Hydration and SEO: How it works and why it matters

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Hydration and SEO- How it works and why it matters

If your site runs on a framework like Next.js or Nuxt, hydration shapes how your pages become interactive, but it’s rarely explained in terms that matter to SEOs.

It’s more approachable than it sounds. Here’s what hydration is, how it works, where it affects SEO (and where it doesn’t), and how different frameworks handle it.

What is hydration?

Hydration is the process of JavaScript running in your browser “taking over” the static HTML built on the server, turning it into a page you can actually interact with.

Here’s the process:

  • The server builds complete, fully formed HTML and sends it to your browser. You see the content right away, but it isn’t interactive. The buttons don’t work yet, and nothing responds to clicks.
  • Hydration happens when the page’s framework (Next.js, Nuxt, SvelteKit, and others) finishes loading. It walks over the existing HTML, attaches event listeners, and reconnects the visible markup with the logic that makes it work.
  • After hydration, the page behaves like a normal interactive app.

Server-rendered HTML paints quickly, which is great for first impressions and often for Largest Contentful Paint (LCP). As the timeline below shows, traditional hydration means the page isn’t actually usable until hydration finishes.

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Hydration adds interactivity, not content

Hydration doesn’t add content to the page. The text, images, and layout already arrived from the server. It only adds behavior, wiring up the existing HTML so it can respond to you. Put simply, before hydration you can read the page, and after hydration you can use it.

You can see this side by side below. The only difference between the two pages is whether the button responds.

Don’t confuse hydration with the rendering pattern, which determines where and when the page is built. Server-side rendering (SSR), static site generation (SSG), and client-side rendering (CSR) each decide how much of the page arrives as finished HTML versus how much JavaScript builds later in the browser.

Because hydration runs on server-rendered (SSR) and static (SSG) pages, the content is already present in the initial HTML. Google can index that content from the initial HTML instead of relying on the render step, which is more reliable than a client-rendered blank shell.

When hydration becomes an SEO problem

Most of the time, hydration isn’t directly an SEO issue. It only becomes one when something breaks, usually a mismatch. This is when the server’s HTML and what the framework builds in the browser don’t agree.

A mismatch typically comes from one of a few sources:

  • Content rendered from a browser-only API that the server can’t access, like localStorage.
  • A value that changes between the server and client, such as new Date().
  • A third-party script or browser extension that alters the DOM before the framework hydrates it.
  • Invalid HTML that the browser rewrites in the background, producing a structure the framework didn’t expect.

In these cases, hydration can’t reconcile the two versions, so the framework throws out the mismatched part and re-renders it. The exact process depends on the framework.

Here, a <time> value from new Date() renders differently on the server and in the browser, forcing a re-render.

That creates problems on three fronts. The re-render makes the page feel sluggish (INP) and shifts the layout (CLS). It can also leave the page outright broken because event listeners may fail to attach, causing buttons and forms to stop working.

In severe cases, because Google may read the raw server HTML before rendering the JavaScript, it can index the version that’s about to be discarded, storing content visitors never actually see.

Developers can resolve these issues by fixing the underlying causes of the mismatches. For example, they can use valid HTML so the browser doesn’t rewrite it behind the scenes.

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How to spot hydration problems on a live site

Hydration errors aren’t as explicit on a live site as they are during development. Start by checking the browser’s Developer Tools console for hydration or JavaScript warnings, then use these additional checks:

  • Watch the page load for content that shifts, flickers, or stays unresponsive.
  • Run important templates through Google Search Console’s URL Inspection tool to see how the page is rendered.
  • Crawl in JavaScript-rendering mode (Screaming Frog, Sitebulb) to compare rendered output against raw HTML at scale.

How frameworks handle hydration

Modern frameworks take different approaches to hydration, including ways to reduce or skip it, to balance performance, interactivity, and JavaScript execution.

The most common approaches are:

  • Full hydration: The whole page hydrates in one go. It sounds simple, but it ships the most JavaScript and puts the most work on the main thread.
  • Partial hydration: Only the interactive bits (“islands”) hydrate. The static parts stay as plain HTML and never get touched. Astro’s islands architecture is built around this.
  • Progressive hydration: The page hydrates in pieces, either as sections scroll into view or on a schedule, instead of all at once. Angular’s incremental hydration works this way.

Two newer approaches sidestep hydration:

  • React Server Components: Some components render entirely on the server and ship zero JavaScript, so there’s nothing to hydrate on the client.
  • Resumability: It skips hydration completely. The page picks up exactly where the server left off, with no components re-running on load. Qwik does this. It’s also the newest of these approaches and the least battle-tested.

Here’s how they compare:

Technique What hydrates JavaScript shipped Example
Full hydration The entire page Most Next.js (Pages Router)
Partial hydration (islands) Only interactive components Less Astro
Progressive hydration The page, in pieces over time Same total, spread out Angular
React Server Components Nothing (for server-only parts) Less Next.js (App Router)
Resumability Nothing, hydration is skipped Least Qwik

What this means for your site

Most of the time, hydration isn’t an SEO problem. It only becomes one when the server’s HTML and the browser’s rendered version disagree.

Newer frameworks leave less room for that to happen because each generation ships less JavaScript and does less work in the browser. Still, the mismatches that do surface matter, especially when search engines index a version of the page your visitors never see.

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Used or cited: The two ways brands appear in AI search

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Used or cited: The two ways brands appear in AI search

Ranking within Google’s traditional search results provides diminishing returns. Ads, AI Overviews, and other search engine results page (SERP) features push organic links further down the page.

As the search landscape changes, how should brands adapt to ensure they’re represented in AI-powered responses?

The more you know about how AI engines use your brand’s information and when they cite it, the better you can use AI search to your advantage. With that knowledge, you can move beyond whether AI models know your brand and start developing your own AI visibility strategy.

Collapse of the click economy

It’s important for most brands to understand AI search and begin developing an AI SEO strategy as quickly as possible. While a full transformation from organic to AI search appears to be years away, AI SEO may eventually replace traditional SEO.

