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Category: PPC

How AI Ads are Revolutionizing Sales Growth for Brands
I recently discovered that Google’s transition to AI-powered ads is transforming how brands engage with consumers, significantly boosting their performance in search results.

Google claims these AI-driven advertising tools are yielding impressive outcomes, with some retailers reporting substantial increases in sales as Google continues to innovate the functionality of ads within AI-driven searches.

The big picture. The anticipated disruption of Google’s search by AI chatbots like ChatGPT hasn’t happened. Instead, Google’s ad revenue continues to rise, illustrating that AI is enhancing search dynamics rather than replacing them.

By the numbers:
- Alphabet Inc. exceeded $400 billion in revenue by 2025.
- Q4 ad revenue jumped 13.5% YoY to $82.28 billion.
- YouTube ads saw a nearly 9% YoY climb, reaching $11.38 billion.
What’s happened. Google is integrating ads into its AI-powered search features, such as AI Mode using Gemini, while unveiling ad formats tailored for conversational searches. A new ‘business agent’ initiative helps brands like Poshmark and Reebok manage their AI representation.

Driving the results. Innovative campaigns, like Performance Max and AI Max, align ads with more nuanced conversational search intents. Google notes that AI Mode queries tend to be two to three times longer, providing better context and connecting users with fitting products. Aritzia, for example, has seen an 80% rise in revenue with AI Max.

How it works. The AI system assesses a retailer’s website and creative assets, interpreting user intent from conversational searches. It matches products and messages dynamically and in real time, crucial as 15% of daily searches are entirely novel.

Why we care. Google’s evolution from keyword-focused to intent-driven and AI-matched advertising enables more precise consumer engagement when they’re ready to purchase. As search becomes increasingly conversational, AI-powered ad formats are essential to stay competitive.

Zoom in. Google is exploring new formats like ‘direct offers’ which personalize promotions when users show buying intent. Using Gemini, these trials with brands like E.l.f. Beauty, Chewy, and L’Oréal analyze conversational context and behavior.

Commerce push. Google is advancing its commerce agenda with a Universal Commerce Protocol developed with Shopify, facilitating purchases directly within AI interactions.

Yes, but. Google isn’t alone in exploring AI-driven search ads. Early results vary; Amazon reports limited success with its AI shopping assistant, and OpenAI and Perplexity AI are navigating their monetization strategies.

What they’re saying. Google presents itself not as a retailer but as a ‘matchmaker,’ emphasizing how AI creates more relevant, personalized ads while allowing brands to control their message and foster user trust by displaying the right product at the perfect time.

What’s next. Though Google has no immediate plans to insert ads directly into Gemini, it will continue enhancing ad offerings within AI Mode, focusing on personalized promotions and AI-driven shopping experiences.

Bottom line. AI isn’t replacing traditional search; instead, it’s reshaping it. For Google, that means more conversational, targeted, and sometimes much more profitable advertising.

Dig deeper. Curious for more insights? Discover how Google’s AI ads are achieving an 80% sales boost for some brands here.

Inspired by this post on Search Engine Land.
April 7, 2026
Discover How Google Ads Now Appear in Mobile Image Searches

I’ve recently discovered that Google has begun integrating sponsored ad units directly within the Images tab of mobile search results. This exciting new placement is accessible to eligible campaigns without requiring any changes to their existing keyword targeting.

What’s happening? Every time I check the Images tab on Google Search via mobile, I may now encounter sponsored units tucked within the image grid. Each ad displays a complete image creative as the primary visual element alongside text, and it is prominently labeled “Sponsored,” aligning with Google’s standard ad labeling throughout search results.

How it works. It amazes me how eligible campaigns can seamlessly serve into the Images tab without altering any keyword targeting or campaign structure. This placement leverages existing image assets, positioning advertisers who run Search or Performance Max campaigns with compelling visual creatives to gain the most. Thankfully, there’s no need to set up separate image-only campaigns.

