I’ve heard that Apple plans to launch more ads within App Store search results in 2026, enhancing their ad inventory but maintaining their focus on relevance, not bid amount.
What’s changing? New ads are set to appear in-line with App Store search results, sitting alongside organic listings. Existing top-result ads will remain. And guess what? There’s nothing we need to do to get into these new placements — bidding won’t help.
What Apple is saying: According to guidance Apple shared with Apple Insider, relevance remains key: “If your app isn’t relevant to what the user is searching for, it won’t be displayed — no matter how much you’re willing to pay,” an Apple rep said.
They also mentioned that apps irrelevant to a user’s query won’t even make it to the auction, regardless of bid size. While relevance and bids matter, relevance is the real gatekeeper.
Why I care: As Apple expands its ad inventory, the competition might heat up, and this could affect how often ads show up during user discovery. Their relevance-first policy suggests that mere bidding isn’t enough, putting a premium on keyword strategy and creative finesse.
Without placement control, aligning closely with user intent seems to be the winning strategy for better exposure.
What I can control: The creative side still matters a great deal. Preparing multiple ad variations to align with different audiences or keyword themes can be a game-changer. If there’s no custom creative, Apple will auto-generate ads from the app’s product page.
Billing stays the same: Apple confirmed no pricing changes. We’ll continue to pay per tap or per install, depending on our current setup.
The big picture: Apple has been ramping up its ads business steadily. It added ads to the Today tab in 2022 and recently rebranded Apple Search Ads to Apple Ads, signaling its broader ambitions despite resisting traditional auction dynamics found elsewhere.
The bottom line: Apple is increasing ad density in the App Store search but not advertiser control. More ads are on the way — just not the ability to buy your way into better positions.
Recently, I’ve been exploring LinkedIn’s Reserved Ads feature, which is now open to all managed advertisers. This exciting update lets me secure the prized top-of-feed placement, fundamentally boosting visibility and engagement for my B2B campaigns.
LinkedIn has now made Reserved Ads accessible to all managed accounts, allowing me to grab the first ad slot in the feed. This prime location guarantees premium visibility for my advertising efforts.
What’s new. With Reserved Ads, I can secure top-of-feed placement at a consistent rate, ensuring predictable delivery and enhanced reach. According to LinkedIn, this format drives up to 75% higher dwell time, 88% higher view-through rates, and achieves 99% of forecasted impressions, making it a powerful choice for my marketing strategy.
How it works. These ads appear in the most visible ad slot on LinkedIn’s feed and support a variety of Sponsored Content formats like Video, Single Image, and Carousel Ads. My LinkedIn account representative assists me in reserving this valuable inventory and setting the pricing strategy.
Why we care. For me, LinkedIn Reserved Ads are a game-changer, providing guaranteed top-of-feed placement. This increases my campaign’s visibility and engagement, helping me stand out in the competitive B2B space. The premium positioning enhances brand recall and influences potential leads early in the funnel.
Explore the future of payments with Oustia's sleek new card reader! Secure, stylish, and efficient — the perfect tool for modern businesses.
The predictable delivery and fixed pricing models mean I can plan my campaigns with more certainty, while also building high-quality retargeting audiences for future conversions.
The big picture. By utilizing Reserved Ads, I’m effectively bridging brand awareness and demand generation. Anchoring my campaigns at the top of LinkedIn’s feed enables me to create higher-quality retargeting pools, with LinkedIn reporting up to a 101% increase in mid-funnel engagement as a result.
The bottom line. LinkedIn’s Reserved Ads provide me, as a B2B marketer, with a predictable way to command attention and transform it into significant demand.
I’ve noticed that Google has recently made a significant change to its Ad Manager by removing the unified pricing rules. This change allows publishers like me to set different price floors for various bidders, potentially causing a shift in programmatic auction pricing.
In practical terms, this means I can now specify that one buyer must bid at least $5 while others might have a lower minimum of $2. Interestingly, Google has also rebranded “unified pricing rules” to just “pricing rules.”
Before 2019, I had more flexibility to set higher floors specifically for Google, which helped balance its data advantages. However, this was all put on hold when uniform pricing was mandated, a decision that didn’t go unnoticed by regulatory bodies in the U.S. and Europe.
Why does this matter to me? With the return of bidder-specific pricing rules, the auction dynamics shift. Higher floors for certain buyers could influence win rates and CPMs, ultimately affecting my advertising strategies and inventory.
Regulatory pressure seems to be a catalyst for this rollback. For instance, the U.S. accused Google of anti-competitive behavior, which resulted in proposals to end unified pricing. Meanwhile, Europe fined Google €2.95 billion, demanding it cease self-preferencing within the ad tech supply chain.
