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.
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.
I’ve noticed a significant shift in Google Ads as they now allow us to optimize bidding for view-through conversions (VTC) in Android App campaigns. This change highlights a growing emphasis on video-driven performance.
In the past, VTC was a subtle, behind-the-scenes signal within Google’s system. Now, it’s a visible option that allows me to focus on conversions that occur after an ad is seen, rather than clicked.
The shift. It’s evident that Google is steering app advertising away from traditional click-focused strategies, encouraging an approach centered around influence and incremental results. This is particularly beneficial for platforms like YouTube and in-feed video advertising.
This update means our bidding strategies align more intuitively with the actual ways users discover and install apps today.
Why it matters to me. This flexibility allows me to go beyond mere clicks, enhancing measurement metrics for video-centric app campaigns. It’s an exciting validation for those of us invested in upper-funnel marketing activities.
Who benefits the most? Advertisers who prioritize video content and focus on creating awareness and engagement. This is a game-changer for teams oriented towards long-term growth, not just immediate installs.
What I’m keeping an eye on:
How Google’s attribution models affect campaign reliance
Potential shifts in Cost-Per-Acquisition expectations
The growing importance of creative quality over click-centric strategies
First seen by. I came across this update thanks to Rakshit Shetty, a Senior Performance Marketing Executive who first spotted this change.
Bottom line. Google is elevating view-based data for app campaigns to a priority status, marking a shift towards a performance marketing strategy led by AI and agnostic of sales funnels.
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.
When I watch a TV commercial that truly connects with me, it’s more than just a fleeting moment of entertainment. It triggers curiosity, encourages me to search online, and often leads to making a purchase.
This is precisely why the “Breaking TV Ads Report,” collaboratively launched by Kinetiq and DAIVID, should be on every search marketer’s radar.
The report ranks the top-performing new TV ads in the U.S., combining Kinetiq’s real-time ad detection with DAIVID’s AI-driven creative analytics to identify which ads truly stand out, why they connect with audiences, and what brands can learn from their success.
It’s a powerful reminder that search doesn’t begin with typing into Google, it starts with a spark in our mind.
As Barney Worfolk-Smith, chief growth officer at DAIVID, said to me via email:
“Search + TV matter – together. TV can boost search volume by up to 60%, and even more in well-coordinated campaigns. AI has altered, and will continue to shape, the TV-to-search relationship, though the principle remains constant: impactful, emotive TV advertising leads to all favorable brand outcomes – search being a prominent one. It’s also key to note that search volume itself is an invaluable indicator of TV ad effectiveness.”
How LeBron James and Indeed Captured Attention
In the first issue of the “Breaking TV Ads Report,” one commercial stood out: Indeed’s “What If LeBron James’ Skills Were Never Seen?”
The ad traces James’s journey from his early days, linking it to Indeed’s “skills-first” hiring message, resonating with viewers due to its authenticity and star power.
Indeed’s ad sparked 11% higher intense positive emotions and garnered 7% more attention than an average U.S. TV ad according to DAIVID. It was among the top 10 ads, alongside campaigns from TikTok, Subaru, and Taco Bell, each with themes revolving around family, mentorship, and belonging.
These ads aren’t merely entertaining stories – they ignite search actions.
When an emotional bond is formed with a brand message, I, like many others, am compelled to explore more – often turning to Google or YouTube for details, reviews, or purchase options.
In 2011, Google introduced the “Zero Moment of Truth” concept, emphasizing that the initial “stimulus” step, like a TV ad, precedes the ZMOT buying journey step.
For many search marketers, focus remains on the measurable second step – insights from clicks and conversions – neglecting the initial step which drives search but often feels like it drains our budgets.
However, research over the past decade indicates that TV advertising significantly extends into search behavior:
In 2015, a Google and Nielsen study revealed TV ads could increase branded search queries by up to 20%, often within just hours after airing.
By 2022, Thinkbox found UK TV advertising provided the strongest multiplier effect on search, social, and web traffic.