Why it matters to us. This move significantly expands Google’s paid search real estate. For those of us engaged in product-led or catalog-heavy advertising, the Images tab is crucial, as it often serves as the starting point for purchase-intent discoveries — and now, our ads can appear right in that moment. If we are using robust image assets in our campaigns, we might be enjoying incremental impressions without any effort on our part.

The big picture. I’m noticing that this placement behaves more like a visual discovery surface rather than traditional paid search. While we should expect high impression volumes, the click-through rates might be lower, similar to display or Shopping ads instead of conventional text ads. Yet, the assist value in multi-touch conversion paths could be quite significant, especially for retail and direct-to-consumer brands. It’s an upper-funnel reach strategy, not a last-click channel.

What we should watch. Even though Google hasn’t officially announced it, nor is there a specific reporting breakdown for these Image tab placements yet, it’s crucial for us to monitor our impression share and segment data closely. This will help us understand its contribution, and whether it impacts organic image visibility for our competitors.

First seen. The innovative placement was first noticed by Google Ads Expert Matteo Braghetta, who shared this update on LinkedIn. At the time of writing, Google hasn’t published any official documentation regarding this development.

Inspired by this post on Search Engine Land.

April 7, 2026
Top 5 AI Strategies for Effective Lead Generation
When I dive into AI-driven advertising, it’s clear that our lead generation strategies must evolve. Here’s what I’m focusing on to make the most of these exciting tools.

Many of today’s PPC tools cater to ecommerce, but that’s not to say they can’t benefit lead gen. It just takes a more intentional approach on my end.

Even though lead gen with AI demands creativity and adaptation of traditional ecommerce tools, they don’t always apply in the same manner. Here’s how I’m ensuring success.

Disclosure: As a Microsoft employee, my examples might lean towards Microsoft Advertising. However, the principles I discuss apply broadly across platforms.

1. Fix your conversion data first

This is the single most crucial step as AI becomes more intertwined with media buying. Changes in attribution models, privacy policies, platform interactions, and consumer behavior mean I frequently question if my data reflects reality.

My initial step is always to audit my CRM or lead management system. I ensure the data I send to advertising platforms is clean, consistent, and intentional.

While data issues often arise from human decisions over technical faults, I never overlook essential technical checks:
- I confirm that conversions fire consistently.
- I regularly review conversion goal diagnostics.
- I validate that status updates and downstream signals flow back as they should.
Since AI systems learn from this data, it’s crucial for me to ensure that the feedback loop accurately reflects my operations.

Dig deeper: How to make automation work for lead gen PPC

2. Make landing pages easy to ingest and easy to understand

Lead gen campaigns can offer users multiple conversion paths. But from an AI standpoint, unclear paths pose a risk.

This means my landing pages need to clearly communicate:
- The action I want users to take.
- What happens after they take action.
- Which conversions are of priority.
Ambiguous conversion paths can confuse both users and systems. If AI crawlers detect inconsistent outcomes, they might question the accuracy of what my site claims, limiting my eligibility for certain placements.

It’s vital for me to use simple language, free of jargon or eccentric terms. This clarity helps AI systems better understand who I am and what I offer, aligning my creative with the right audience.

Using Performance Max campaign builders is a practical test. I review how the system positions my business. If its messaging aligns with my goals, my site is probably clear enough. If not, I take that feedback seriously.

I also utilize AI assistants to gauge how they describe my services. Accurate descriptions mean I’m on the right track; inconsistencies signal needed refinements.

Behavioral analytics tools, like Clarity, offer insights into user engagement on my site and frequency of AI tool crawlers.

Dig deeper: AI tools for PPC, AI search, and social campaigns: What’s worth using now

3. Budget across the entire funnel

Lead gen often faces long conversion cycles, an issue that AI can amplify. AI-driven systems evaluate sentiment, visibility, and contextual signals beyond just last-click performance. Therefore, if my budget only emphasizes immediate traffic, I risk missing significant impact higher in the funnel.

I aim to:
- Budget intentionally across awareness, consideration, and conversion stages.
- Apply the right metrics for each stage.
- Look beyond traffic as the primary success indicator.
In many lead gen models, citations, qualified leads, and eventual revenue provide a more accurate performance story than mere clicks.

