I’m thrilled to share that Microsoft Advertising has just unveiled the Publisher Content Marketplace (PCM). This innovative system allows publishers like us to license premium content to AI products and earn revenue based on its usage.
How It Works. At its core, PCM creates a direct value exchange. As a publisher, I have the freedom to set my own licensing and usage terms. Meanwhile, AI developers can discover and license this content for grounding their algorithms in real-world scenarios. The marketplace also offers detailed usage reports, providing insights into how our content performs and where it contributes the most value.
Designed to Scale. The PCM is a scalable solution designed to eliminate the need for one-off licensing deals. Participation is entirely voluntary, and ownership and editorial independence remain with the publishers. It’s a platform inclusive of everyone from large global publishers to smaller niche outlets like ours.
Why We Care. As AI technology progresses from merely answering questions to making impactful decisions, the quality of content is becoming increasingly crucial. Whether it’s about influencing purchases, finance, or healthcare, AI systems need to tap into premium content, elevating the importance of credibility and trust in our brands.
Early Traction. Microsoft Advertising has partnered with notable U.S. publishers such as Business Insider, Condé Nast, and Hearst to co-design PCM. Initial pilot projects anchored Microsoft Copilot responses to licensed content, with companies like Yahoo as early adopters.
What’s Next. Looking ahead, Microsoft plans to extend the pilot program to more publishers and AI developers who share the belief that as the AI web evolves, the value and governance of high-quality content should be recognized and rewarded.
The Big Picture. In the evolving landscape of AI-driven web interactions, tools are now summarizing, reasoning, and making recommendations through conversation. The effectiveness of these tools hinges on access to trusted and authoritative sources, many of which are under paywalls or in secured archives.
The Tension. The traditional model where publishers provide content in exchange for traffic from platforms is changing. AI is increasingly delivering answers directly, which reduces clicks but still relies on high-quality content.
Bottom Line. For AI to make better decisions, it must have access to superior inputs. Microsoft’s PCM is a strategic move towards establishing a sustainable content economy that supports the next wave of AI innovation.
As an advertiser, I’ve recently noticed that Microsoft Advertising is kicking off 2026 with a fresh batch of updates tailored for search-centric marketers. These updates offer me better control, clearer insights, and more streamlined campaign management across their platform.
Driving the news. In their latest product update, Microsoft has rolled out enhanced Performance Max features, broadened audience targeting options, improved Google import processes, and automated more creative aspects of search ads.
The big picture. Performance Max remains at the heart of these changes. There’s a new customer acquisition goal available in open beta that lets me prioritize new customers or exclusively target them in PMax campaigns geared towards purchase goals. Additionally, I can allocate higher conversion values to new customers, which aids the system in optimizing for long-term growth over short-term revenue.
Alongside these goals, Microsoft has also expanded transparency and controls within PMax. They now offer share of voice metrics, including impression share and losses due to budget or rank, giving me a better understanding of competitiveness in Search and Shopping placements. Plus, asset group-level URL options and tracking templates allow for more granular measurement without needing to reorganize campaigns.
What’s changing under the hood. The process for importing from Google has become more seamless. PMax campaigns now support up to 50 search themes, and asset group imports have become more flexible, meaning that non-eligible images or auto-generated logos won’t block the rest of the asset group from being imported.
Beyond PMax, I’m excited that Content Targeting for Audience ads is now generally available. I can target specific Microsoft-owned placements like MSN and Outlook, or align ads with content categories such as Finance or Travel. A new reporting view also shows where ads actually appear, aiding in refining contextual strategies.
Why we care. These updates furnish me with greater command over how automation propels growth, especially in acquiring new customers. New customer acquisition goals and additional visibility in Performance Max make optimizing for long-term value easier rather than focusing solely on immediate conversions. With smoother imports and smarter creative automation, these advancements allow advertisers like me to enhance performance without giving up visibility or control.
On creative automation. Autogenerated assets are now being rolled out as a default setting for newly created Responsive Search Ads worldwide, excluding China and South Korea. Microsoft reports that advertisers using these assets witness around a 5% increase in CTR, as the system dynamically generates and tests more headlines and descriptions based on website content. Sensitive verticals remain opt-in only, leaving existing RSAs unaffected.
