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.
At the NRF 2026 Conference, I had the opportunity to see Microsoft’s latest innovations firsthand: the Copilot Checkout and Brand Agents features.
Seeing Microsoft roll out its first agentic experiences within Copilot feels like stepping into the future. With Copilot Checkout, I can now shop seamlessly without leaving the conversation, making it easier to switch from browsing to buying.
What’s more intriguing is the introduction of Brand Agents for Shopify sites. Imagine having an AI chat that’s trained on your product catalog, offering personalized shopping experiences that echo your brand’s unique voice. It’s a smart solution for fast and scalable implementation.
Copilot Checkout. I’m excited to share that Copilot Checkout is starting its U.S. rollout on Copilot.com. This feature allows conversational purchases directly within your current chat and integrates with partners such as PayPal, Shopify, Stripe, and Etsy.
For Shopify merchants, enrollment in Copilot Checkout is automatic, though opting out is possible. Non-Shopify merchants interested in joining can apply through a dedicated form.
Check out a glimpse of how it works:
Brand Agents. Now available for Shopify merchants, Brand Agents bring your brand’s voice into every interaction online. I was impressed to see how it leverages a brand’s product catalog to provide crystal-clear answers to product-specific inquiries.
The AI speaks in your brand’s voice, seamlessly guiding customers from browsing to purchasing, and can be set up in just a few hours. Microsoft shared with me that sessions assisted by Brand Agents show higher engagement and conversion rates.
Here’s a video showcasing Brand Agents in action:
Brand Agents insights. Microsoft is innovatively using Microsoft Clarity to provide insights into Brand Agents conversations, helping merchants understand and optimize performance.
Once I activated Brand Agents, I found myself equipped with additional insights to compare and optimize agent-assisted sessions against organic traffic, reinforcing my growth strategies.
Here is where you can view these insights:
Google and OpenAI. It’s fascinating to see how Google and OpenAI are also embracing agentic experiences. Google has introduced agentic checkout, whereas OpenAI announced its Instant Checkout in ChatGPT. Together, these developments mark a significant industry shift towards direct purchasing through AI experiences.
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 learned about a significant ruling that will impact Google’s longstanding agreements with tech giants like Apple and Samsung. This decision means that moving forward, Google will only be able to secure its place as the default search engine on devices for one year at a time. Despite this change, I’m not expecting a major shift in Google’s dominance over the search market anytime soon.
Here’s what’s driving the news: On Friday, Judge Amit Mehta described this one-year cap as a crucial step in enforcing antitrust measures. This follows his 2024 decision, which concluded that Google was unlawfully monopolizing the realms of search and search advertising. According to Business Insider, the requirement aims to enforce fair competition in the industry.
Additionally, Judge Mehta’s earlier ruling outlined restrictions for Google:
Google must avoid any exclusive contracts regarding the distribution of Google Search, Chrome, Google Assistant, and the Gemini app.
They cannot condition licensing agreements of the Play Store on the preloading of these applications on devices.
Revenue sharing cannot be contingent on placing or maintaining these applications on devices beyond one year.
Partners are free to distribute alternative GSEs, browsers, or GenAI products simultaneously.
Why I care: This landscape shift could mean that user searches originate from a wider array of platforms. If AI-powered competitors like OpenAI, Perplexity, or Microsoft make even modest advances, we could see a more diverse and challenging search terrain emerge.
Reality check: In my view, this is more of a bump in the road rather than a disruption. Google’s financial resources, brand strength, and user habits continue to provide significant leverage in annual negotiations.
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.
I witnessed Google take a major step by pulling back its antitrust complaint against Microsoft following a new EU investigation into cloud licensing practices. This decision marks a pivotal moment in the ongoing tug-of-war between tech giants.
Driving the news. Just as the European Commission initiated fresh inquiries into whether Microsoft’s Azure and Amazon Web Services are compliant with the Digital Markets Act (DMA), Google decided to let go of its 2024 complaint. This complaint was primarily focused on what Google considered Microsoft’s unfair cloud licensing strategies. Nevertheless, Google assures us that pulling back doesn’t equate to giving up.
What they’re saying. Giorgia Abeltino, who leads public policy at Google Cloud Europe, emphasized, “We filed our antitrust complaint…to give voice to our customers and partners.” She reaffirmed Google’s commitment to the concerns initially raised.
Why we care. The EU’s deep dive into Microsoft’s cloud operations might just revolutionize the infrastructure supporting various ad-tech tools, measurement systems, and AI workflows. Should regulators enforce changes to Microsoft’s Azure, we may see a more competitive landscape emerging, benefiting us with cost reductions and improved tool interoperability.
Simply put, competition within the cloud domain influences the speed, affordability, and dependability of the tools advertisers depend on daily.
