Tag: A/B Testing

  • Unlocking True ROAS: Insights from a 7-Day Google Ads Attribution Test

    Unlocking True ROAS: Insights from a 7-Day Google Ads Attribution Test

    Have you ever wondered if your Google Ads attribution window is truly representing how your customers purchase? That’s a question I faced when working with one of my clients, a direct-to-consumer (DTC) retailer in a fiercely competitive industry.

    At first, we used the default 30-day click attribution window in Google Ads. But as I discovered, my client’s customers typically converted within 2.2 days. This discrepancy meant that many conversions were mistakenly credited long after the initial interaction.

    I realized that to capture the genuine impact of our advertising efforts, particularly the impulse-buying behavior, we needed a shorter attribution window. So, in January, we transitioned the account from a 30-day to a 7-day click window. Here’s what we found.

    Our main focus was on Meta Ads, the primary recipient of the marketing budget. With both Meta and Google Ads reporting high sales due to the initial 30-day window, it was challenging to assess where advertising dollars were best spent.

    Before making any changes, I delved into the conversion path data, which revealed that customers converted on average in just 2.2 days. A sizable portion of these conversions occurred within a single day.

    Rather than abruptly altering our primary conversion action, we decided to carefully test by setting up a new 7-day conversion as a secondary action. This cautious approach helped us monitor any disruptions.

    The process went as follows:

    ```json
{
  "alt": "Bar chart showing purchase conversions by day, with highest on day less than one.",
  "caption": "Purchase conversions peak sharply on the first day, highlighting immediate customer action.",
  "description": "This bar chart illustrates purchase conversions over a 12-day period, with the highest conversions occurring on 'less than 1 day' after purchase intent. This initial peak shows over 80,000 conversions, while subsequent days show a steep decline, with days 1 to 12 having significantly lower conversions. The x-axis represents days to conversion and the y-axis denotes the number of conversions, providing a clear view of customer behavior patterns."
}
```
    • Step 1: We duplicated the primary purchase conversion, setting a 7-day click window as a secondary conversion action.
    • Step 2: We monitored performance over two weeks.
    • Step 3: We transitioned to primary optimization on January 12, 2026.

    Let’s see what happened after we made this change. By comparing data 30 days post-switch to a previous period, we observed changes and improvements.

    Results:

    • Spend decreased by 6.3%.
    • Conversions rose by 42.9%.
    • Conversion value increased by 52.1%.
    • ROAS jumped by 62.3%.

    The signs were promising, but I still wanted to check the actual business impact. Examining Shopify sales data, I found a 20% increase in total sales and a 30% increase in net profit.

    Our Marketing Mix Modeling (MMM) data revealed:

    • Google’s incremental ROAS improved by 10% to 1.82.
    • Meta’s incremental ROAS fell by 25% to 0.59.

    Clearly, the 7-day window gave us better clarity on channel contribution. But I must admit, we were also refining campaigns, which contributed to these outcomes. Still, performance remained stable, and transparency increased.

    With Google’s window shortened, we successfully limited overlap with Meta, which had previously been capturing credits for conversions likely influenced by other channels. It’s now easier to gauge the incremental impact of our efforts.

    ```json
{
  "alt": "The CapmatchOne logo with a gradient circle and bold text.",
  "caption": "Discover innovation with the CapmatchOne logo, featuring sleek typography and a modern gradient circle.",
  "description": "The CapmatchOne logo features bold, modern typography coupled with a gradient circle, symbolizing connection and innovation. The sleek design conveys a sense of progress and creativity. This image can be used for branding or promotional purposes, appealing to audiences interested in innovative solutions and forward-thinking designs."
}
```

    The quicker attribution provided faster insights into campaign performance, tightening feedback loops for optimization. Here’s how we benefited:

    • Reduced delayed attribution.
    • Enhanced feedback loops for optimization.
    • Improved performance diagnostics.

    This shift also affected Smart Bidding by providing fresher signals for bid strategies, enabling the system to respond quicker to changes like bid adjustments and budget shifts.

    I found that a cleaner attribution structure built stronger confidence for campaign optimizations, helping my client make smarter investments.

    Ultimately, while not a miracle solution, this adjusted approach significantly complemented other campaign enhancements, improving overall strategy.

    Do consider potential trade-offs if you plan to shorten your attribution window like this. Be prepared for an initial dip in reported conversions and a recalibrating phase for smart bidding. Most importantly, ensure this approach aligns with your sales cycle.