Google is already leaning heavily on AI search. As CEO Sundar Pichai said in an April article from The Verge: 

  • “Search had a strong quarter with AI experiences driving usage, queries at an all-time high, and 19% revenue growth.”

At the same time, users are adapting to AI search features. When users encounter an AI-powered summary in search results, they click a blue link just 8% of the time, a Pew Research study found. When they don’t encounter AI summaries, they click blue links 15% of the time.

Although AI search traffic is still limited, it tends to have a higher conversion rate than organic search traffic. AI traffic had a conversion rate of 11.4%, compared to 5.3% for organic search traffic, per a Similarweb study.

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Brand presence within AI engines: Usage vs. citation

Brands can exist in AI systems in two distinct ways: usage and citation.

AI engines ingest information about your brand and use it when responding to search queries. This is somewhat similar to how Google traditionally indexes pages before ranking and serving them in search results.

When AI engines use your content, they may also mention your brand as an unlinked citation. This can drive discovery and may prompt users to search for and engage with your brand.

Citation occurs when an AI engine directly references your brand as a source of information. This may be a link to your web page, a link to your social profile, or a clickable phone link that lets users call you.

Within OpenAI, usage and citation rely on separate technical levers. As OpenAI’s documentation explains, there are four distinct user agents, with OAI-SearchBot and GPTBot deployed separately. Other AI systems have similar controls and measures that point to the same distinction.

Why citations are only part of the AI visibility equation

AI engines often answer questions directly without necessarily citing web sources. This isn’t a new phenomenon. Before AI Overviews, Google tried something similar with featured snippets.

ChatGPT retrieves almost the exact same number of cited (~16.57) and uncited (~16.58) URLs to generate an average response, according to an Ahrefs study. Yet Reddit accounts for more than two-thirds (67.8%) of uncited URLs. As a result, comparing cited and uncited URLs is really a comparison between search results and Reddit API output.

This demonstrates that many AI systems are biased in the uncited information they provide to users. Certain platforms and websites are better than others at helping brands appear in AI answers. Brands that try to force themselves into AI models without understanding where those models source most of their information will be at a distinct disadvantage.

How to improve AI usage and citation for your brand

Start by tracking your brand’s status and progress over time. Run a representative selection of prompts through an AI visibility platform and examine the citation sources. Where do they land, and what does that tell you?

There are many emerging AI citation tracking platforms to choose from. Established platforms like Semrush and Ahrefs have also integrated AI tracking features.

Scale your tracking and research efforts as much as possible. This can be difficult because AI prompt tracking often relies on API calls and is more expensive than traditional search ranking tracking.

As long as your sample is broadly representative, most tracking platforms will pull multiple responses and calculate some type of average. Although the volume of data is smaller, it’s usually quite rich.

Don’t forget to read AI and data vendor studies. They’re valuable sources of information because they show where AI engines pull information from.

Continual monitoring and adaptation are key. Over time, you can place your brand within the sources AI engines rely on most heavily.

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Should you bother with traditional search rankings?

Yes, you should continue to pursue traditional search rankings, but not for the reasons you might think. The connection between organic ranking positions and performance has become much more nebulous.

However, Ahrefs research suggests a correlation between AI citations and Google ranking positions, at least for Google AI Overviews. A July 2025 study found that 76.1% of pages cited in AI Overviews ranked in Google’s top 10 organic search results. For AI Overviews, which may become a dominant force in AI search over the coming years, traditional rankings still seem to matter.

How AI Overview citations rank in the SERPs

AI engines rarely cite generic content that restates what other sources already say, an April study from Semrush found. Content that earns citations adds unique value.

This aligns with Google’s helpful content guidance, which encourages brands to publish original information. Producing content with a unique, trusted, and statistically grounded perspective can also help improve Google rankings.

Since many tactics for earning higher organic rankings can also earn AI citations, there’s no reason to abandon traditional SEO techniques and content strategies.

The growth of AI visibility and the fate of traditional SEO

Both usage and citation require continual tracking and analysis. To increase the likelihood that AI engines use your brand’s knowledge and content, get your brand into the sources each AI model relies on. To earn citations, stay crawlable, rank organically, and say something original.

Classic SEO still earns its keep because the techniques that win organic rankings often earn AI citations as well. Yet the returns are diminishing, and AI SEO may one day replace traditional SEO altogether. That’s still a long way off, so for now, keep ranking, start tracking, and pursue both.

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Google merchant listings support sale duration and product category

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Google has updated its merchant listing structured data to support sale duration and product category property. This is to more align the merchant listing structured data support within Google Search with Google Merchant Center feed support.

Sale duration. Google added a new section on “Sale duration” to the Merchant listing structured data help document. Google said, “this explains how to use the validFrom, validThrough, and priceValidUntil schema.org properties to set the effective range for sale prices, including best practices and examples for placement on either Offer or PriceSpecification nodes.” Google added this because it “aligns schema.org usage with the Merchant Center feed attribute sale_price_effective_date, providing clear instructions and best practices for merchants using structured data.”

Here is that new section:

Product category. Google also updated that document to include support for Product.category property.

Google wrote it has updated the Merchant listing documentation to detail how the Product.category property can be used with both Text and CategoryCode types. “This aligns with the Google Merchant Center feed specifications for the product_type and google_product_category attributes,” Google added. Google also said this is to “help merchants provide both merchant-defined and Google-defined category information within their schema.org markup, enhancing product information for Google Search and Shopping.”

Here is what was added:

Why we care. If you maintain merchant listing structured data for Google, these additions may be useful. Product category support can help Google understand more about the products you are feeding it, which may help with showing that product for more relevant queries. Google’s sale duration support can let you plan your product sales more effectively and efficiently when updating your merchant listing structured data.

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How to get the most from Microsoft Advertising campaigns

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How to get more from Microsoft Advertising than a campaign import

If you’re struggling to scale performance with Microsoft Advertising, you may be treating it as a place to replicate a strategy developed elsewhere.