The backdrop:
I observed that Google accused Microsoft of using restrictive software licensing to make other cloud services less appealing.
This complaint followed closely after Microsoft resolved a related dispute with the cloud advocacy group CISPE.
It’s worth noting that other Microsoft and Amazon sectors, such as Windows and Amazon’s marketplace, are already under the ambit of the DMA.
State of play. While the EU remains vigilant in monitoring cloud competition, Microsoft has opted not to comment on these developments.
Bottom line.Google’s decision to withdraw isn’t a retreat but rather a shift of focus as the regulatory battleground opens new fronts with EU scrutiny on Microsoft and AWS drawing sharper lines with tougher regulations soon on the horizon.
In the ever-changing world of search, I’ve come to see Answer Engine Optimization (AEO) as an essential approach for brands looking to gain visibility on platforms focused on direct answers. While Google reigns supreme in the search domain, other engines like Bing serve crucial roles in delivering precise, authoritative responses to searches. With Bing’s unique algorithms and its seamless integration into Microsoft’s ecosystem, I’ve noticed that businesses have specific opportunities to optimize their content for AEO. Let me guide you through Bing’s significance in AEO and actionable strategies to harness the power of non-Google answer engines.
Understanding Bing’s Role in AEO
Bing, Microsoft’s search engine, processes millions of queries daily, powering numerous answer-driven platforms like Cortana, Microsoft Edge, and Windows Search. Unlike Google, which casts a wider search net, Bing’s approach is more aligned with providing direct answers, often emphasizing structured data, rich snippets, and clarity. From my experience, I’ve seen how Bing’s AEO environment rewards content that provides quick, accurate responses, making it a strategic platform for brands targeting specific or localized audiences.
The algorithm Bing uses focuses on relevance, authority, and user experience, integrating AI-driven features like natural language processing to grasp conversational queries, increasingly common in voice search and virtual assistants. For businesses, what I’ve learned is that optimizing for Bing means employing strategies that deviate from Google-centric SEO methods.
Key Strategies for Optimizing for Bing in AEO
1. Leverage Structured Data and Schema Markup
Bing thrives on structured data to interpret and showcase content in answer boxes, knowledge panels, and rich snippets. By implementing schema markup—be it FAQ, How-To, or Product schemas—I’ve seen that Bing gains a clearer understanding of the content’s context, increasing its chances of featuring as a direct answer. Make sure your site’s schema aligns flawlessly with Bing’s Webmaster Guidelines to make a significant impact.
2. Focus on Conversational and Long-Tail Queries
Bing excels at handling natural language queries, especially those framed as questions (like “What is the best way to clean a laptop screen?”). To capture this demand, I’ve found optimizing content for long-tail keywords and conversational phrases effective. Creating FAQ sections, blog posts, or landing pages that directly target common inquiries in your field, in a language that mirrors user speech, works wonders.
3. Prioritize Content Clarity and Authority
Bing appreciates well-organized, readable, and authoritative content. Using clear headings, bullet points, and concise paragraphs enhances readability. To establish authority, I’ve always included credible sources, author bios, and current information. Bing tends to favor content from trusted domains, so focusing on high-quality backlinks and a solid domain reputation has been crucial.
4. Optimize for Local and Visual Search
With a strong emphasis on local search, especially for businesses linked with Microsoft Maps, I’ve learned to ensure business listings are accurate on Bing Places, incorporating location-specific keywords into content. Additionally, Bing’s growing visual search capabilities mean that optimizing images with descriptive alt text, high-quality resolution, and relevant metadata enhances discoverability.
5. Align with Microsoft’s Ecosystem
Bing’s integration with Microsoft products presents unique AEO opportunities. Content tailored for Bing often finds its way into Windows Search or Cortana results. To best leverage this, I ensure my site is mobile-friendly, considering most Microsoft users access Bing via Edge or Windows devices. Additionally, using Microsoft Advertising can effectively complement organic AEO efforts.
Why Bing Matters for AEO
While Google dominates search traffic, Bing’s user base—holding around 7-10% of the U.S. market—cannot be overlooked. Bing primarily serves enterprise customers, older demographics, and users tied to Microsoft’s ecosystem, offering brands a chance to reach untapped audiences. In an environment that’s often less competitive, I’ve found that with focused AEO strategies, it’s easier to achieve greater visibility.
Conclusion
Bing plays a crucial role in AEO for brands seeking to expand beyond Google. Through the use of structured data, optimization for conversational queries, emphasis on content clarity, and alignment with Microsoft’s ecosystem, the potential power of Bing as an answer engine becomes apparent. As AEO shapes the future of search, investing in platforms like Bing ensures that brands stand out in an answer-driven world.
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.
