I was excited to hear that Microsoft Advertising is now expanding LinkedIn profile targeting to connected TV campaigns. This update offers advertisers like me a fresh opportunity to engage professional audiences by integrating LinkedIn data with streaming inventory.
Navah Hopkins, the Product Liaison, unveiled this development at the SEM Stories event on May 14. It’s a game-changer for us in the advertising space.
Why I care. Microsoft stands out by offering unique access to LinkedIn audience data. Extending these capabilities to connected TV formats that previously lacked such precise professional targeting is a big deal in an expanding digital advertising landscape.
For B2B advertisers like myself, this integration bridges the critical gap between brand exposure and measurable performance.
What’s new. According to Hopkins, we can now target CTV audiences by leveraging LinkedIn profile attributes that reflect users’ professional roles, which is a fantastic addition.
This means I can engage with viewers based on:
Industry
Job function
Company category
Professional identity signals
Hopkins framed this feature as an avenue to create meaningful audience lists, moving beyond mere click-based intent signals.
The bigger picture. This announcement aligns with Microsoft’s broader goal to offer AI-driven, audience-centric advertising experiences.
Hopkins emphasized the merging of brand and performance marketing, noting how AI is reshaping traditional marketing funnels.
Connected TV is at the core of this evolving conversation. Historically a branding-heavy channel, CTV often lacked the attribution robustness of search or shopping campaigns. LinkedIn-based targeting could make such campaigns more strategic for those of us who prioritize performance while requiring precise audience control.
This update also bolsters Microsoft’s standing against competitors in both the streaming and B2B advertising sectors.
What to watch. There are still questions regarding market availability, measurement capabilities, the granularity of LinkedIn audience segmentation in CTV, and privacy or compliance considerations for professional audience targeting.
Nonetheless, this advancement offers Microsoft a new edge in the crowded CTV market, allowing advertisers like me to achieve increased audience precision without compromising on scale.
Embrace audience engineering to influence AI decisions, manage ad spend wisely, and connect with high-value customers through creativity and data.
I’m witnessing a significant transformation in the paid media landscape as platforms shift from manual targeting to AI-driven audience discovery. This change is redefining how we approach advertising, with automation tools consolidating campaigns, obscuring data, and favoring prediction algorithms over manual selection.
This transition requires me to innovate by mastering the art of audience engineering. By doing so, I ensure I’m equipped with strategies to thrive in this evolving landscape.
The End of Manual Targeting as I Knew It
Previously, I depended on detailed keyword lists and demographic filters to pinpoint my ideal audience. I directed platforms about where to focus and paid to access the desired market.
However, these options are now outdated:
Google has transitioned to Performance Max, which eliminates keyword-specific targeting in favor of more fluid groups and signals.
Meta’s Advantage+ automates demographic focus, turning my role into that of a signal provider instead of an audience selector.
Microsoft’s inclusion of this model confirms this is an industry-wide evolution.
While traditional targeting seems to have vanished, it has merely moved to the internal structures of the platforms where algorithms dictate the direction based on their indigenous data.
The Rise of Audience Engineering
My role shifts from targeting to engineering as it becomes more about guiding algorithms than manually selecting audiences.
From Targeting to Teaching
The distinction is crucial. Traditionally, targeting emphasized choosing audiences, but now it’s about educating AI with comprehensive conversion data, targeted creativity, and insightful first-party data.
Previously, I might have targeted CFOs with job filters, but now I feed the AI robust data (e.g., “deal closed” signals) to characterize valuable prospects and devise creative content tailored to their needs.
The New Competitive Discipline
Embracing this transformation gives me an edge. By finetuning conversion signals, honing creative content, and fortifying data systems, I ensure our performance remains robust.
The performance gap now relies on the quality of signals, making audience engineering pivotal for success.
The Three Levers that Now Drive Targeting
I focus on optimizing these three crucial AI inputs to ensure effective audience segmentation:
1. Conversion Signal Quality
By providing the algorithm with relevant business outcomes rather than superficial metrics, I encourage it to find results that truly matter.
