Starting in January 2026, I’ll see Google updating its Pharmaceutical policy for AdMob Authorized Buyers. This update allows ads for prescription drugs and services in certain markets without needing Google certification. However, they will tighten restrictions on what remains prohibited.
What’s changing? Google’s policy will now be called “Pharmaceutical products and services.” This change permits Authorized Buyers to promote prescription drugs and services legally in specific countries, without requiring Google certification as is usually demanded in Google Ads.
Although access is broadening, the basic rules remain stringent. The policy modifications intend to enhance clarity and readability rather than reducing enforcement.
Why do I care? This update lets me tap into pharmaceutical advertising inventory without needing Google certification, creating fresh opportunities and competition in programmatic auctions. However, it places more compliance responsibility on my shoulders, increasing the risk of policy violations if geo-targeting and creative controls aren’t precise.
I should consider that even non-pharma advertisers might experience changes due to increased demand and ad presence affecting pricing, brand safety, and placement strategies.
What’s still banned? Ads related to clinical trials, miracle cures, illicit drugs, addiction services, crisis hotlines, and experimental treatments remain banned across Google Partner Inventory.
Looking deeper. While Google is opening access, it’s also transferring responsibility to me as a buyer. By removing certain certification requirements for Authorized Buyers but maintaining strict controls, compliance risk is pushed firmly onto buyers and publishers.
What should I do now? As an app publisher using AdMob, I should review category blocking and ad controls to ensure unwanted pharma ads are excluded, especially as more inventory becomes permissible. I need to prepare for enforcing rules country-by-country and carefully audit creatives.
I’ve learned that broad match now operates alongside Smart Bidding. It’s fascinating how drift happens, why it’s important, and how to align performance with genuine intent.
Broad match, once synonymous with “more reach, less relevance,” now depends on a machine learning layer to define relevance.
Over time, Google has nudged us, the advertisers, towards fewer complexities like fewer match types and more automation.
Since July 2024, broad match has become the default for new Search campaigns, signaling a shift in how we ought to think about it.
If you’re stuck in the mindset of broad match being the “loosest match type,” you’re stuck in 2016, and that’s where problems like CPC inflation and irrelevant leads arise.
Today’s broad match works within a system, collaborating with query matching, Smart Bidding, conversion signals, and optional tools like audiences and negatives.
Google leverages broad match as a growth mechanism for Smart Bidding campaigns rather than a solitary reach tactic.
In this article, I explore the changes, Google’s motivations behind them, and safe practices to maintain standards while using broad match.
The real risk with broad match isn’t relevance, it’s direction
Broad match tends to drift rather than fail completely.
With shallow optimization goals, broad match coupled with Smart Bidding can find quick ways to meet them, sometimes resulting in:
Queries that trigger cheap forms without real sales potential.
Users who convert but never purchase.
Leads that look good in Google Ads but don’t end up profitable.
Even when everything seems fine in the interface, the account might drift away from commercial intent.
This illustrates why understanding broad match’s current behavior is crucial.
What broad match actually is now
Broad match no longer stands alone as a keyword setting but works within a larger optimization system.
It’s built to work with Smart Bidding
Google specifies that broad match is intended to run with Smart Bidding, as bidding decisions are now made during auctions using signals like:
Device
Location
Time of day
Query context
User behavior
Broad match increases eligible queries. Smart Bidding evaluates which ones merit investment.
Running broad match without Smart Bidding deviates from its intended design.
Google has materially improved broad match matching
Google claims that recent AI enhancements have uplifted broad match campaigns using Smart Bidding by 10%.
This doesn’t imply broad match is inherently safe, but Google feels its matching layer justifies broader use.
It’s no longer positioned as optional
Starting July 2024, new Search campaigns activate broad match by default.
The campaign-level setting enforces broad match when conversion-based Smart Bidding is active, marking a significant paradigm shift.
Why Google wants advertisers to adopt broad match
Google’s rationale is straightforward:
Search behavior is increasingly unpredictable and long-tail.
Manual keyword lists fail to keep up with language and intent shifts.
Machine learning can interpret intent at auction time better than rigid logic.
