Ever since learning about Google’s latest update to its YouTube and Discover Feed ad requirements, I’ve been intrigued by the clarification on election-related ads. This change, effective April 2026, doesn’t alter enforcement but provides much-needed transparency.
Why it matters. As someone navigating the complex landscape of YouTube and Discover ad placements, I understand how tightly regulated these spaces are. Historically, election ads have been surrounded by ambiguity. Now, the update helps clear up that confusion without imposing additional restrictions.
What’s new (and what’s not). It’s interesting to note that election ads are now clearly exempt from specific YouTube and Discover Feed ad requirements. However, no changes in enforcement mean that if compliance was achieved before, there’s no need for advertisers to shift gears.
Why we care. With this update, I’ve noticed how Google aims to eliminate the haze surrounding election ads on YouTube and Discover. Although these ads don’t need to meet placement-specific requirements, adherence to Google Ads policies remains essential, offering clearer guidance and more predictable campaign launches.
Zoom in. For election ad campaigns, this exemption is beneficial since these ads aren’t required to comply with the targeted YouTube and Discover Feed ad guidelines. However, advertisers must pass the Election Ads verification within the ad’s targeted region.
Between the lines. It’s vital to recognize this as a documentation clarification rather than a policy change. Google is distinguishing between the unique requirements for YouTube and Discover ads and its overarching ads policy framework.
What advertisers should do. If you’re running political campaigns, it’s crucial to maintain your verification status and continue adhering to Google Ads policies. Despite the exemption, keeping up with regulations is necessary for a smooth advertising process.
I’m thrilled to share that Microsoft is simplifying the process of expanding Google PMax campaigns into Microsoft, allowing us to enjoy greater visibility and control over our campaign performance.
Microsoft Advertising is launching several updates to make managing, measuring, and migrating Performance Max campaigns more straightforward, especially for those of us already familiar with Google Ads.
Driving the news. Microsoft now allows us to import Google PMax campaigns with new customer acquisition (NCA) goals, a feature that’s been part of Microsoft since earlier this year.
The update is live for all advertisers now, enabling us to transfer campaigns focused on first-time buyers more seamlessly, without having to start from scratch.
What’s new. Microsoft ensures that when we import Google PMax campaigns with NCA goals, they will be retained if they don’t already exist in our account. Our existing settings won’t be overwritten.
Regarding audience lists:
Google website visitor segments transform into Microsoft remarketing lists.
Google’s “all visitors” and “all converters” lists map to similar lists on Microsoft.
For unsupported lists like Customer Match, we may need to use alternate options.
I’ve also noticed that Microsoft takes a cautious approach with “unknown” customers, categorizing them as existing customers to avoid inflating new customer conversion counts.
Why we care. This initiative could streamline cross-platform campaign expansion and reduce the hassle of rebuilding, making it simpler to test Microsoft’s PMax inventory. Plus, enhanced landing page reporting and search term insights offer a clearer picture of campaign performance, aiding our optimization and budget decisions.
More visibility for PMax. Microsoft is integrating landing page (Final URL) reporting for PMax campaigns, allowing us to review spend, clicks, impressions, conversion value, and ROAS by landing page.
We can also break this information down by campaign, asset group, and other dimensions.
Additionally, Microsoft stated that search term reporting will become more apparent by default, with more transparency updates such as auction insights and publisher URL metrics rolling out soon.
Other key updates:
Seasonality adjustments now support portfolio bid strategies, aiding short-term promotions.
Campaign name limits have increased, enabling up to 400 characters for easier management.
Autogenerated assets are improving ad relevance and performance by filling in underused Responsive Search Ads.
Merchant Center users can directly update store names and domains without needing support.
The bottom line.These updates simplify scaling across platforms, save time on campaign setups, and enhance our visibility into campaign performance, giving us greater control over efficiency and outcomes.
I’ve been noticing the rapid transformation in how brands are tracking user behavior online. With privacy laws tightening and browser extensions increasingly blocking data, the demand for cleaner data from ad platforms is higher than ever. This change urged me to explore server-side tagging as a solution.
By implementing server-side tagging, I’ve managed to reduce data loss while collecting cleaner, privacy-compliant data. This approach is invaluable, especially considering the experiences I’ve had with providers like Elevar and Littledata.