Dig deeper: Lead gen PPC: How to optimize for conversions and drive results

4. Clean up your feeds and map data

I might assume I don’t have a “feed” in my lead gen setup, but that assumption puts me at a disadvantage.

Feeds provide AI systems with insights into my business structure and services. Keeping a simple Excel feed can grant platforms valuable context, even if my site isn’t massive.

Proper feed hygiene increases understanding. I use clear, specific columns, adhere to platform standards, and ensure full category representation.

$```json { "alt": "Spreadsheet with URLs and custom labels, including comments.", "caption": "Explore how URLs are paired with custom labels in this straightforward spreadsheet layout.", "description": "The image displays a spreadsheet containing two columns: 'Page URL' and 'Custom label'. It lists URLs alongside corresponding custom labels like 'MARKET_PAGE;REGION' and 'SINGLE_SERVICE;MONTHLY_SUBSCRIPTION'. The sheet also includes comments for guidance, indicating that rows with '#' are ignored. This setup is typical for organized digital marketing or web development projects, allowing efficient tracking and categorization." } ```$

On the local level, I claim and maintain all map profiles for accuracy. Consistent information is crucial. If I use call tracking, I carefully review labels to prevent attribution chaos caused by AI pulling mismatched data.

Adjust for potential AI-driven inflation in reporting and ensure changes reflect in conversion goals.

5. Pressure-test your creative for clarity

AI might mix, match, or shorten creative assets, meaning I often get one chance through a single headline to convey my entire value proposition.

If my selling points need multiple elements to make sense, that’s a risk. I review my creative to ensure it stands alone, communicating:
- What I do
- Who I help
- Why it matters
Lack of clarity can cause AI-driven placements to quickly become muddled.

Dig deeper: Why creative, not bidding, is limiting PPC performance

The fundamentals that still move the needle

Lead gen doesn’t need to be overly complex. Most impactful actions remain the same: clean data, clear messaging, rational budgeting, and disciplined execution. What’s shifting is attribution and the value AI places on different signals.

The fundamentals win out. AI merely highlights weaknesses and scales strengths. Emphasizing clarity, accuracy, and comprehensive funnel alignment sets up the best future performance.

Inspired by this post on Search Engine Land.
April 7, 2026
Mastering Paid Search: Strategy Over Keywords

In my extensive three-decade career, I’ve witnessed keywords dominate the landscape of paid search. However, in today’s world, they have become just a part of a larger puzzle. What truly drives performance now is strategy.

I remember spending weeks meticulously researching keywords, crafting strategies around them, and managing every aspect, from bid adjustments to audience targeting. It was the foundation of success in this industry.

We used to focus heavily on precise placements, structured URLs, and audience targeting, primarily with Google’s influence leading the charge. Our profession thrived on the tactical control this model offered.

We enjoyed the ability to identify which queries triggered ads and make informed decisions to optimize budgets accordingly. Sometimes we would even segment ad groups intricately to maximize returns.

What Changed Across Platforms

Now, advertising has embraced a significant shift: automation, driven by AI, has taken over critical tasks like bidding and creative assembly. While keywords remain relevant, they serve as just one of many signals that AI systems use.

With tools like AI Max for Search, Google has transformed keywords from being the focal point to just signals in guiding ad delivery. It’s fascinating how AI now uses elements like existing keywords and landing page content to enhance performance.

Advertisers employing AI Max often experience notable gains, with some campaigns seeing up to 27% more conversions. Integrating it with other tools like Performance Max can further amplify reach across various platforms.

Dig deeper: Google Ads no longer runs on keywords. It runs on intent.

The New Primary Levers

When I mention strategy as the new keyword, I mean focusing on specific inputs shaping ad performance. These include conversion data quality, a critical factor for systems like Google’s Smart Bidding, which relies on quality data to optimize campaigns.

We now prioritize which conversions hold the most value. It’s a shift from purely manual adjustments to strategic evaluations that highlight what truly matters for campaign success.