Google positions broad match as a growth tool for Smart Bidding campaigns, providing algorithms with more opportunities to optimize for conversions.
You might not agree with this philosophy, but when advertising on Google Search, you’re part of this system.
A framework for using broad match without losing control
Broad match expands your reach. Maintaining control requires thoughtful constraints.
Conversion goals that reflect quality, not convenience
Smart Bidding optimizes based on defined conversion actions and values.
If your primary conversions are low-intent, broad match will scale this low intent.
Successful setups often include:
Optimizing for deeper conversion actions.
Applying conversion values to identify lead quality tiers.
Importing offline conversions, like qualifying leads or revenue.
This tackles the issue of associating cheap volume with success.
Intent filters through audience signals
Broad match identifies queries. Audience signals dictate ad visibility for those queries.
Audiences should provide context, not just report data:
Customer lists favor known buyers.
Remarketing lists for measured expansion.
Audience insights to recognize quality-segment correlations.
Even in observation mode, these signals help verify if broad match growth benefits the right areas.
Negative keyword structures that scale
With broad match, negative keywords transform from mere cleanup to structural elements.
Effective accounts often include:
Account-level shared negative lists for terms like jobs, free, definition.
Campaign-level exclusions aligned with intent boundaries.
Regular search term reviews, crucial early on.
Broad match naturally explores, while negatives determine its limits.
Brand controls to protect intent
Google’s brand controls can substantially reduce unwanted behavior in broad match.
These controls include:
Brand inclusions restrict matching to queries featuring specified brands.
As I dive into the latest updates from Google, I’m thrilled to share that they have now introduced native location targeting controls to Demand Gen campaigns. This update allows advertisers, like myself, to implement more precise geo-targeting, making our campaigns even more effective.
Recently, Google Ads started rolling out these new location targeting options specifically for Demand Gen campaigns. These new options bring these campaigns closer in functionality to Search, which is great news for enhancing our ad strategies.
What’s new? Now, I have the ability to choose explicitly between ‘Presence or interest’ and ‘Presence only’ when setting up Demand Gen campaigns. These options are readily available directly within the campaign interface, streamlining the process by eliminating the need for manual exclusions.
Why this matters for us. Up until now, targeting precision in Demand Gen was somewhat of a challenge. By making ‘presence only’ targeting a native feature within campaign setup, Google helps us avoid common workarounds and reduces the risk of geo-leakage. This means cleaner traffic, more accurate measurements, and increased confidence in our campaign performance.
The bigger picture. Demand Gen is crafted for reaching audiences in the upper and mid-funnel across platforms like YouTube, Discover, and Gmail. With these enhanced location controls, I’m now more assured that my impressions and clicks are from users situated in the target markets I’m aiming for.
Where I noticed it first. This exciting update was first spotted by the Google Ads specialist, Marcin Wsół, whose insights I follow on LinkedIn.
The takeaway for us. With these improved location targeting capabilities, setting up Demand Gen campaigns is now much simpler, giving me greater control and ensuring our budget stays focused within intended regions.
I’ve recently been intrigued by how Bayesian testing allows Google to measure incrementality with just $5,000. It’s fascinating how this modern approach opens up new possibilities for advertisers.
Through these tests, advertisers like me can now explore lift measurement options without needing big enterprise budgets, as reported by Search Engine Land.
This change immediately raises an important question: How exactly does Google achieve accurate measurements of incrementality with significantly less data?
Previously, achieving reliable lift measurements demanded substantial budgets, lengthy test timelines, and the patience to handle inconclusive outcomes.
Given this context, Google’s claim of delivering precise results with merely $5,000 seems almost too good to be true. But it isn’t just marketing fluff; it’s a utilization of innovative mathematical models.
This transformation is powered by a testing methodology that emphasizes probability and learning, rather than aiming for absolute certainty.
Understanding this new approach is crucial for accurately interpreting these incremental results and for enhancing our PPC strategies.
Before we delve deeper, let’s quickly revisit some key Bayesian terms that marketers often encounter.
Glossary: Bayesian terms for search marketers
Prior: What we assume before the test begins.
Posterior: Updated belief after analyzing the data.
Credible interval: It shows the likely range of the result.