Import can get you live fast. Performance comes from adding human judgment, Microsoft-specific structure, foundational measurement, controls tailored to your business needs, and a broad range of creative assets that help AI understand your products or services.

The strongest accounts share a common approach: Import is a starting point, visual creatives unlock more demand and better performance, and AI performs best when you give it the right structure, creative, measurement, and guardrails.

Here’s how to use Microsoft-specific mechanics to improve performance while avoiding common pitfalls.

Note: I’m a Microsoft employee, and this article was written as objectively as possible. I’ve also included hidden gems sourced from the community that highlight favorite features where relevant.

1. Start with import, but don’t stop there

Import is useful because it removes friction. It can bring over structure, assets, and settings from Google, Meta, or Pinterest so you can launch faster. The mistake is assuming a successful import means your Microsoft Advertising strategy is finished.

Imported campaigns preserve yesterday’s assumptions. Microsoft Advertising still requires decisions about budget, bidding, audiences, creative, measurement, reporting, and AI-powered opportunities.

Decide whether sync helps or holds you back

One of the most important import decisions is whether future changes from the source platform should continue syncing to Microsoft Advertising. If your goal is to mirror another platform, automatic sync may reduce overhead. If your goal is to build a Microsoft-specific strategy, automatic sync can quietly erase the optimizations you make after launch.

To access the full list of import settings, go to Manual import > Advanced settings. Review which settings should stay, which should change, and which Microsoft-specific opportunities weren’t part of the original structure.

Review budgets, bids, currency, and Microsoft-only options

Imported budgets may not reflect the opportunity or efficiency available, especially if you consolidate campaigns with ad-group-level controls. 

Imported bids may preserve assumptions from another platform instead of giving Microsoft Advertising room to optimize for its own auction dynamics, audiences, and conversion data.

Review Microsoft-specific settings after import

Import also can’t choose Microsoft-specific opportunities for you. Review these settings after launch:

  • LinkedIn profile targeting: Bid up or down, observe, and use LinkedIn profile data as a Performance Max audience signal. Microsoft supports Company, Industry, Job Function, and Seniority.
  • Ad-group-level scheduling and location targeting: Override campaign-level schedules and location targets at the ad group level. You have access to the same settings, including whether ads serve in the user’s time zone or the account’s time zone.
  • Impression-based remarketing: Target, exclude, or adjust bids based on someone seeing your ad. As long as there’s at least one Audience ads campaign or ad group in the source seed lists (up to 20), any campaign can target any other campaign (for example, search can target search). Impression-based remarketing doesn’t require an existing email list or pixel, and members can remain on the list for up to 30 days after a single impression.
  • Multimedia ads: Visual-heavy ads that occupy a unique position on the SERP and are eligible to serve in Copilot. They have their own auction and can appear on the same SERP as your text ad without competing against it. You can bid more aggressively for Multimedia ads and create them in standard search campaigns by selecting that ad type instead of responsive search ads (RSAs).
  • Cross-account portfolio bidding: If you need to launch a new account for the same brand, you can allow it to benefit from the conversion data of an existing account.
  • Microsoft Clarity: A free behavioral analytics tool that helps you understand how people and AI engage with your site. It can reveal whether landing pages create friction, are easy for people and AI to understand, and which grounding queries (the searches AI performs in the background) your site naturally appears for and whether they align with your target search terms.
  • Creative and editorial considerations: Microsoft has stricter advertising policies than many other platforms, but it also offers some unique capabilities, such as allowing exclamation points in headlines and disclaimers of up to 500 characters that don’t take up ad space. Note: If you enable disclaimers, your ads will only serve when the disclaimers can appear alongside them.

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2. Build the signal foundation before optimizing

Account-level settings may seem overly technical. In practice, they determine whether AI receives clean signals or learns from bad data. Settings like business attributes also let you communicate why customers should choose your business.

Verify conversion tracking and attribution before changing bids

The best bidding strategy can’t compensate for incomplete conversion data. Microsoft Advertising provides account-level settings that help ensure conversion and attribution data flow correctly, including:

  • Microsoft Click ID (MSCLID): Helps connect ad clicks to conversion activity.
  • View-through conversions: Help you correctly attribute the role of visual creatives in the path to conversion.
  • Simplified conversion setup: Enables intelligent conversion action creation.

Without verified tracking, it’s easy to blame bidding, keywords, audiences, or creative when the real problem is incomplete or inconsistent conversion data.

If your organization relies heavily on UTM parameters, validate how auto-tagging and manual tagging interact. The goal is clean reporting, not duplicated parameters or attribution confusion caused by mislabeling.

Treat creative inputs as signals

When enabled, Microsoft Advertising can use images from your landing pages to create more compelling, relevant ad experiences that better match a potential customer’s intent and context. If you have strong, well-maintained landing pages, this can improve creative asset coverage without having to manually build every image variation for every campaign type.

AI-optimized creative works best when your site already contains brand-safe, relevant, high-quality imagery. If your pages contain images you wouldn’t want appearing in ads, or if the imagery is sparse, text-heavy, or doesn’t represent the offer well, upload the assets you want the system to use. Auto-retrieved images reduce creative friction. They don’t replace creative strategy.

Use account-level negatives carefully

Account-level negatives help eliminate unwanted traffic patterns across your account. Microsoft supports phrase and exact match negatives. If you want to eliminate a root problem, a phrase match negative is likely the better option. For a specific search term, an exact match negative may work better. Neither negative match type accounts for close variants.

Use account-level negatives only for terms you’re confident shouldn’t serve anywhere in the account. Keep nuanced exclusions at the campaign or ad group level.

3. Use structure and controls to help AI perform

Microsoft Advertising gives you useful controls, but the goal isn’t to micromanage every lever. Give AI cleaner inputs, stronger guardrails, and fewer structural problems to solve. Use human judgment to guide the system.

Concentrate signals instead of fragmenting them

Ad-group-level location and ad schedule settings can reduce the need to create duplicate campaigns or split budgets across multiple accounts.

