I see performance marketing under more pressure than it has faced in a decade. Budgets are flat or shrinking, expectations keep rising, and AI is quickly raising the standard for what “good” performance actually looks like.
For years, I watched performance marketing rely on a familiar playbook. When performance plateaued, teams added another vendor. When targeting weakened, they bought another dataset. When activation became difficult, they layered on more technology. But as budgets tighten and the demand for immediate ROI grows, constantly expanding the stack is no longer sustainable.
The challenge I see for enterprise marketers is not that they lack data. It is that they struggle to operationalize the data they already have.
At the same time, AI is revealing a hard truth about modern marketing architecture. Most AI failures are not really model failures. They are data failures. Even the most advanced agent, model, or automation workflow cannot make up for fragmented customer profiles, disconnected activation systems, or stale audience definitions. Yet much of the customer data platform conversation still centers on launching more AI agents.
I think that misses the point.
The real question is not whether a platform has an AI agent. It is whether my data foundation can support the leap from automating tasks to partnering on strategic outcomes.
For too long, the industry treated self-service as the north star. The goal was to help marketers avoid engineering tickets and data science queues. That made sense for the last decade, but it also turned marketers into manual operators of complex systems. The new bar is not just self-service. It is self-directed performance at scale.
I see a fundamental shift in the marketer’s job-to-be-done. We are moving away from the operational burden of building and managing audiences and toward the strategic work of setting outcomes. Instead of spending the day wrangling segments, I can define the goal, whether that is maximizing customer lifetime value or reducing churn, and let the system suggest the best audience definitions and activation paths. When intelligent agents are connected to a clean data foundation, I move from managing technology to orchestrating outcomes. That is the new blueprint for performance.
At mParticle, we describe this approach as a performance engine: a model where the data foundation and activation layer work as one connected system. The goal is not simply to collect customer data. It is to make that data immediately useful for performance outcomes.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
Audience Agent is one example of that idea in action. I can describe what I want in plain language, such as high-value customers who have not repurchased in 60 days, and the agent proposes the underlying logic for me to review and approve.
For me, the shift is not about handing everything over to automation. It is about working in a marketer-led workflow with an expert collaborator beside me. The longer I work with it, the better it understands the business, the data, the customers, and the patterns that actually move performance. That understanding is only as strong as the data foundation behind it, and ours was built for this long before AI made the need obvious. The marketer leads. The agent elevates and expands the work. Together, they push what is possible.
That same philosophy shows up in capabilities such as Audience Expansion and Household Reach. Audience Expansion helps me identify additional high-potential users directly from first-party datasets, without depending on third-party lookalike audiences or outside data sources. It gives teams more precise control over the balance between scale and quality.
Household Reach addresses one of digital marketing’s most persistent blind spots: buying decisions rarely happen in isolation. By using first-party customer data and enriching it with trusted third-party signals, Household Reach helps marketers engage the full decision-making unit, not only the individual who converted first.
The key distinction is simple. I only need to bring my first-party data. The householding solution handles the rest, helping me reach more of the household without spending extra resources building additional audiences or manually configuring campaigns.
What connects these approaches is a different mindset. Better performance should not require more vendors, more engineering resources, or more external data. It should come from extracting more value from the customer relationships brands already understand.
In this era of intense performance pressure, I believe the advantage will go to marketers who stop looking for more vendors to solve every problem. Success will not come from adding more tools to the stack. It will come from using a stronger data foundation to meet rising expectations and activate more of the data we already own.
When I work on a site built with a framework like Next.js, Nuxt, SvelteKit, or a similar JavaScript framework, I pay close attention to hydration. It is the step that turns server-rendered HTML into an interactive page, but it is often explained in a way that does not connect clearly to SEO.
I think hydration is easier to understand when I separate content from behavior. The content may already be visible, but the page may not be fully usable until the browser finishes connecting that content to the JavaScript behind it.
What I mean by hydration
Hydration is the process where JavaScript in the browser takes over the static HTML that was built on the server. The server sends a complete page first, and then the framework attaches the logic that makes buttons, menus, forms, filters, and other interactive pieces actually work.
Here is how I usually explain the sequence. First, the server builds the page and sends fully formed HTML to the browser. I can see the content quickly, but the page is not interactive yet. Then the framework loads, walks through the existing HTML, attaches event listeners, and reconnects the visible markup to the application logic. Once that is done, the page behaves like a normal interactive app.
This is why server-rendered HTML can feel fast at first. It can paint quickly and often helps with first impressions and Largest Contentful Paint (LCP). The tradeoff is that, with traditional hydration, the page may appear ready before it is actually usable.
Hydration adds interactivity, not content
The most important distinction I keep in mind is this: hydration does not add the main content to the page. The text, images, and layout should already be present in the server-rendered HTML. Hydration only adds behavior by wiring that HTML to the JavaScript that responds to clicks, typing, taps, and other user actions.
A hydration timeline shows the gap between content appearing and a page becoming usable: HTML is visible first, but buttons only work after hydration completes.
Put simply, before hydration I can read the page. After hydration, I can use it.
I also avoid confusing hydration with the rendering pattern itself. Server-side rendering (SSR), static site generation (SSG), and client-side rendering (CSR) describe where and when the page is built. Hydration describes what happens after server-rendered or statically generated HTML reaches the browser and needs to become interactive.