First-party data, enriched and well-structured, is paramount. It’s akin to the foundational keyword research of the past, vital for driving performance on today’s platforms.

Creative assets have evolved beyond mere deliverables; they’re now strategic signals that AI uses to target effectively. These visuals and messages have become an integral part of how we engage audiences.

The quality of landing pages and websites has also taken on new importance. AI determines relevance based on post-click experiences, emphasizing the need for seamless user journeys.

Dig deeper: In Google Ads automation, everything is a signal in 2026

What It Means for Practitioners

Our roles have adapted to these changes. It’s less about managing keywords or bids manually and more about creating strategic frameworks that guide AI systems effectively.

Subject-matter experts like us now focus on ensuring data quality, defining creative strategies, and identifying when human intervention is necessary.

We guide AI through a careful mix of conversion architecture, audience signal quality, and creative frameworks rather than traditional methods of keyword lists and bidding.

It’s crucial to understand how these advanced systems and platforms operate, as well as to emphasize the signals that matter most. Building strong first-party data and strategic frameworks will enhance AI capabilities and redefine the future.

Embracing this evolution, practitioners focusing on strategy over technical execution positions will find themselves best equipped to thrive in this changing landscape.

The keyword list remains, but our primary focus now is on strategy.

Dig deeper: 4 times PPC automation still needs a human touch

Inspired by this post on Search Engine Land.

April 3, 2026
Unlock E-commerce Success: Master Google Shopping & Amazon Ads

As I delve into the world of e-commerce, I’m constantly amazed by how paid search can transform business growth. Platforms like Google Shopping and Amazon Ads are game-changers, offering high conversion rates and efficient spending when campaigns are crafted thoughtfully.

These platforms are adept at capturing high-intent demand, providing the crucial data to expand my campaigns. They connect search queries directly to revenue streams, letting me pinpoint which terms are boosting sales so I can allocate my budget wisely.

However, the true test lies in organizing campaigns to effectively leverage this data.

Why does paid search excel in e-commerce? It’s all about intent and data. Google and Amazon thrive on search-driven environments. When someone seeks a product, they’re clearly expressing their needs. I don’t need to make inferences; I’m delivering precisely what customers want.

Moreover, Google Shopping and Amazon Ads offer unparalleled keyword-level revenue data. This insight helps me understand conversion rates and costs better. Amazon, in particular, shines with its granular product and category level revenue visibility.

Together, this data forms a powerful feedback loop. By analyzing which terms tie back to revenue, I can strategically shift my spending and enhance my return on ad spend (ROAS) over time. On Amazon, higher conversion rates even boost organic rankings, reducing future acquisition costs.

My success in search campaigns hinges on creating multi-funnel structures. While the concept remains consistent, execution varies based on campaign types, settings, and bidding strategies.

I implement campaign architectures that utilize wide-net, low-cost discovery initiatives to explore the search landscape. High-intent converters funnel into dedicated performance campaigns with strategic bidding. This approach not only strengthens ROAS but also enhances rankings and fosters scalable growth.

Dive deeper: Ecommerce PPC: 4 takeaways that shape how campaigns perform

Embarking on Google Shopping, the priority sculpting method, inspired by Martin Roettgerding, is invaluable. Utilizing a three-layer campaign structure, I route keywords into distinct campaigns based on their performance.

This strategy optimizes spending on discovery keywords and directs investment toward high-performing, high-intent terms. The Google Shopping priority settings are pivotal; high-priority campaigns initially serve at lower bids.

Layer 1 focuses on capturing branded search traffic through a Performance Max campaign, maintaining an assetless format to focus on shopping inventory and avoid bleeding into other channels.

Layer 2, the catch-all, casts a wide net, experimenting with search terms to gather conversion data, while Layer 3 dedicates budget to best-performing terms, aligning with high-ROAS strategies.

Amazon’s multi-tier campaign structure offers its own set of advantages, like higher conversion rates and the intricate connection between ad spend and organic rankings. Campaigns are organized at the SKU level, employing research, ranking, and performance tiers.