    In summary, the core objective wasn’t merely updating platform metrics. It was about improving insights and facilitating well-informed decisions. The right solution depends on the congruence between your attribution settings and actual buying behaviors.


    Inspired by this post on Search Engine Land.


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  • Revamp Your Testing Strategy: Avoid Costly Mistakes in 2026

    Revamp Your Testing Strategy: Avoid Costly Mistakes in 2026

    If I hear “always be testing” one more time, I might just scream. It was excellent advice back in 2016, but in 2026, it’s more like watching your budget go up in flames.

    Back then, with flexible budgets and forgiving platforms, chaotic testing methods were all the rage. Launching multiple audience tests at once or swapping several creative variables was the norm. Why not, right?

    But times have changed. We’re dealing with tighter budgets, longer learning phases, and fragmented signals. Now, a poorly structured test can distort results for weeks, compounding your performance issues rapidly.

    Modern experimentation has become both costly and risky. Instead of sticking with outdated practices, why not leverage agentic AI? I’m not talking about using AI as a quick fix to churn out more ad variants—that’s just burning budgets faster.

    Instead, it’s time to employ agentic AI to craft smarter experimentation systems.

    The Real Cost of Unstructured Testing

    In the “always be testing” era, launching random tests was as common as Oprah giving away cars or Taylor Swift packing stadiums. We’d throw ideas around at the start of the week, hoping for a pleasant surprise by Friday.

    These days, the costs are astronomical. Algorithms thrive on stability. Research shows that ad sets stuck in learning phases have CPAs 20-40% higher than stable ones.

    Every significant change in creative, audience, or budget risks resetting this learning. Run overlapping tests that each cause resets? You’re essentially imposing a volatility tax on all your media spend.

    Then there’s the issue of waste. Most A/B tests yield no significant lift. If you’re not discerning about what tests to run, you’re wasting resources to confirm that most ideas are inconsequential. Without proper guardrails, “always be testing” spirals into “always be destabilizing.”

    From Random Tests to a Real Experimentation Engine

    We’re shifting focus now. It’s no longer about “AI, write me 10 new headlines.” It’s about “AI, craft the most efficient next experiment within our budget, considering our risk tolerance and current learning status.”

    This transition from just generating creatives to configuring a comprehensive experimentation framework is where the real advantage lies.

    Here’s a seven-step guide to evolve testing from a mere habit to a strategic powerhouse.

    Step 1: Set Hard Guardrails (Humans Draw the Lines)

    Before integrating AI into your testing strategy, establish constraints. Without these, AI has no context. With them, it becomes a disciplined strategic ally.

    Define and document five key constraints.

    • Budget allocation: Dedicate a fixed percentage, like 10%, exclusively for testing.
    • Maximum volatility: “Ensure no test increases CPA by more than 15% over five days.”
    • Learning phase sensitivity: Tailor reset criteria for each platform.
    • Leading indicators: Use early signals (CTR, engagement drops) to terminate underperforming tests before they impact significantly.
    • Brand risk: Define untested areas (like avoiding discount-heavy strategies in upscale markets).

    Maintain these in a single document (e.g., experimentation-guardrails.md) to guide AI in ensuring test viability. Your AI agent must refer to this before suggesting any tests.

    Step 2: Let AI Audit Your Experiment History

    Most teams have amassed data over time but don’t utilize it effectively. Feed your last six months of test results into an AI system to analyze changes, duration, performance shifts, statistical relevance, and platform resets.

    Have it spot patterns like:

    • Over-tested variables: Testing CTA buttons multiple times with negligible results? That’s not a useful variable.
    • False failures: Tests often fail due to lack of statistical significance. AI can verify statistical power and highlight inconclusive outcomes.
    • Volatility patterns: Your highest CPA weeks might not be market shifts or poor ads but the result of multiple simultaneous tests.

    This is the essence of AI as your analytical partner.

    Step 3: Write Real Hypotheses

    Instead of jumping straight from concept to launch, let AI enforce hypothesis discipline.

    • Weak: “Let’s test a new headline.”
    • Strong: “Emphasizing ‘faster time-to-value’ over ‘ease of use’ could boost demo requests by 10-15% among mid-market companies, as analysis shows speed is crucial for them.”

    Documenting hypotheses builds institutional knowledge. Later, when someone suggests retesting “speed messaging,” you’ll know past results and reasoning.

    Step 4: Risk-Score Every Proposed Test

    Budget and algorithm stability are limited. Your AI agent should evaluate proposed tests on five criteria, assigning a risk score.