P-value: Frequency-based probability indication.
Traditional A/B testing, which most PPC advertisers know even if unknowingly, follows frequentist statistics.
These conventional A/B tests use metrics like p-values and fixed sample sizes to evaluate if changes reach statistical significance, often restricting smaller-budget tests.
In contrast, Bayesian testing veers away from this binary framework, instead asking, “Given all we know, how likely is this result to be true?”
Let’s see how Google legitimately manages to make $5,000 tests work effectively by embracing priors combined with its extensive data resources.
Google’s strategy rests on informed priors, hierarchically modeling, and probability assessments based on extensive campaign history.
This enables a competent analysis even with modest budgets, thus transforming limited data insights into actionable intelligence without averaging noise across campaigns.
Bayesian methods provide flexibility and adapt as more data is gathered, making them ideal for dynamic marketing environments, unlike their frequentist counterparts.
As more data rolls in, Bayesian tests evolve, relying increasingly on real results rather than priors, ensuring refined decision-making from smaller experiments to large-scale trials.
Using Bayesian inference, Google allows advertisers to derive directional insights without needing enormous budgets, effectively bridging gaps where frequentist testing falls short.
Takeaways for advertisers interested in Bayesian testing include understanding the diminishing role of priors as data accumulates, needing a discerning approach to interpreting outcomes.
To conclude, this mathematical ingenuity leverages Google’s vast data resources, offering a practical perspective over traditional methods, empowering PPC campaigns with more cerebral decision-making.
I recently discovered that Google has supercharged its Merchant Center with some noteworthy additions. If you’re like me, always on the lookout for ways to make your product listings pop, this update is exciting!
Google’s Product Studio is now equipped with three creative features that add flair to your product images. Previously, it was all about generating images, but now there’s so much more on offer.
What’s New: Imagine transforming your static product pictures into engaging short videos with just a few text prompts. Product Studio now makes it easy to do just that, perfect for creating eye-catching ads for social platforms.
Another cool feature is the one-click background removal. This tool is fantastic for making your product images look clean and professional, allowing products to stand out more vividly in Shopping visuals.
The third addition is really handy—enhancing image resolution. It lets us upscale older, lower-quality images to meet today’s high visual standards, ensuring our listings look their best.
Why We Care: High-quality images are crucial for boosting Shopping performance. However, creating and updating these assets has always required time and effort. These new features speed up the process and keep us from relying heavily on design teams.
The Big Picture: Google’s integration of AI-powered tools within Merchant Center is a game-changer. By making it easier to animate and enhance images, Google lowers the barriers to testing creative content—essential for maximizing campaigns.
What to Watch: For those of us with limited creative resources, these tools could be a massive time-saver. As Google pushes for more video-focused and visually enhanced ad formats, staying ahead with these updates will be vital.
First Seen: I came across this exciting update thanks to a post by Senior PPC Specialist, Vojtěch Audy.
For years, I’ve been fascinated by how PPC advertisers navigate the complexities of Google’s campaigns, especially Performance Max (PMax).
While the automation behind PMax is impressive, the lack of transparency has often been a source of frustration for me and many others.
Thankfully, Google has finally started to address some of these concerns with the introduction of the new Channel Performance report.
This guide is designed to help you understand the report, its benefits, and how you can leverage it effectively.
The Channel Performance report represents a major shift in how we can view and assess campaign performance.
Located under Campaigns > Insights and Reports > Channel Performance (beta), it’s a pre-built network report offering tabular and flow diagram data.
It’s currently exclusive to Performance Max campaigns but could potentially expand to other types in the future, hinting at a broader applicability.
Previously, getting insights into channel performance required tedious manual reports, or at best, third-party tools with limited capabilities.
Now, the Channel Performance report provides a direct, Google-native solution to this problem.
The report has two primary components: an account-level view and a campaign-level view, complete with a data table and a Sankey diagram.
The account-level view offers a new perspective with a convenient table displaying campaign and channel metrics, making it easier to analyze at a glance.
This view allows for sorting by different metrics, which is a handy way to compare and prioritize campaigns.
