I’ve just discovered a game-changing update from Google Ads that’s making my life a whole lot easier. Now, Google Ads shows per-product campaign eligibility, which makes spotting gaps and overlaps a breeze.
With this new feature, I can see exactly which campaigns my products are eligible for, right within the Products section. This has transformed the way I approach campaign tracking.
How it works. I find the new dashboard in the Products section incredibly useful. It includes:
A table that shows product details, status, issues, and priority flags
A line graph summarizing campaign status trends
Filters that let me segment eligibility views
A pop-up panel listing “Eligible” and “Not eligible” campaigns per product
Why we care. This update helps me quickly identify products that are missing from essential campaigns or unintentionally overlapping, especially in Shopping and Performance Max. It saves me the hassle of bouncing between different campaign views to diagnose issues.
The big picture: These changes allow me to swiftly spot products not running in expected campaigns and identify overlap before it’s a budgeting issue, all while minimizing time spent on troubleshooting.
Between the lines. It’s clear that Google is focusing on giving advertisers like me more precise control over Shopping campaigns, a key factor in product-level optimization and profitability.
When. The feature is available now in Google Ads.
First seen. I first learned about this update thanks to Hana Kobzová from PPC News Feed.
I’ve discovered that Google Ads now offers ready-to-run experiments directly within the Experiments page, making it easier for me to test optimizations quickly without a complicated setup.
These suggested experiments are based on my account’s setup and performance data, helping me uncover new ways to enhance results.
How it works: The platform provides suggestions for testing various bidding strategies, creative variations, and new campaign features, all accessible right in the Experiments dashboard.
Every recommendation comes with a pre-configured setup, so I can either launch them immediately or adjust the settings to better fit my needs. These suggestions are conveniently displayed alongside the standard Create Experiment option, streamlining the process.
Why I care: Google’s effort to simplify experiment setups significantly decreases the time and effort I need to put into testing. It allows me to act swiftly on optimization ideas and maintain a consistent flow of improvements. However, I still review each test configuration to ensure it aligns with my campaign goals and doesn’t lead to unnecessary resource expenditure.
Zoom in: For instance, I might see a prompt suggesting I enable final URL expansion to boost campaign performance. These recommendations appear as pop-ups inside the Experiments interface, guiding my decisions with relevant insights.
The big picture: Google is embedding more automated guidance into Ads workflows, nudging me towards continuous testing and pursuing data-driven optimizations.
First seen:This update was first spotted by PPC News Feed owner, Hana Kobzová, shedding light on these helpful enhancements.
I’ve noticed that when I leave Performance Max campaigns running without proper setup, they tend to focus on getting easy conversions, often leading to a rise in low-quality leads. While this can quickly rack up conversion numbers, the quality isn’t always great. Google tends to prioritize cheaper conversions, benefiting their revenue, but not necessarily my pipeline.
Many times, brands are surprised by these results after following Google’s sales advice too closely. Although low CPA metrics look tempting, they can often mask the fact that these new leads aren’t contributing to the real growth of my business.
That said, with the right adjustments, Performance Max can be optimized to generate high-quality leads. Building these ‘guardrails’ effectively is key to success, and I’m here to share what I’ve learned.
This guide will walk you through which strategies work for improving lead quality, tactics that don’t deliver desired results, and the notable differences between using Performance Max in Google versus Bing.
How to Improve Lead Quality in PMax Campaigns
Here are the actionable steps I’ve found to consistently impact lead quality:
Focus on conversion goals that align with higher quality targets. Try targeting metrics like closed-won leads or sales-qualified leads, which provide more valuable insights than just form fills. For this to work, ensure my CRM is accurately tracking offline conversions.
Utilize high-value audience signals. Target more specific behaviors, such as users who have ‘booked a meeting’ rather than just anyone who converts.
Concentrate on the correct audiences. Exclude irrelevant segments, and use Customer Match to help Google’s algorithms find users similar to my best customers.