    • Budget impact (e.g., less than 5% vs over 15%).
    • Algorithm disruption level (minor update vs new campaign).
    • Audience overlap.
    • Brand sensitivity.
    • Learning value.

    High risk with low learning potential? Drop it. Low risk with high potential? Proceed.

    Example: Testing a new positioning statement is risky in a paid campaign. Your AI might suggest verifying it with organic LinkedIn posts first. Low risk. High insight.

    Step 5: Pre-test With Synthetic Audiences

    This under-utilized AI application can simulate how varied personas might respond to messaging, saving real-world testing costs.

    Research by Stanford and Google DeepMind has shown digital agents match human survey responses with 85% accuracy and mimic social behavior with 98% accuracy.

    While not a replacement for actual data, synthetic audiences serve as a cost-effective early test.

    Define demographic archetypes such as the Skeptical CMO, Growth-focused VP, and margin-driven CFO, and test their responses to messaging.

    For example, you may find that phrases like “All-in-One” are seen negatively, prompting a shift to terms like ‘Integrated’.

    Step 6: Sequence Tests, Don’t Stack Them

    Tweaking audience, creative, and landing pages simultaneously teaches you nothing. Your AI should monitor campaigns to avoid conflicts and recommend proper test sequencing.

    A sensible approach is to:

    • Weeks 1-2: Audience testing.
    • Weeks 3-4: Creative tests with the proven audience.

    When unavoidable, establish clear control groups to maintain data integrity.

    Step 7: Build A Living Knowledge Base

    Treating tests as one-off experiments overlooks their value. Have AI summarize each test by assessing:

    • Success reasons.
    • The audience impacted.
    • Lift durability.
    • Variable interaction.

    Over time, this database can provide unmatched advantages. Anyone can access the same audience targeting, but few have a database of 100+ customer insights.

    The Bigger Shift: From Activity to Architecture

    “Always be testing” may have worked in a growth-centric era, but in 2026, success comes from “always be compounding intelligence.”

    Instead of maximizing tests, build a competitive edge through structured, risk-aware experiments that maintain algorithm stability and tie directly to revenue.

    When asked why you’re not testing more, show your testing architecture and confidently say, “We’re building an intelligence engine, not just running experiments.”

    Because intelligence compounds.


    Inspired by this post on Search Engine Land.


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  • Markdown vs. HTML: Impact on AI Bot Traffic Explored

    Markdown vs. HTML: Impact on AI Bot Traffic Explored

    I embarked on a journey to uncover whether AI crawlers favored Markdown over HTML. By conducting a controlled experiment, I aimed to see if serving content in Markdown format would result in increased bot traffic. After analyzing data from 381 pages over the span of three weeks, I’m eager to share what I discovered.

    The results of this experiment could provide valuable insights for those interested in enhancing the visibility of their content through strategic formatting. Stay tuned as I reveal the intriguing findings.


    Inspired by this post on Try Profound Blog.


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  • Unlock Quick Wins with Google Ads Recommended Experiments

    Unlock Quick Wins with Google Ads Recommended Experiments

    I’ve discovered that Google Ads now offers ready-to-run experiments directly within the Experiments page, making it easier for me to test optimizations quickly without a complicated setup.

    These suggested experiments are based on my account’s setup and performance data, helping me uncover new ways to enhance results.

    How it works: The platform provides suggestions for testing various bidding strategies, creative variations, and new campaign features, all accessible right in the Experiments dashboard.

    Every recommendation comes with a pre-configured setup, so I can either launch them immediately or adjust the settings to better fit my needs. These suggestions are conveniently displayed alongside the standard Create Experiment option, streamlining the process.

    Why I care: Google’s effort to simplify experiment setups significantly decreases the time and effort I need to put into testing. It allows me to act swiftly on optimization ideas and maintain a consistent flow of improvements. However, I still review each test configuration to ensure it aligns with my campaign goals and doesn’t lead to unnecessary resource expenditure.