My favorite feature is the ability to switch segments, offering insights into ‘ads using product data’ versus ‘ads not using product data’, which was a significant challenge in understanding PMax campaigns.
Upon switching to the campaign-level view, you’ll notice a striking Sankey diagram that visualizes user interactions from impressions to conversions.
Though visually impressive, the data table below is more reliable for detailed analysis, showing performance metrics by channel and ad type.
For a deeper dive, I recommend exporting the data and using it in spreadsheets for comprehensive analysis.
However, the report has some drawbacks, like the misleading proportions in the Sankey diagram and lack of ratios in the data table.
Despite this, it offers valuable insights into which channels are genuinely delivering results, enabling you to maximize asset and traffic quality.
Utilizing placement data for quality control and customizing reports through Google Sheets can enhance your strategy.
Google has promised future features like API access, which will expand the report’s utility significantly.
As we continue to explore these insights, the challenge lies in accurately interpreting the data to make informed decisions.
I’ve noticed that Google’s AI-powered bidding can truly be enticing. It promises to optimize my campaigns if I just feed it my conversion data and set a target, allowing me to focus on the bigger picture of strategy.
The idea is that machine learning will take care of everything else. But, what Google doesn’t really highlight is that its algorithms prioritize Google’s outcomes, which might not align with my goals.
As I delve into 2026, it’s clearer than ever that with Smart Bidding becoming more opaque and Performance Max absorbing more campaign types, discerning when to direct the algorithm—and when to take charge—has become an essential skill for exceptional PPC managers.
AI bidding can yield impressive results, but there’s also a risk of it undermining profitable campaigns by prioritizing volume over efficiency. The key isn’t in the technology itself but in knowing when the algorithm requires direction, tighter constraints, or a complete override.
This article will guide you through:
How AI bidding actually operates.
Recognizing the warning signs when it’s failing.
The intervention points where human judgment is crucial.
How AI Bidding Actually Works – And What Google Doesn’t Tell You
Smart Bidding offers various strategies, such as Target CPA, Target ROAS, Maximize Conversions, and Maximize Conversion Value. Each uses machine learning to predict conversion likelihood and adjusts bids in real time.
The algorithm evaluates numerous signals during auctions—device type, location, time of day, and more—to determine an optimal bid. During the “learning period” of typically seven to 14 days, the algorithm probes the bid landscape to understand the conversion probability curve.
Although Google advises patience during this phase, sometimes campaigns get stuck in perpetual learning and fail to stabilize.
Google’s Optimization Goals vs. Your Business Goals
The algorithm optimizes for metrics that increase Google’s revenue, which might not align with my profitability goals. For instance, setting a Target ROAS at 400% might prompt the system to maximize total conversion value, focusing on spending the full budget rather than understanding the varied nuances of my business.
My business goals might require a different approach, such as a specific volume threshold or maintaining varying margin requirements across products. The algorithm doesn’t account for these intricacies, like cash flow constraints.
Key Signals the Algorithm Can’t Understand
While AI bidding is effective, it has its limitations. Without intervention, several factors may go unaccounted for, like seasonal patterns, product margin differences, and changes in market conditions.
For example, the algorithm might not recognize the distinction between products with different profit margins. A $100 sale on Product A with a 60% margin is distinct from a sale on Product B with a 15% margin, yet the algorithm treats them equally, highlighting the need for profit tracking and margin-based segmentation.
Warning Signs Your AI Bidding Strategy Is Failing
The Perpetual Learning Phase
Extended learning periods are a major red flag. If my campaign’s “Learning” status persists for over two weeks, it indicates a problem. The causes could range from low conversion volume to frequent changes that reset the learning phase.
When to Intervene
Boost the budget to speed data collection.
Relax the target for higher conversions.
Switch to a less aggressive strategy, like Enhanced CPC.
Budget Pacing Issues
Healthy AI campaigns show smooth budget pacing. If I observe erratic patterns like front-loaded spending or consistent underspending, it signals a lack of algorithm confidence.
The Efficiency Cliff
This refers to when performance starts strong but then deteriorates. It’s usually visible in Target ROAS campaigns where, month after month, the ROAS declines as the algorithm exhausts efficient segments and expands into less qualified traffic.