Optimize campaign settings smartly. Examples include using brand exclusions, targeting high-performing geos, strategic scheduling, analyzing search themes, and employing site link extensions to channel traffic efficiently.
Refine forms for better lead filtering. Integrate reCAPTCHA to deter bots, implement field validation to block disposable domains, and include quality-check questions such as how they heard about my company or if they have budget allocations.
Some common optimizations don’t significantly enhance lead quality:
Switching bid strategies offers minimal impact.
Adding more assets or budget doesn’t inherently improve lead caliber.
I’ve learned to be cautious when seeking help from Google support, as results can vary.
Important Differences Between Google and Bing PMax Campaigns
Google and Bing both offer Performance Max campaigns, but they differ significantly. Google’s expansive network includes search, display, YouTube, discovery campaigns, and Gmail. If not carefully managed, this can lead to spam-driven conversions, particularly from display and YouTube.
Bing’s campaigns, on the other hand, focus on Bing search and their audience network, which covers display, Outlook, and MSN. I haven’t observed significant performance differences, but staying updated with platform changes is crucial.
Performance Max Isn’t Broken, but It Needs Control
Entering PMax for lead generation with caution is a wise approach. Although promising for ecommerce revenue, lead quality demands stringent campaign guidelines. For instance, preventing misaligned conversions for a luxury retailer requires effective PMax guardrails.
Considering Google’s shift towards automation and AI, it’s essential to continuously test and adapt. Recent updates like channel-level reporting and exclusion options offer new tools to shape my campaigns.
Achieving quality leads and a healthy ROI is possible by navigating the algorithm strategically. If past PMax efforts were paused due to poor returns, revisiting and applying lessons learned could significantly improve future outcomes.
When I opened Google Ads recently, I noticed something intriguing. Google is now directly promoting its AI Max feature right inside the campaign settings. This is a bold move, as it places advertisements for their own tools directly in front of advertisers like me.
What’s happening: I saw promotional messages for AI Max specifically for Search campaigns when accessing my campaign settings panel.
These notifications show up during my usual account audits and updates.
It’s essentially Google’s way of internally advertising its own tools to me.
Why it matters to me. Seeing these ads within the platform highlights Google’s strategy to push AI adoption. It makes me wonder if this will nudge advertisers like myself towards tools that minimize manual input, potentially reshaping how I manage campaigns.
Encountering ads in a platform that’s already a paid advertising service is quite unprecedented. It feels like a subtle shift towards more aggressive product adoption strategies by Google.
The big picture from my perspective. Although Google often rolls out AI features, actively promoting them within our regular workflows is a more assertive step towards encouraging us to adopt new features.
What I should watch for. I’m curious if this promotional strategy will extend to other features within Google Ads and how other advertisers will react to seeing marketing within their management tools.
First observation. This notification was first spotted by Lead Gen PPC Specialist Julie Bacchini, who shared her experience on LinkedIn.
I recently discovered that Performance Max now includes built-in A/B testing for creative assets. This feature offers advertisers a straightforward way to measure and enhance their advertising strategies.
Google is introducing a beta feature that allows me and other advertisers to conduct structured A/B tests on creative assets within a single Performance Max asset group. This setup enables me to split traffic between two sets of assets and evaluate performance through a controlled experiment.
Why it matters to me. In the past, creative testing within Performance Max was often guesswork. With Google’s new native A/B asset experiments, I can now perform controlled tests directly within PMax without needing to launch separate campaigns.
How it works for me. I select one Performance Max campaign and asset group, then define a control asset set using my existing creatives and a treatment set with new alternatives. Shared assets can be utilized across both versions. After setting a desired traffic split, like 50/50, the experiment runs for several weeks, allowing me to apply the winning assets based on actual performance data.
Why this is beneficial for me. Conducting tests within the same asset group isolates the impact of the creatives I’ve designed, minimizing interference from changes in campaign structure. This controlled split allows me to obtain clearer reporting, helping my team make data-driven decisions based on solid performance metrics rather than assumptions.