Each tier serves a unique purpose, managed by differing advertising cost of sales (ACOS) targets, tailored for profitability. The research tier explores broad keyword possibilities, performance tiers maximize returns on proven converters, and ranking tiers drive organic positions aggressively.

Both Google Shopping and Amazon Ads offer unique opportunities in the e-commerce landscape. Whether aiming for short-term gains on Amazon or long-term brand building via Google, using these platforms synergistically can propel a business to new heights.

Inspired by this post on Search Engine Land.

April 3, 2026
How Clarity Beats Creativity in ChatGPT Ad Performance
I recently delved into an intriguing analysis by Adthena, which examined over 40,000 daily ChatGPT ad placements. What stood out to me was how these ads are evolving into a streamlined, high-intent messaging format, specifically tailored for users who are already deep in the decision-making process.

The big picture: ChatGPT ads are gravitating towards a style that’s concise, well-structured, and highly contextual. This approach emphasizes precision over persuasion, signaling a shift from traditional creative advertising to real-time, intent-driven assistance.

By the numbers:
- The average headline is just 30 characters long, consisting of about 5 words.
- Body copy averages 116 characters and roughly 19 words.
This makes it clear that every word needs to be purposeful, enhancing clarity or directly driving conversion.

What’s working: The dominant pattern I observed involves a “Brand: Benefit” headline structure, which clearly delineates the brand name from the value proposition. This works well because users in conversational settings prioritize immediate clarity over intrigue.

In this environment, brand recall is essential, especially as ads often start with the brand name—ideal for users evaluating rather than discovering options.

Headlines have become succinct, resembling functional labels more than traditional slogans. This brevity continues in the body copy, usually composed of two concise sentences: one proving a point and another offering a subtle prompt.

Context mirroring has emerged as a distinguishing feature. The best ads expertly reflect a user’s query or environment, suggesting real-time message tailoring—a level of AI-native targeting that transcends basic keyword matching.

Concrete value signals are vital. The dollar symbol and specific numerical claims, such as prices or performance metrics, significantly outperform generic promises. Numbers naturally instill credibility, which is crucial in a context where users are actively researching and comparing.

Low-friction offers—like trials or demos described with the word “free”—are the most effective conversion drivers. They lower the commitment threshold for users still exploring options.

Calls to action are direct and action-focused, using phrases like “Shop now,” “Compare,” or “Book,” steering away from generic prompts like “Learn more.”

The overall tone is calm, confident, and measured, with minimal punctuation like exclamation points or question marks. This aligns more with the voice of helpful guidance than traditional advertising hype, allowing ads to blend naturally into conversational contexts.

Why we care: ChatGPT ads target users with high intent, where clarity and relevance trump creativity or storytelling. In a conversational space, ads compete against genuinely helpful answers, so precise and value-driven copy truly stands out.

This brings advantages to early adopters as the format becomes standardized, rewarding those who use shorter, structured messaging.

Between the lines: While ChatGPT ads share characteristics with paid search—focused on intent and relevance—they must seamlessly fit into dialogues, respond to users with high intent, and present messages that feel supportive rather than disruptive.

The takeaway is that success in ChatGPT advertising increasingly relies on precision, relevance, and credibility over emotion or brand storytelling. Achieving this means perfectly integrating at the moment when users need clear, trustworthy information.

Dig deeper: Check out the complete infographic shared by Adthena CMO Alex Fletcher on LinkedIn.

Inspired by this post on Search Engine Land.
April 2, 2026
Avoid These Costly Google Ads Mistakes for Ecommerce Success
Expanding beyond paid social? Discover how I learned to structure campaigns, control spend, and unlock demand without depending solely on the Meta playbook.

My paid social campaigns were thriving. I understood my audience intimately, had a tight creative process, and watched results improve each year. Naturally, when leadership proposed expanding into Google Ads, I was thrilled—envisioning it as a new revenue channel.

But sticking to our existing strategy only led to difficult conversations. Google demands different tactics—intent signals and campaign structures vary, and common budget-draining mistakes aren’t always obvious. Many brands mirroring their Meta strategy end up with flashy dashboards but disappointing balance sheets.