From an SEO perspective, that distinction matters. When a page uses SSR or SSG correctly, the core content is already in the initial HTML. Google can discover and index that content from the HTML before depending on a JavaScript render step, which is generally more reliable than sending a mostly empty client-rendered shell.
When I see hydration become an SEO problem
Most of the time, I do not treat hydration itself as an SEO problem. It becomes a problem when hydration breaks, usually because the HTML created on the server does not match what the framework expects to create in the browser.
That kind of mismatch can happen when content depends on browser-only APIs such as localStorage, when a value changes between server and client rendering such as new Date(), when a third-party script or browser extension changes the DOM before hydration finishes, or when invalid HTML causes the browser to rewrite the structure before the framework can attach to it.
Before hydration, a server-rendered page can be read but not used; after hydration, JavaScript adds behavior so elements like the Subscribe button respond.
When the two versions do not line up, the framework may throw away the mismatched section and re-render it in the browser. The exact behavior depends on the framework, but the SEO and performance risks are similar.
For example, if a <time> value is generated with new Date(), the server may output one value while the browser generates another. That mismatch can force a re-render, even though the page appeared to load correctly at first.
I worry about this because it can hurt the page in several ways. A re-render can make the page feel sluggish, which can affect Interaction to Next Paint (INP). It can shift the layout, which can affect Cumulative Layout Shift (CLS). It can also break user actions if event listeners fail to attach properly, leaving buttons, menus, or forms unresponsive.
In severe cases, Google may read the raw server HTML before JavaScript finishes rendering and then index content that visitors never actually see after the page re-renders. That is the scenario I want to avoid most: search engines and users experiencing different versions of the same page.
The fix is usually not an SEO trick. It is a development fix. I want the underlying mismatch removed by using valid HTML, avoiding browser-only logic during server rendering, stabilizing values that change between server and client, and controlling third-party scripts that alter the DOM too early.
When server HTML and browser-rendered content disagree, hydration may discard and rebuild the page, creating layout shifts, broken UI and potential SEO indexing problems.
How I spot hydration problems on a live site
Hydration errors are usually easier to catch in development than on a live site, but I still look for a few practical signals. I start with the browser’s Developer Tools console and check for hydration warnings, JavaScript errors, or framework-specific mismatch messages.
Then I watch the page load carefully. If content flickers, shifts, disappears, reappears, or stays unresponsive for longer than expected, I treat that as a sign worth investigating.
I also use Google Search Console’s URL Inspection tool on important templates to see how Google renders the page. For larger sites, I prefer crawling with JavaScript rendering enabled in tools like Screaming Frog or Sitebulb so I can compare rendered output against raw HTML at scale.
How I think about different hydration approaches
Modern frameworks handle hydration in different ways, and I think of those differences as a balance between performance, interactivity, and how much JavaScript must run in the browser.
Full hydration means the entire page hydrates in one pass. It is straightforward, but it usually ships the most JavaScript and asks the browser to do the most main-thread work. Next.js Pages Router is a common example of this model.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
Partial hydration hydrates only the interactive pieces, often called islands. Static sections remain plain HTML and do not need client-side JavaScript. Astro’s islands architecture is a well-known example of this approach.
Progressive hydration hydrates the page in pieces over time. A framework may hydrate sections as they scroll into view or as browser resources become available. Angular’s incremental hydration follows this general pattern.
React Server Components take a different path by letting some components render entirely on the server and ship no client-side JavaScript for those server-only parts. In those cases, there is nothing for the browser to hydrate for that portion of the page. Next.js App Router uses this model.
Resumability goes further by trying to skip hydration entirely. Instead of re-running components on load, the page resumes from the state the server already produced. Qwik is the main example here, although I still view it as newer and less battle-tested than some of the older patterns.
When I compare these techniques, I look at what hydrates, how much JavaScript ships, and how much work the browser must do. Full hydration touches the entire page and usually ships the most JavaScript. Partial hydration touches only interactive components and ships less. Progressive hydration spreads the work over time. React Server Components reduce hydration for server-only parts. Resumability aims to avoid hydration altogether.
What this means for my SEO work
I do not assume hydration is bad for SEO. In most cases, it is simply part of how modern server-rendered and statically generated sites become interactive.
What I do watch closely is whether the server HTML and the browser-rendered version agree. If they do, hydration is usually a performance and user experience consideration. If they do not, hydration can become a visibility problem, especially when Google indexes a version of the page that users never see.
Newer frameworks reduce some of this risk by shipping less JavaScript and doing less work in the browser, but they do not remove the need for careful implementation. For me, the practical takeaway is simple: make sure the important content is present in the initial HTML, keep server and client output consistent, and test how search engines actually render the page.
I often get asked why I “only” run each prompt one time per day.
For me, the answer comes down to signal quality. Running a prompt once daily gives me enough consistent data to understand performance without overloading the process with unnecessary repetition.
The statistics show that a single daily run is plenty. It gives me a reliable view of how prompts behave over time, while keeping the workflow focused, efficient, and easier to interpret.
I am seeing OpenAI roll out the ability to upload audience lists inside ChatGPT Ads. The new option appears under the “Tools” section and is labeled “Audiences.”
My read is that this gives advertisers a way to target campaigns based on the audience lists they upload to the platform, which should make ChatGPT Ads more useful for more precise ad targeting.
A new Audiences area appears in ChatGPT Ads Manager, inviting advertisers to upload customer lists for campaign targeting and audience filtering.