I’ve seen advertisers create separate campaigns solely to accommodate different geographies or schedules. In many cases, those settings can be managed at the ad group level, resulting in a simpler structure and more concentrated conversion volume.

That consolidation matters because automated bidding generally performs best with stronger, more consistent signals. A practical benchmark is to aim for at least 30 conversions in 30 days, where possible. That level of steady signal gives automated bidding a better chance of making stable decisions than a fragmented structure with thin conversion volume.

Use scheduling, location, and disclaimers as guardrails

Location targeting deserves review. Microsoft Advertising supports geographic targets, radius targeting, and exclusions, but city-, county-, metro-, or DMA-level strategies may be more practical than forcing ZIP codes.

If Microsoft doesn’t support a specific location target, it defaults to the next-highest level (for example, ZIP code to city or city to DMA). If you need narrow targeting, consider using exclusions.

Avoid unnecessary learning volatility

Large bid or budget changes can create performance volatility as the system adjusts. As a general rule, keeping bid or budget changes below 15% over a 14-day period can help reduce avoidable learning volatility. Larger changes may still be necessary, but make them intentionally rather than accidentally resetting the system’s learning rhythm.

Seasonality adjustments help when you expect a temporary conversion rate change because of a sale, event, promotion, or other short-term spike. Data exclusions help when conversion tracking breaks or reports misleading data you don’t want automated bidding to learn from. These tools aren’t bidding hacks. They protect automation from learning the wrong lesson.

Use conversion value rules whenever possible

The best way to communicate with the bidding algorithm is through conversion value rules grounded in accurate conversion tracking. They let you create if/then statements for devices, audiences, and locations to add a monetary amount to or multiply your conversion value.

Microsoft supports bid adjustments across audiences, devices, demographics, locations, and time. Multiple adjustments can compound. If a user qualifies for several categories at once, your bid may become more aggressive than intended.

Before adding another layer, ask whether you truly want to spend more to reach that audience, in that location, on that device, during that time. If you want the algorithm to understand value, meaningful conversion values and conversion value rules are usually stronger signals. If values aren’t reliable, CPA-oriented bidding with carefully chosen adjustments can still work.

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4. Use audiences, inventory, and creative to shape demand

Microsoft’s differentiated audiences, inventory, and creative formats can help you generate and shape new demand rather than only capture existing demand.

Use LinkedIn profile targeting intentionally

LinkedIn profile targeting remains one of the most unique audience capabilities in Microsoft Advertising. You can apply bid adjustments based on company, industry, job function, and seniority. 

Multiple targets within the same LinkedIn profile category act as “or” statements, while targeting across categories narrows the signal. A company target plus a seniority target is more restrictive than choosing two companies, which is useful when intentional and expensive when accidental because bid adjustments compound.

For B2B advertisers, this can be especially useful, but it isn’t limited to enterprise brands. Any business selling to specific professional audiences can use these signals to prioritize valuable traffic.

For example, if a brand is trying to reach someone traveling for work with local experiences or travel gear, it might bid up on someone with a “Business development” job function in an industry with a conference taking place in the next two to three weeks.

Build audiences from exposure, not just site visits

Traditional remarketing relies on someone visiting your website. Impression-based remarketing introduces another option: building audiences based on people who’ve been exposed to your advertising. 

A prospect may not click the first time they encounter your brand, particularly in formats such as Audience ads, Premium Streaming, or Multimedia ads. Impression-based remarketing lets you continue that conversation later rather than treating the initial exposure as a failed interaction. An impression can be the starting point for an audience strategy.

Reevaluate search partners and exclusions

Many advertisers disable search partners because they assume they behave like display network expansions on other platforms. Search partner inventory is still search inventory, and Microsoft provides publisher visibility, so you can evaluate it rather than reject it based on assumptions. 

Recent Microsoft studies have shown a 45% improvement in conversion rates and a 20% reduction in low-quality impressions tied specifically to Search Partner inventory, independent of advertiser optimization.

If specific publishers aren’t performing, use the available controls. You can manage unlimited exclusion lists at the MCC account level, and each list can exclude up to 2,500 URLs. If you need to protect a campaign’s ability to target a placement, such as when running Performance Max and Audience ads simultaneously, exclude domains surgically rather than cutting off useful inventory.

Use Multimedia ads to expand your SERP presence and build impression-based remarketing lists

Multimedia ads participate in their own auction and can appear in prominent visual placements on the search results page. A traditional search ad and a Multimedia Ad can both appear for the same brand, increasing your presence on the results page. 

Multimedia ads can be enabled at the campaign level, with ad-group-level decisions that help direct budget toward or away from the format.

They also matter because they can amplify your visual presence, serve as ads in Copilot, and qualify for impression-based remarketing. Their value isn’t limited to direct-click performance. They can connect search visibility, visual storytelling, and remarketing strategy.

Use Audience ads to expand reach

Audience ads (display, native, and video) can be a controlled way to expand reach, support full-funnel strategy, and build remarketing inputs that inform other parts of the account.

Audience ads support audience strategies, placement preferences, content category controls, and creative preview before launch. For organizations that require legal, brand, product, or executive approval, preview capability can simplify the review process.

Use creative and editorial details to reduce friction

Microsoft Advertising has editorial policies you should understand rather than assuming every platform evaluates ads the same way. Claims such as “best,” “number one,” or other superiority language need clear landing page support. 

Microsoft Advertising also allows some emphasis you might not expect, such as one exclamation point in headlines, but that flexibility doesn’t remove the need for substantiated claims and clean final URLs.

Editorial issues often get misdiagnosed as platform friction. In many cases, the issue is a specific asset rather than the entire ad, but final URL problems are more fundamental and can prevent an ad from serving.

Extensions and visual assets can also help brands communicate more value before users reach the landing page, especially in competitive categories where plain text may not provide enough differentiation.

5. Treat PMax, AI Max, and Copilot as AI opportunities with guardrails

Microsoft’s approach to AI is most useful when viewed as an augmentation rather than a replacement. Human-centered AI should enable thoughtful scale while preserving advertiser consent, transparency, and trust.