From my experiences, six frequent mistakes can cause substantial damage before they’re even noticed. They’re what I’ve seen most often with ecommerce brands transitioning to Google Ads—and each error is reversible.

Mistake 1: Treating Google like a retention channel

Utilizing Google Ads for retention and brand defense is possible, but relying solely on it as a strategy is problematic. I often notice brands new to the platform diving straight into Performance Max. Initially, the ROAS shines bright, making everyone happy. However, when the right question surfaces—”Are we truly growing or just capturing purchases?”—issues arise.

For example, a client approached me with branded search and retargeting doing most of the work in PMax—a mere tax on demand already created elsewhere, leading to stagnant revenue. Although ad spend was soaring, growth wasn’t.

Acquiring new customers requires a different setup, like:
- Shopping campaigns to highlight products to new audiences.
- Search campaigns centered on non-branded, high-intent keywords.
- Layered PMax configurations to bypass defaulting to easy conversions.
When Google grants vast access to new audiences, focusing solely on closing disregards most of this opportunity.

Dig deeper: Ecommerce PPC: 4 takeaways that shape how campaigns perform

Mistake 2: Not knowing how to leverage Google’s core levers

Although paid social expertise is somewhat transferable to Google, I’ve observed four major gaps. Let me share them with you in more detail.

Search intent: Social media ads interrupt, but search ads meet users actively seeking your offerings, transforming campaign structure, ad copy, and keyword targeting entirely.

Data feed optimization: An optimized product feed enhances visibility and targeting in Shopping or Performance Max campaigns.

Keyword research: Understanding match types and search intent is critical for reach and cost efficiency.

Landing pages: Engaging landing pages outperform product pages for high-intent but unfamiliar visitors.

Dig deeper: 7 Google Ads search term filters to cut wasted spend

Mistake 3: Allowing operational issues to interrupt campaign momentum

Consistent data is key for Google’s algorithms. Every unintended campaign pause can reset learning, causing weeks of degraded performance and wasted spend.

Common disruptions include:
- Payments: Bill lapses, leading to campaign pauses, overshadow the actual cost when factoring in downtime recovery.
- Tracking and feed integrity: Broken pixels and feed errors silently degrade performance.
Setting up automated alerts and regular audits can prevent these costly errors.

Mistake 4: Overly granular campaign structures

Detail-oriented advertisers may over-segment campaigns, believing it provides control. However, widespread budget allocation hinders Google’s automation from optimizing effectively.

Instead, tight, well-funded campaigns optimize better and are more manageable.

Dig deeper: How to find and fix the root cause of low conversions

Mistake 5: Leaving campaigns on Max Conversion Value without ROAS targets

Max Conversion Value aims for conversion volume, neglecting cost efficiency. A realistic ROAS goal encourages the algorithm to maximize efficiency. Setting this correctly is crucial.

Dig deeper: How each Google Ads bid strategy influences campaign success

Mistake 6: Underfunding campaigns, keeping them in learning mode

Underfunding during the learning phase results in indefinite stalled progress. Adequately funding new campaigns from the outset fosters quicker, more accurate results.

Expanding beyond Meta to include Google is a strategic move, accessing actively expressed demand. These pitfalls aren’t deterrents but guideposts for smoother transitions and optimized strategies.

For early adopters, start with my guide on expanding from Meta to Google Ads. If seeking further optimization, learn how to sidestep Google’s automation traps.

Inspired by this post on Search Engine Land.
April 2, 2026
Unveiling Google’s PMax Timeline: Boost Your Ad Strategy

Recently, I discovered that Google has launched an exciting new feature for Performance Max campaigns. As an advertiser, I’m always on the lookout for tools that provide clearer insights, and this new channel performance timeline view does just that. It offers a comprehensive breakdown of how different channels like Search, YouTube, and Display contribute to my campaign results over time.

What’s New

The latest update introduces a timeline graph that showcases channel-level contributions over a selected period, complete with investment and performance filters. This means I can quickly identify which channels are excelling and which ones might need a bit more attention.