Traffic Quality Deterioration
Even when metrics seem fine, qualitative signals might suggest otherwise. I might notice a drop in engagement or shifts in geographic targeting, indicating the algorithm is prioritizing cheaper clicks which don’t necessarily convert better.
The Search Terms Report Reveals the Truth
Regularly exporting the search terms report helps identify issues. I look for irrelevant expansions or low-intent queries that consume budget with little conversion value, such as a luxury retailer finding clicks for “free furniture donation pickup.”
Strategic Intervention Points: When and How to Take Control
Segmentation for Better Control
When it comes to AI bidding, a one-size-fits-all approach might not work for diverse business models. By segmenting my campaigns, I can tailor algorithms to meet specific goals, using separate campaigns for high- and low-margin products or different regional performances.
Bid Strategy Layering
Sometimes, a hybrid approach serves better. I might run a Target ROAS under normal conditions and adjust it manually during peak times to capture volume, or use Maximize Conversion Value with bid caps to honor unit cost constraints.
The Hybrid Approach
Pairing AI with manual campaigns can optimize effectiveness. Allocating a percentage of the budget to each allows for capturing valuable traffic through manual efforts while still leveraging AI for broader campaign management.
COGS and Cart Data Reporting (Plus Profit Optimization Beta)
Google now supports reporting cost of goods sold and cart data, allowing a clearer view of profitability within Ads reporting. Although still in testing, this feature could soon enable profit-focused bidding rather than revenue-focused, enhancing performance analysis.
AI bidding thrives under solid fundamentals, such as sufficient conversion volume and a stable business model with clear margins. In these contexts, AI often surpasses manual bidding by processing more variables than a human possibly could.
This tends to hold true for mature ecommerce accounts, stable lead generation programs, and SaaS models with predictable conversion paths.
Preparing for AI-First Advertising
As Google continues to simplify advertisement management through automation, my role has evolved from bid management to being an AI strategy director. My focus is now on setting clear goals, providing context, and intervening when needed.
Despite the reduction in advertiser control, certain strategic decisions remain human-driven, ones that require intelligence beyond what an algorithm alone can provide.
Master the Algorithm, Don’t Serve It
AI-powered bidding is a remarkable tool for optimization that delivers unparalleled results when conditions are optimal. However, the key lies in mastering it, ensuring that my business context informs the algorithm’s decisions, and knowing when to take control to align it with my strategic goals.
The strongest PPC leaders today are those who don’t just manage bids but helm the systems that manage them.
As someone deeply involved in marketing, I’ve seen how the explosion of marketing channels and touchpoints has made measuring success a truly strategic endeavor.
I’ve noticed that click-based attribution models—such as last-click and first-click—are still widely used as standard. Yet, as I delve deeper into these metrics, I realize they’re becoming less effective as standalone measures.
These models dominate executive dashboards, giving me pause because this reliance can impose significant limitations.
In my experience, click-based metrics can indeed be valuable for understanding digital interactions. However, it’s risky for executives to center major strategies and budget allocations solely around clicks, as this can lead to neglecting vital parts of the customer journey—parts that truly count.
In this article, I want to explore:
What click-based attribution really captures.
How it falls short in a complex, multi-channel world.
The risks of over-relying on click metrics for business decisions.
Alternative measurement approaches that better align marketing with actual business results.
Ways marketing leaders, like myself, can guide executives toward more comprehensive outcome-focused frameworks.
My goal isn’t to dismiss clicks; they have their place. They should, however, provide context rather than serve as the core measure of success.
What Does Click-Based Attribution Actually Measure?
Click-based attribution tracks ad clicks and assigns conversion credit to the responsible marketing touchpoints. In my role, I observe that models vary—first-click, last-click, linear, time-decay, to name a few—but fundamentally, they all divide credit along the user journey differently.
Platforms tend to default to click-based models because clicks are straightforward to capture and report. However, their clarity can often mislead.
I’ve learned that click-based attribution hinges entirely on user interaction with tracking links. Without a click, or with delayed decisions, important touchpoints might be misattributed or entirely overlooked.
While this approach might work in simplistic funnels, today’s customer journeys are multi-device and multi-channel, quickly diminishing the value of clicks in context.