The bottom line.Microsoft Advertising’s January updates aim to make automation more user-friendly, quantifiable, and advertiser-friendly, particularly for those of us managing Performance Max across multiple platforms.
As an advertiser, I’m excited to share that Microsoft is empowering Performance Max campaigns by expanding search themes to 50. This change offers us more control, allowing better alignment with high-intent customer searches.
Just ahead of next week’s major announcements, Microsoft Advertising has confirmed that we can now incorporate up to 50 search themes in our campaigns, a notable increase from previous restrictions.
Why this matters to me. Search themes serve as strategic signals that guide Performance Max toward the search queries and intent patterns that we prioritize. With this expanded capacity, I have more room to refine how automation interprets customer demand, especially for businesses with diverse product lines or complex structures.
This update also means I no longer need to compress different intents into a limited number of themes or run multiple campaigns just to portray various product offerings.
The bigger picture. Microsoft’s focus is shifting towards signal-based control rather than strict keyword targeting. By combining search themes with LinkedIn profile targeting and other audience signals, including impression-oriented remarketing, I can better target high-value customers rather than a generic audience.
What I’m looking forward to. Next week, Microsoft’s Advertising blog will feature additional updates, suggesting this change is part of a larger initiative to make Performance Max not only more flexible but also more responsive to advertisers’ needs without undermining the automation logic.
Where I first learned about this. Microsoft Product Liaison Navah Hopkins shared these insights on LinkedIn, along with hints of upcoming updates slated for next Wednesday, January 14th.
The bottom line for me. By increasing the number of available search themes to 50, Microsoft is improving our control over Performance Max, not through additional complexity, but by widening the range of pivotal signals.
Here’s how LinkedIn professional attributes enhance intent, automation, and creative decisions in Microsoft Advertising.
Using LinkedIn targeting within Microsoft Advertising allows me to align creative strategies with the perfect audience. By engaging with this thoughtfully, I can apply professional insights to intent-driven inventory without breaking the bank.
The key is understanding how these targeting methods collaborate across different campaign types. In this guide, I’ll walk you through leveraging LinkedIn data within Microsoft Advertising, including:
LinkedIn in Search campaigns, including Multimedia ads.
Using LinkedIn insights for an enhanced audience strategy.
Performance Max targeting signals.
Audience reach and composition insights via Audience Planner.
Disclosure: As a Microsoft employee, I’ve kept this article objective, focusing on LinkedIn targeting mechanisms, targeting action items, reporting, and message mapping strategies.
LinkedIn Profile Targeting in Search
Microsoft Advertising search campaigns fully support LinkedIn profile targeting, allowing me to layer professional attributes on top of keyword targeting. The supported attributes include:
Company
Industry
Job function
These audiences can be utilized across Microsoft‑owned environments, such as Bing Search, Microsoft Edge, Microsoft Start, and other eligible search surfaces, provided users are signed in.
In search, LinkedIn targeting works as a contextual guide rather than a standalone target. Keywords carry the main weight, while LinkedIn data helps me adjust my response when professional relevance is present.
How to Approach It
Start with keywords that already convert: LinkedIn targeting enhances existing intent with proven keywords. I apply bid adjustments to campaigns or ad groups where search terms already demonstrate business value, potentially increasing bids by 10%-15% for aggressive bidding or more aggressive adjustments when impression share is lost to rank.
Choose one professional dimension first: I begin with either company, industry, or job function instead of applying all three simultaneously. This approach prevents double-bidding on potential customers.
Use bid-only mode to establish a baseline: Observation mode provides performance clarity before I make delivery decisions. This acts as audience research to identify who engages profitably.
LinkedIn Professional Demographics in Audience Ads
Audience Ads leverage LinkedIn Professional Demographics as both a targeting and observation layer, introducing professional context into native, display, and video formats tailored for scalable reach.
Audience Ads aren’t driven by keyword intent; however, Professional Demographics anchor delivery and insights in real-world business contexts, bridging broad reach with professional relevance.
