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Tag: A/B Testing

Unlocking the Secrets of Google Discover Headline Formats
I recently delved into a fascinating study on Google Discover headline formats, looking at a staggering 3.4 million articles. The results were eye-opening and showed that a simple headline rewrite often doesn’t yield the expected lift.

You might have come across these bold statements before:
- Quote-led headlines outperform plain declarative ones by nearly 29%.
- Question headlines underperform both, sometimes by 24%.
- Format drives the result: Rewrite a statement as a quote, or add that magic word, and you should expect a real lift.
To put these claims to the test, I examined 1,674,518 English articles and 1,690,295 French articles from the 1492.vision Discover corpus. That’s quite a hefty sample size!

What I found was a deeper flaw than just numbers. It turns out that all three claims treat headline format as a leverage point for visibility. However, the data clearly shows that the impact of a headline’s format mainly reflects the publisher’s audience and the specific Discover surface used.

One striking analysis was Simpson’s paradox. An anomaly that, once noticed, appeared across the entire dataset.

Here’s what we’re really measuring:

Rather than clicks from Discover, our metric is hits per article: how often an article appears across the 1492.vision fleet. This serves as a proxy for visibility.

The dataset was limited to editorial articles, excluding platforms like YouTube because they have different headline norms. We’ll dive back into these at the end, as they bring more clarity than anything else.

Why is volume important? The crux of the argument depends on slicing this vast dataset by publisher, Discover surface, topic, and language while still keeping enough data in each segment for valid insights. This is where the real difference between numbers and insights, and between a genuine format effect and a statistical illusion, lies.

Here’s a sneak peek: when you pool all publishers together, a clear gradient appears with quote-led headlines leading the pack and statements trailing.

The frequently cited +29% is actually a conservative estimate for editorial pieces: quote-led headlines achieve a +37% lift in English and +48% in French. Even questions don’t lag behind as much as expected since they outperform statements to some extent (+7% EN, +16% FR).

Though claim 1 appears understated and claim 2 misguided at the aggregate level, these are the observations on which most headline advice leans. Let’s delve further to understand what the data is really revealing.

Let’s shift to the hidden aspects, starting with publishers. The raw comparison isn’t effectively between quotes and statements. It’s more about one set of publishers versus another because the publishers employing quotes often differ from those who don’t.

Some media, like celebrity-focused outlets, regional newspapers, and sites attuned to trending topics, gravitate towards quotes, and naturally earn more Discover hits compared to entities that prefer factual presentations.

This is a prime example of Simpson’s paradox: a strong trend at the aggregate level that fades or reverses when segmented into groups.

To focus on the format itself, publishers must each be their own baseline: comparing quotes with statements within the same publishing entities while controlling for audience and topic diversity.

So, the question is, how does each format fare on its own? Let me walk you through the rest of this journey as we unpack these layers.

Inspired by this post on Search Engine Land.
June 15, 2026
Mastering Paid Social Creative Testing for Optimal Results

I’ve realized that when it comes to paid social creative testing, platforms are quite adept at recognizing when our creative variations are almost identical. This means that coming up with unique concepts can be far more valuable than simply making minor adjustments.

From my experience, increasing the volume of ads doesn’t necessarily enhance performance. When my accounts are flooded with similar variations, it fragments budgets, prolongs learning phases, and complicates the process of drawing meaningful insights.

The real strength of today’s top advertisers lies in their focus on distinct concepts rather than quantity. They delve into audience psychology, craft emotionally resonant messages, and explore different angles and formats, all aimed at giving algorithms clearer signals to work with.

What Meaningful Creative Testing Actually Looks Like

I’ve often found myself mistaking each new asset as a fresh opportunity in the algorithm’s eyes, but that’s not necessarily so. Merely uploading a large number of ads doesn’t equate to meaningful differentiation.

For instance, if the only change in five creatives is the color of overlay text, platforms like Meta still see them as nearly identical in message, audience, and visuals. This overlap means our ads might just end up competing amongst themselves.

Meaningful creative testing is deeply rooted in psychological triggers, messaging, and differentiated angles. It should change how the audience experiences the ad and how algorithms perceive it.

It’s most effective when concepts truly differ. That’s why I emphasize different emotional hooks, motivations, and creative formats to see noteworthy performance changes.