More details. I can upload raw or hashed emails and phone numbers and use them as audience filters for campaigns running on ChatGPT Ads.
A ChatGPT Ads audience upload form shows how advertisers can add customer lists, choose identifier type, and submit CSV or TXT files for campaign targeting.
What it looks like. I spotted screenshots of the feature from Craig Graham and Joss Froggatt on LinkedIn. Here is what the Audiences option looks like in the platform:
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
Why I care. I see this as another sign that OpenAI is continuing to build more customization and targeting controls into its new ChatGPT Ads platform.
For advertisers and marketers, audience uploads could make the platform more practical and more performance-focused. If the targeting works well, it may help improve conversions, strengthen ROI, and make ChatGPT Ads a more serious option in paid media plans.
When I see Microsoft Advertising campaigns struggle to scale, the issue is often not the platform itself. It is usually that the account is being treated as a copy of a strategy built somewhere else.
Importing campaigns can get me live quickly, but it is only the beginning. Real performance comes when I add human judgment, Microsoft-specific structure, clean measurement, business-specific controls, and enough creative assets to help AI understand what I am actually selling.
The strongest accounts I see have a shared pattern: import is the starting point, visual creative opens more demand, and AI works best when I give it the right structure, signals, measurement, and guardrails.
Here is how I approach Microsoft Advertising when I want more than a simple campaign import.
Note: I’m a Microsoft employee, and I have written this as objectively as possible. I have also included community-sourced hidden gems where they help highlight useful features.
1. I start with import, but I do not stop there
Import is useful because it removes friction. It can bring over campaign structure, assets, and settings from Google, Meta, or Pinterest so I can launch faster. The mistake is assuming that a successful import means the Microsoft Advertising strategy is finished.
Imported campaigns often preserve yesterday’s assumptions. I still need to make Microsoft-specific decisions about budget, bidding, audiences, creative, measurement, reporting, and AI-powered opportunities.
Decide whether sync helps or holds the account back
One of the first choices I review is whether future changes from the source platform should keep syncing into Microsoft Advertising. If I only want to mirror another platform, automatic sync can reduce maintenance. If I want to build a Microsoft-specific strategy, automatic sync can quietly overwrite the optimizations I make after launch.
To see the full list of import settings, I go to Manual import > Advanced settings. From there, I review which settings should stay, which should change, and which Microsoft-specific opportunities were never part of the original structure.
Review budgets, bids, currency, and Microsoft-only options
Imported budgets may not match the opportunity or efficiency available in Microsoft Advertising, especially when I can consolidate campaigns and use ad-group-level controls instead.
Imported bids can also carry assumptions from another platform. I want Microsoft Advertising to have room to optimize for its own auction dynamics, audiences, and conversion data.
A PPC expert highlights LinkedIn Profile Targeting as a Microsoft Advertising hidden gem, especially for B2B campaigns that need to reach senior decision influencers.
Review Microsoft-specific settings after import
Import cannot choose Microsoft-specific opportunities for me. After launch, I review the settings that can materially change performance.
LinkedIn profile targeting: I can bid up or down, observe performance, and use LinkedIn profile data as a Performance Max audience signal across Company, Industry, Job Function, and Seniority.
Ad-group-level scheduling and location targeting: I can override campaign-level schedules and location targets at the ad group level, including whether ads serve in the user’s time zone or the account’s time zone.
Impression-based remarketing: I can target, exclude, or adjust bids based on someone seeing my ad. It does not 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: These visual-heavy ads have their own auction, can appear on the same SERP as my text ad, and may also serve in Copilot.
Cross-account portfolio bidding: If I need to launch a new account for the same brand, I can let it benefit from conversion data in an existing account.
Microsoft Clarity: I can use this free behavioral analytics tool to understand how people and AI engage with my site, where landing pages create friction, and which grounding queries may connect AI systems to my content.
Creative and editorial considerations: Microsoft has stricter advertising policies than many platforms, but it also allows useful capabilities such as exclamation points in headlines and disclaimers of up to 500 characters that do not take up ad space. If I enable disclaimers, my ads will only serve when the disclaimers can appear alongside them.
2. I build the signal foundation before optimizing
Account-level settings can look overly technical, but I treat them as the foundation for AI performance. They determine whether automation learns from clean data or from messy, misleading signals. Settings such as business attributes also help me communicate why customers should choose the business.
Verify conversion tracking and attribution before changing bids
Even the best bidding strategy cannot make up for incomplete conversion data. Before I blame bids, keywords, audiences, or creative, I verify that conversion and attribution data are flowing correctly.
Microsoft Click ID (MSCLID): This helps connect ad clicks to conversion activity.
View-through conversions: These help me understand the role visual creative plays before a conversion happens.
Simplified conversion setup: This enables intelligent conversion action creation.
Without verified tracking, it is easy to diagnose the wrong problem. What looks like a bidding issue may actually be incomplete or inconsistent conversion data.
If the organization relies heavily on UTM parameters, I also validate how auto-tagging and manual tagging interact. My 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 landing pages to create more relevant ad experiences. If the site has strong, brand-safe, well-maintained imagery, this can improve creative coverage without forcing me to manually build every variation for every campaign type.
AI-optimized creative works best when the site already gives it good material. If the pages include images I would not want in ads, or if the imagery is sparse, text-heavy, or poorly matched to the offer, I upload the assets I want the system to use. Auto-retrieved images reduce friction, but they do not replace creative strategy.