What I’ve learned so far. Early testing indicates that shorter experiments—especially those under three weeks—can yield unstable results, particularly in accounts with lower volume. I’ve found that extending the test duration and avoiding simultaneous campaign changes significantly enhances reliability.
My takeaway. Performance Max is evolving into a more testable platform. I now have the ability to validate creative decisions using built-in experiments, reducing reliance on trial and error approaches.
Source of insight. A Google Ads expert noticed the update and shared insights on LinkedIn.
When I first heard about Performance Max, I was skeptical. It seemed like an unfinished product, but over the past 18 months, Google has made significant improvements in transparency and control. If you haven’t revisited Performance Max since its early days, now is the perfect time to take another look.
As I learned from Mike Ryan at SMX Next, the advancements are worthy of attention.
Taking a Fresh Look at Performance Max
Performance Max evolved from Smart Shopping campaigns, introduced with much excitement in 2019. Yet, industry experts quickly pointed out issues with transparency and control, which Google is only now beginning to address.
Smart Shopping took away vital controls critical for managing campaigns effectively. Essential features like promotional controls and search term reporting vanished, leaving many of us feeling limited.
Fortunately, Performance Max reintroduces much-needed functionality, enhancing what was once lacking.
Understanding Performance Max Search Terms
In my experience, search terms are crucial for understanding the effectiveness of our campaigns. With Performance Max, Google has added a unique match type that brings detailed and scriptable data, allowing us to optimize with precision.
Search Term Insights vs. Campaign Search Term View
Initially, Google introduced search term insights, grouping queries into categories. Unfortunately, these lacked depth as they didn’t provide essential cost data.
The game-changer, though, is the new campaign-level search term view, offering access to more metrics and clearer visibility on performance.
While these insights are only available at the search network level, they offer significant improvement over past limitations.
Search Theme Reporting
Through Performance Max, I’ve realized search themes act as a positive targeting method. By checking conversion data and the source of traffic, I can ascertain the value of search themes, identifying whether they contribute effectively or remain underutilized.
Search Term Controls and Optimization
Negative Keywords
At first, negative keywords in Performance Max were limited, which was frustrating. But now, they are fully supported and much more robust, giving me the control I need to fine-tune performance.
Brand Exclusions
While Performance Max tends to favor brand queries because of their high intent, I’ve noticed that using negative keywords provides a stronger solution for ensuring optimal performance without leakage.
Optimization Strategy
My strategy involves identifying non-performing search terms with higher-than-average clicks but zero conversions, making them strong candidates for exclusion. This approach prevents overcorrection while maintaining a focus on impactful terms.
Modern Optimization Approaches
Instead of spending countless hours manually reviewing search terms, I leverage automation. Using the API for high-volume accounts and scripts for mid-range volumes significantly optimizes my workflow.
Channels and Placements Reporting
Channel Performance Report
One of the tools I now rely on is the channel performance report, offering insights across different networks like Discover and Display. Though interpreting some diagrams can be tricky, it provides valuable data on how different channels perform.
Channel and Placement Controls
Placement Exclusions
Through API and Report Editor data, I focus on excluding specific placements that seem irrelevant or pose risks, particularly in sensitive content areas like politics and children’s videos on YouTube.
Tools for Placement Review
For reviews, especially in other languages, I’ve found that using Google Sheets’ translation function is effective. It helps me quickly determine the relevance of YouTube placements without relying on external systems.
Search Partner Network
The inability to opt out of the Search Partner Network can be frustrating. However, I mitigate this by prioritizing exclusions where performance is subpar compared to the Google Search Network.
Device Reporting and Targeting
Device Analysis
Analyzing device performance provides deeper insights into how specific products perform across different devices. This often reveals advantages or challenges when compared to competitors.
Device Targeting Considerations
Splitting campaigns by device can hurt data volume, impacting machine learning effectiveness. It’s crucial to weigh the benefits of splitting against the potential for data fragmentation.
Conclusion
Reflecting on Performance Max’s evolution, it’s evident that Google has made impressive strides in offering advertisers like myself more control and transparency. While it’s not without flaws, it’s a far more effective tool for ecommerce success now than ever before.