According to Google, this update should simplify the process for publishers and advertisers like me to work with competing ad tech solutions, while aiming to minimize disruption. They view this as part of broader strategic changes across display, video, and app ads.
Industry reactions appear positive. Jason Kint from Digital Content Next mentioned that the change brings meaningful relief, as unified pricing previously reduced yield. It also signals compliance with regulatory pressures, potentially averting stricter remedies.
Ultimately, after more than six years, I feel like I’m regaining some control over the pricing in Google Ad Manager. This shift is less about Google’s product strategy and more about responding to intense antitrust scrutiny.
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 been exploring how Microsoft’s Copilot is revolutionizing search advertising by transforming our daily conversations into actionable insights for advertisers. It provides a window into user intent, reducing wasted spend, and boosting ROAS significantly.
In fact, Microsoft reports a 13-fold increase in ROAS when users interact with Copilot before conducting a search. By tapping into billions of first-party data across platforms like Bing and LinkedIn, Copilot can identify high-value audiences and help advertisers make every dollar count.
The mechanics of conversational search are intriguing. Users tend to provide AI like Copilot with more detailed queries, offering richer context compared to traditional search bars. This shift creates multiple ad opportunities from a single detailed conversation, potentially transforming the advertising landscape.
A recent campaign I ran for a university highlights this transformation in action. Shifting from broad keywords to detailed, conversational queries allowed us to sharply decrease wasted impressions and costs, while significantly boosting engagement.
It got me thinking about how advertisers can transition to this model effectively. Besides technological integration, it requires a strategic realignment to capture the conversational demand using structured data and cross-channel strategies.
Especially with Gen Z, addressing authenticity concerns becomes crucial. They value real interaction, so ads need to feel native and relevant, not generic or intrusive. Using behavioral data from platforms like Activision, we can target more effectively without crossing into ‘stalker-ish’ territory.
As we relearn how to engage with this audience, I see the balance between utility and authenticity as the key to long-term success. The rise of AI in advertising continues to create an exciting new economic landscape, driven by precision rather than sheer volume.
I’ve been diving into some recent updates from Google regarding keyword match types, especially for those of us working with AI Overviews (AIO) and AI Mode ad placements. It’s crucial to understand these changes, particularly for those testing AI Max and using various match-type strategies. Let’s break it down so we can all optimize our ad reach effectively.
Why this matters to us. As the digital advertising landscape embraces AI-powered placements, it’s more important than ever to grasp which keywords are ready to serve ads and avoid unintentionally limiting our ad reach or misjudging performance metrics.
In May’s developments. When I followed the conversation between Marketing Director Yoav Eitani and Google’s Ads Liaison, Ginny Marvin, it was clarified that ads can serve either above or below an AI Overview—or appear within—but not in both placements simultaneously. Marvin stated, “Your ad could trigger to show either above/below AIO or within AIO, but not both at this time.”
When we talk about ad placements, it turns out both exact and broad match keywords can trigger ads above or below AIO. However, only broad match keywords (or those using keywordless targeting) have the privilege to appear within the AI Overviews.
What’s different now. In a later discussion with Paid Search specialist Toan Tran, Marvin provided further insight into Google’s updated eligibility criteria. Before this update, the presence of an exact match keyword could block a broad match keyword from filling AIO spots. But thanks to Google’s revisions, that’s no longer an issue.
Marvin detailed, “The presence of the same keyword in exact match will not prevent the broad match keyword from triggering an ad in an AI Overview, since the exact match keyword is not eligible to show Ads in AI Overviews and hence not competing with the broad match keyword.”
This adjustment means that with exact and phrase match keywords not qualifying for AI Overview placements, they won’t compete with broad match keywords in those auctions. So, a broad match can still trigger successfully even if its exact match counterpart is present.
The broader perspective. Google’s strategic update strengthens the distinction between traditional keyword matching and AI-powered intent matching. Ads in AI Overviews now depend on a keen understanding of both user queries and AI-generated content, requiring broader targeting signals.
The takeaway for us. If you, like me, are pushing into AI Max and AIO placements, it’s clear that broad match and keywordless strategies are key to tapping into Google’s AI-driven ad spaces. Exact and phrase match keywords might not appear in AI Overviews, but crucially, they won’t stop us from leveraging broad matches.
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.
Have you ever felt uneasy managing large catalogs in Google Performance Max, almost like you’re handing over your wallet to an algorithm? I sure have.
La Maison Simons faced a similar struggle. With too many products and not enough control, they decided to rebuild their segmentation using Channable Insights. This change turned their perplexing campaign into a revenue powerhouse.