    ```json
{
  "alt": "Google Ads dashboard highlighting recommended experiments and campaign options.",
  "caption": "Explore Google Ads' recommended experiments to enhance your campaign performance. Navigate through various options to optimize your ad efforts.",
  "description": "This image displays a section of the Google Ads dashboard, focusing on recommended experiments. The screenshot shows a highlighted experiment option encouraging users to 'Turn on Final URL expansion' for improved campaign performance. The sidebar features navigation options including campaigns, ad groups, and assets. A button to 'Create Experiment' is prominently displayed, inviting users to engage with the suggested optimization. Keywords: Google Ads, dashboard, recommended experiments, campaign optimization."
}
```

    Zoom in: For instance, I might see a prompt suggesting I enable final URL expansion to boost campaign performance. These recommendations appear as pop-ups inside the Experiments interface, guiding my decisions with relevant insights.

    The big picture: Google is embedding more automated guidance into Ads workflows, nudging me towards continuous testing and pursuing data-driven optimizations.

    First seen: This update was first spotted by PPC News Feed owner, Hana Kobzová, shedding light on these helpful enhancements.


    Inspired by this post on Search Engine Land.


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  • Unlock Creative Success with Performance Max A/B Testing

    Unlock Creative Success with Performance Max A/B Testing

    I recently discovered that Performance Max now includes built-in A/B testing for creative assets. This feature offers advertisers a straightforward way to measure and enhance their advertising strategies.

    Google is introducing a beta feature that allows me and other advertisers to conduct structured A/B tests on creative assets within a single Performance Max asset group. This setup enables me to split traffic between two sets of assets and evaluate performance through a controlled experiment.

    Why it matters to me. In the past, creative testing within Performance Max was often guesswork. With Google’s new native A/B asset experiments, I can now perform controlled tests directly within PMax without needing to launch separate campaigns.

    How it works for me. I select one Performance Max campaign and asset group, then define a control asset set using my existing creatives and a treatment set with new alternatives. Shared assets can be utilized across both versions. After setting a desired traffic split, like 50/50, the experiment runs for several weeks, allowing me to apply the winning assets based on actual performance data.

    Why this is beneficial for me. Conducting tests within the same asset group isolates the impact of the creatives I’ve designed, minimizing interference from changes in campaign structure. This controlled split allows me to obtain clearer reporting, helping my team make data-driven decisions based on solid performance metrics rather than assumptions.

    ```json
{
  "alt": "Google Ads interface showing options to choose experiment type and test variables.",
  "caption": "Exploring Google Ads: A look at the platform's options for testing and optimizing ad campaigns, featuring performance and asset management tools.",
  "description": "The image showcases the Google Ads interface where users can select an experiment type to test different assets, goals, and campaign types. Highlighted sections include options to test campaign features such as assets, campaign types, and custom variables. The interface also allows selection between different campaign types like App, Demand Gen, and Performance Max. Notable is the emphasis on creating and testing creative assets like text, images, and videos to optimize ad performance. Keywords: Google Ads, experiment type, campaign testing, asset management."
}
```

    What I’ve learned so far. Early testing indicates that shorter experiments—especially those under three weeks—can yield unstable results, particularly in accounts with lower volume. I’ve found that extending the test duration and avoiding simultaneous campaign changes significantly enhances reliability.

    My takeaway. Performance Max is evolving into a more testable platform. I now have the ability to validate creative decisions using built-in experiments, reducing reliance on trial and error approaches.

    Source of insight. A Google Ads expert noticed the update and shared insights on LinkedIn.


    Inspired by this post on Search Engine Land.


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  • Google’s New Search Ad Feature: External Endorsements Tested

    Google’s New Search Ad Feature: External Endorsements Tested

    I recently discovered that Google’s testing a fascinating new feature in Search ads. They’re incorporating third-party endorsements, complete with publisher logos and quotes, to offer a layer of external validation for paid results.

    This experiment places brief endorsements from external publishers right under the ad description, showcasing the third party’s name, logo, and favicon.

    What’s showing up. I first spotted this test when Sarah Blocksidge, Marketing Director at Sixth City Marketing, shared a screenshot on Mastodon. In that example, a Search ad included the line “Best for Frequent Travelers,” attributed to PCMag, along with the publication’s favicon.

    The endorsement is positioned directly beneath the ad copy, making it visually distinct from the standard text written by advertisers.

    Why we care. If this feature is expanded, it could transform Search ads to mirror product reviews more closely, potentially granting advertisers with substantial third-party validation an edge in highly competitive auctions.