Know what Performance Max is designed to enable

Performance Max can be powerful, but it requires a different mindset from traditional campaign structures. Asset groups aren’t ad groups. There’s no asset-group-level equivalent to ad-group negatives, and you can’t force one asset group to take priority over another.

Performance Max is designed for AI-driven allocation. If strict control is your priority, traditional Search, Shopping, and Audience campaigns may provide clearer governance. The best way to influence Performance Max is through:

  • Strong audience signals: Include impression-based remarketing and LinkedIn profile targeting, which are unique to Microsoft.
  • Relevant creative: Copilot can pull creative from your landing page and adapt existing creative with tonal shifts, rewrites, or formatting improvements.
  • Thoughtful search themes: Including the same search themes as your exact match keywords makes it harder for Performance Max because exact match keywords take priority in the auction.
  • Meaningful conversion tracking: Ensure you have accurate conversion tracking and conversion values because Performance Max needs conversions to perform effectively.
  • Landing pages that clearly communicate the offer: Your landing page is a critical part of the matching and creative logic. If you don’t use clear language, the algorithm may struggle to identify which queries are a good match. It also makes it harder for people to do business with you.

If you run the same search theme as an exact match keyword, there’s a strong chance the exact match keyword will serve instead of the Performance Max campaign. Use search themes as testing grounds rather than duplicating exact match keywords.

Performance Max website URL reporting provides URL-level visibility into spend, clicks, impressions, and conversions. This gives you more to work with than impression-only reporting and can make automated campaign testing easier to justify.

Separate campaigns when budget separation matters

If budget separation matters, create distinct campaigns rather than forcing multiple business objectives into a single Performance Max campaign. Microsoft’s campaign capacity of 300 Performance Max campaigns, compared with Google’s 100, can be useful when meaningful budget priorities require separation.

For example, if you have two equally important products with drastically different tROAS goals, you wouldn’t want them to share budgets because there’s no way to specify which asset group or product should take priority. They’re better served in separate campaigns with distinct budgets and tROAS goals aligned to their margins.

The rule is simple: If related assets and audiences can share a budget, consolidate Performance Max campaigns to strengthen conversion volume. If budget separation matters, build that control at the campaign level instead of trying to force it through asset groups.

Evaluate AI Max and Copilot for new opportunities

AI Max now addresses many of the use cases that once made Dynamic Search ads valuable. If your goal is to let Microsoft AI better match queries, creative, and landing pages, AI Max may be the better place to focus your testing.

That doesn’t mean you should abandon existing high-performing campaigns. It means you should be intentional about whether you’re investing in legacy dynamic functionality or AI-powered capabilities built on Microsoft’s latest technology.

Ads can appear in relevant Copilot experiences when Microsoft determines there’s clear commercial intent and the ad may help the user. Ads have served in Copilot since 2024. The goal isn’t to force ads into AI answers. It’s to preserve a useful experience for the user.

Copilot isn’t a separate campaign type you manually opt into. Performance Max, AI Max, exact, phrase, and broad match search campaigns, Multimedia ads, and Shopping ads are all eligible to serve in Copilot. Performance Max and AI Max have the easiest time serving in Copilot because they can adapt to AI-driven experiences.

Use generative AI as a creative workflow and diagnostic tool

Copilot can help you brainstorm, rewrite, refine, and adapt creative across workflows, including Performance Max, responsive search ads, Multimedia ads, Audience ads, and other campaign types where you need to adjust tone, rewrite copy, or develop stronger variations. Copilot doesn’t replace the marketer. It reduces friction between strategy and iteration.

Ad Studio can generate new creative assets and make adjustments such as background modifications, seasonal refinements, location-specific tailoring, and additional aspect ratios. Its best use isn’t replacing the brand team. It’s accelerating iteration once the overall creative strategy is established.

AI-generated assets can also help diagnose how clearly your site communicates. If the outputs accurately represent your business, your site is likely sending clearer signals. If they consistently miss the mark, your landing pages, messaging, or content structure may be confusing both AI systems and people. The Performance Max campaign generator can serve as a useful diagnostic shortcut for the same reason. 

6. Use reporting and Clarity to diagnose before blaming the auction

No amount of AI, bidding nuance, or audience strategy can compensate for poor measurement. Microsoft Advertising provides extensive reporting visibility, and you should use it before making media-only decisions.

Use transparent reporting to make better decisions

Microsoft provides visibility into every search term that generates a click as part of its transparency approach. That visibility can reveal whether a query is:

  • Genuinely wasteful: There’s no business case for targeting that search.
  • An AI-driven match: It may seem questionable until you examine the customer journey with behavioral analytics.
  • A landing page issue masquerading as a traffic problem: Before adding a negative keyword, evaluate post-click behavior to determine whether the landing page or conversion tracking is the real issue.

Use Microsoft Clarity before you make campaign changes

Microsoft Clarity answers an important question: What happens after the click? It can show whether users engage with the page, get confused, abandon forms, encounter technical issues, or complete actions that aren’t being tracked correctly.

Clarity should be part of your diagnostic process before making campaign changes.

  • If people arrive and get stuck, the issue may be the landing page experience.
  • If they complete the desired action but conversions don’t appear in Microsoft Advertising, the issue may be tracking.
  • If they arrive and immediately disengage, the issue may be creative alignment, traffic quality, or the offer itself.

Clarity can also help you understand how AI systems interact with your content, including the grounding queries that led AI systems to cite your domain and recommendations for improving citation opportunities.

If AI systems cite your domain as relevant, that can validate your content strategy. If they don’t, or if the queries reveal mismatches, that may point to gaps in how your content communicates its value.

Every click they win is a customer you lose.

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See who’s stealing your traffic

Apply Microsoft-specific optimizations

You can import existing campaign structures and assets while also taking advantage of Microsoft-specific capabilities. AI can play a central role, serve as an occasional assist, or be used selectively, though scaling becomes more difficult without some level of AI adoption.