Using tools like Offline Conversion Imports (OCI) and the Conversions API (CAPI), I ensure our data highlights genuine sales by leveraging value-based bidding techniques.
2. Creative as a Targeting Mechanism
With no demographic filters, my creative content now acts as the primary targeting tool, filtering users through its message.
If my creative targets niche pain points, the AI connects with users aligned with that perspective, even without traditional filters.
3. First-Party Data as Competitive Moat
Our customer lists and engagement signals become core learning elements for the algorithm, replacing third-party signals and offering a competitive edge.
Essentially, I’m arming the AI with a guide to discover the most profitable audiences.
How This Plays Out in Real Campaigns
The journey to AI-led targeting isn’t just theoretical. Within our agency, managing over $215 million in media spend annually, we have evaluated this approach across different platforms, witnessing its power firsthand.
Advantage+ Audiences in Practice
One long-standing client had a specific perception of their audience based on a vast history of accurate data. Initially, our campaigns ran with tightly controlled targeting to maintain efficiency.
Transitioning to Advantage+ allowed for data-driven optimization, revealing an unexpectedly lucrative older demographic, improving their click-through rates by 37% and conversion rates immensely.
Broader AI-optimized targeting cut costs and raised revenue — outperforming past manual methods.
By aligning goals with data and creative, we found valuable segments conventional targeting schemes previously overlooked.
Microsoft PMax Placement Transparency and Advanced Audience Signal Targeting
Another client benefited from a Microsoft PMax test, effectively targeting high-intent prospects using internal data across several Microsoft networks, seeing notable increases in performance metrics each month.
This trial highlighted the importance of combining strategic oversight with smart AI deployment, enhancing the algorithm’s reach while maintaining disciplined campaign direction.
The balance between scale and strategic input preserved efficiency and bolstered overall performance.
The Risks Nobody is Talking Enough About
While automated targeting offers significant advantages, it’s essential to understand its limitations. Here’s what I strive to avoid:
Garbage In, Garbage Out
Poorly defined conversion objectives, weak data quality, or junk data hinder performance and mislead the algorithm. Feeding it quality information and focused outcomes is crucial.
An overly broad goal without distinct signals results in quantity over quality, which doesn’t necessarily translate to business success.
The Self-Reinforcement Trap
If the seed data has biases, the AI will continuously optimize for those biases, possibly neglecting valuable audience segments.
These underrecognized biases present inherent risks in leveraging automated systems without mindfulness.
Automation Without Oversight
Platforms promote broad automation, but I recognize the need for continued oversight to realign campaigns with business goals.
Constant monitoring is essential to ensure objectives are met, avoiding a passive management style.
Creative Complacency
As automation advances, creative strategy becomes a crucial differentiator and shouldn’t be neglected.
Crafting compelling creative that addresses core customer issues is vital in distinctively standing out.
How to Put Audience Engineering into Practice
Here’s how I integrate audience engineering into everyday operations:
Restructure Creative: Focus on intent signals, addressing what beliefs inspire conversion.
Predefine Guardrails: Establish performance boundaries before unleashing the algorithm, allowing for better campaign control.
The Future Belongs to Audience Engineers
The era of manual targeting is closing, but precision remains crucial. Audience engineering acts as an invaluable skill, unlocking AI’s full potential to achieve maximum results in this dynamic landscape.
Expanding beyond paid social? Discover how I learned to structure campaigns, control spend, and unlock demand without depending solely on the Meta playbook.
My paid social campaigns were thriving. I understood my audience intimately, had a tight creative process, and watched results improve each year. Naturally, when leadership proposed expanding into Google Ads, I was thrilled—envisioning it as a new revenue channel.
But sticking to our existing strategy only led to difficult conversations. Google demands different tactics—intent signals and campaign structures vary, and common budget-draining mistakes aren’t always obvious. Many brands mirroring their Meta strategy end up with flashy dashboards but disappointing balance sheets.