Use account-level negatives carefully
Account-level negatives can eliminate unwanted traffic patterns across the account. Microsoft supports phrase and exact match negatives. If I need to remove a root problem, phrase match is often the better option. If I need to block a specific search term, exact match may work better. Neither negative match type accounts for close variants.
I only use account-level negatives for terms I am confident should not serve anywhere in the account. More nuanced exclusions belong at the campaign or ad group level.
3. I use structure and controls to help AI perform
Microsoft Advertising gives me useful controls, but my goal is not to micromanage every lever. I want to give AI cleaner inputs, stronger guardrails, and fewer structural problems to solve.
Microsoft reports a 20% reduction in low-quality Search Partner Network impressions, crediting earlier invalid activity detection, stronger quality signals, and tougher enforcement.
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 have seen advertisers create separate campaigns only to support different geographies or schedules. In many cases, I can manage those settings at the ad group level, simplify the structure, and concentrate conversion volume.
That matters because automated bidding usually performs better with stronger, more consistent signals. When possible, I aim for at least 30 conversions in 30 days. That level of signal gives automated bidding a better chance to make stable decisions than a fragmented structure with thin conversion volume.
Use scheduling, location, and disclaimers as guardrails
I always review location targeting. 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 does not support a specific location target, it defaults to the next-highest level, such as ZIP code to city or city to DMA. If I need narrow targeting, I look closely at exclusions.
Avoid unnecessary learning volatility
Large bid or budget changes can create volatility while the system adjusts. As a general rule, I try to keep bid or budget changes below 15% over a 14-day period when I want to avoid unnecessary learning disruption. Larger changes may still be necessary, but I make them intentionally.
Seasonality adjustments help when I 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 that I do not want automated bidding to learn from. These tools are not bidding hacks. They protect automation from learning the wrong lesson.
Use conversion value rules whenever possible
The cleanest way I can communicate value to the bidding algorithm is through conversion value rules grounded in accurate conversion tracking. These rules let me create if/then logic for devices, audiences, and locations, then add a monetary amount or multiply 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, the bid may become more aggressive than I intended.
Before I add another layer, I ask whether I truly want to spend more to reach that audience, in that location, on that device, at that time. If I want the algorithm to understand value, meaningful conversion values and conversion value rules are usually stronger signals. If values are not reliable, CPA-oriented bidding with carefully chosen adjustments can still work.
Microsoft Advertising reports network-level gains, with indexed conversion rates up 45% and indexed cost per conversion down 1.5%, tied to cleaner traffic quality.
4. I use audiences, inventory, and creative to shape demand
Microsoft’s differentiated audiences, inventory, and creative formats can help me generate and shape new demand instead of only capturing demand that already exists.
Use LinkedIn profile targeting intentionally
LinkedIn profile targeting is still one of the most distinctive audience capabilities in Microsoft Advertising. I 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 two company targets. That can be powerful when intentional and expensive when accidental because bid adjustments compound.
For B2B advertisers, this can be especially useful, but it is not limited to enterprise brands. Any business selling to specific professional audiences can use these signals to prioritize valuable traffic.
For example, if I am trying to reach someone traveling for work with local experiences or travel gear, I might bid up on a “Business development” job function in an industry with a conference happening in the next two to three weeks.
Build audiences from exposure, not just site visits
Traditional remarketing depends on someone visiting my website. Impression-based remarketing gives me another option: building audiences from people who have been exposed to my advertising.
A prospect may not click the first time they see the brand, especially in formats such as Audience ads, Premium Streaming, or Multimedia ads. Impression-based remarketing lets me continue the conversation later instead of treating the first exposure as a failed interaction. An impression can become the starting point for an audience strategy.
Reevaluate search partners and exclusions
Many advertisers disable search partners because they assume the inventory behaves like display network expansion on other platforms. I do not start with that assumption. Search partner inventory is still search inventory, and Microsoft provides publisher visibility, so I can evaluate it directly.
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 are not performing, I use the available controls. I can manage unlimited exclusion lists at the MCC account level, and each list can exclude up to 2,500 URLs. If I need to protect a campaign’s ability to target a placement, such as when Performance Max and Audience ads run together, I exclude domains surgically instead of cutting off useful inventory.
A PPC strategist highlights a practical Microsoft Advertising tactic: run multimedia ads separately from branded search to expand visibility without self-competition.
Use Multimedia ads to expand SERP presence
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 my presence on the SERP.
I can enable Multimedia ads at the campaign level and then use ad-group-level decisions to direct budget toward or away from the format.
They matter because they can amplify visual presence, serve as ads in Copilot, and qualify for impression-based remarketing. Their value is not limited to direct-click performance. They can connect search visibility, visual storytelling, and remarketing strategy.
Use Audience ads to expand reach
I use Audience ads, including display, native, and video, as 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 make review much easier.
Use creative and editorial details to reduce friction
Microsoft Advertising has editorial policies I need to understand instead of 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 I might not expect, such as one exclamation point in headlines, but that flexibility does not remove the need for substantiated claims and clean final URLs.
Editorial issues are often misdiagnosed as platform friction. In many cases, the issue is one specific asset rather than the entire ad. Final URL problems are more fundamental and can prevent an ad from serving at all.
Extensions and visual assets can help brands communicate more value before users reach the landing page, especially in competitive categories where plain text may not provide enough differentiation.