The Hidden Costs of Creative Volume

Pushing for creative volume over value can lead to inefficiencies in performance, squander resources, and weigh down our advertising processes with unnecessary complexity.

I’ve noticed that when an account is overwhelmed with low-value creatives, our analysis becomes convoluted, pulling attention away from more strategic, high-level planning.

Fragmented Budgets and Longer Learning Phases

Every new addition requires data for the platform to optimize its delivery. When budgets scatter across too many similar creatives, the data fragments, making it harder for algorithms to collect sufficient conversion signals, delaying proper progression through the learning phase.

Instead of investing in solid concepts, my budget often disperses across small-scale tests that hardly reach statistical significance, providing little insight for future efforts.

The Analysis Tax

When an account teems with assets featuring minor differences, it diverts attention from broader strategic discussions, trapping us in data minutiae.

I’ve learned it’s more productive to analyze broader creative strategies rather than dwell on minor performance metrics.

Misaligned KPIs

While speed and output are important, they shouldn’t solely define success. When volume dictates KPIs, it results in optimizing for delivery over strategic differentiation. A balance between production efficiency and deeper strategy is crucial.

How to Build Higher-Value Creatives

If merely tweaking existing creatives isn’t yielding results, how can we consistently create high-value ads? The key is leveraging genuine audience insights from reviews, social media comments, and other authentic sources instead of just chasing trends.

Identifying recurring themes or concerns allows me to craft messages that resonate more deeply. High-value creative doesn’t require high-budget productions; often, raw, low-fi content outperforms polished material.

Ultimately, impactful advertising stems from powerful messages, not just high production standards.

Source: AEKSA (Meta Ad)

Strategically Feed the Machine

Balancing between creative value and volume is key. I often use a two-phase framework: first focusing on macro-testing for value, then micro-testing for volume.

Phase 1: Macro-Testing for Value

Initially, I focus on exploring different concepts and testing diverse creative hypotheses to identify winners.

Phase 2: Micro-Testing for Volume

Once I determine a winner, I introduce volume by making iterations to refine and maximize the creative’s impact.

Test variations like different hooks, pacing, and CTAs to ensure the highest efficiency, strategically optimizing concepts that have proven their value.

The Weekly Creative Audit

By moving to a value-first approach, I help my organization escape the content mill trap. I regularly audit ad accounts by asking: Are our ads distinct? What insights drove our winners? Is the data guiding our strategy?

Slow Down the Content Treadmill

Algorithms reflect human behavior and can’t fabricate interest or turn weak messages into profits. It’s essential to provide strategic value, assess data, and leverage impactful concepts to drive growth.

Inspired by this post on Search Engine Land.

May 29, 2026
How Custom Visuals Doubled My Website’s Organic Traffic

Over the past six months, I’ve been on a journey to discover how custom visual assets can enhance SEO performance. I decided to test different design elements across 47 articles on a high-traffic accounting education website.

The experiment involved featured images, infographics, and videos used in both new and existing content. Interestingly, some visuals significantly boosted organic traffic, while others didn’t justify the investment.

Instead of showing that any image can help, my goal was to uncover the ROI of bespoke design elements that could consistently improve organic traffic.

Infographics emerged as the clear winner, with an astounding 110% average increase in organic traffic on the articles that used them.

This taught me a crucial lesson: Custom visuals supercharge already popular pages. They enhance strong content but can’t breathe new life into struggling articles.

Inspired by this post on Search Engine Land.

May 15, 2026
Boost PPC Performance by Measuring Paid Social Impact
I sometimes find it challenging to measure the true impact of my paid social campaigns on PPC performance. Despite not always seeing conversions directly within the social platform, these ads can significantly influence my overall marketing efforts.

To truly understand how paid social affects my other marketing channels, including PPC, I’ve found a few strategies that help me set up and measure effective tests.

Step 1: Determine Your Hypothesis

I always start by clarifying what I want to learn from my tests. Defining a realistic hypothesis that I can evaluate with available data is crucial.

For example, I often use the following hypothesis to measure the influence of social traffic on PPC:
- Search lift hypothesis: Increasing social media spend will boost brand search volume and PPC CTRs.
- Logic:
  - Social ads build brand awareness, prompting more people to search for my brand during research and purchase stages.
  - As more people become familiar with my brand, they tend to click on PPC ads more, regardless of search terms, enhancing both brand and non-brand CTRs.
  - Exposure to my brand boosts trust, potentially increasing conversion rates.
- Measurement:
  - Track impression and click volume for branded terms.
  - Monitor CTR changes for brand and non-brand terms.
  - Observe conversion rate changes for these terms.
My hypothesis varies, sometimes focusing on the lift from social spend or a surge in direct traffic.