From my experiences, six frequent mistakes can cause substantial damage before they’re even noticed. They’re what I’ve seen most often with ecommerce brands transitioning to Google Ads—and each error is reversible.
Mistake 1: Treating Google like a retention channel
Utilizing Google Ads for retention and brand defense is possible, but relying solely on it as a strategy is problematic. I often notice brands new to the platform diving straight into Performance Max. Initially, the ROAS shines bright, making everyone happy. However, when the right question surfaces—”Are we truly growing or just capturing purchases?”—issues arise.
For example, a client approached me with branded search and retargeting doing most of the work in PMax—a mere tax on demand already created elsewhere, leading to stagnant revenue. Although ad spend was soaring, growth wasn’t.
Acquiring new customers requires a different setup, like:
Shopping campaigns to highlight products to new audiences.
Search campaigns centered on non-branded, high-intent keywords.
Layered PMax configurations to bypass defaulting to easy conversions.
When Google grants vast access to new audiences, focusing solely on closing disregards most of this opportunity.
Mistake 2: Not knowing how to leverage Google’s core levers
Although paid social expertise is somewhat transferable to Google, I’ve observed four major gaps. Let me share them with you in more detail.
Search intent: Social media ads interrupt, but search ads meet users actively seeking your offerings, transforming campaign structure, ad copy, and keyword targeting entirely.
Data feed optimization: An optimized product feed enhances visibility and targeting in Shopping or Performance Max campaigns.
Keyword research: Understanding match types and search intent is critical for reach and cost efficiency.
Landing pages: Engaging landing pages outperform product pages for high-intent but unfamiliar visitors.
Mistake 3: Allowing operational issues to interrupt campaign momentum
Consistent data is key for Google’s algorithms. Every unintended campaign pause can reset learning, causing weeks of degraded performance and wasted spend.
Common disruptions include:
Payments: Bill lapses, leading to campaign pauses, overshadow the actual cost when factoring in downtime recovery.
Tracking and feed integrity: Broken pixels and feed errors silently degrade performance.
Setting up automated alerts and regular audits can prevent these costly errors.
Mistake 4: Overly granular campaign structures
Detail-oriented advertisers may over-segment campaigns, believing it provides control. However, widespread budget allocation hinders Google’s automation from optimizing effectively.
Instead, tight, well-funded campaigns optimize better and are more manageable.
Mistake 5: Leaving campaigns on Max Conversion Value without ROAS targets
Max Conversion Value aims for conversion volume, neglecting cost efficiency. A realistic ROAS goal encourages the algorithm to maximize efficiency. Setting this correctly is crucial.
Mistake 6: Underfunding campaigns, keeping them in learning mode
Underfunding during the learning phase results in indefinite stalled progress. Adequately funding new campaigns from the outset fosters quicker, more accurate results.
Expanding beyond Meta to include Google is a strategic move, accessing actively expressed demand. These pitfalls aren’t deterrents but guideposts for smoother transitions and optimized strategies.
Struggling with restricted targeting? Dive into my guide on how to drive conversions using intent signals, creative messaging, and offline data, especially when remarketing isn’t an option.
Have you ever experienced that “Eligible (Limited)” status in your Google Ads account? As a lawyer, college administrator, or financial services provider, I know how challenging it can be when your remarketing lists and exact match keywords aren’t working as expected.
Feeling like Google Ads is your adversary in sensitive interest categories can be frustrating, but there are valid reasons for these regulations. More importantly, strategies exist to overcome them.
In this article, I will explain the personalized advertising policies, their implications for your account, and share five tactics you can implement to achieve success with Google Ads.
Why does Google have personalized advertising policies?
Google’s policies are rooted in legal requirements and ethical standards, as detailed in their official documents. In the U.S., legislation like the Fair Housing Act and employment laws prohibit discrimination based on age, gender, or location. This means Google can’t allow you to exclude individuals based on such demographics.