So, what exactly is server-side tagging, and in which situations does it really shine? Let’s dive into the details!
What is server-side tagging?
Traditionally, tracking scripts ran directly in the browser. However, with server-side tagging, these scripts operate on a server I control, giving me more control over data processing.
Here’s how it works: instead of sending data straight to multiple third parties from the browser, events are sent to a first-party server endpoint, often using a Google Tag Manager server-side container. The server then processes, enriches, and forwards this data to tools like Meta and Google Analytics.
This setup provides benefits such as more data control, a cleaner page performance, and better compliance with privacy laws.
Moreover, server-side tagging grants me the flexibility to enrich and transform data before it reaches ad platforms, standardizing event names, filtering out low-quality events, and adding custom parameters for better audience segmentation.
Is server-side tagging right for you?
While server-side tagging isn’t a one-size-fits-all solution, many brands find it essential, particularly if you:
You need to meet strict privacy or compliance requirements
Server-side setups allow for greater control over how data is processed and shared, supporting compliance with regulations like GDPR and CCPA.
You want faster website performance
In my experience, client-side tracking can slow your page down, but server-side tagging shifts data processing to the server, resulting in faster websites.
You want more accurate tracking (despite ad blockers)
Ad blockers can hinder client-side scripts, but server-side tagging circumvents many of these restrictions, making your data collection more reliable.
You’re investing heavily in paid media
For those heavily invested in platforms like Meta and Google Ads, achieving better data accuracy can significantly impact return on ad spend.
How to implement server-side tagging
When it comes to implementing server-side tagging, you have two main options: building it internally or using a service provider.
Option 1: Internal setup
Choosing an internal setup gives me complete control but requires technical expertise and ongoing maintenance. This involves setting up a GTM server-side container and adding logic for data processing.
Option 2: Use a server-side tagging service
Platforms like Elevar and Littledata offer turnkey solutions that integrate seamlessly with existing tools, allowing me to focus on strategy rather than technicalities.
Our direct experience: Littledata vs. Elevar
In my experience with Littledata and Elevar, each caters to different needs. Littledata is ideal for emerging brands with simpler tech stacks, while Elevar is suitable for those outgrowing entry-level solutions.
Investing in server-side tagging has transformed how I handle data, ensuring that I remain compliant with privacy laws while boosting site performance and data reliability across all my platforms.
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.
During a recent study, I discovered that Reddit stands out as the most-cited domain in AI-generated answers. In fact, it’s ahead of heavyweights like YouTube and LinkedIn, thanks to an analysis of 30 million sources conducted by Peec AI, a tool specializing in AI search analytics.
The findings: I’ve learned that Reddit claims the top spot across various AI platforms including ChatGPT, Google AI Mode, Gemini, Perplexity, and AI Overviews. Top contenders YouTube, LinkedIn, Wikipedia, and Forbes are right behind. Platforms like Yelp and G2 frequently appear when searching for recommendations.
As I delved deeper into the research, it became clear which domains the AI models tend to lean on:
ChatGPT values Wikipedia, Reddit, and editorial sites like Forbes.
Google shows preference for platforms such as Facebook and Yelp.
Perplexity favors Reddit, LinkedIn, and G2 for queries within the B2B realm.
Why we care: The insight that resonated with me was the importance of having authority beyond just our own websites. Brands that consistently feature on reputable third-party platforms have a better chance of being cited by AI.
Why these sources? It’s fascinating to see how AI systems are wired to prioritize both authority and authentic user input:
I’ve found that Reddit excels because it mirrors genuine user discussions.
YouTube shines in video citations, owing to their comprehensive transcripts and descriptions.
Wikipedia not only serves real-time data but also acts as a foundation for training datasets.
About the data: The analysis spanned 30 million sources, providing a comprehensive look at how often domains are directly cited in AI answers, effectively revealing what shapes these responses.
According to a recent, though unverified, report, Google Gemini’s AI is designed to tailor its responses based on the user’s tone, intent, and emotional context. This fascinating development suggests that the AI aligns its answers with the emotional backdrop of each query.
Why This Matters. If this information holds true, it means that the responses generated by AI might vary significantly, depending on how we phrase our queries, rather than just on the data available. This could change the way we engage with search engines.