Step 2: The Test

Setting up test parameters is my next step. It’s essential to avoid simply comparing results before and after changes due to possible seasonal effects. A geographic split test is typically my go-to method.

In this test, I increase social spend in specific geographies and analyze PPC data from these areas versus others. While selecting geographies, I control for various factors, such as regional televised sports events or confined TV commercials, to ensure my test results are valid.

It’s crucial to compare control and experimental groups by similar factors like income levels and region types. I also ensure my budget can accommodate anticipated increases in social spent, preventing budget limitations from skewing results.

Evaluating the impression share before and after allows me to ensure budget constraints don’t impact my outcomes.

Step 3: The Measurement

When starting measurement, I keep it simple, comparing platform data to see changes prompted by stopping social spend across all channels like TikTok, LinkedIn, Facebook, etc.

Upon halting social spending, I’ve observed mixed conversion rate results, with some regions showing increases and others decreases, though an overall drop in conversions was common.

Depending on my analytics setup, I delve into more complex analyses, looking at conversion touchpoint differences, visitor overlap rates between social and paid search, or different attribution models.

Before initiating any tests, I ensure that my measurement capabilities are robust enough to understand and interpret results accurately.

Step 4: Evaluation Beyond Test Criteria

While running tests, I measure results against my hypothesis but also look at additional variables that may provide further insight.

In one case, a brand I tested on believed they could cut down on brand advertising without affecting their search volume. However, a drop in common brand terms contradicted this. An evaluation across various factors showed unpredictable results that required expanded analysis.

I rely heavily on my experience to sniff out anomalies and conduct further internal evaluations.

When results seem unexpectedly drastic, I question whether it’s a quirk or if other factors, like recent AI-driven changes, are silently influencing outcomes.

What to Do With Your Social Impact Tests

The test setup is straightforward:
- Define your hypothesis.
- Choose how to test, preferably using a geographic split.
- Ensure you can measure the outcomes appropriately.
- Run the tests and evaluate the hypothesis-related metrics.
- Assess additional metrics for further insights or testing ideas.
For some, social channels like Facebook are top converters, while others see poor outcomes in isolation, necessitating tests to guide budget allocation strategies.

In these scenarios, companies with substantial social media spending reduce to test impact, while others might increase spending to assess performance changes.

Results vary widely across companies, with some seeing significant performance lifts and others noticing minimal changes, underscoring the need for personalized testing.

Conducting geographic split tests can offer incredible insights into how social media campaigns bolster or detract from other marketing channels.

Inspired by this post on Search Engine Land.
April 28, 2026
Unlock Early Features with Google’s App Labs for Advertisers

I recently discovered that Google is quietly testing something quite intriguing—a new “App Labs” beta in Google Ads. This development is offering app advertisers early access to experimental campaign features before they’re available to everyone.

What’s new? There’s a new dedicated tab within the App advertising hub. Here, advertisers like me can explore limited-time experiments, provide valuable feedback, and take a sneak peek at tools still in development.

Why do I care? Well, Google providing early access means I get a chance to test, learn, and optimize before competitors catch on. This early adoption could give my advertising efforts a significant performance edge, helping me adapt more quickly as new tools standardize.

Zoom in. Features in App Labs are essentially short-run tests. They’re not guaranteed to roll out on a permanent basis, but they offer Google real-world feedback while giving me a first-mover advantage.

Between the lines. This is essentially a sandbox for app campaigns and signals that Google values advertiser input early in the product cycle.

What to watch. As an early adopter, I might see performance advantages by testing and adapting to features long before my competitors are even aware of them.

First seen. I first heard about this update from Google Ads expert Thomas Eccel, who spotted it and shared the news on LinkedIn.

Inspired by this post on Search Engine Land.

April 23, 2026
Discover Why ‘Ugly’ Ads Could Boost Your Marketing Success
For years, I’ve been told to stick to a set of guidelines: always use top-notch creatives, maintain a polished brand, follow scripts, and adhere to platform-recommended formats.