In 2024, Comscore identified that coordinated TV and digital campaigns deliver stronger engagement, prompting “second-screen” actions.
In essence, successful TV campaigns quickly translate into search demand – sometimes within mere minutes.
For those of us in SEO and PPC, this generates a clear call to action: be ready to capitalize on these moments.
The Integration of TV and Search by Leading Brands
Prominent brands have effectively demonstrated that coordinated TV stories and search strategies boost performance across both channels.
Apple: Building Curiosity to Ignite Search
Apple’s product launches exemplify cross-channel synergy. Airing an iPhone ad leads to skyrocketing search for “iPhone 17 Pro Max” or its release date.
Following major campaigns, Apple’s branded search traffic can see a up to 40% spike, per Semrush data.
Apple crafts its TV ads to spur questions, not provide answers – nudging viewers to seek more online, where Apple’s search-optimized content completes the user journey.
Progressive: Tying Humor to Searchability
Progressive’s “Flo” campaign is a lesson in how consistent creative narration cultivates search interest.
The campaign’s narratives arouse curiosity, leading to increased branded searches like “Progressive car insurance” or “Flo from Progressive.”
Their media team precisely aligns search and display campaigns with TV schedules, ensuring spikes in interest are met with ready search ads.
Coca-Cola: An Ad Both Shareable and Searchable
Coca-Cola’s historic success with “Share a Coke” underlines TV’s capacity to drive search behavior.
The original campaign, born in Australia in 2011, replaced Coke logos with popular names, enhancing emotional connections and boosting sales globally through a focus on personalization.
The 2025 relaunch targets Gen Z, fostering digital and in-person connections, featuring personalized cans and new interactive tools.
Strategies like QR codes invite consumers to Google “custom Coke” or “share a Coke names.”
Data insights support their approach. By monitoring spikes in branded searches and social mentions, Coca-Cola fine-tuned its campaign strategies.
Assessing Creative Success with Real Audience Indicators
The “Breaking TV Ads” report stands out due to its data-centered approach to measuring creativity.
Kinetiq deploys propietary technology to capture TV ads across the U.S., while DAIVID’s AI gauges emotional responses and attention, yielding a comprehensive creative effectiveness score based on real audience experience.
In today’s fleeting media landscape, such insights are vital to understanding which narratives break through, directly connecting with downstream behaviors like searches or site visits.
As Kinetiq CEO Kevin Kohn highlighted, this partnership offers marketers a panoramic understanding of TV and CTV advertising – not only insights into aired content, but its audience resonance.
This type of insight is what performance marketers, like me, need to bridge the gap between creative resonance and measurable outcomes.
In February 2025, Neal Mohan, YouTube’s CEO, shared that TV has overtaken mobile, becoming the primary device for YouTube viewing in the U.S., according to Nielsen.
Search marketers can apply insights from the Breaking TV Ads Report in various strategic ways:
Expect search spikes: With emotionally charged or celebrity-driven TV ads, branded search activity is likely to rise. Tailor PPC budgets, ad messaging, and keywords to match campaign themes and taglines.
Target intent-rich moments: TV spots spark “navigational” and “informational” queries. Ensure that organic content – landing pages, FAQs, YouTube videos – caters to such queries.
Coordinate search campaigns with TV airings: Use ad scheduling to sync with TV airings or streaming releases. Nielsen Catalina Solutions research shows that coordinated efforts can greatly amplify conversion rates.
Monitor branded search as a creative KPI: Tracking branded search volume can signal advertising impact. Utilize Google Trends or Search Console for tracking shifts post major media campaigns.
Adopt emotional cues in marketing copy: Insights from DAIVID highlight the need for emotionally resonant headlines, ad extensions, and meta descriptions that align with TV-driven sentiment.
Why Cross-Channel Strategies Are the Future of Performance Marketing
Traditionally seen as a response channel, search today functions as the connective tissue between inspiration and action.
Whether it’s a QR code at the end of a TV ad, or a YouTube masthead following a TV broadcast, search seamlessly bridges storytelling and sales.