5. I treat PMax, AI Max, and Copilot as AI opportunities with guardrails
I find Microsoft’s approach to AI most useful when I view it as augmentation rather than replacement. Human-centered AI should help me scale thoughtfully while preserving consent, transparency, and trust.
A marketer highlights how Microsoft Clarity surfaces real user friction, from mobile testing gaps to visitors tapping images they mistake for links, offering useful context for ad and landing page optimization.
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 are not ad groups. There is no asset-group-level equivalent to ad-group negatives, and I cannot force one asset group to take priority over another.
Performance Max is built for AI-driven allocation. If strict control is the priority, traditional Search, Shopping, and Audience campaigns may provide clearer governance. When I want to influence Performance Max, I focus on the inputs that matter most.
Strong audience signals: I include impression-based remarketing and LinkedIn profile targeting, which are unique to Microsoft.
Relevant creative: Copilot can pull creative from the landing page and adapt existing creative with tonal shifts, rewrites, or formatting improvements.
Thoughtful search themes: I avoid duplicating exact match keywords as search themes because exact match keywords take priority in the auction.
Meaningful conversion tracking: I make sure conversion tracking and conversion values are accurate because Performance Max needs conversions to perform effectively.
Clear landing pages: The landing page must communicate the offer clearly. If it does not, the algorithm may struggle to match the right queries, and people may struggle to do business with me.
If I run the same search theme as an exact match keyword, there is a strong chance the exact match keyword will serve instead of the Performance Max campaign. I prefer to use search themes as testing grounds rather than duplicates.
Performance Max website URL reporting gives me URL-level visibility into spend, clicks, impressions, and conversions. That gives me 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, I create distinct campaigns instead of forcing multiple business objectives into one Performance Max campaign. Microsoft’s capacity of 300 Performance Max campaigns, compared with Google’s 100, can be useful when budget priorities genuinely need separation.
For example, if I have two equally important products with drastically different tROAS goals, I would not want them to share budgets because I cannot specify which asset group or product should take priority. Separate campaigns with distinct budgets and tROAS goals are usually a cleaner fit.
My rule is simple: if related assets and audiences can share a budget, I consolidate Performance Max campaigns to strengthen conversion volume. If budget separation matters, I 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 my goal is to let Microsoft AI better match queries, creative, and landing pages, AI Max may be the better place to test.
That does not mean I abandon existing high-performing campaigns. It means I stay intentional about whether I am 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 is clear commercial intent and the ad may help the user. Ads have served in Copilot since 2024. The goal is not to force ads into AI answers. It is to preserve a useful experience for the user.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
Copilot is not a separate campaign type I 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 there because they can adapt to AI-driven experiences.
Use generative AI as a creative workflow and diagnostic tool
Copilot can help me brainstorm, rewrite, refine, and adapt creative across Performance Max, responsive search ads, Multimedia ads, Audience ads, and other campaign types. It does not 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. I see its best use as accelerating iteration once the creative strategy is already clear.
AI-generated assets can also help me diagnose how clearly the site communicates. If the outputs accurately represent the business, the site is probably sending clearer signals. If they repeatedly miss the mark, the landing pages, messaging, or content structure may be confusing both AI systems and people. The Performance Max campaign generator can be a useful diagnostic shortcut for the same reason.
6. I use reporting and Clarity before blaming the auction
No amount of AI, bidding nuance, or audience strategy can compensate for poor measurement. Microsoft Advertising provides strong reporting visibility, and I 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. I use that visibility to understand what is really happening behind performance changes.
Genuinely wasteful: There may be no business case for targeting that search.
An AI-driven match: The query may look questionable until I examine the customer journey with behavioral analytics.
A landing page issue disguised as a traffic problem: Before I add a negative keyword, I evaluate post-click behavior to see whether the landing page or conversion tracking is the real issue.
Use Microsoft Clarity before making campaign changes
Microsoft Clarity answers one of the most important questions in campaign diagnostics: what happens after the click? It can show whether users engage with the page, get confused, abandon forms, run into technical issues, or complete actions that are not being tracked correctly.
I want Clarity in the diagnostic process before I make major campaign changes.
If people arrive and get stuck, the issue may be the landing page experience.
If they complete the desired action but conversions do not 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 me understand how AI systems interact with my content, including the grounding queries that led AI systems to cite the domain and recommendations for improving citation opportunities.
If AI systems cite the domain as relevant, that can validate the content strategy. If they do not, or if the queries reveal mismatches, that may point to gaps in how the content communicates value.
I apply Microsoft-specific optimizations deliberately
I can import existing campaign structures and assets while still taking advantage of Microsoft-specific capabilities. AI can play a central role, act as an occasional assist, or be used selectively, but scaling becomes harder without some level of AI adoption.
Testing Microsoft Advertising does not require a massive investment. It does require getting the fundamentals right: conversion tracking, bid-to-budget ratios, and creative that reflects the channel’s visual nature.
When I get those fundamentals right, Microsoft Advertising gives me search term transparency, GDPR-compliant impression-based audiences, and opportunities to reach people across the surfaces where they work, live, and play.
For most of the past decade, I treated organic marketing as a visibility game. I wanted brands on Page 1, inside featured snippets, and in front of the people already searching.
That north star has moved.
When I spoke at SMX Advanced on June 5, the question I put to the room was not simply, “How do I get a brand found?” The harder question was, “How do I get that brand chosen?”