The key lies in understanding available data, using modern tools to streamline processes, and applying performance insights strategically to achieve the best results.
I’ve always found it challenging to keep my Google Ads campaigns running smoothly without a hitch. When I heard about Google Ads’ new diagnostics hub for data connections, I knew I had to explore it. This tool promises to catch issues early, which could significantly enhance my conversion tracking and overall campaign performance.
Recently, Google Ads introduced a data source diagnostics feature within their Data Manager. It’s designed specifically to help people like me monitor the health of my data connections. The tool is a lifesaver, flagging issues linked to offline conversions, CRM imports, and tagging mismatches.
How it works. The dashboard is centralized, and it assigns clear connection status labels like Excellent, Good, Needs Attention, or Urgent. It also provides actionable alerts, which is a huge plus for me. I can easily identify problems such as refused credentials, formatting errors, or failed imports. Additionally, there’s a run history that displays recent sync attempts and error counts.
Why we care. I’ve noticed that when conversion data breaks, campaign optimization collapses too. It’s the minor data connection failures that can distort conversion tracking and weaken automated bidding. This diagnostics tool is crucial as it helps my team and me spot and fix issues early, safeguarding our campaign performance and reporting accuracy. If you’re relying on CRM imports or offline conversions like I am, it’s truly a needed safety net.
Who benefits most. If you’re running complex conversion pipelines like I do, including Salesforce integrations and offline attribution setups, this feature is a game-changer. It addresses disruptions that could otherwise ripple through our bidding and reporting process.
The bigger picture. As we increasingly depend on accurate first-party data for automated bidding, having visibility into data pipelines has become as crucial as the campaign settings themselves.
Bottom line. Google Ads has effectively given us an early warning system for data failures, helping us fix broken connections before they affect performance.
First seen. I learned about this update when Digital Marketer Georgi Zayakov shared it on LinkedIn. I’m grateful to Georgi for sharing this valuable insight.
I’ve noticed a significant shift in how Google Ads operates. No longer is it about simply targeting keywords. Now, it’s all about understanding and leveraging user intent. Here’s what this evolution means for eligibility, structure, and PPC strategy.
Most PPC teams, myself included, have operated on autopilot: compiling keyword lists, assigning match types, and structuring ad groups around search terms. This was the norm.
However, Google’s auction process has transformed. Search interactions are evolving into more conversational experiences. People engage with AI as if they’re having a dialogue, asking follow-up questions and refining their inquiries. AI now reasons through a question before linking it to suitable ads.
Today, the auction isn’t kicked off by a keyword but by the user’s implied intent. If I’m still relying on exact and phrase match structures, I’m planning for a system that’s no longer there. It’s time to embrace intent as the foundation—not the specific words typed, but the underlying goals they signify.
With this intent-first approach, I find a more resilient strategy. It allows me to effectively design campaigns, creativity, and metrics, especially as Google rolls out new AI-focused formats.
While keywords still play a role, they no longer serve as the framework.
Recently, I’ve learned about changes happening under the hood during a search.
Google’s AI now utilizes a method called “query fan out,” which breaks down complex queries into subtopics and conducts simultaneous searches to provide a comprehensive response.
The auction begins even before users finish typing. Importantly, AI can deduce commercial intent from purely informational searches.
For example, if someone asks, “Why is my pool green?” Google understands they’re troubleshooting, not shopping, but identifies potential product needs and displays ads for pool-cleaning supplies. The AI’s reasoning layer recognizes the solution products offer.
This change in auction logic focuses on matching offerings to the user’s inferred intent, rather than merely matching keywords to queries. Recognizing this shift is crucial, or I risk misinterpreting the user journey.
I’ve come to appreciate the intricacies of an intent-first approach. It doesn’t eliminate the need for keyword research but changes how I prioritize keywords. Now, I align campaigns to the user’s intent.
This strategy encourages me to consider:
What problem is the user addressing?
What stage of decision-making are they in?