These ads let me apply company, industry, and job function as professional audience layers, which I can use to observe performance trends or influence delivery, depending on campaign objectives.
How to Approach It
Start in observation to understand natural performance: By observing performance trends in Professional Demographics, I learn which industries, job functions, or company types naturally engage with Audience Ads before imposing delivery constraints.
Let LinkedIn data inform creative, not just delivery: In content-rich environments, creative matters more than targeting alone. I use insights from high-performing professional segments to shape tone, examples, and value framing in my messaging.
Align format choice with professional mindset: Different formats perform distinct roles. For example, native and display formats excel in awareness and education within professional segments, while video supports storytelling and industry-specific narratives. Professional Demographic insights guide the most suitable formats for varied business audiences.
LinkedIn Data in Performance Max: Guiding Automation with Purpose
LinkedIn profile targeting is available within Performance Max campaigns, where it functions as an audience signal. These signals help the system identify professional profiles most likely to yield profit for my business and influence budget allocation.
Within Performance Max, professional signals are most effective when representative and directional, rather than exhaustive, providing the system a strong starting point.
How to Approach It
Select signals that reflect your best customers, not every customer: Using LinkedIn attributes to describe my most valuable segments is crucial, especially if different personas represent varying ROAS/CPA goals, as this affects PMax campaign asset groups’ shared ROAS/CPA bidding.
Pair LinkedIn signals with strong conversion definitions: Automation improves when reinforced by clear success metrics. Ensuring at least 30 conversions over a 30-day period is vital for autobidding effectiveness.
Allow time for learning: Audience signals need sufficient volume to influence delivery, so I avoid frequent changes during the initial learning period (two weeks). Afterward, budget adjustments up to 15% can be made without triggering learning period fluctuations.
Aggregated LinkedIn audience reporting is divided by company, industry, and job function, letting me analyze how professional segments contribute to campaign performance. This reporting, found under Reporting > Professional demographics, includes LinkedIn targeting or audiences applied through predictive targeting.
How to Approach It
Look for consistency across time, not single spikes: Patterns emerging over weeks or months are more actionable than short-term anomalies. I allow “observation” audiences ample time to prove themselves or use Audience Planner for informed decisions at scale.
Use reporting to inform creative and bids together: Upon identifying outperforming professional segments, I scrutinize messaging and bidding before initiating changes. It’s crucial to confirm creative resonance without overbidding.
Avoid over-segmentation early: Excessive audience segmentation can weaken signal strength, especially when conversion scarcity is a concern.
Bidding with LinkedIn Audiences
In Microsoft Advertising, I use bid adjustments alongside automated strategies, enabling flexibility in how LinkedIn audiences influence auctions. Overlapping audiences can amplify bid adjustments, necessitating overlap awareness as part of my bid strategy.
Effective bidding adjustments should be incremental and reversible, aiming for calibration rather than acceleration.
Creative Strategy: Professional Relevance Without Narrow Assumptions
LinkedIn targeting controls ad visibility, but creative determines engagement. Professional cohorts encompass a variety of experiences, identities, and viewpoints. My aim is effective creative that respects diversity while remaining relevant to shared contexts.
Effective creative exhibits professional empathy, addressing challenges, goals, and constraints without reliance on stereotypes.
How to Approach It
Anchor creative in shared problems, not titles: I focus on challenges common to roles and seniority levels within a LinkedIn targeting segment.
Keep language inclusive and adaptable: I avoid assumptions about background, experience, or decision-making authority.
Use AI tools to localize, not homogenize: Adapting tone or examples by region or industry while preserving message intent is crucial.
Test creative alongside audience layers: I evaluate messaging performance within LinkedIn segments to refine both together.
Extending LinkedIn Insights Across B2B Campaigns
LinkedIn targeting in Microsoft Advertising provides an opportunity to combine professional expertise with intent-driven media scalably, in a privacy-conscious and economical manner.
Teams already using LinkedIn Ads can leverage this strategy to extend learnings into additional inventory via automation, amplifying reach and efficiency.