Ethically, remarketing can become invasive, especially in high-stakes industries like healthcare. If you’re running a rehab center, trailing someone across the internet with ads about their struggles is intrusive. Google’s policies help maintain user privacy in such cases.
What can’t you do in a sensitive interest category?
Operating in housing, employment, credit, healthcare, or legal services means restricted audience targeting. Here’s what you’ll miss out on:
Website or App Remarketing Lists: Targeting past visitors is off the table.
Customer Match: Uploading and targeting email or phone lists is not permitted.
YouTube Audiences: Targeting based on video interactions is restricted.
Custom Segments: You can’t create audiences based on specific searches or website visits.
Moreover, in categories like housing, further demographic targeting like age or ZIP code may also be stripped away.
The good news: What can you do in a sensitive interest category?
Despite these restrictions, there’s still much you can utilize. Here’s what you have at your disposal:
Keywords and Feeds: Intent-driven strategies are perfect for Search, Shopping, and Performance Max.
Google Audiences: Use Affinities, In-Market, and Life Events segments as allowed.
Optimized Targeting: AI-driven targeting is still viable for certain ad types.
Content Targeting: Target ads based on keywords, topics, and placements.
Conversion Tracking: Maintain conversion tracking and utilize Enhanced Conversions.
5 strategies to win in sensitive categories
Thinking outside the box can yield results, even without remarketing. Let me share five strategies that work:
1. The “Separate Domain” strategy
For businesses offering a mix of sensitive and non-sensitive services, avoid having your entire account restricted. By placing sensitive services on a separate domain, you maintain the flexibility of using full Google Ads capabilities for your main business.
2. Choose Demand Gen over Display
Opt for Demand Gen when using image or video ads. My experiences show it attracts higher-quality audiences in restricted niches.
3. Lean into Phrase and Broad Match
While Exact Match keywords might seem appealing, the algorithm often restricts narrow queries. Consider using Phrase or Broad Match, giving you the chance to target users querying the same concept differently.
4. Feed the AI with offline conversion tracking
For industries like law and finance, where online conversions are rare, provide Google with offline conversion data. This step trains the algorithm, ensuring smart bidding leverages real-world outcomes, even with privacy guidelines in mind.
5. Creative-Led Targeting
In cases where user lists are off-limits, let your creatives do the talking. Your visual and textual ads should be clear on who they’re meant for, improving conversion by weeding out unfit viewers.
Navigating Google Ads in sensitive areas isn’t easy, but it’s achievable. By focusing on what users seek and fine-tuning your messaging, you can deliver outstanding results.
This piece is part of my Search Engine Land series: Everything you need to know about Google Ads in under 3 minutes, where Jyll discusses critical Google Ads features to help you maximize your advertising results.
I recently discovered an important update from Google affecting how I run Demand Gen campaigns using Lookalike user lists. Starting April 30, Google will block creating duplicate Lookalike lists via the Google Ads API and return an error code for any breaches.
This update might seem quiet, but its implications are significant, especially for those of us utilizing automated systems or third-party tools. Google is now enforcing a uniqueness check to prevent duplicates that have identical seed lists, expansion level, and country targeting.
Why do I care about this change? An unaddressed error could disrupt the workflow of my campaigns if I don’t update my integrations in time.
Here’s what I plan to do:
Audit my current Lookalike lists and reuse those that already align with my goals instead of creating new ones.
Update my API error handling processes to catch the new DUPLICATE_LOOKALIKE error code in versions v24 and above, or RESOURCE_ALREADY_EXISTS in older versions.
The bottom line is, while this change is housekeeping, the deadline is firm. I need to ensure my campaigns are technically prepared before the end of April to maintain stability in Google’s systems.
When I first started thinking about Google Ads retargeting, I assumed it was all about banner ads chasing people across the web. But I’ve since learned that our first-party data is now the fuel for AI performance in advertising.