Testing Microsoft Advertising doesn’t require a large investment, but it does require getting the fundamentals right, including conversion tracking, bid-to-budget ratios, and creative that reflects the channel’s visual nature.

When you get those fundamentals right, Microsoft Advertising offers search term transparency, GDPR-compliant impression-based audiences, and opportunities to reach people across the surfaces where they work, live, and play. 

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Google Search Console gains reporting on social and video platforms

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Google Search Console has released what it calls platform properties, which is a way to see how well your social and video content is performing within Google Search. Google will let you see the performance of your content on Instagram, TikTok, X and YouTube performs on Google Search.

More details. Google wrote, “Now, you can easily track which search terms lead people to your Instagram, TikTok, X and YouTube content on Search, and see exactly how your audience is interacting with your posts.” You can see this information within Google Search Console’s performance report, insights report and achievement sections.

  • Performance report: View your total clicks, impressions, and additional metrics. Filter and sort this data to see which specific posts and queries are driving the most traffic. If you prefer to analyze your performance using another tool you can export the data.
  • Insights report: View a high-level overview of your recent traffic trends, your top-performing posts, and how people discover your account on Google.
  • Achievements: Track your growth and celebrate milestones, such as reaching a new threshold for total clicks from Google Search in the last 28 days.

This is similar to the social channels details we had in the Search Console insights reports.

Here is a screenshot of my X account performance report:

Here is a screenshot from Google’s blog post of the insights report:

How to set it up. You will need to verify your platform property within your Google Search Console account, here is how to do that:

  • Open Search Console
  • Go to the Search Console verification page, or open the property selector dropdown anywhere in Search Console and click “Add property.”
  • Select one of the four available platforms: Instagram, Tiktok, X, YouTube.
  • Follow the onscreen verification steps to securely authorize the connection.

Slow rollout. Google said platform properties will roll out gradually over the coming weeks, so there is a chance you won’t see this yet. To learn more about platform properties and how to set them up, see Google’shelp center documentation. We actually saw Google publish this help document a few weeks ago, but then it was quickly removed.

Search profiles. This is different from the new search profiles feature, which actually has its own analytics.

Why we care. Previously, Google has not given us a real way to see how our content performs on domains/properties we do not own. But now, we are going to have access to see how our content performs of domains/properties we do not have developer access to. This is pretty cool and I am excited to see what we can learn from this.

Be the brand AI recommends.

See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.

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Building a brand worth finding: Signals that fuel discovery

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Building a brand worth finding: Signals that fuel discovery

For most of the past decade, organic marketers have operated with a clear north star: visibility. Get on Page 1. Get in the featured snippet. Get seen.

That north star has now moved.

The question I put to the room at SMX Advanced on June 5 wasn’t, “How do you get found?” It was the even harder question: “How do you get chosen?”

In 2026, the answers are no longer the same, and the gap between them is where most brands lose ground.

In AI search, your reputation precedes you 

The complex, multi-touchpoint user journey of exploration, evaluation, and conversion has been dramatically compressed. A single AI prompt now does the same work that previously required a dozen searches, three Reddit threads, and a couple of comparison sites.

AI search doesn’t reward the brand that shouted the loudest in paid media or stuffed the most keywords into its metadata. Instead, it rewards the brand with the strongest reputation in the spaces that matter, the same spaces the user journey once encompassed. Reddit threads, comparison sites, and the like all get ingested by LLMs and blended into a single synthesized answer. 

AI search citation material

In other words, your brand is no longer just what you say it is. It’s how AI understands your brand, and the algorithm is reading everything everyone else has ever said about you.

Brand-owned content, across websites and social channels, will always be self-serving and promotional. AI looks for third-party validation to back up those claims.

This shift changes everything for organic marketers. Our job has gone beyond building visibility to building a brand that, once found, is correctly understood and ultimately chosen. Those are three distinct challenges, and they require three distinct strategies.

Be the brand AI recommends.

See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.

See your AI visibility

Found: Be present in your audience’s actual search ecosystem

The first challenge is still about discoverability, but we now work with a much broader canvas than just Google. Consumers now discover brands across ChatGPT, Reddit, YouTube, TikTok, Google, Quora, LinkedIn, and, yes, word of mouth. There are dozens of entry points into the discovery ecosystem, and you need to be credibly present in the ones your specific audience actually uses.

This starts with understanding your audience’s sources of influence: the publications, platforms, communities, and voices they genuinely trust. The intersection of semantic relevance, domain authority, and audience affinity tells you which third-party properties are worth targeting. For one B2B audience, that might be Wired, Tom’s Guide, or a LinkedIn group that actively discusses vendors in a particular vertical. For another, it’s r/smallbusiness and a Substack newsletter with 40,000 subscribers.

Once you know where your audience spends time, you can create content and earn your spot in the conversation in those exact places. This is targeted, audience-first, performance-driven PR and organic strategy that puts your brand in the conversations already happening at the decision point.

The data underscores the importance of earned media. Across the top commercial sectors we analyzed, 93% of AI search citations come from third-party sources. If you’re only investing in content that sits on your own domain, you’re invisible to the systems now doing the heavy lifting of brand discovery.

Understood: Consistent signals across every surface

Getting found is necessary, but it’s not a standalone effort. If you’re getting found by machines, your brand is understood well enough to be surfaced.

LLMs go beyond crawling your website. They synthesize a consensus picture of your brand from everything about you that exists online: reviews, Reddit discussions, press coverage, YouTube commentary, Trustpilot ratings, forum threads, and more. If those signals conflict with what you’re saying about yourself, you have a problem.

A brand that claims premium positioning but has thousands of articles questioning whether it’s actually luxury, a history of heavy discounting, and a Trustpilot score of 1.3 isn’t going to be recommended by AI as a premium option, regardless of how well-crafted its homepage copy is. The algorithm has read the whole story, not just your version of it.

This means brand messaging consistency is now an SEO issue. Every piece of owned, earned, and paid content needs to reinforce the same core associations — because the model is building a cohesive picture from all of it. Conflicting signals confuse customers and actively suppress your AI visibility.