The value lies not in complexity, but in alignment – aligning data, mechanics, and human behavior enhances results.
Key takeaways:
LinkedIn profile targeting is fully accessible in Search and Performance Max on Microsoft surfaces.
Professional attributes act as targeting layers in search and optimization signals in Performance Max.
An observation-first approach fosters understanding before commitment.
Aggregated reporting aids informed optimization without revealing individual data.
Empathy-anchored creative fosters professional relevance.
When I use LinkedIn data with curiosity and care, it offers a way to view audiences more clearly rather than control them more tightly. For B2B advertisers navigating complex buying journeys, such clarity often becomes the most valuable optimization.
Recently, I discovered that Microsoft Advertising has introduced asset-level editorial reviews, a game-changer for anyone running ad campaigns. This new feature allows us to see individual ad components like headlines and images get reviewed separately. If one part is non-compliant, it won’t hold back the whole ad, ensuring that compliant components keep running smoothly.
Here’s What’s New: Announced back in June, this feature provides a granular view of ad approvals. Now, I can easily spot which specific asset might be causing issues, instead of having to guess why an entire ad wasn’t approved.
Why I Care: This update is a relief because it minimizes campaign disruptions and speeds up the approval process. No more resubmitting entire ads just to fix one small mistake. I can now address the exact problematic asset swiftly.
How it Enhances the Workflow: The platform now flags disapproved elements right in the dashboard. It gives a clear warning when something is blocked and provides a detailed asset status, making it easy to stay on top of my campaigns.
The Bottom Line: This more precise system replaces the old all-or-nothing approval process, letting compliant ads run uninterrupted and putting more control in my hands as an advertiser. It’s definitely a step forward in ad management!
I’ve been exploring how Microsoft’s Copilot is revolutionizing search advertising by transforming our daily conversations into actionable insights for advertisers. It provides a window into user intent, reducing wasted spend, and boosting ROAS significantly.
In fact, Microsoft reports a 13-fold increase in ROAS when users interact with Copilot before conducting a search. By tapping into billions of first-party data across platforms like Bing and LinkedIn, Copilot can identify high-value audiences and help advertisers make every dollar count.
The mechanics of conversational search are intriguing. Users tend to provide AI like Copilot with more detailed queries, offering richer context compared to traditional search bars. This shift creates multiple ad opportunities from a single detailed conversation, potentially transforming the advertising landscape.
A recent campaign I ran for a university highlights this transformation in action. Shifting from broad keywords to detailed, conversational queries allowed us to sharply decrease wasted impressions and costs, while significantly boosting engagement.
It got me thinking about how advertisers can transition to this model effectively. Besides technological integration, it requires a strategic realignment to capture the conversational demand using structured data and cross-channel strategies.
Especially with Gen Z, addressing authenticity concerns becomes crucial. They value real interaction, so ads need to feel native and relevant, not generic or intrusive. Using behavioral data from platforms like Activision, we can target more effectively without crossing into ‘stalker-ish’ territory.
As we relearn how to engage with this audience, I see the balance between utility and authenticity as the key to long-term success. The rise of AI in advertising continues to create an exciting new economic landscape, driven by precision rather than sheer volume.
I recently stumbled upon some intriguing developments from Bing, as they are experimenting with a new ad format that closely resembles Google’s approach. This revamped ‘Sponsored results’ grouping could potentially lead to more accidental ad clicks, given how seamlessly these paid listings blend with the organic search results.
Picture this: Microsoft is testing a redesign for search ads in Bing, wherein multiple sponsored links are grouped under a single ‘Sponsored results’ label. There’s also a handy ‘Hide’ button to collapse the ad block entirely, adding a layer of user control that’s quite novel.
What’s Happening? It was Sachin Patel who first noticed this Bing test in action, sharing screenshots and a video that spotlight this new layout. Interestingly, in the current test, only the first ad in the group is marked with a label. Any subsequent ads are listed without labels beneath it. This feature allows users to click ‘Hide’ to collapse these ads and ‘Show’ to display them once more.