As brands increasingly embrace connected TV (CTV) and streaming, the lines between “brand” and “performance” marketing will increasingly blur.
Creative effectiveness data helps bridge that gap by highlighting which emotional and visual cues drive search and conversions.
The “Breaking TV Ads” report is a vital reminder that the most impactful search strategies start long before the search itself.
They start with captivating attention and sparking emotions, usually on the biggest screen in the house.
I recently stumbled upon some intriguing developments from Bing, as they are experimenting with a new ad format that closely resembles Google’s approach. This revamped ‘Sponsored results’ grouping could potentially lead to more accidental ad clicks, given how seamlessly these paid listings blend with the organic search results.
Picture this: Microsoft is testing a redesign for search ads in Bing, wherein multiple sponsored links are grouped under a single ‘Sponsored results’ label. There’s also a handy ‘Hide’ button to collapse the ad block entirely, adding a layer of user control that’s quite novel.
What’s Happening? It was Sachin Patel who first noticed this Bing test in action, sharing screenshots and a video that spotlight this new layout. Interestingly, in the current test, only the first ad in the group is marked with a label. Any subsequent ads are listed without labels beneath it. This feature allows users to click ‘Hide’ to collapse these ads and ‘Show’ to display them once more.
Understanding the Mechanism. The design clusters ad units in such a way that blurs the lines between paid and organic content. By consolidating ad labeling to just one header, it makes each ad appear more like a standard search result.
Looking Back. Google introduced a similar approach not too long ago, and it quickly drove discussions around unintended ad clicks. According to a recent poll conducted by Barry Schwartz on X, a remarkable 63% of respondents admitted to inadvertently clicking on Google Ads results due to this new grouping.
Bing following suit might indicate a broader industry trend in the labeling and display of search ads.
Why Should We Care? Bing’s new grouped ‘Sponsored results’ format could potentially raise ad visibility and enhance click-through rates by making ads blend more seamlessly with organic listings. The ‘Hide’ button introduces a refreshing control element for users, though the single-label approach may still lead to increased accidental clicks, as observed with Google’s recent redesign, potentially resulting in higher bounce rates.
Should Microsoft decide to implement this change broadly, it could significantly impact campaign performance, attribution, and spending efficiency across Bing’s search platform.
Initial Observations. This layout change was first shared by Sachin Patel, who took to X with his findings.
The Takeaway: While the experiment remains limited for now, if Bing rolls this format out extensively, it could lead to increased engagement — whether intended or accidental — and renew discussions about how clearly ads are disclosed in search results.
I can’t contain my excitement as Google unveils the Developer Assistant for the Google Ads API. This breakthrough tool allows us, as advertisers and developers, to leverage natural language to create, manage, and export Ads API queries effortlessly.
Google has introduced the Google Ads API Developer Assistant v1.0, an innovative Gemini CLI extension. It empowers us to interact with the Ads API seamlessly, transforming our everyday language into instant answers, functional code, and even real-time API calls.
How it works: Embedded within the Gemini CLI, the assistant utilizes project contexts from GEMINI.md and configuration files to generate precise code tailored to our specific environment. With a simple query like, “How do I filter by date in GAQL?”, I receive immediate assistance. If I describe a task, such as “Show me campaigns with the most conversions in the last 30 days,” it provides both the GAQL query and a well-optimized Python script using the google-ads-python client library.
Key features include: The ability to execute read-only API calls directly from the terminal, presenting the results in cleanly formatted tables. Plus, any tabular data can be exported to CSV, filed neatly in a dedicated directory. All code generated by the assistant is automatically organized within a saved_code/ folder for easy access.
Why it matters to us: The Google Ads API is immensely powerful yet complicated. This new Developer Assistant simplifies our workflow drastically, making it quicker and more efficient for teams to create, refine, and optimize Google Ads API workflows—the core of comprehensive campaign management and reporting.
By converting natural language into GAQL queries and operational code, it minimizes technical obstacles and speeds up our ability to glean insights that could lead to better optimization strategies. The ease of one-command execution and CSV exports means we spend less time dealing with coding complexities and more on boosting performance.