In 2026, those answers are no longer the same. The distance between being discovered and being selected is where I see many brands losing ground.
In AI search, my reputation shows up first
The old user journey was messy and multi-step. People explored, compared, checked reviews, read Reddit threads, visited comparison sites, and moved toward a decision over time. Now, a single AI prompt can compress much of that process into one synthesized answer.
AI search does not reward the brand that shouts the loudest in paid media or stuffs the most keywords into metadata. I see it rewarding the brand with the strongest reputation in the places that matter. Reddit discussions, review sites, comparison pages, expert commentary, forums, and editorial coverage are all being absorbed by large language models and blended into recommendations.
In other words, my brand is no longer defined only by what I say about it. It is shaped by how AI understands it, and AI is reading what everyone else has said, too.
Owned content on websites and social channels will always carry a promotional bias. AI systems look for outside validation to support, challenge, or clarify those claims.
That changes the work of organic marketing. I can no longer stop at visibility. I have to build a brand that is found, correctly understood, and ultimately chosen. Those are three separate challenges, and I need a strategy for each one.
Found: I need to appear where my audience actually looks
The first challenge is still discoverability, but the canvas is much wider than Google. People now discover brands through ChatGPT, Reddit, YouTube, TikTok, Google, Quora, LinkedIn, and word of mouth. I have to understand which of those entry points matter most to the specific audience I want to reach.
That starts with mapping the sources my audience genuinely trusts: the publications, platforms, communities, creators, analysts, newsletters, and peer groups that influence their decisions. The intersection of semantic relevance, domain authority, and audience affinity tells me which third-party properties are worth pursuing.
For one B2B audience, that might mean Wired, Tom’s Guide, or an active LinkedIn group where buyers discuss vendors in a specific vertical. For another, it might be r/smallbusiness or a Substack newsletter with 40,000 engaged subscribers.
Once I know where the audience spends time, I can create useful content, earn credible mentions, and participate in the conversations already shaping decisions. This is audience-first, performance-driven PR and organic strategy, not generic brand awareness.
AI search leans heavily on outside validation: this chart shows third-party communities, reviews, and earned media driving 93% of citations versus 7% from owned channels.
The data makes the case even stronger. Across the top commercial sectors analyzed, 93% of AI search citations came from third-party sources. If I only invest in content on my own domain, I risk being invisible to the systems now doing much of the brand discovery work.
Understood: I need consistent signals everywhere
Getting found matters, but it is not enough on its own. If machines are surfacing my brand, they also need to understand it accurately.
LLMs do more than crawl my website. They build a consensus picture from everything available online: reviews, Reddit discussions, press coverage, YouTube commentary, Trustpilot ratings, forum threads, and more. If those signals conflict with the story I am telling about myself, I have a real problem.
If I claim premium positioning while thousands of articles question whether the brand is truly luxury, heavy discounting is part of the public record, and review scores are poor, AI is unlikely to recommend that brand as a premium option. The model has read the broader story, not just the homepage copy.
That is why brand messaging consistency has become an SEO issue. Owned, earned, and paid content all need to reinforce the same core associations. Conflicting signals do not just confuse customers; they can weaken AI visibility.
Digital PR plays a critical role here because it helps shape the external narrative. Through strategic media placements, expert commentary, and search-informed coverage, I can influence what journalists write, what audiences remember, and what models learn.
I also have to think beyond one obvious keyword. The query fan-out, or the range of prompts a potential customer might use, requires positive and consistent answers across every touchpoint an LLM might evaluate.
Chosen: I need trust signals that influence the decision
The third challenge is the hardest and probably the most important. Trust has always been an SEO currency, but as clicks decline and zero-click search becomes more common, trust matters even more.
According to an Ahrefs study, brand appearance in AI Overviews is most strongly correlated with branded web mentions. In practical terms, that means the number of times a brand is positively named across authoritative third-party sources is becoming one of the most powerful signals organic marketers can influence.
That is also the core output of strong digital PR. Based on the last 4,000 pieces of U.S.- and U.K.-based coverage driven for clients, 91% of AI search citations included expert insight rather than branded content or product pages.
That tells me expert-backed, editorially independent coverage is critical. Internal experts are now one of the most valuable assets a brand has. Brands that invest in real thought leadership, original research, and data-backed studies are giving both people and AI systems stronger reasons to trust them.
The three content formats I see consistently supporting LLM inclusion are product roundups and listicles that place a brand inside trusted “best of” editorials, reliable data-backed research that journalists and LLMs can cite, and expert thought leadership that positions real people as credible voices in their category.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
What does not work is chasing inauthentic mentions through artificial link schemes, fake expert personas, or manufactured coverage. Google has already flagged these kinds of tactics in its GEO guidance, and models are getting better at distinguishing genuine authority from manipulated signals.
The reputational risk is also high. If I try to manufacture authority and get caught, I do not just lose visibility. I damage the trust I was trying to build.
This cannot be 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 flow of credible, expert-backed third-party coverage do not just appear more often in AI responses. They appear with more confidence.
Frequency and freshness both matter. A one-off PR campaign is not enough. I need to treat credible external validation as an always-on strategic investment.
The framework I use in practice
When I think about brand discovery in 2026, I come back to three words: found, understood, and chosen.
Found: I map the audience’s real sources of influence and make sure the brand is credibly present across the fragmented ecosystem where discovery now happens.