Lately, while navigating ad accounts or simply scrolling through feeds, I’ve noticed something intriguing. The ads that grab my attention often defy these rules. They’re less polished, scrappier, and sometimes referred to as ‘ugly ads.’ What’s fascinating is that they’re outperforming the traditional, polished ones.

More brands are deliberately breaking so-called best practices to stand out. It’s important to remember that these practices represent an average of what worked for others in the past. By the time a strategy becomes a platform-recommended rule, it might have already lost its edge.

This is why defying best practices can lead to success — but only if you understand the reasons behind them.

Why Breaking Best Practices Enhances Ad Performance

Before diving into what to change, it’s crucial to understand the rationale behind existing rules. Platforms like Meta and TikTok have dual objectives:
- They aim for you to spend money on ads.
- They want to keep users engaged on their platforms.
The best practices they promote are designed to ensure a seamless experience, encouraging ads to resemble others. The issue is that familiarity eventually breeds invisibility. When I adhere too closely to the rules, my ads risk blending into the background noise, overlooked by users.

Highly-produced ads often scream ‘this is an ad,’ prompting users to skip them before my message hits home. In contrast, when my ad resembles something a friend might share, users’ defenses remain down longer, potentially transforming a scroll into a conversion.

This is why many top-performing ads today don’t appear traditionally polished or on-brand. They break patterns instead. Consider:
- Grainy phone footage.
- Notes app screenshots.
- Green-screened reactions or commentary videos.
- Other lo-fi formats that outperform studio-quality creatives.
Source: TikTok Ads Manager

To implement this, I started intentionally reducing my production value and experimented with formats like point-of-view (POV) shots tailored to various personas.

Dig deeper: TikTok ad creative has a shorter shelf life. Here’s how to keep up

Founder-Led Ads: Reviving the Human Touch

Many brands have adopted guidelines that make them seem faceless and untouchable. They refrain from showing a messy office, an unpolished founder, or anything that challenges their corporate script. However, others are discarding that playbook, embracing founder-led ads that deviate from the polished executive version.

There’s a catch.

Breaking the rules works only when it’s genuine. I’ve learned that faking authenticity is easy to spot and can backfire. This was evident in a viral series of videos where McDonald’s CEO appeared to present a new burger, but his execution was criticized for being stiff and unconvincing.

As shown in a Dineline video, his performance appeared staged. Contrarily, Burger King’s president presented their burger with no hesitation, offering a genuine and relatable moment.

The distinction was evident: One was a product pitch, and the other felt authentic.

If my leadership doesn’t genuinely believe in the product, neither will my customers. Rule-breaking should allow us to be real, rather than simply appear unpolished.

Source: Dineline on YouTube

The Comment Hook Hijack

You’ve probably encountered video hook best practices like ‘show the product in the first two seconds and state the value prop clearly.’ Sound familiar?

Imagine my ad starting with a screenshot of a negative comment, like one for a skincare product stating, ‘This probably smells like old socks, and does it even work?’ My ad would then show the founder confidently disproving this in an unscripted manner, applying the product.

Though this breaks the positive-association rule, it leverages viewers’ curiosity about digital conflicts. By the time they realize it’s an ad, they might already be engaged.

Source: TikTok Creative Center

The Rebel’s Safety Net

I learned not to abandon all polished assets just yet.

Rule-breaking is strategic, and often misunderstood when the ’80/20 rule’ is ignored.

Switching completely to shaky phone footage isn’t wise. Keeping 80% of the budget in traditional ads while using 20% for testing unconventional ones can be effective.

Next testing campaign, I plan to try:
- The silent test: Running a silent ad with bold captions to stand out in a noisy feed.
- The UI ghost: Using static images resembling platform notifications to pause scrolling.
- The algorithmic trust fall: Disabling auto-optimizations in a campaign to test creative performance without constraints.
Don’t Follow the Rules; Understand Them

Best practices are a guide, not a strategy. To move beyond them, I do it systematically.

I start by questioning the rule’s existence, evaluating its current relevance, and testing its opposite in a structured manner. Comparing traditional and lo-fi approaches helps me understand user engagement better.

In an environment where brands play it safe, those who understand and strategically break the rules will capture attention and conversions. My goal is to learn faster than the competition, skipping guesswork.

Inspired by this post on Search Engine Land.
April 22, 2026
Unlock Demand Gen’s Potential: Test Creative Impact with Uplift
I often find that platform reporting can lead me astray when trying to gauge the real impact of Demand Gen creative. To get a clear picture, conducting controlled experiments can validate if my creative work genuinely boosts conversions.