What role does the product play in solving their issue?
Realizing that the same intent can emerge from various queries and that identical queries can express different intents based on context has been illuminating. Phrases like “Best CRM” might indicate a need for feature comparison or a readiness to purchase; Google’s AI can now make those distinctions, and so should my campaigns.
This shift is more mental than tactical. While I still build keyword lists, they’re now organized by intent rather than match type. My ad copy speaks directly to user goals instead of echoing search terms.
Moving from keywords to intent isn’t merely a tactical alteration—it’s a strategic lens through which I plan for future campaigns, especially as Google enhances its AI-driven ad formats.
Reorganizing campaigns around intent rather than keywords has its immediate effects, impacting eligibility and landing page efficacy while fundamentally influencing system learning.
By 2026, Google Ads automation has transformed drastically, with signal quality becoming paramount for exceptional performance. In this post, I’ll guide you on how signals drive these changes and how you can align them for optimal outcomes.
Back in 2015, I had tight control over my PPC campaigns. I directed Google on which keywords to pursue, set manual bids, and handled budgets with precision. Skillful use of spreadsheets allowed me to efficiently manage vast keyword inventories.
Those meticulously controlled days have faded. Now, in 2026, automation steers the wheel, moving beyond being a mere helper to a key driver of our advertising success. Fighting it is futile; embracing it is wise.
Automation has evened the playing field, liberating time for PPC marketers like me. But effectiveness now hinges on understanding how automation gleans insights from our data.
This piece delves into the intricacies of Google Ads signals, illustrating how to preserve their quality and prevent automation from veering off course.
The Mechanics of Signals in Automation
Contrary to seeing Google’s system as a mystery, it requires input of robust signals to perform optimally. Accurate signals lead to triumph; flawed data gears us for failure.
Automation runs on the signals I provide. AI interprets these signals, adjusting bids and targeting with unparalleled precision and efficiency.
While traditional documentation might suggest a primary focus on audience segments, the reality is that automation learns from a broader spectrum of signals.
Decoding What Qualifies as a Signal
In my experience, every component in a Google Ads account serves as a signal—shaping Google’s algorithm to determine successful advertising strategies.
Structural elements, budgets, conversion quality, and more provide insights into user intent, modeling a detailed blueprint for targeting.
The entire ecosystem, from landing pages to real-time data, contributes—guiding the AI in its decision-making process.
Here’s what stands out:
Conversion Actions: These signal what success looks like for my business.
Keyword Signals: Essential for decoding user search intent.
Creative Signals: Influences user attraction via visual cues.
Landing Page Signals: Ensures alignment with user expectations.
Bid Strategies: Communicates my advertising priorities to Google.
Innovation in signal interpretation has shifted, with the introduction of campaign total budgets, indicating a comprehensive financial commitment to Google.
Retailers, like Escentual.com, witnessed increased traffic through this approach, showcasing how signal precision offers tangible results.
Understanding Auction-Time Realities
Every user search triggers a unique bid calculation based on myriad signals, moving beyond generalized assumptions to precise decision-making.
This tailored approach ensures identification of “pockets of performance,” optimizing for predicted user outcomes aligned with our objectives.
Without quality signals, however, the system is left with assumptions, demonstrating the critical nature of providing accurate inputs.
Identifying and Prioritizing Signals
Not all signals wield equal influence. I’ve recognized that conversion signals bear the most weight, providing essential guidance for AI performance.
Conversion Dominance
Accurate conversion tracking underpins robust algorithmic learning, crucial for successful B2B and eCommerce advertising.
Enhanced Conversions and First-Party Data
In an era where third-party cookies disintegrate, relying on enriched data tracking is invaluable for understanding user interactions.
Quality audience signals and custom segments are imperative, enabling nuanced targeting, especially in niche markets.
Signal Category
Specific Input
Weight
Importance
Primary
Offline Conversion
Critical
Speaks to profit, not mere leads.
Primary
Value-based Bidding
Critical
Prioritizes profitable products.