One of my go-to strategies in Google Ads is retargeting, which involves showing ads to individuals who already know about my business. If you still see retargeting as merely display campaigns with flashy banners, we’re missing out on the transformative potential of “Your data segments.”
I want to dive deeper into how we can use our proprietary audience data in innovative ways while also steering clear of common pitfalls as we move into 2026 and beyond.
The concept of “Your data segments” in Google Ads is a nuanced take on retargeting. Essentially, it represents all the retargeting lists in our accounts, rebranded under Google’s parlance.
Google Ads offers a suite of retargeting options, akin to what you’d find on platforms like Meta or LinkedIn. I find grouping them into four main categories quite helpful:
Website Visitors: This category targets visitors to our website, tracked through Google Tag Manager or Google Analytics.
App Users: If your brand has a mobile app, pulling data from Firebase or another analytics tool into Google Ads lets us retarget app users.
Customer Match: This is the ultimate form of retargeting. We can upload our proprietary data like email addresses to Google Ads to find these very users across Google’s platforms.
Content Engagers: This targets individuals who’ve interacted with our content on platforms Google owns. This includes YouTube viewers or users entering from search results, known as the Google Engaged Audience.
Now, when it comes to uploading “your data segments,” some might wonder if it’s worthwhile without an immediate plan for retargeting. Interestingly, these segments do more than just aid ad targeting.
Even absent any retargeting campaigns, uploading these lists can enhance Smart Bidding and Optimized Targeting. For example, providing a customer list signals to Google, “These are our real buyers.” Even if I don’t use this for direct audience signals in Performance Max, Google can leverage it for understanding likely converters.
Various campaigns handle audience data differently, so having clarity on these approaches is crucial for crafting an effective targeting strategy.
For instance, in Search, Shopping, and Display campaigns, we have three tactics with our data segments: Targeting, Observation, and Exclusion. Meanwhile, Performance Max and App Campaigns allow the inclusion of data segments within the audience signal and recently added exclusion options.
If new to retargeting, Demand Gen campaigns are a solid starting point since they emphasize visual storytelling, harmonizing well with our lists.
A pitfall I’ve encountered? Over-segmenting. The urge to create detailed lists like “Tuesday cart visitors” can arise, but unless your ad spend is exceptionally high, such granularity could hinder us. Google’s AI flourishes with dense data, so simplicity is key for efficiency.
Keeping strategies straightforward and trusting the AI with our unique data can lead to powerful retargeting outcomes.
This guide is part of the ongoing Search Engine Land series, where we explain Google Ads features for optimal results in under three minutes.
I’ve discovered a game-changing PPC framework that not only predicts user intent but also extends beyond traditional search methods to connect your content with the right audience.
Search marketing continues to thrive, with Google reaching over $100 billion in ad revenue in just one quarter, primarily driven by search ads. However, relying solely on search won’t yield the results many businesses anticipate anymore.
During the SMX Next event, I learned from Google Ads Coach Jyll Saskin Gales that genuine performance now hinges on integrating traditional search with an expansive PPC strategy.
The challenge with traditional Search Marketing
In my experience as a search marketer, I excel at reaching individuals actively searching for what I offer. Yet, there’s an entire audience segment that aligns with my target market but hasn’t started their search journey.
The actual opportunity lies at the crossroads of user intent and audience fit.
Consider the term [vacation packages]. This could be queried by different groups like a family with kids, honeymooners, or retirees. While the keyword remains the same, each group requires unique messaging and offers.
Understanding targeting capabilities in Google Ads
There are two primary targeting types I focus on:
Content targeting places ads in specific locations.
Audience targeting displays ads to particular user types.
For instance, targeting [flights to Paris] is content targeting, while targeting users “in-market for trips to Paris” uses audience targeting. Google’s in-market audiences are crafted by analyzing various signals like user searches, browsing behavior, and location data.