New Findings. At the heart of this revelation is a system called upcast_info. As reported by Elie Berreby, head of SEO and AI search at Adorama, this system seems to provide the blueprint for how Gemini processes user queries, aiming to:
Reflect the user’s tone, energy, and purpose.
Acknowledge emotions before formulating a response.
Deliver answers from the user’s perspective.
Implications. Instead of maintaining a neutral stance, the AI’s responses could:
Emphasize negative perspectives (“Why is X bad?”).
Highlight positive aspects (“Why is X great?”).
Should the public sentiment toward a topic be negative, the AI might intensify that sentiment. As the report indicates:
AI mirrors prevalent emotional signals.
It doesn’t offer the balancing act usually provided by traditional search result links.
The Role of Query Framing. The emotional tone of a query can impact:
The choice of sources cited.
The style of summaries presented.
The overall tone and substance of the answers.
Google’s AI Overviews already demonstrate shifts in tone that align with the intent of queries, providing potential insight into the mechanics behind these changes.
Unsubstantiated Information. Google has yet to confirm this leak. As Berreby mentions: “I’ve decided to share just a portion of the leaked internal system data publicly. It’s not a security exploit or major breach, just a minor leak.”
I’ve noticed a significant shift in the SEO industry toward senior, strategy-focused roles. As AI increasingly handles execution tasks, the demand for seasoned strategists has grown, along with an increase in salaries and responsibilities that span multiple channels.
The change in hiring trends is evident when looking at a recent Semrush analysis of 3,900 job listings. It appears companies are now prioritizing leadership skills, innovative experimentation, and cross-channel visibility over purely technical execution.
Why it matters to me. The landscape for SEO careers and skillsets is evolving. Entry-level positions are mostly focused on execution, while leadership roles require a firm grasp of strategy across various domains such as search, AI assistants, and paid channels, ensuring they drive significant revenue.
What’s changing now. Senior roles account for 59% of job listings, clearly dominating the landscape. In contrast, mid-level positions like specialists and managers are less prevalent, with only 15% and 10%, respectively.
Companies are redirecting their budgets towards strategic roles as AI tools begin to absorb more of the technical workload.
The shift in skills. The skills in demand now extend beyond traditional SEO to include coordination, experimentation, and decision-making capabilities:
Project management is mentioned in over 30% of the listings, highlighting its importance.
Communication is highlighted in 39.4% of non-senior roles, indicating its fundamental role in the industry.
Experimentation is noted in 23.9% of senior roles, compared to just 14% of other roles.
Technical SEO appears in approximately 6% of postings, showing its niche but crucial role.
Tools and channels. The modern SEO toolkit now includes analytics, paid media, and comprehensive data tools.
Google Analytics is cited in up to 47.7% of job listings, underlining its importance.
Google Ads features in 29% of the listings, showcasing its growing relevance.
Demand for SQL skills is rising, especially at the senior level.
AI tools, such as ChatGPT, are increasingly mentioned, reflecting their future role in SEO.
AI expectations. AI literacy is shifting from being a nice-to-have to an essential skill:
31% of senior roles now reference AI capabilities.
Nearly 10% of listings highlight familiarity with LLMs.
Concepts such as AI search and AEO are increasingly common in job descriptions.
Pay and positioning. SEO is being increasingly recognized as a vital business function:
The median salary for senior roles has reached $130,000, markedly higher than the $71,630 for other roles, with some positions offering even more.
Preferred degrees are leaning towards business and marketing, reflecting the strategic emphasis.
Remote work prevalence. Remote options are available in over 40% of job listings, indicating a shift towards flexible work environments across all levels.
About the data. This analysis by Semrush covers 3,900 SEO job listings in the U.S., gathered from Indeed as of November 25. The roles were deduplicated and segmented by seniority before a semantic keyword extraction analysis was applied.
I’ve got some exciting news to share—Reddit has just opened up its Pro publishing tools to all publishers! No more waiting lists. Now, anyone can dive into the public beta and ramp up their content distribution and engagement strategies, all for free.
Why this matters to us. Reddit Pro offers me a centralized hub to monitor where my content spreads, simplifying my posting process, and helping me pinpoint the right communities to engage with. It’s transforming Reddit from being a place of manual posting to a well-organized distribution channel.