The big picture: Google positions the assistant as a dual-purpose tool—a learning aid for beginners and a productivity enhancer for experienced users. For newcomers, the use of natural language commands significantly lowers the learning curve.
For advanced users like me, features such as code generation, automatic file management, and command-line execution streamline and minimize repetitive tasks involved in daily API operations.
Getting started is straightforward: Ensuring you have a Google Ads API token, a configured google-ads.yaml, Python 3.10+, the Gemini CLI, and a local clone of the google-ads-python library is essential. A setup script handles the cloning process, with full instructions available on GitHub.
What’s next: Google invites early users to provide feedback, suggest features, and engage with the community on the Discord channel as the platform evolves with more enhancements and AI-driven tools.
The bottom line: By enabling developers to query, code, and execute using everyday language, Google is transforming the Google Ads API into a faster, more intuitive, and broadly accessible tool.
As someone who navigates the complexities of Google Ads, I know the mere mention of ‘Recommendations’ can send shivers down your spine. It’s like a pop-up that corners you on every platform screen—when you’re tweaking keywords, setting campaigns, or batching bids, even when you’re simply checking on things!
I’ve had countless emails from clients fretting over why their ‘Optimization Score’ has suddenly dipped. In this article, I want to demystify what Google Ads Recommendations really are, dispel some myths, and share some tactical advice on how to handle them.
Why do Google Ads Recommendations get such a bad rap?
So why this widespread disdain? To me, it’s plain: the expectations simply don’t align. While the system tailors Recommendations to our accounts, it often lacks the nuance needed for unique business goals.
The algorithm’s designed to spot patterns and suggests tweaks based on what’s working in other accounts. Say you only use Exact and Phrase match keywords—the system might suggest ‘Test Broad Match’ because, theoretically, it could broaden reach, but it may not align with budget constraints or niche specifics.
Bear in mind, Recommendations initially served as a tool for Google Ads sales reps to identify potential client improvements. In their hands, human insight ensured suggestions were relevant. Now, the human filter is absent, making Recommendations feel less tailored.
Is the Optimization Score really that important?
When Google tells you your Optimization Score is low, it’s tempting to perceive it as a failing report card. Many fall into the trap of blindly accepting every suggestion just to see that 100% score light up.
Let me be candid: resist the urge. This score doesn’t reflect performance but rather measures how actively you are reviewing recommendations. Dismissing a suggestion has the same impact on your score as applying it. So, keep your score at 100% if it’s crucial to your Google Partner status—but otherwise, let it slide.
Decoding Recommendations vs. Real Issues
Recommendations might pop up anywhere across the ad platform, not just the designated tab. You’ll see them during account setup, keyword addition, or bid adjustment. These prompts can set off alarms due to their visibility.
Remember, blue or yellow notifications are mere suggestions. Red or purple signals require immediate attention, potentially indicating a billing error or disapproved ad. Maintain a calm head, and only adopt changes that align with your objectives.
Are Recommendations just Google’s strategy to boost spending?
An argument often made is that Recommendations aim to skyrocket spending, subtly capturing more dollars. And sure, Google is profit-driven, but they understand you’ll curb spending if returns don’t justify the expense.
Suggestions are twofold: some aim at increasing reach and expenditure, while others focus on ROI and account refinements that might not increase costs but enhance efficiency.
Turn Off Auto-Apply Recommendations
It’s crucial to mention Auto-Apply Recommendations when discussing these aspects. It’s a feature Google previously championed, enabling automatic implementation of suggestions without checks. Thankfully, it’s losing focus now.
To take control, head to the Recommendations tab, switch to All Campaigns, click Auto-Apply Settings, and ensure all selections are unchecked. Keep the reins in your hands—Google doesn’t need unsupervised access to your budgets, bids, or keywords.
Recommendations aren’t inherently good or bad. They are mere prompts to evaluate and test. Listen to your instincts: review, test if promising, or move on if irrelevant.
This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature and how to maximize it efficiently.
