings, Discover impressions and clicks, and share of voice plus sentiment in key communities.
• Local, global, identity: map-pack visibility and Business Profile actions, review volume and rating, international performance by market, and knowledge-panel presence and accuracy.

One measurement principle matters more than any specific metric: track presence across the portfolio, not just sessions on your site. The failure mode I keep warning about, holding rankings while vanishing, is invisible to any single-surface dashboard. It only shows up when you deliberately look across surfaces. So build the habit of a periodic Search Everywhere review that pulls together your classic-search health, your hand-checked AI citations, your commerce and social presence, and your identity accuracy into one picture. That review is the instrument panel for the whole framework, and running it monthly is what keeps a portfolio strategy from quietly collapsing back into a Google-only strategy.

What this looked like in practice

Let me make this concrete, because frameworks are easy to admire and hard to trust until you see one run in production. The clearest example from my own work is The Ideal Card, a digital business card platform I helped build and scale as VP. The results that matter here: it grew past 55,000 customers, roughly doubled revenue year over year, gained 44% market share, and built more than 200 partnerships. Underneath those numbers was a discovery strategy that looked exactly like Search Everywhere Optimization, before I had fully named it.

The lesson that shaped everything came early and it stung. We had a genuinely good product with real features, and we assumed, as most product teams do, that better features would win. They did not, at least not on their own. What actually moved the business was being discoverable at every point where a potential customer might look. Discovery beat features. A superior product nobody finds loses to an adequate product everyone finds. Once I internalized that, the strategy inverted from build more to be found everywhere.

So we built the structured truth first. Clean, consistent product and organization data, so every engine understood what The Ideal Card was and who it served. Answer-first content around the real questions people asked about digital business cards, which fed both classic search and the answer surfaces. We treated identity seriously: consistent facts across every profile and listing, so the brand read as one coherent, trustworthy entity everywhere it appeared. That foundation is what let a relatively young brand punch above its weight in category understanding.

Then we distributed that truth across surfaces. Content optimized for search intent, not just keywords. Presence in the communities and comparison contexts where people research tools like ours, so the brand was corroborated by more than its own marketing. Product data structured cleanly enough to surface wherever it needed to. Partnerships, all 200-plus of them, which did double duty: real business relationships that also strengthened our entity and authority by associating us with recognized names. We were not doing nineteen disconnected campaigns. We were shipping one body of structured truth to the places our audience actually looked.

The compounding effect is what convinced me this was a repeatable framework and not a lucky run. Each surface reinforced the others. Strong content earned authority, which improved rankings, which drove the engagement and reviews that fed trust signals, which made the brand more citable and more discoverable on the next surface. Doubling revenue year over year is not one channel working. It is a portfolio compounding, where growth on each surface lowered the cost of growth on the next.

The specific optimization results I have delivered across this body of work map directly onto the framework. Lifting organic traffic by roughly 45% came from exactly the Cluster 1 and Cluster 2 discipline I described: topic clusters, real depth, answer-first structure, clean technical health, and genuine E-E-A-T. Improving conversion by roughly 25% came from the behaviors that serve both machines and humans: fast, stable pages, clear direct answers, complete product truth, and trust signals present at the decision point. The same structured truth that pleased the algorithms pleased the buyers. That is not a coincidence, it is the core mechanic. When you build content and data clear enough for a machine to parse and confident enough for a machine to cite, you have also built content clear enough for a human to understand and confident enough for a human to buy.

I have run versions of this managing a $2.75 million budget at 3x ROI and growing sales around 35% year over year in other seats, across Shopify Plus, WordPress, Webflow, WooCommerce, Wix, and Magento. Different platforms, different products, same pattern. Build the structured truth once, structure it so every engine can read it, distribute it to the surfaces your audience uses, and let the portfolio compound. The tools changed. The discipline did not. And every time, the brands that treated discovery as a portfolio outgrew the ones that treated it as a Google-shaped channel.

Where search goes next

I will make a few predictions, because a framework should be forward-compatible, not just current. The direction of travel is clear even if the exact timing is not, and preparing for it now is cheap while catching up later will be expensive.

First, agents move from answering to acting, and this is the biggest shift on the horizon. Today most AI systems describe options and hand the decision back to you. Tomorrow they will increasingly take the action: booking the appointment, comparing and buying the product, filling the form, completing the transaction on your behalf. That is why AAO, Agent Action Optimization, sits in the framework already even though it feels early. When an agent is the one transacting, the brands it can actually operate on will win, and the brands whose critical actions are illegible to a machine will be silently skipped. Prepare by making your prices, availability, specifications, and core actions structured and machine-completable now, before agent-driven commerce is the norm rather than the experiment.

Second, discovery becomes AI-first for a growing share of queries. More journeys will begin, and increasingly end, inside an AI answer rather than a results page. That does not eliminate the underlying web, it changes who gets seen: the sources the AI trusts and cites. So the value migrates to being the cited source, which rewards exactly the behaviors this framework centers, structured truth, genuine expertise, consistency, and corroboration from the wider web. The brands that invested in being quotable and verifiable will be the ones AI systems reach for. The brands that only optimized for clicks will find the clicks intermediated away.

Third, the surfaces will keep multiplying, not consolidate. It is tempting to hope this all collapses back into one engine you can master, but the trend is the opposite. New platforms become search engines, TikTok did, Reddit effectively did through AI citations, and more will. New answer engines and assistants launch. New identity systems emerge. The count was not always nineteen and it will not stay nineteen. This is precisely why I built the framework around behaviors and clusters rather than a fixed checklist of surfaces. When surface number twenty arrives, you will not need a new strategy. You will point your existing structured truth at it, because a new engine that reads clear, structured, trustworthy, verifiable information will find you already speaking its language.

Fourth, verification and trust get more important, not less, as AI-generated content floods every surface. When anyone can generate plausible content at scale, the scarce and valuable thing becomes provable authenticity: real experience, real identity, real corroboration. The systems will increasingly favor sources they can verify, which pushes E-E-A-T, KGO, and even Web3 identity from nice-to-have toward essential. The brands that built a strong, consistent, verifiable identity will be trusted. The brands that did not will be indistinguishable from the noise.

So what do you prepare for, concretely? Make your truth machine-legible, because every future surface will read machines-first. Make your identity verifiable and consistent, because trust is the currency that appreciates. Make your critical actions completable by software, because agents are coming for the transaction. Build genuine expertise and earn genuine corroboration, because that is what survives when generation is free. None of that is speculative. It is the same five behaviors, aimed a little further downfield.

I want to close on the idea this whole framework rests on, because it is the thing that outlasts any specific surface or engine. I call it Structured Truth. The brands that win discovery are not the ones with the cleverest tactics for one platform. They are the ones that have built a single, clear, structured, trustworthy, verifiable body of truth about who they are and what they offer, and made it legible to every machine that mediates human attention. Search did not get replaced. It multiplied into nineteen surfaces and counting. You do not chase all of them. You build one body of Structured Truth so complete and so clear that they come to you. That is Search Everywhere Optimization, and it is the most durable growth investment I know how to make.