Here’s the scoop. I can now easily sign up for Reddit Pro, verify my domain (usually within three business days), and jump into the Links tab. With Reddit Pro, I can:
Keep track of where my content is shared all over Reddit.
Quickly auto-import articles through RSS, speeding up my posting.
Receive AI-powered tips on the most relevant communities to connect with.
Reddit has also rolled out some features based on early adopter feedback:
Community snapshots that display rules, stats, and top discussions.
Community notes that let me track strategy and context over time.
By the numbers. Back in 2025, Reddit revealed there were over 55 billion views of publisher-related discussions. Since some publishers started testing in September, they saw:
A 46% uptick in median post views.
An almost doubled amount of profile views.
A 48% climb in median comments.
What else to look forward to. Reddit is also expanding profile flairs to every Pro user. This means I can organize posts on my profile, making it easier for users to browse my coverage and get involved with stories.
I recently stumbled upon a tricky issue in Google Ads Editor that’s affecting many advertisers. A bug is causing structured snippet extensions copied between accounts to unintentionally stay linked. Whenever I change the language setting in one account, it seems to magically update the extension in another account too.
Why this matters to us. For those of us running multi-market campaigns, this bug could introduce hidden inconsistencies, especially if we’re managing accounts that require different languages.
What I’ve been experiencing. This issue came to light for digital marketer Marcin Wsół while handling Czech and Slovak e-commerce accounts. A change in snippet language in one account inadvertently altered the same setting in another.
The extensions appear separate at first glance but act like they’re mysteriously synced.
Zoom in on the details. If you use the Google Ads web interface, you can temporarily correct this, but any further edits in Editor might cause the language settings to toggle again.
A deeper issue. This bug isn’t confined to cross-account use. PPC News Feed founder Hana Kobzová discovered that even copying structured snippets within the same account can lead to incorrect language settings after making additional edits.
Reading between the lines. For those of us who depend on bulk edits in the Editor, there’s a risk of unintentionally overwriting localization settings, which could lead to mixed messaging across our markets.
The bottom line. Until Google fixes this, I recommend double-checking structured snippet languages after copying or editing in Google Ads Editor, especially when you’re working across different accounts or regions.
When this issue was first seen. This was initially identified by Marcin Wsół and later reported by PPC News Feed.
Heidi Sturrock, a seasoned paid search consultant, shared her insights with me in a recent episode of PPC Live The Podcast. With over two decades of industry experience, Heidi discussed a memorable campaign blunder that surprisingly turned into a strategic win, as well as her experiences with AI Max across numerous accounts.
Heidi’s career story includes a significant misstep she made while running a competitor campaign using broad match without negative keywords. Launched on a Friday, this led to a weekend surge of calls from irate customers of a competitor, a situation both alarming and chaotic for the client’s call center.
Unexpectedly, her client saw potential in this turmoil. Instead of dwelling on the mistake, they chose to transform these calls into sales opportunities by offering a discounted first month to switchers. By dividing the campaign for better focus, they turned a problem into a pathway for growth.
From this experience, I learned two valuable lessons: never initiate major campaigns on a Friday and ensure all stakeholders are involved in client meetings. Having both the business owner and the sales leader aware of the situation allowed for quick, effective problem-solving.
When facing a mistake, I’ve realized the importance of halting the issue swiftly, taking responsibility, and presenting a clear plan for resolution. Clients value honesty, and this approach can reinforce trust even in difficult times.
Common failures in account management often involve misaligned attribution windows and undue focus on secondary KPIs. It’s crucial to align metrics with the primary goals, ensuring that higher CPCs are understood within the broader context of achieving ROAS targets.
Regarding AI tools, Heidi’s exploration of AI Max across various accounts delivered mixed results. Success often hinged on the availability of comprehensive historical data and well-defined goals. Her advice is to experiment gradually and prepare upcoming guidelines on her blog.
For those in the industry, embracing technological changes, especially in AI, is essential. Mastering these tools can propel us ahead as marketers.
Stay connected with Heidi on LinkedIn or visit HeidiSturrock.com for her expert guides, including tips on crafting effective ad copy. Also, catch her live at SMX Advanced in Boston this June, where she’ll participate in an engaging expert panel discussion.