The three types of content targeting
Keyword targeting: Engage users when they search on Google, extending to dynamic ad groups and Performance Max.
Topic targeting: Present ads next to content about specific subjects in display and video campaigns.
Placement targeting: Present ads on particular websites, apps, YouTube channels, or videos where my ideal customers already engage.
The four types of audience targeting
Google’s data: Prebuilt segments include detailed demographics, affinity segments, in-market segments, and life events, usable by any advertiser across most campaigns.
Your data: Target website visitors, app users, and those engaging with my Google content using Customer Match, though remarketing is restricted for sensitive topics.
Custom segments: Convert content targeting into audience targeting by crafting segments based on search behavior, interests, and user site or app preferences. Names vary across campaigns, such as “custom segments” and “custom search terms” in video.
Automated targeting: This entails optimized targeting, audience expansion, and lookalike segments deriving new users from existing data.
Building a targeting strategy
To construct a cutting-edge targeting strategy, I need to address these two essential questions:
How can I leverage Google Ads to promote my offer?
How can I connect with a specific audience using Google Ads?
For instance, targeting Google Ads professionals for lead generation software could involve building tailored segments targeting users of the Google Ads app, visitors of industry-relevant sites like searchengineland.com, or searchers utilizing specific Google Ads terms like “Performance Max.”
Layering in content targeting, such as YouTube placements on industry educational channels and topic targeting around search marketing, enhances my outreach.
Strategies for sensitive interest categories
In cases where I operate within restricted categories like legal or healthcare, and cannot employ custom segments or remarketing, non-linear targeting becomes crucial. I focus entirely on the audience and ignore direct offers. Selecting any Google data audience with an overlapping potential and letting creative content filter it out helps tremendously.
Employ industry-specific terminology, acronyms, and visuals that resonate with and are recognizable to my target audience. Others will likely disregard it.
Remember: High CPCs aren’t the enemy
From my perspective, low-quality traffic poses the real challenge. It’s more beneficial to incur a $10 click with a 10% conversion rate than a $1 click with an infinitesimal 0.02% conversion rate.
When analyzing targeting strategies, I focus on conversion rates and cost per acquisition instead of merely cost per click.
Search alone can’t deliver the results you’re used to
By expanding beyond traditional search keywords and incorporating content and audience targeting, I can ensure the right people see my ads and achieve robust results.
Watch: Building a Modern Targeting Strategy Like a Pro + Live Q&A
I’ve noticed that YouTube has recently upgraded its Promotions tool, offering creators like us a smarter way to reach our audience. Now, we can target viewers based on their interests rather than just simple demographics like age, gender, or location. This change is making things more personal and effective!
What’s new: With the latest update, we can target specific interest categories, such as Food & Dining. These categories are crafted from aggregated, anonymized data, giving us insights based on viewing habits and search behaviors.
For example, if someone frequently searches for recipes and enjoys watching cooking videos, YouTube may place them within a food-related interest segment, allowing us to tailor our promotions more precisely.
How it works: YouTube uses patterns it detects across Google services to infer viewers’ interests, applying these insights on a broad scale while keeping individual data private.
Why this matters: As creators investing in promotional videos, we can now target audiences based on their true interests, making our ads more effective and as viable as traditional Google Ads.
The big picture: Historically, YouTube’s promotion tools have felt somewhat blunt, relying heavily on demographics. This new interest-based approach aligns with a full-funnel advertising strategy, making paid promotions notably appealing for:
Growing channels looking to build a dedicated audience
Established creators experimenting with new content formats
Brands working with creators to widen their reach
What’s next:
Currently, this feature is only available on desktop
We can expect a mobile rollout in the near future
First seen: This upgrade was first discovered by Google Ads Specialist Georgi Zayakov, who shared the news on LinkedIn.
Bottom line: YouTube is equipping us with better tools to connect with the right viewers. Instead of just increasing viewer numbers, we’re now closer than ever to narrowing the gap between creator marketing and traditional digital advertising.
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.