    What Google says. A spokesperson from Google Ads confirmed that this is a “small experiment” being conducted:

    ```json
{
  "alt": "1Password sponsored search result with links to sign up and explore services.",
  "caption": "Explore the features of 1Password through their sponsored search result, including sign-up and business solutions.",
  "description": "This image displays a sponsored search result for 1Password, an online security and password management platform. It features the 1Password website link, a brief description, and options to sign up or utilize various services such as 1Password for Business and Generate Secure Passwords. The ad highlights their security management offerings and mentions features like a free trial and business trust. Keywords include password management, security, 1Password, and business solutions."
}
```
    • “This is a small experiment we are currently running that explores placing third-party endorsement content on Search ads.”

    However, Google hasn’t revealed any specific details regarding eligibility, the content sourcing process, or how endorsements are chosen.

    What we don’t know yet. It’s not yet clear if advertisers will be able to opt into this feature, request specific endorsements, or influence which third-party sources are displayed. Google hasn’t clarified whether this test is linked to existing review extensions, publisher partnerships, or other trust and safety initiatives.

    What to watch. Should Google decide to broaden this experiment, the prominence of third-party credibility could significantly impact ad performance, shifting focus from advertiser claims to external validation at the search stage.

    For the moment, this intriguing test is limited, but it offers a glimpse into how Google might continue to merge ads, trust signals, and editorial-style context within search results.

    Dig Deeper. Screenshot shared on Mastodon.


    Inspired by this post on Search Engine Land.


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  • Discover Google Ads’ Game-Changing Experiment Center

    Discover Google Ads’ Game-Changing Experiment Center

    I’m thrilled to share that Google Ads has launched a transformative new Experiment Center, providing us advertisers with a centralized platform to test strategies and analyze their impact before scaling them up.

    What’s new. With Google’s latest update, we now have access to a comprehensive help page introducing the Experiment Center. This innovative dashboard merges traditional Experiments and Lift Studies, allowing us to handle tests regarding bidding, targeting, and creatives. Simultaneously, we can measure brand, search, or conversion lift, all in one place.

    Why it matters. Previously, experimenting within Google Ads was a bit scattered. Different tests lived in separate areas, making it cumbersome to streamline our strategies. A unified hub simplifies this process drastically, reducing complexity and enabling us to confidently validate our strategies before increasing our budgets.

    How it works: The new layout is a breath of fresh air, enhancing setup and reporting efficiency. Now, key insights from our tests are displayed together, rather than being spread out across various tools. This consolidation allows us to quickly compare outcomes, grasp the impacts, and take action faster.

    Between the lines. Google is clearly investing heavily in experimentation, and the Experiment Center is the latest in a line of updates. With enhancements like expanded A/B testing in Shopping and Performance Max campaigns, alongside the new Campaign Mix Experiments beta, this platform equips us with the tools needed to adapt to an automated landscape, ensuring our strategies remain impactful and clear.

    Bottom line: If you haven’t already, it’s time to dive into the Experiment Center. Formalize your testing around bidding, targeting, and creative strategies, leveraging lift studies and experiments to validate your approaches before rolling them out on a larger scale.


    Inspired by this post on Search Engine Land.


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  • Unlock Ad Performance with Google’s Mix Experiments Beta

    Unlock Ad Performance with Google’s Mix Experiments Beta

    I’ve discovered that Google is introducing a fascinating new tool called Campaign Mix Experiments (beta). This innovative framework allows me and other advertisers to experiment across various campaign types, budgets, and settings all within a single, unified setup.

    How it works:

    As an advertiser, I can create up to five experiment arms, each with its own unique combination of campaigns. This means I can include the same campaign in multiple arms and distribute traffic among them.

    Google’s mix experiments support a wide range of campaigns, including Search, Performance Max, Shopping, Demand Gen, Video, and App campaigns, though it does exclude Hotels.

    I’m able to customize traffic splits starting at a minimum of 1%, and the results are adjusted to the smallest split for a fair comparison — ensuring accuracy in our findings.

    What I can test:

    The beta provides an exciting opportunity to explore and test budget allocation across different campaign types. I can also assess account structures, varying between consolidation and fragmentation.

    It allows me to examine differing bidding strategies, targeting options, and feature adoptions, alongside studying cross-channel performance interactions, beyond just individual campaign impacts.

    Why I care. With this new tool, I can go beyond individual campaign testing, gaining insights into how various campaign types interact and identifying which combinations yield the most substantial business outcomes.

    Reporting details: I can monitor results through the Experiment summary and campaign-level reporting, selecting from confidence intervals like 95%, 80%, or 70%, and focus on key metrics such as ROAS, CPA, conversions, or conversion value.

    Best practices:

    I make sure to keep the experiment arms similar, only altering one variable at a time. I align the total budgets across these arms unless budget allocation itself is the variable being tested.

    It’s advised to avoid shared budgets and significant changes while the experiment is underway, and to run these tests for at least six to eight weeks to ensure the results are statistically reliable.

    Between the lines: Google is shifting the focus from a single-campaign victory to understanding how the right mix of efforts can lead to success, especially as automation reshapes the landscape.

    Bottom line: By utilizing campaign mix experiments, I gain a realistic view of how different campaign types and financial plans work collaboratively. This empowers me to make informed decisions on where my spending truly adds value.


    Inspired by this post on Search Engine Land.


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  • Unlocking Success: A/B Testing for Google Shopping Ads

    Unlocking Success: A/B Testing for Google Shopping Ads

    I recently learned that Google Ads is running a fascinating experiment, allowing select advertisers to A/B test different product titles and images in Shopping Ads. This feature, known as “product data experiments,” provides insightful results within three to four weeks.

    Who gets it? At the moment, only a small group of merchants have access to this test, according to Google Ads Liaison Ginny Marvin. However, it seems broader availability is on the horizon.

    Why we care. The impact of product titles and images on Shopping ad performance is significant. Yet, traditionally, advertisers face challenges in testing changes without affecting live results. This new feature promises a much-needed opportunity for experimentation within product feeds.

    ```json
{
  "alt": "LinkedIn exchange between two users discussing a product teased at GML.",
  "caption": "An engaging LinkedIn conversation about a product teased at GML, hinting at limited testing and future availability.",
  "description": "A LinkedIn conversation between two users discussing a product that was teased at GML the previous year. The conversation highlights limited current testing among select merchants and anticipates broader availability. One user humorously asks for influence to move higher on an internal list. Keywords: LinkedIn, conversation, GML, product tease, merchants, availability, testing."
}
```

