This past year, PPC has been anything but static – it has evolved. As I explored the insights from 2025, I found these articles resonated deeply. They addressed crucial questions like maintaining a competitive edge, eliminating wasteful spending, collaborating with automation, and gearing up for the future.
Join me as I take you through the links to the top 10 most-read PPC columns on Search Engine Land from 2025, crafted by our incredible experts.
Though it might seem challenging, even the smallest businesses can carve out their niche and captivate customers. Discover the strategies that make this possible. (By Sophie Logan. Published Sept. 16.)
Update your optimization techniques for 2025 with innovative approaches to keywords, Performance Max, and audience targeting. (By Pauline Jakober. Published Feb. 6.)
With increasing CPCs, understanding the pace of this inflation and comparing it to the consumer price index is essential for shaping your ad strategies. (By Mark Meyerson. Published April 16.)
AI is bridging the gap between organic and paid search. Learn how integrating SEO and PPC can enhance your visibility and brand presence. (By Jen Cornwell. Published Oct. 6.)
PPC scripts have limitations, but with vibe coding, you can remove obstacles and transform complex seasonal data into practical planning tools. (By Frederick Vallaeys. Published Aug. 21.)
Streamline your ad creation process without losing your core message. Leveraging generative AI can help craft engaging, personalized copy that truly connects. (By Jason Tabeling. Published Aug. 1.)
Discover filtering techniques that refine targeting, reduce unnecessary clicks, and reveal new keyword opportunities. (By Menachem Ani. Published July 22.)
Enhance your campaign management with Google Ads scripts. Uncover insights, actionable tips, and use cases for leveraging automation to improve performance. (By Frederick Vallaeys. Published Jan. 9.)
As clicks become scarcer, maintaining visibility requires precise targeting and value-based bidding. Achieving this ensures your prominence in both paid and organic searches. (By Sarah Stemen. Published Oct. 7.)
With Google’s environment becoming more automated, some PPC tactics are now obsolete. Discover what to eliminate and what to focus on for the coming year. (By Sarah Vlietstra. Published Nov. 4.)
I recently learned that Google has made a significant change by lowering the minimum audience size requirement for its Ads platform to just 100 active users. This adjustment now makes it far easier for advertisers, both large and small, to harness the power of remarketing and customer lists without the previous constraints.
What’s new: Now, advertisers can utilize audience segments with as few as 100 users across platforms like Search, Display, and YouTube. This includes both remarketing lists and customer lists. Excitingly, this same 100-user limit also applies to Audience Insights, slashing the previous threshold from 1,000.
Catch up: The shift toward these smaller audience thresholds began in May. At that time, Google had already reduced the minimum user requirement for Customer Lists in Search campaigns from 1,000 to just 100 users. This marks a clear trend towards making audience targeting more inclusive.
Why this matters: Smaller accounts and niche advertisers now have the opportunity to implement audience strategies that were once unattainable due to those larger size thresholds. By bridging this gap, Google removes a longstanding barrier to advanced targeting and personalization within Ads.
What to watch: I’m curious to see how advertisers will leverage these more precise, smaller segments and whether performance or privacy safeguards will evolve to align with this broader access.
First seen: This update first caught the eye of Web Marketing Consultant, Dario Zannoni, who shared the news on LinkedIn.
I’m excited to share that Google has expanded the Performance Max Channel reporting to MCCs, providing us advertisers with unprecedented insights across accounts. This new update allows me to see how PMax spends and performs across various channels, all in one place.
The Channel Performance report, which was previously available only per account, is now accessible in some manager (MCC) accounts. This is particularly thrilling as I’ve been eager for Google to confirm this rollout, and now it’s happening in live environments!
Why it’s important to me: This MCC-level visibility means I can efficiently analyze Performance Max’s spend allocation across different channels like Search, Display, YouTube, Discover, Gmail, and Shopping without having to dive into separate accounts. It’s a fantastic time-saver for managing large portfolios.
What I’m paying attention to: I’m keen to see when this feature becomes widely available across all MCCs. Plus, I’m hoping Google might introduce deeper metrics or export options to further enhance our analysis.
This development was first noticed by Mike Ryan from Smarter Ecommerce. He’s also published a helpful guide on using Google’s Channel Performance reports. His insights have been invaluable!
Conclusion: With MCC-level Channel Performance, Google is moving closer to demystifying Performance Max, particularly for agencies requiring extensive cross-account insights. It’s a welcome change for many of us strategizing at scale.
Recently, I discovered that Microsoft Advertising has introduced asset-level editorial reviews, a game-changer for anyone running ad campaigns. This new feature allows us to see individual ad components like headlines and images get reviewed separately. If one part is non-compliant, it won’t hold back the whole ad, ensuring that compliant components keep running smoothly.