Step 1: Stop segmenting by category
Initially, Simons divided campaigns by product category. It seemed like a good idea until their popular sweater consumed the entire budget, leaving less visible or new products unnoticed.
Static segmentation brought limited visibility and sluggish decision-making. Marketers were trapped with manual tweaks, while Google auto-focused on what’s already succeeding.
Step 2: Segment by performance
With Channable Insights, product-level data like ROAS and clicks now fuel dynamic grouping:
Products automatically transition between segments based on performance. As Etienne Jacques, Digital Campaign Manager at Simons, expressed:
“One super popular item no longer takes all the money.”
Step 3: Shorten your analysis window
Instead of the usual 30-day signals, Simons decided to use a rolling 14-day window. This means quicker reactions, more accurate decisions, and less wasted spend in a fast-paced catalog.
Step 4: Push the strategy across channels
Why limit the strategy to Google? Simons applied the same segmentation across:
Meta
Pinterest
TikTok
Criteo
This cross-channel consistency amplifies optimization.
Step 5: Watch the metrics climb
Simons unlocked impressive results without increasing ad spend:
ROAS growth: from ~800% to ~1500%
CPC decrease: $0.37 to $0.30
CTR lift: 1.45% to 1.86%
14% increase in average order value
1300% ROAS for New Arrivals campaigns
Faster workflows and fewer manual tweaks
Even previously invisible products turned into unexpected profit drivers with a spot in the limelight.
Step 6: Treat automation as control, not chaos
Automation has restored marketing control rather than taking it away. Now, teams can learn from data and actively influence product growth instead of leaving everything to PMax autopilot.
Your action plan
Classify products as Stars, Zombies, and New Arrivals.
Automate campaign reassignment based on real-time data.
Refresh product insights every 14 days.
Roll out segmentation logic to every paid channel.
Scale what wins – test what’s yet to succeed.
Aiming for Simons-style ROAS gains without raising ad spend? Start with a free feed and segmentation audit to enhance your product data quality.
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.
As a seasoned PPC professional, I’ve learned the hard way that even experts can fall victim to default settings. It’s become clear to me how crucial it is to thoroughly double-check every campaign setting.
On episode 334 of PPC Live: The Podcast, I chatted with Sophie Fell, Head of Paid Media at Liberty Marketing Group. We delved into a memorable PPC mishap involving location targeting, illustrating how minor oversights can escalate into significant issues—but also how to resolve them effectively.
Sophie shared a story where she inadvertently launched a campaign with worldwide location targeting. The campaign quickly amassed 1,500 leads, which appeared promising until she realized they were from unintended locations.
At first glance, such a spike in leads seemed like a triumph, yet we soon saw it as a cautionary tale. Upon further investigation, the reason was clear: the location settings were misconfigured. This experience taught us the importance of scrutinizing results that seem unusually favorable.
The client noticed the mistake around the same time as Sophie. She addressed the situation with honesty, acknowledging the error, clarifying the misstep, and resolving it promptly. This transparency was crucial in maintaining trust, even if the client felt understandably frustrated.
This wasn’t a case of lacking expertise; rather, it was about rushing through processes and assuming reviews had been done. We’ve all made assumptions that trip us up, and this incident was a stark reminder of the dangers inherent in default settings.
Once the issue was corrected, Sophie’s campaign achieved exceptional results, hitting targets early and surpassing revenue goals by £3.5 million. This success wasn’t defined by the initial error but by the way it was handled.
Nowadays, Sophie double-checks campaign settings multiple times for assurance. She examines settings during any unusual performance shifts and ensures results are thoroughly vetted. Her key takeaway: post-launch reviews often catch what pre-launch overlooks.
When mistakes occur, Sophie advises: pause, assess, and be transparent. It’s critical to take responsibility, explain the error, and detail preventive measures. Errors only escalate into issues if mishandled.
In her audits, Sophie frequently encounters outdated accounts, over-reliance on brand campaigns, and misapplied automation tools. She emphasizes the ongoing importance of aligning keywords, ads, and landing pages, even in the era of AI-driven marketing.
Discussing mistakes is vital—many assume industry veterans no longer err, but learning never stops. Sharing these experiences fosters junior confidence, enhances leadership, and propels industry evolution.
I believe a healthy team culture tolerates experimentation and accountability. Sophie highlights the need for clear testing frameworks, budget constraints, and openness. Teams claiming perfection often lack innovation.
The key takeaway? Regularly verify your campaign settings. Platforms evolve, defaults change, and assumptions can lead astray. Ensuring campaigns align with intentions prevents mishaps.