Secondary
Customer Match Lists
High
Offers AI a model audience.
Tertiary
Keywords
Medium
Identifies search semantics.
Pollutant
Soft Conversions
Negative
Skews intent towards lower value.
Proper signals form the foundation for successful automation, requiring constant vigilance and correction of detrimental factors like signal pollution.
Combating and Correcting Signal Drift
Signal drift occurs when automation diverges from desired outcomes. Identifying subtle shifts in user targeting and making strategic corrections is key.
By tightening conversion signals, reinforcing audience data, and refining campaign structures, I can steer systems back to intended paths.
Reinforce Audience Patterns: Update lists and segments.
Adjust Campaign Structure: Separate high and low intent traffic.
Remaining proactive is about guiding automation, ensuring the system aligns with my business goals while leveraging Google’s robust AI insights.
Building a Winning Signal Strategy
Creating a coherent signal strategy in 2026 requires segmenting data wisely, isolating brand traffic, and differentiating products by ROAS for clarity in campaign objectives.
Achieving Competitive Edge
In a landscape where automation is universally accessible, the true advantage lies in the quality of signals I feed to Google.
By protecting these signals and timely correcting any drift, I ensure Google’s automation works for me, transforming it into a powerful asset in my advertising arsenal.
Have you been noticing those high CPCs? It might not be due to aggressive competition or your bidding strategy. What if the real issue lies in your ad quality? By improving your Quality Score, you can achieve better results without overpaying.
If you’re finding that CPCs are on the rise, it might not be about your budget or competitive bids. The hidden issue could be low ad quality, something I’ve come to recognize over time.
Allow me to guide you through understanding the most foundational metric in your Google Ads account— the Quality Score. If you want to stop overpaying and start winning on merit, you need to grasp how the Quality Score truly works.
Before we continue, let’s clear up any confusion. Google presents numerous “scores” and diagnostics. However, Quality Score is the one that genuinely matters and can’t be ignored.
Ad strength focuses on your ad-level diagnostics, ensuring your responsive ad adheres to best practices. Although important, it doesn’t affect auction performance directly.
Optimization score is more of a guiding tool for using Google’s recommendations, not a reflection of true campaign success.
However, Quality Score goes deeper. It plays a vital role, as it summarizes the quality of your ads at the keyword level. Its interaction with your bid decides your Ad Rank, influencing your ad’s position on the SERP and the cost per click.
Here’s the basic formula: Ad Rank = price × quality. The 1–10 score you see reflects the real-time quality evaluation Google conducts for every search. Simple, yet crucial.
Let’s set up your Google Ads dashboard to locate your Quality Score. Navigate to your Keywords report and add these columns: Quality Score, Expected CTR, Ad Relevance, and Landing Page Experience.
I recommend analyzing Quality Scores at the ad group level instead of focusing on individual keywords. If most of your keywords score a 7 or higher, you’re on the right path. Should they average a 5 or below, start improving ad quality swiftly.
Let’s address the three components you can improve:
1. Ad Relevance: The ‘message match’ involves ensuring that your keywords match both the ad and its landing page. Use Dynamic Keyword Insertion to automate this, or manually check that keywords feature in your ad copy and landing pages.
2. Landing Page Experience: The “Delivery” can be evaluated using PageSpeed Insights. If your landing page receives a “Below average” rating, the reasons often involve slow load times, inadequate mobile experiences, or poor navigation.
3. Expected CTR: The “Popularity Contest” shows that Google favors ads that get clicks. You can use Auction Insights for competitive analysis or check the Google Ads Transparency Center to see and learn from competitors’ successful ads.
While aiming for a 10/10 Quality Score everywhere may be overambitious, a regular quality review can pinpoint underperforming ad groups. Tackle the most lagging components to gradually lift your results.
Improving ad quality involves more work than just raising budgets, but the rewards of increased clicks at lower costs are undeniable.
This piece is part of our ongoing Search Engine Land series, presenting concise insights into Google Ads. In quick reads, Jyll covers diverse features to optimize your Google Ads results.