Here’s What’s New: Announced back in June, this feature provides a granular view of ad approvals. Now, I can easily spot which specific asset might be causing issues, instead of having to guess why an entire ad wasn’t approved.
Why I Care: This update is a relief because it minimizes campaign disruptions and speeds up the approval process. No more resubmitting entire ads just to fix one small mistake. I can now address the exact problematic asset swiftly.
How it Enhances the Workflow: The platform now flags disapproved elements right in the dashboard. It gives a clear warning when something is blocked and provides a detailed asset status, making it easy to stay on top of my campaigns.
The Bottom Line: This more precise system replaces the old all-or-nothing approval process, letting compliant ads run uninterrupted and putting more control in my hands as an advertiser. It’s definitely a step forward in ad management!
I’ve learned that broad match now operates alongside Smart Bidding. It’s fascinating how drift happens, why it’s important, and how to align performance with genuine intent.
Broad match, once synonymous with “more reach, less relevance,” now depends on a machine learning layer to define relevance.
Over time, Google has nudged us, the advertisers, towards fewer complexities like fewer match types and more automation.
Since July 2024, broad match has become the default for new Search campaigns, signaling a shift in how we ought to think about it.
If you’re stuck in the mindset of broad match being the “loosest match type,” you’re stuck in 2016, and that’s where problems like CPC inflation and irrelevant leads arise.
Today’s broad match works within a system, collaborating with query matching, Smart Bidding, conversion signals, and optional tools like audiences and negatives.
Google leverages broad match as a growth mechanism for Smart Bidding campaigns rather than a solitary reach tactic.
In this article, I explore the changes, Google’s motivations behind them, and safe practices to maintain standards while using broad match.
The real risk with broad match isn’t relevance, it’s direction
Broad match tends to drift rather than fail completely.
With shallow optimization goals, broad match coupled with Smart Bidding can find quick ways to meet them, sometimes resulting in:
Queries that trigger cheap forms without real sales potential.
Users who convert but never purchase.
Leads that look good in Google Ads but don’t end up profitable.
Even when everything seems fine in the interface, the account might drift away from commercial intent.
This illustrates why understanding broad match’s current behavior is crucial.
What broad match actually is now
Broad match no longer stands alone as a keyword setting but works within a larger optimization system.
It’s built to work with Smart Bidding
Google specifies that broad match is intended to run with Smart Bidding, as bidding decisions are now made during auctions using signals like:
Device
Location
Time of day
Query context
User behavior
Broad match increases eligible queries. Smart Bidding evaluates which ones merit investment.
Running broad match without Smart Bidding deviates from its intended design.
Google has materially improved broad match matching
Google claims that recent AI enhancements have uplifted broad match campaigns using Smart Bidding by 10%.
This doesn’t imply broad match is inherently safe, but Google feels its matching layer justifies broader use.
It’s no longer positioned as optional
Starting July 2024, new Search campaigns activate broad match by default.
The campaign-level setting enforces broad match when conversion-based Smart Bidding is active, marking a significant paradigm shift.
Why Google wants advertisers to adopt broad match
Google’s rationale is straightforward:
Search behavior is increasingly unpredictable and long-tail.
Manual keyword lists fail to keep up with language and intent shifts.
Machine learning can interpret intent at auction time better than rigid logic.
Google positions broad match as a growth tool for Smart Bidding campaigns, providing algorithms with more opportunities to optimize for conversions.
You might not agree with this philosophy, but when advertising on Google Search, you’re part of this system.
A framework for using broad match without losing control
Broad match expands your reach. Maintaining control requires thoughtful constraints.
Conversion goals that reflect quality, not convenience
Smart Bidding optimizes based on defined conversion actions and values.
If your primary conversions are low-intent, broad match will scale this low intent.
Successful setups often include:
Optimizing for deeper conversion actions.
Applying conversion values to identify lead quality tiers.
Importing offline conversions, like qualifying leads or revenue.
This tackles the issue of associating cheap volume with success.
Intent filters through audience signals
Broad match identifies queries. Audience signals dictate ad visibility for those queries.
Audiences should provide context, not just report data:
Customer lists favor known buyers.
Remarketing lists for measured expansion.
Audience insights to recognize quality-segment correlations.
Even in observation mode, these signals help verify if broad match growth benefits the right areas.
Negative keyword structures that scale
With broad match, negative keywords transform from mere cleanup to structural elements.
Effective accounts often include:
Account-level shared negative lists for terms like jobs, free, definition.
Campaign-level exclusions aligned with intent boundaries.
Regular search term reviews, crucial early on.
Broad match naturally explores, while negatives determine its limits.
Brand controls to protect intent
Google’s brand controls can substantially reduce unwanted behavior in broad match.
These controls include:
Brand inclusions restrict matching to queries featuring specified brands.
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.
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.