Understanding the Mechanism. The design clusters ad units in such a way that blurs the lines between paid and organic content. By consolidating ad labeling to just one header, it makes each ad appear more like a standard search result.
Looking Back. Google introduced a similar approach not too long ago, and it quickly drove discussions around unintended ad clicks. According to a recent poll conducted by Barry Schwartz on X, a remarkable 63% of respondents admitted to inadvertently clicking on Google Ads results due to this new grouping.
Bing following suit might indicate a broader industry trend in the labeling and display of search ads.
Why Should We Care? Bing’s new grouped ‘Sponsored results’ format could potentially raise ad visibility and enhance click-through rates by making ads blend more seamlessly with organic listings. The ‘Hide’ button introduces a refreshing control element for users, though the single-label approach may still lead to increased accidental clicks, as observed with Google’s recent redesign, potentially resulting in higher bounce rates.
Should Microsoft decide to implement this change broadly, it could significantly impact campaign performance, attribution, and spending efficiency across Bing’s search platform.
Initial Observations. This layout change was first shared by Sachin Patel, who took to X with his findings.
The Takeaway: While the experiment remains limited for now, if Bing rolls this format out extensively, it could lead to increased engagement — whether intended or accidental — and renew discussions about how clearly ads are disclosed in search results.
I’ve recently discovered some exciting updates from Microsoft Ads that promise to enhance the way we manage and analyze our advertising campaigns.
With these updates, we now have the ability to see individual asset disapprovals—meaning that if a particular image, headline, or text in our ad doesn’t meet standards, it can be addressed without disrupting the entire campaign. What a relief to know that one small glitch won’t pause everything!
Additionally, conversion reporting has become much more transparent. We can track how long it takes for 90% of conversions to be recorded post-click, whether they happen online or offline. This clarity is essential for refining our bidding strategies and assessing campaign performance with better precision.
These changes tackle two big challenges we face as advertisers: minimizing wasted spend due to blanket ad disapprovals and clearing up the murkiness of conversion lag.
Why this matters to me.
The ability to address ad issues at the asset level keeps our campaigns live and our revenue steady, while the new conversion metric improves data accuracy. It helps me make informed decisions on bidding and pacing across platforms, ultimately leading to better resource management.
In short, these enhancements make managing Microsoft Ads more predictable and efficient.
The updates were initially shared by John Sargent on LinkedIn and confirmed by Microsoft Ads Liaison Navah Hopkins. For those of us handling multi-platform campaigns, this is a major step forward in gaining better control and clearer data insights.
In today’s fast-evolving search landscape, I’ve realized that Answer Engine Optimization (AEO) is crucial for brands wanting to dominate platforms like Bing. Unlike traditional SEO that focuses on page rankings, AEO is all about delivering concise, accurate answers to user queries. It often takes the form of featured snippets, knowledge panels, or direct responses. With its growing user base and unique features such as AI-powered Copilot, Bing offers a prime opportunity for businesses to optimize for answer-driven searches. In this guide, I’ll share strategies to enhance your AEO efforts for Bing.
Bing’s search engine is tailored to provide quick, relevant answers, utilizing its integration with Microsoft’s AI technologies. Features like the “People Also Ask” section, rich snippets, and Copilot’s conversational responses prioritize content that directly addresses user intent. To succeed in AEO, I need to ensure my content aligns with Bing’s preference for structured, authoritative, and user-focused information.
Focusing on question-based content is essential since Bing excels at answering queries. I begin by researching questions that my target audience frequently asks, using Bing Webmaster Tools or platforms like AnswerThePublic. For instance, if I’m in the fitness industry, I might target queries like “What is the best workout for beginners?” or “How to lose weight safely?”
I organize my content to answer these questions clearly. By using headers (H1, H2) to pose the question followed by a concise, factual response, I make it easy for Bing to pull answers from my well-organized content. Utilizing bullet points, numbered lists, or tables can also enhance readability. It’s important that my answers are comprehensive yet succinct, ideally under 100 words, for snippet eligibility.
Securing featured snippets—prime real estate in Bing’s search results—requires analyzing existing snippets for my target keywords. I identify gaps or opportunities to provide better answers. For example, if a snippet lacks detail, I create content that’s more thorough but still concise.