Understood: I work to make sure everything said about the brand tells a consistent story, matches the desired positioning, and reinforces the associations that drive preference.
Chosen: I continuously build genuine trust signals through earned coverage, expert commentary, and third-party validation, so that when a person or machine compares the brand with a competitor, credible external evidence tips the decision in my favor.
The brands winning in organic search right now have not unlocked some secret technical trick. They have built reputations worth recommending, and they have made sure machines can understand those reputations clearly.
That is where I believe organic marketing has to go next. Instead of chasing the algorithm, I need to build something worth finding, worth understanding, and worth choosing.
I’m seeing Google Search Console get a useful new reporting layer for social and video content through what Google calls platform properties. This gives me a way to understand how my content on Instagram, TikTok, X, and YouTube is performing in Google Search.
The big change is that I can now connect supported social or video accounts to Search Console and see how people find that content through Google. Instead of only analyzing websites I own or manage directly, I can begin looking at search visibility for content hosted on third-party platforms.
Google said this update makes it possible to track which search terms lead people to Instagram, TikTok, X, and YouTube content in Search, along with how audiences interact with those posts. I’ll be able to review this data inside the performance report, insights report, and achievements sections of Google Search Console.
A Google Search Console dropdown highlights the new platform property flow, with the rustybrick X profile appearing as a selectable property for reporting.
In the performance report, I can review total clicks, impressions, and other key metrics. I can also filter and sort the data to see which posts and queries are driving the most traffic, and if I want to analyze it somewhere else, I can export the data.
In the insights report, I can get a higher-level view of recent traffic trends, top-performing posts, and the ways people are discovering my account through Google Search.
A Google Search Console platform property view shows how an X profile appears in Search, pairing 28-day click and impression trends with the queries driving visibility.
The achievements section adds another useful angle by helping me track growth milestones, such as reaching a new threshold for total clicks from Google Search over the last 28 days.
This feels similar to the social channel details that previously appeared in Search Console insights, but platform properties look like a more direct way to verify and analyze these accounts.
A Google Search Console Insights view highlights how YouTube posts are gaining visibility in Search, with 17.8K clicks and traffic broken down by web, video, Discover, and image search.
To set this up, I need to verify a platform property inside my Google Search Console account. I can start by opening Search Console, going to the Search Console verification page, or using the property selector dropdown anywhere in Search Console and choosing “Add property.”
From there, I select one of the currently supported platforms: Instagram, TikTok, X, or YouTube. Then I follow the onscreen verification steps to securely authorize the connection.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
Google said platform properties will roll out gradually over the coming weeks, so I may not see the option in my account right away. For setup details, Google points users to its help center documentation. The help document had briefly appeared a few weeks earlier before being removed, so this release makes the feature official.
What stands out to me is the access this gives marketers, creators, and SEOs. Google has not traditionally given us a clear way to see how our content performs on domains or properties we do not own. With platform properties, I can finally start seeing how my social and video content performs in Google Search, even when I do not have developer access to those platforms. That opens up a much better view of search-driven visibility beyond my own website.
I’m seeing traditional Google rankings deliver less predictable value than they once did. Ads, AI Overviews, and other search engine results page features are pushing organic links farther down the page, which means visibility no longer depends only on where a brand ranks in the classic blue-link results.
As search keeps shifting, I believe brands need to ask a more practical question: how do I make sure my brand is represented accurately inside AI-powered answers?
The more I understand how AI engines use brand information and when they cite it, the easier it becomes to build a real AI visibility strategy. This moves the conversation beyond whether an AI model “knows” a brand and toward how that brand can earn presence, trust, and discoverability in AI search.
The click economy is shrinking
I think most brands should start learning AI search and building an AI SEO strategy now. A full shift from organic search to AI search may still be years away, but the direction is clear enough that waiting creates risk.
Google is already leaning hard into AI search. In an April article from The Verge, CEO Sundar Pichai said that search had a strong quarter, with AI experiences driving usage, queries reaching an all-time high, and revenue growing 19%.
Users are changing their behavior too. A Pew Research study found that when people see an AI-powered summary in search results, they click a blue link only 8% of the time. When no AI summary appears, that click rate rises to 15%.
AI search traffic may still be smaller than organic traffic, but I would not dismiss it. According to Similarweb, AI traffic converted at 11.4%, compared with 5.3% for organic search traffic. That makes AI visibility worth tracking even before it becomes the dominant traffic source.
How I separate AI usage from AI citation
I think about brand presence in AI systems in two main ways: usage and citation.
Usage happens when an AI engine ingests information about a brand and draws on that information when answering a query. In some ways, this reminds me of how Google traditionally indexed pages before ranking and serving them in search results.
When an AI engine uses brand content, it may mention the brand without linking to it. Even an unlinked mention can matter because it can create discovery, influence perception, and prompt users to search for the brand directly.
Ahrefs data shows most Google AI Overview citations still come from high-ranking organic pages, with 76.10% in the top 10 and a smaller share outside the top 100.
Citation is different. A citation happens when an AI engine directly references a brand as a source of information. That reference might be a link to a web page, a social profile, or even a clickable phone link that lets someone contact the business.
Within OpenAI, usage and citation appear to depend on separate technical systems. As OpenAI’s documentation explains, OAI-SearchBot and GPTBot are deployed separately among four distinct user agents. Other AI systems have their own controls, but the same broader distinction still applies.