Digital PR plays a critical role here. It engineers the external narrative. Through strategic media placements, expert commentary, and search-informed coverage, you shape what journalists write about you and what the models learn about you. The query fan-out (the range of prompts a potential customer might use) requires positive, consistent answers across every touchpoint where an LLM might look.

Get the newsletter search marketers rely on.

See terms.

Chosen: Earning the trust signals that tip the decision

The third challenge is the hardest and arguably the most important. Trust has always been an SEO currency. As clicks decline and zero-click search becomes the norm, its importance has only grown.

Brand appearance in AI Overviews is most strongly correlated with branded web mentions (i.e., the number of times your brand is positively named across authoritative third-party sources), according to an Ahrefs study. This is essentially digital PR’s core output, and it is one of the most powerful levers available to organic marketers right now.

Based on the last 4,000 pieces of U.S.- and U.K.-based coverage we’ve driven for our clients, 91% of AI search citations include expert insight rather than branded content or product pages. This means expert-backed, editorially independent coverage is critical. That’s why your internal experts are your most valuable asset in the current landscape, and why brands investing in genuine thought leadership, original research, and data-backed studies are pulling ahead.

The three content formats consistently driving LLM feature inclusion are product roundups and listicles (getting your brand into trusted “best of” editorials), reliable data-backed research that journalists and LLMs cite as authoritative, and expert thought leadership that positions your people as the go-to voices in your category.

What doesn’t work (and what Google has explicitly flagged in its recent release of GEO guidelines) is seeking inauthentic mentions using methods like artificial link schemes, fake expert personas, and manufactured coverage. Models are increasingly adept at distinguishing genuine authority from manufactured signals, and the reputational downside of getting caught is severe.

None of this works as a one-time effort. Multiple studies, including research from Waseda University, have identified a correlation between AI brand visibility and content recency.

Brands that maintain a steady drumbeat of credible, expert-backed third-party coverage don’t just appear more often in AI responses. They appear more confidently.

Frequency and freshness of coverage both matter. It’s not enough to deliver a one-off PR campaign. This is a strategic investment that needs to be “always on.”

If AI can’t find you, customers won’t either.

Track your visibility across AI search, uncover missed opportunities, and grow your presence where customers are asking questions.

See your AI visibility

The framework in practice

When talking about brand discovery in 2026, three words are essential: 

  • Found: Map your audience’s real sources of influence and be credibly present there — across the fragmented ecosystem where discovery now happens.
  • Understood: Ensure that everything said about your brand tells a consistent story: one that matches how you want to be positioned and reinforces the associations that drive preference.
  • Chosen: Continuously generate genuine trust signals through earned coverage, expert commentary, and third-party validation so that when a human or a machine is deciding between you and a competitor, the weight of credible external evidence tips in your favor.

The brands winning in organic search right now haven’t cracked some new technical trick. They’ve built a reputation that’s genuinely worth recommending and made sure the machines know it. Stop chasing the algorithm and start building something worth finding.

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ChatGPT Ads rolling out audience lists

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ChatGPT ads

OpenAI is rolling out the ability to upload audience lists to ChatGPT Ads. The option is under the “Tools” section and is named “Audiences.” I assume this lets ChatGPT Ads target based on the audience lists you upload to the ad platform for better ad targeting.

More details. You can upload raw or hashed emails and phone numbers to use as audience filters for your campaigns on ChatGPT Ads.

What it looks like. We spotted screenshots of this from Craig Graham and Joss Froggatt on LinkedIn – here are those screenshots:

Why we care. OpenAI continues to add more customizations and targeting options to its new ChatGPT Ads platform.

This should make for a more robust ad platform for advertisers and marketers and improve the overall conversions and ROI of the ads.

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OpenAI can generate ChatGPT Ads for you

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ChatGPT ads

OpenAI rolled out a new feature to let ChatGPT Ads generate ads for you – I suspect using AI. The feature is available under the “Add new ad” option, and says “generate ads for you.”

The advertiser or marketer can then opt to let ChatGPT create the ad, and then review, edit and approve the ad for delivery on the ChatGPT Ad platform.

What it looks like. Anthony Higman posted a screenshot of this feature on the social media platform named X. Here is that screenshot:

As you can see, it says, “We generated an ad variation based on your website and campaign settings. Review, edit as needed, and activate when you’re ready.” Then you can “Review and create.”

There is also a quick duplicate ad option that Higman spotted as well:

Why we care. It makes sense that OpenAI would leverage AI to help advertisers create ads. The hope is it would lead to more ads being created and submitted on ChatGPT Ads sooner. This way the AI company can earn more money on ChatGPT.

As a marketer, you should be very careful when using AI to generate ads. Make sure to review the ads that were generated for you carefully so they meet your branding and marketing criteria, including your ROI goals.

Be the brand AI recommends.

See where your brand appears in AI search, where competitors are winning, and what it takes to become the answer AI recommends.

See your AI visibility

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ChatGPT commands 92% of AI referral traffic. Here’s what 6.77 million sessions reveal.

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AI traffic search

Twelve months ago, the industry was betting on which AI platform would win discovery. Perplexity looked like the search-native challenger. Copilot looked like the enterprise Trojan horse. Neither bet paid off.

Previsible (disclosure: I’m its CPO and co-founder) just published its third AI Traffic Study, analyzing 6.77 million LLM-driven sessions. The data shows consolidation. Monthly LLM sessions grew 9.9x, reaching 644,478 in May 2026. And 92.4% of that traffic comes from one platform.

The plateau was a pause

In mid-2025, AI traffic appeared to be peaking in some sectors. It wasn’t.

Sessions rose from 65,249 in November 2024 to 396,278 by August 2025, then dropped sharply in November 2025, before hitting new highs of 428,203 in February 2026 and 644,478 in May.

That November dip needs context.