The Problems with Solely Relying on Click-Based Attribution
When I examine today’s buyers, I see that they rarely follow neat, linear paths—an assumption made by click-based models.
Instead, buyers interact across many devices, channels, and may even engage through offline touchpoints. Consider social media, AI like ChatGPT, or brand recognition from videos, influencers, or website content.
Many valuable interactions go untracked by clicks, though they meaningfully influence buyer perception and conversion readiness.
Imagine a buyer: they watch a video on LinkedIn, then research your product through third-party reviews and your case studies on your website. Days later, they directly Google your brand and make a purchase.
In click-based systems, only the final branded search click would be credited, overlooking all previous touchpoints that educated and persuaded the customer.
Such blind spots aren’t trivial; they form a canyon between reality and measurement.
I recently discovered that uncontested ads might be silently eating away at my holiday budget. Even when I’m the sole bidder, my CPCs remain stubbornly high. Here’s how I began to reclaim those wasted dollars.
This holiday season, Google Search and Shopping Ads are projected to surpass a staggering $70 billion in spending. However, many advertisers, myself included, overlook a critical flaw in Google’s auction system that drains our funds—even in the absence of competitors.
The team at BrandPilot identifies this issue as the “Uncontested Google Ads Problem,” a significant yet often ignored source of wasted ad spend during peak times.
During SMX Next, I learned from John Beresford, the Chief Revenue Officer at BrandPilot, about a little-known quirk in Google’s auction logic. It’s fascinating how this can lead advertisers like me to overspend on our brand terms, shopping placements, and category keywords because Google doesn’t automatically lower our CPCs when no one else is bidding.
Instead of enjoying lower costs as the sole bidder, I found myself paying the same high rate as if competitors were still active. It’s a situation that unfolds thousands of times a day for major brands, and like me, many marketers don’t even realize it.
In John’s session, we explored:
Understanding why “competition gaps” are far more frequent than we think.
Discovering how uncontested moments can warp CPCs, even on brand keywords.
The potential of real-time auction visibility—and how AI is revolutionizing the field.
He also shared how advertisers are deftly reclaiming wasted spending and channeling it back into growth, without giving up impression share, traffic, or revenue.
Identify why CPCs are artificially high when competitors are missing.
Calculate the true financial impact of the Uncontested Ads Problem on your budget.
Execute AI-driven bidding and suppression strategies to avoid self-bidding and increase ROAS.
If you’re managing Google Search or Shopping campaigns this holiday season, this session is a must-see. Learn how to keep Google from sneaking off with your budget and start converting those savings into real performance improvements.
I’ve discovered that Google has quietly introduced a new feature in their Performance Max (PMax) campaigns, allowing advertisers like us to access video assets directly from the Merchant Center. This seemingly small adjustment is poised to make a significant impact on how we handle retail and e-commerce ads.
How it works. As part of this update, Google Ads now enables us to:
Auto-surface product-associated videos directly from Merchant Center during the PMax setup process.
Shorten creative workflows for our retail and e-commerce teams, saving us valuable time.
Improve product-to-creative alignment, thereby enhancing ad relevance.
Boost performance especially for those of us managing extensive SKU catalogs.
Why this matters. This update is a game-changer because it eliminates a key friction point within PMax: the challenge of integrating high-quality, product-relevant videos into our campaigns. By streamlining this process and pulling videos directly from the Merchant Center, Google is enhancing the connection between inventory and creative assets. This means higher ad relevance, greater engagement, and improved overall performance.
For brands like ours that have vast SKU inventories, this feature significantly accelerates the workflow and guarantees comprehensive video coverage — something we used to find challenging and resource-draining.
The bigger picture. It seems that Google is on a mission to expand PMax’s creative capabilities. From integrating social video imports to this new Merchant Center video feature, there’s a clear intention to make PMax more user-friendly for advertisers heavily involved in commerce.
First seen. This update caught my attention thanks to senior performance marketing executive, Rakshit Shetty, who shared his insights on LinkedIn.
The bottom line. Although it’s a subtle change, it’s undoubtedly a meaningful victory for brands operating at scale in the eCommerce space.