    What it does. By comparing variations of product titles and images, advertisers can identify which combinations lead to higher sales, all without committing changes to their entire feed.

    Context. Previously teased at Google Marketing Live, this feature builds on earlier tests allowing A/B experiments in some Performance Max campaigns, suggesting a larger trend towards increased experimentation across automated formats.

    ```json
{
  "alt": "Google Merchant Center Experiments tab displaying a product data experimentation feature.",
  "caption": "Explore new heights in sales with Google Merchant Center's A/B testing for product data. Boost your campaign performance effectively!",
  "description": "The image shows the 'Experiments' tab in Google Merchant Center Next's interface. It highlights a feature allowing A/B testing for product titles and images, aimed at improving sales performance. A promotional message encourages merchants to increase sales through data experiments, with results expected in 3 to 4 weeks. A 'Find out more' button is visible, inviting further exploration."
}
```

    Big picture. With Google Ads increasingly embracing automation, tools for controlled testing like this become essential. They give advertisers the insight needed to understand performance drivers, particularly in Shopping and feed-based campaigns.

    Credit. I discovered this news through a screenshot shared by Duane Brown, the founder of Take Some Risks, on LinkedIn.

    What to watch. Should this feature be widely rolled out, product data experiments could become a key optimization tool for Shopping Ads and fulfill a long-standing request from advertisers focused on feed performance.


    Inspired by this post on Search Engine Land.


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  • Unlock Performance Max Potential with A/B Asset Testing

    Unlock Performance Max Potential with A/B Asset Testing

    Google has rolled out a new Beta feature that allows us, Performance Max advertisers, to A/B test asset sets. This expansion takes last year’s retail experiment to an exciting new level, now available for all campaigns.

    With this update, I can compare two sets of assets while keeping the ‘common assets’ steady across both versions. By accessing the Experiments page under the Assets sub-menu, I can determine which creative combinations yield the best results.

    I saw a similar experiment rolled out for retail campaigns last year, and I’m thrilled to see it expand to all Performance Max campaigns.

    Why it matters to me. Performance Max campaigns rely heavily on automation, often making it difficult for me to test specific creative assets. This new capability gives us more control over asset-level performance without compromising the integrity of the entire campaign.

    The big picture. From my perspective, tests must run for at least four weeks to consider the learning phase of P-Max and ad delivery stabilization. While the results aren’t immediate, they’ll allow me to make more informed choices about which images, headlines, and videos drive engagement.

    Between the lines. Asset-level A/B testing could be a pivotal factor in enhancing my Performance Max ROI, particularly when managing diverse creative and asset formats.

    First seen. This update caught my attention when web marketer Dario Zannoni highlighted it on LinkedIn.

    The bottom line. Although still in Beta, this experiment type offers a new degree of transparency and control over automated campaigns, potentially transforming how I approach asset strategies in Performance Max.

    Dig deeper. For more insights on this feature, check out About Performance Max optimization experiments: A/B testing assets (Beta).


    Inspired by this post on Search Engine Land.


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