Sessions fell 50% in one month, driven almost entirely by ChatGPT referrals dropping from 448,412 to 213,345. Other platforms held steady. This was likely a model-related change; we’ve seen modest product tweaks massively swing referral traffic, like last fall when many sites lost half their ChatGPT traffic because the model began favoring Wikipedia and Reddit. Sessions recovered to 442,609 by December.

The lesson: one vendor’s product decisions can halve your AI traffic overnight. Plan for the volatility.

Consolidation, not competition

When we last published in December 2025, ChatGPT held roughly 84% share, followed by Perplexity at 8.9%, Gemini at 4.5%, Copilot at 2.1%, and Claude at 0.6%. Six months later, the field has collapsed toward the leader.

Across the full dataset, ChatGPT commands 92.4% of trackable LLM referral traffic, growing 12.8x over 19 months with no sign of slowing. It’s the only LLM sending meaningful referral volume at scale. Optimizing for “AI visibility” without prioritizing ChatGPT means optimizing for an abstraction.

Important framing: this measures standalone LLM referral traffic. AI discovery inside Google’s own results, including AI Overviews, almost certainly drives more AI traffic than all standalone platforms combined, but it operates on a different measurement paradigm and is excluded here.

The challengers flipped

The surprise isn’t at the top. It’s who’s moving underneath.

Claude

Claude grew 64x, from 133 sessions in November 2024 to 8,528 in May 2026, and overtook Perplexity in March 2026 for the first time. It stayed ahead.

Claude was flat through 2025, then accelerated 4x in two months as its agentic tools and enterprise integrations gained adoption. The enterprise advantage the industry expected Copilot to win may be materializing for Claude instead.

If your audience includes technical buyers, developers, or professional services, Claude visibility is now material, and the window for early positioning is open.

Gemini

Gemini is the quiet number two: 3.2x growth with almost no volatility. Its Workspace and Android integration mean referral numbers likely undercount its real discovery footprint.

Perplexity & Copilot

Perplexity peaked at 17,507 monthly sessions in March 2025 and has fallen 61% since. Copilot collapsed 96% from its August 2025 peak, from 8,651 sessions to 339.

Neither is a growth bet for traffic acquisition anymore. Both are shifting toward keeping users inside their own experiences: browsers, agents, and modes where they don’t need to send you traffic at all.

Where LLMs send users, and why it should change your roadmap

The study’s most actionable finding isn’t market share. It’s landing pages.

ChatGPT sends 28.8% of its traffic to internal search results pages. Across industries, roughly 25% of AI-referred traffic lands on internal search.

The model trusts your domain but can’t pick the right page, so it sends users to your search box and lets them navigate. This pattern persists across verticals and time periods, suggesting it’s structural to retrieval-augmented generation rather than a temporary quirk.

Think about what that means. The model did the hard work of choosing your domain. Your internal search UX now determines whether that high-intent visit converts or bounces.

For most sites, internal search is a neglected navigation feature, not an acquisition surface. That has to change.

The vertical view tells several different stories:

  • SaaS traffic lands on search pages (34.6%).
  • Publisher traffic lands on news pages (54%), yet against 120+ million organic sessions, publisher penetration is 0.11%; publishers produce the content LLMs cite and capture almost none of the resulting traffic.
  • Ecommerce traffic lands on product pages, with purchase intent already formed.
  • Education traffic lands directly on course pages (52%), bypassing marketing content.
  • Health traffic lands on About pages (42.1%), with users evaluating the source before the content.
  • Legal traffic spreads across blog, about, contact, and location pages: the full evaluation arc.

The platforms have personalities, too:

  • ChatGPT and Gemini are search-pattern models: domain trust, page-level uncertainty.
  • Perplexity and Claude are content-selection models that pick specific pages and over-index on long-form.

If your strategy depends on editorial content driving qualified traffic, Perplexity and Claude matter disproportionately to their share.

What to do now

  • Optimize for ChatGPT first. Expand elsewhere when volume justifies it.
  • Monitor Claude. It overtook Perplexity in March. Early positioning compounds.
  • Treat product pages as AI entry points. Product pages capture 43% of e-commerce LLM traffic. Structured, comparable product data is a discoverability requirement now.
  • Make pricing machine-readable. “Contact us for pricing” gives AI systems nothing to summarize, compare, or recommend.
  • Prioritize internal search. It’s an acquisition tool, not a navigation feature.
  • Track AI traffic by page type, not site-wide. Your site average hides where AI traffic concentrates. Your pricing page might run 3x your site-wide penetration.

The next question is the one nobody has answered: conversion rate by LLM platform. Which platforms send users who buy, and which send users who bounce?

We built this dataset to answer that. If the last 19 months are any indication, the answers will change faster than most teams are ready for.

About the data

166 GA4 properties, November 2024 through May 2026, spanning SaaS, ecommerce, finance, legal, health, insurance, education, publishing, and ticketing. All 166 properties are present throughout the full 19-month window, so the trajectories reflect behavioral change rather than sample expansion.

The report

You can find the full report at previsible.io.

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SMX Now: Build better site architecture for SEO, AI, and users

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Beyond navigation advanced architecture and AI

Advanced architecture is no longer just technical structure. It determines whether your content can be found, understood, and surfaced by search engines and AI systems.

Our next SMX Now on July 15 features Shari Thurow, co-founder, information scientist, and search director at the Information Architecture Gateway. She’ll explain how advanced architecture works and where most AI, SEO, and site development workflows fall short.

The session introduces a five-phase framework Thurow has tested through decades of client work with organizations including Microsoft, Google Cloud, Abbott Laboratories, CVS Pharmacy, WebMD, Sony Music, the Library of Congress, Best Buy, and Merriam-Webster. You’ll learn how architecture decisions shape labeling systems, wayfinding networks, taxonomy, wireframes, and AI access to valuable content.

It also challenges long-standing misconceptions, including the three-click rule, the idea that taxonomy is only a hierarchy, and the belief that AI can generate effective wireframes without a deeper architectural model.

You’ll leave with a practical framework for building sites that communicate more clearly with users, search engines, and human-centered AI systems.

Save your spot

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