Why citations do not tell the whole story
I do not see citations as the full AI visibility picture. AI engines often answer questions directly without citing web sources, and this pattern is not entirely new. Before AI Overviews, Google was already moving in that direction with featured snippets.
Ahrefs found that ChatGPT retrieves almost the exact same number of cited and uncited URLs to generate an average response: about 16.57 cited URLs and 16.58 uncited URLs. But Reddit made up 67.8% of uncited URLs, which means comparing cited and uncited URLs is often really a comparison between search results and Reddit API output.
That matters because AI systems are not neutral in the uncited information they surface. Some platforms and websites are simply more influential than others. If I try to push a brand into AI answers without understanding where the model gets its information, I am working at a disadvantage.
How I would improve brand usage and citation
I would start by tracking the brand’s current AI visibility and monitoring progress over time. That means running a representative set of prompts through an AI visibility platform, reviewing the sources that get cited, and asking what those sources reveal about the model’s preferences.
There are already many AI citation tracking tools available, and established platforms like Semrush and Ahrefs have added AI tracking features as well. I would choose a tool based on the prompts, markets, and engines that matter most to the brand.
I would also scale tracking and research as much as budget allows. AI prompt tracking often depends on API calls, so it can cost more than traditional rank tracking. Still, the data is usually richer, even when the sample size is smaller.
As long as the prompt sample is broadly representative, most platforms can pull multiple responses and calculate an average. That gives me a more useful view of recurring patterns instead of relying on one-off answers.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
I would keep reading studies from AI platforms, SEO vendors, and data providers too. Those reports are valuable because they show which sources AI engines rely on and where brands may have the best chance to appear.
The key is continual monitoring. Over time, I can work to place a brand inside the sources AI engines already trust and use most heavily.
Why I still care about traditional rankings
Yes, I still think traditional search rankings matter, but not for the same reasons they used to. The relationship between organic position and business performance is less direct now, especially as SERP features and AI answers absorb more user attention.
At the same time, Ahrefs research suggests a relationship between AI citations and Google rankings, at least inside Google AI Overviews. A July 2025 study found that 76.1% of pages cited in AI Overviews ranked in Google’s top 10 organic results. If AI Overviews become a dominant AI search experience, traditional rankings will still influence visibility.
I also pay attention to content quality. Semrush found that AI engines rarely cite generic content that simply repeats what other sources already say. The content that earns citations usually contributes something distinct.
That fits closely with Google’s helpful content guidance, which rewards original information and useful perspective. In my view, content with trusted data, original insight, and a clear point of view can support both Google rankings and AI citations.
Because many classic SEO tactics can also support AI citations, I would not abandon traditional SEO. I would treat it as part of a broader visibility strategy that now includes AI usage, AI citations, and brand presence across trusted third-party sources.
Where I think AI visibility is heading
Both usage and citation need ongoing tracking and analysis. If I want AI engines to use a brand’s knowledge and content, I need to understand which sources each model relies on and help the brand appear in those places. If I want citations, I need the brand’s content to stay crawlable, rank well, and say something original.
Classic SEO still earns its place because the same work that improves organic visibility can often improve AI visibility too. But returns from traditional rankings are changing, and AI SEO may eventually become the primary discipline. For now, I would keep ranking, start tracking, and build for both usage and citation.
I’m seeing Google expand merchant listing structured data with support for sale duration and the Product.category property. The update brings Google Search’s merchant listing structured data closer to the capabilities already available in Google Merchant Center feeds.
Sale duration. Google added a new Sale duration section to its Merchant listing structured data documentation. In that update, Google said the guidance explains how to use the validFrom, validThrough, and priceValidUntil schema.org properties to define the effective date range for sale prices.
I find this useful because Google’s guidance also covers best practices and examples for placing those properties on either Offer or PriceSpecification nodes. Google said the change aligns schema.org usage with the Merchant Center feed attribute sale_price_effective_date, giving merchants clearer instructions for handling sale price timing in structured data.
Google's sale duration guidance shows merchants how to define when a sale price starts and ends in structured data, including Offer and UnitPriceSpecification JSON-LD examples.
Here is the new sale duration section Google added:
Product category. Google also updated the same Merchant listing structured data documentation to include support for the Product.category property.
Google’s merchant listing guidance now shows how product categories can mix custom text labels with Google Product Category codes in structured data.
Google wrote that the documentation now explains how Product.category can be used with both Text and CategoryCode types. According to Google, this aligns with Google Merchant Center feed specifications for the product_type and google_product_category attributes.
From my perspective, this makes the structured data more practical for merchants because it lets them provide both merchant-defined and Google-defined category details directly in schema.org markup. Google said this can enhance product information for Google Search and Shopping.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
Here is what Google added for product category support:
Why I care. If I maintain merchant listing structured data for Google, these additions are worth reviewing. Product category support can help Google better understand the products being provided, which may improve how those products match relevant queries.
I also see sale duration support as a practical improvement for planning promotions. When I update merchant listing structured data, I can now define sale price timing more clearly and align that markup more closely with Google Merchant Center feed behavior.
I use Conductor’s MCP Server to ground the AI tools my team already relies on in verified AEO and SEO intelligence, instead of depending on a stale snapshot of the web.
A bold launch visual introduces an AEO and SEO Intelligence Layer, framing verified search and AI visibility data as a modern layer for marketing teams.