Demand Gen campaigns shine across YouTube, Discover, and Gmail, but they also bring a challenge—what I call the “attribution illusion.” It’s frequent for me to question whether reported conversions are truly incremental or if users would have converted through search regardless.

Google introduced asset uplift experiments in November, allowing me to measure the impact of my Demand Gen creative using an A/B split test. This feature helps replace assumptions with clearer insights into what’s truly driving results.

Relying heavily on creative instinct or standard reporting can misdirect efforts and waste valuable resources on underperforming assets. Google’s A/B testing capabilities empower me to isolate the impact of individual assets, preventing such outcomes.

Why attribution doesn’t equal incrementality

For example, if someone views a Demand Gen ad on YouTube but doesn’t click, only to search for my brand later and convert, Google might still credit the Demand Gen campaign. This attribution reflects correlation more than causation.

To measure accurately, I need to understand the scenario without showing the creative. Withholding test assets from a portion of the target audience helps establish a baseline.

The difference in conversion rates, or any key KPI between groups exposed to the ad and those not, reveals the actual incremental lift the creative drives.

Dig deeper: Why incrementality is the only metric that proves marketing’s real impact

What you need before testing creative uplift

Launching experiments without enough data for statistical significance is a common misstep. Before testing, I ensure campaigns meet necessary prerequisites to avoid inconclusive or invalid results.

Conversion volume

Google suggests having at least 50 conversions across test groups during the experiment for accurate lift measurement. If primary conversions fall short, I consider optimizing the test around micro-conversions like “Add to Cart.”

Budget minimums

Experiments require continuous, uninterrupted spending. A limited budget stopping my campaign early skews data for the control group.

The campaign budget must be sufficient to run for at least four weeks or until statistically significant results are achieved.

Creative isolation

I test one new variable at a time to determine if a specific asset drives uplift, keeping all other campaign elements unchanged.

Dig deeper: Why Demand Gen is the most underrated campaign type in Google Ads

How to run an asset uplift test in Google Ads

Running a creative uplift test in Google Ads is now more streamlined. Here’s how I set up a valid experiment.

1. Define a clear hypothesis

Each scientific test starts with a clear hypothesis. I avoid tests without defined objectives. For example:
- Bad hypothesis: “Let’s see if our new video works.”
- Good hypothesis: “Adding user-generated content (UGC) to our Demand Gen asset group will drive a 10% incremental lift in ‘purchase’ conversions compared to standard static image carousels.”
Navigate to the Experiments interface

In my Google Ads account, I navigate to Campaigns > Experiments. I create a new experiment, selecting Asset tests provided by you for a Demand Gen campaign.

Configure a 50/50 split

I define a 50/50 cookie-based split to ensure both groups have equal historical data and algorithm weighting, preventing users from being in both test arms.

My existing campaign becomes the control, and the new asset campaign serves as the treatment.

Lock your variables

Once started, I practice extreme discipline by not altering audiences, targeting, or making drastic bid and budget changes.

Any changes during the test can introduce noise, affecting the statistical significance of results.

Set the duration

I run experiments for at least four weeks. Week 1 is a learning period, and Weeks 2 to 4 provide actionable data.

Longer conversion cycles in B2B SaaS might require six to eight weeks.

Dig deeper: What it takes to make demand gen work for B2B and ecommerce

What your experiment results actually mean

Upon completion, I review the Experiments dashboard for each arm’s performance and confidence intervals across metrics to validate my hypothesis.

Outcome 1: Positive lift (statistically significant)

A positive lift with 95% confidence means my creative asset adds real value. I calculate incremental cost per acquisition (iCPA) by dividing the treatment group’s ad spend by incremental conversions over the control.

This iCPA becomes my benchmark for further scaling.

Outcome 2: Negative lift

Creatives may underperform, perhaps being too disruptive or skipped in ads. Pausing these assets is crucial to let data direct budget choices over personal preference.

Outcome 3: Inconclusive result

If results are negligible and don’t confidently attribute conversions after four weeks, I might extend the test for more data. If still inconclusive, trying a drastically different creative asset is my next step.

Prove creative impact with incrementality testing

Creative remains a powerful differentiator for performance. Creating high-quality video or UGC is one thing, but proving its impact with scientific rigor strengthens my creative decisions.