The chart features helpful visual cues—like a yellow box highlighting channel performance evolution over time, and a pink box indicating different ad types, such as All Ads, Ads Using Product Lists, and Ads Using Video.

Why I Care

Managing Performance Max campaigns across multiple channels often left me guessing about where my budget was working best. This new view provides valuable insights into channel-level trends, allowing me to adjust strategies or budgets more efficiently. If I notice YouTube underperforming while Search is thriving, I can now make informed decisions without relying purely on guesswork or exported data.

The Big Picture

This new view empowers me to evaluate PMAX performance more effectively, without relying solely on Google’s automated decisions. Now, I can see consistent underperformance or excellence across channels, which guides my budget and asset strategies moving forward.

The Bottom Line

Though it’s not full transparency, this update is a significant move in the right direction. I now have a more structured way to detect trend anomalies in PMax campaigns early and make necessary adjustments to optimize performance.

First Spotted

This feature was first noticed by Axel Falck, Head of Search at Le Mage du SEA, who shared his insights on LinkedIn.

Inspired by this post on Search Engine Land.

April 2, 2026
Unveiling Auto-Applied Google Ads Experiments: Speed Up Your Results

I recently discovered that Google Ads now includes an auto-apply setting for its experiments feature, which is activated by default. This means that once an experiment determines a winning variant, it can automatically implement that change without waiting for manual review. A real time-saver, but there’s more to consider.

Here’s how it works: as advertisers, we can select between two modes when evaluating results – directional outcomes or statistical significance with varying confidence levels of 80%, 85%, or 95%. However, it’s reassuring to know there’s a safety net; if any chosen success metric performs significantly worse during testing, the system won’t proceed with automatic changes.

Why it matters to me. Experiments are incredibly powerful within a Google Ads account, allowing us to test ideas without risking the existing campaign’s performance. While automating the application of results could streamline testing phases, this process eliminates a crucial checkpoint where we often catch unintended outcomes that might impact active campaigns.

The potential pitfall. One limitation is that experiments currently accommodate only two success metrics. This might mean that a third, important metric could suffer unnoticed if it’s not one of the chosen ones, as the system’s guardrails only protect what we’ve explicitly instructed Google to watch, not every significant factor.

The takeaway. While the auto-apply feature serves as a helpful shortcut for straightforward tests, when conducting significant experiments, it’s worth going the extra mile for manual review. It’s best to let the experiment play out fully, ensure accuracy and thoroughness, and examine all data before making a final call.

First observed by professionals. This update did not go unnoticed; it was first picked up by Google Ads specialist Bob Meijer, who shared his insights on LinkedIn.

Inspired by this post on Search Engine Land.

April 1, 2026
Bing’s Expanded Product Carousel Boosts Advertiser Visibility

I’ve noticed that Bing is testing a double-rowed sponsored product carousel in its shopping results. As someone who keeps an eye on these updates, this change could offer substantial visibility boosts for Microsoft Shopping advertisers.

The test, first spotted by Digital Marketer Sachin Patel, caught my attention when he noticed the broader layout while searching for cushions on Bing. This new format combines a significant double-rowed sponsored carousel, prominently paired with organic results below.

Why this matters to me: If Bing decides to roll out this format broadly, I foresee a significant increase in screen space dedicated to sponsored products. This extra visibility typically translates to higher click-through rates, especially for those running Microsoft Shopping campaigns. The visually appealing double-row carousel puts Bing’s shopping ads on par with similar offerings by Google Shopping.

Here’s the catch: The test seems to be in its early stages, as not all users, including seasoned industry experts like Mordy Oberstein, are seeing this expanded format. When I checked myself, I noticed a more compact layout, hinting at Bing’s ongoing experimentation.

The takeaway: Bing often experiments with its search engine results pages without officially rolling them out. As a retailer using Microsoft Shopping, it’s crucial for me to stay alert for any increase in product impressions if the format becomes more widespread.

Initially discovered. This testing phase was initially spotted by Sachin Paten, who shared his insights and a screenshot on X.

Inspired by this post on Search Engine Land.

April 1, 2026

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