To signal to Bing that my content is answer-ready, I use schema markup like FAQPage or HowTo. I ensure my page loads quickly and is mobile-friendly, as Bing prioritizes user experience. Additionally, I include related keywords and synonyms to align with Bing’s natural language processing capabilities.
Bing heavily relies on structured data to understand and display content in knowledge panels or rich results. By implementing schema.org markup, I can categorize my content, whether it’s an article, product, or event. For example, a recipe page with Recipe schema can appear as a rich result with ratings and cooking times.
I test my structured data using Bing’s Markup Validator to avoid errors. Consistent use of schema across my site improves Bing’s ability to extract answers, increasing my chances of appearing in direct response features.
Establishing my site’s credibility is crucial since Bing values authoritative sources. I publish content from recognized experts, cite reputable sources, and maintain an active presence on platforms like LinkedIn or X to boost my brand’s visibility. Bing’s algorithm considers external signals, so earning backlinks from trusted sites can elevate my content’s ranking for answer-driven queries.
I regularly update my content to reflect the latest information, as Bing favors fresh, accurate data. For example, a blog post on “2025 tax laws” is revised annually to maintain relevance.
Bing’s Copilot feature, which responds to conversational queries, makes it vital for me to optimize for natural language. I write content in a conversational tone, mirroring how users phrase questions verbally. Instead of focusing on keywords like “best laptops 2025,” I might target queries like “What are the best laptops to buy in 2025?” including long-tail keywords and phrases that align with voice search trends.
Using Bing Webmaster Tools helps me track which queries drive traffic and identify opportunities for new content. I analyze click-through rates and refine underperforming pages by improving clarity or adding visuals like infographics, which Bing often highlights in results.
In conclusion, optimizing for Bing’s answer engine requires a strategic blend of question-focused content, structured data, and authority-building. By aligning with Bing’s AI-driven features and understanding user intent, I can capture valuable real estate in search results. I encourage you to start implementing these AEO tactics today and stay ahead in 2025’s competitive digital landscape.
Inspired by this post on AnswerEngineOptimization.blog.
In the ever-evolving world of AI-driven advertising, I’ve noticed that Performance Max campaigns have become absolutely crucial. Both Google and Microsoft offer these innovative opportunities, allowing advertisers to bring together creative assets, audience signals, and automation into a single seamless campaign type.
While Google and Microsoft share this foundational concept, they execute it uniquely. I am excited to offer an in-depth comparison of Google PMax and Microsoft PMax as they stood toward the end of 2025, hoping to shed light on the intricacies that could shape your 2026 advertising strategies.
What I found universally true across both platforms is the replacement of ad groups with asset groups. These groups encompass a blend of creatives, such as images and headlines, along with audience signals, but also carry an absence of any prioritization.
Significantly, PMax is built for automation. Both platforms request the use of Maximize Conversions or Maximize Conversion Value strategies, underlining the need for conversion tracking that can keep pace with no less than 30 conversions in a month.
Goal alignment is another crucial aspect. I realized that accurate reflection of business goals in your campaigns is imperative, for an artificially low ROAS target will likely backfire by yielding unexpectedly lower returns.
Search term visibility is an area where Google offers broader negative keyword support, unlike Microsoft who is still piloting this feature. However, Microsoft’s PMax creatives have been involved in AI placements longer, demonstrating proven results and thus indicating a stronger track record in this area.
Google’s PMax has evolved impressively, offering tools such as channel-level reporting and video asset support, which are particularly beneficial for visual marketing endeavors.
On the flip side, Microsoft’s edge, especially for B2B advertising, includes higher campaign limits, impression-based remarketing, and the integration of LinkedIn targeting signals, appealing for advertisers looking at high-quality lead generation.
Reflecting on both platforms, I believe PMax should be seen as a tool for incrementality rather than a replacement for proven search campaigns. The optimal approach involves leveraging both platforms’ strengths, whether it’s Google’s affinity for creative automation or Microsoft’s prowess in B2B targeting and remarketing.