Asset uplift experiments provide evidence of Demand Gen’s budget worthiness to stakeholders. When I start with a holdout test, establish a baseline, and let data guide my creative roadmap, the results speak for themselves.

Dig deeper: The Google Ads Demand Gen playbook

Inspired by this post on Search Engine Land.
April 21, 2026
10 Years of PPC Insights: When Breaking Rules Pays Off

I’ve spent a decade delving into PPC strategies and what I’ve learned is that chasing ‘best practices’ often limits true performance potential. Real growth stems from daring to deviate and experiment with new methods.

PPC conversations frequently revolve around sticking to best practices. These mandates include maintaining clean account structures, controlling match types, scaling budgets incrementally, ensuring campaigns don’t overlap, and keeping everything logical and easy to explain.

While these fundamentals do promote consistency and prevent inefficiencies, they are not the secret to achieving significant gains.

Looking back, many of the most impactful improvements came from testing unorthodox ideas that didn’t neatly fit into the established frameworks, but instead aligned with how platforms like Google Ads and Meta actually operate. These platforms don’t optimize for best practices, but rather for signals, prompting a rethink in approach to performance.

Control Still Matters: Revisiting SKAGs

In several accounts, reintroducing Single Keyword Ad Groups (SKAGs) for high-intent, high-revenue keywords led to improved performance. Ad relevance shot up, conversions grew, and query matching became more precise. It’s not about reverting to old structures, but recognizing where control adds value.

The narrative that machine learning abolishes the need for such control is overly simplistic. My experience shows that precision matters, but only in contexts where the intent justifies it.

Harnessing Broad Match with Control

Historically, broad match has been met with skepticism due to its expansive nature. However, combining broad match with aggressive negative keyword management allows Google to explore broadly while you shape the output through strategic query mining.

By continuously refining query inputs, broad match can expand reach without compromising relevance, redefining how control is applied.

When Visibility Trumps Efficiency: Target Impression Share

Target Impression Share often supports defensive strategies, but applying it to high-value, non-branded terms can boost SERP dominance even at the cost of efficiency. In such cases, ensuring visibility can outweigh concerns over cost efficiency, especially when aiming for market dominance rather than mere competition.

Focusing on Conversion Quality: Weighting Over Tracking

Most lead generation accounts capture multiple conversion actions, but treating them equally can lead to suboptimal interpretations. In one instance, assigning different values based on conversion likelihood—like prioritizing phone calls—shifted optimization to improve conversion quality rather than volume.

This approach emphasizes what’s truly valuable, ensuring platforms optimize effectively based on input.

Competitor Bidding: Leveraging Existing Intent

Despite their reputation for inefficiency, competitor campaigns succeed by capturing existing intent. Users searching for competitor brands often convert thanks to their advanced position in the decision process, proving crucial when strategically managed with clear positioning and relevant landing pages.

Rethinking Top-of-Funnel Keywords

Although often removed for low conversion rates, top-of-funnel keywords can indirectly enhance account performance by strengthening remarketing pools and audience signals, thus supporting high-intent campaign efficiency.

These queries play an unseen but vital role in driving conversions across the account.

Trusting the Data Over Assumptions

Initial audience hypotheses frequently miss the mark, whereas data often pinpoints the most efficient converters. By trusting data and adjusting strategies accordingly, accounts can improve performance by aligning with audience realities.

Revisiting Account Structure’s Role

While clean setups simplify management, they’re not always the most effective. Controlled overlaps between campaigns can leverage shared signals for better auction outcomes, challenging the notion that rigid structures lead to optimal performance.

Treating Product Feeds as Dynamic

In Shopping campaigns, product feeds are often overlooked. Yet, revisiting and adjusting feed details—like product titles and attributes—can significantly enhance product visibility and click-through rates, underscoring their strategic importance.

Retargeting: A Hub for Testing Strategy

Retargeting is not just about conversions; it’s ideal for testing variations in messaging and creative content due to its high-intent audience. Successful test results can then be confidently scaled, reframing retargeting as a strategic testing ground.

The Real Secret Behind Top Account Success

Over the years, I’ve realized that outperformance doesn’t stem from strictly adhering to playbooks, but from understanding and influencing platform signals and stepping beyond conventional boundaries to outperform beyond expectations.

Inspired by this post on Search Engine Land.

April 9, 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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