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.
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.
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.
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.
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.
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:
“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.
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.
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.
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.
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.
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.
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.
As I type my search query in Google, I’ve noticed an interesting change. The usual AI Mode button is sometimes replaced by a striking blue ‘Send’ button right in the search box.
Google is currently testing this new feature. Traditionally, the AI Mode button appears on the right side of the search box, but it seems this might be changing. As soon as I start typing, the ‘Send’ button takes its place.
What it looks like. Recently, I came across a post by Shameem Adhikarath, who shared a video of this new feature on X.
From the video, it’s clear that when I start typing my query, the AI Mode, Lens, and Microphone buttons vanish, leaving behind this new blue ‘Send’ button.
Interestingly, the familiar plus sign remains unaffected, sticking around as always.
Why this matters. While this is currently just a test, it could have significant implications. If implemented, it might mean fewer users are directed to Google’s AI Mode, prompting more straightforward searches.
For those of us who rely on AI Mode, this change could make accessing it a bit more challenging, urging us to adjust how we initiate searches.
I’ve recently been intrigued by how Bayesian testing allows Google to measure incrementality with just $5,000. It’s fascinating how this modern approach opens up new possibilities for advertisers.
Through these tests, advertisers like me can now explore lift measurement options without needing big enterprise budgets, as reported by Search Engine Land.
This change immediately raises an important question: How exactly does Google achieve accurate measurements of incrementality with significantly less data?
Previously, achieving reliable lift measurements demanded substantial budgets, lengthy test timelines, and the patience to handle inconclusive outcomes.
Given this context, Google’s claim of delivering precise results with merely $5,000 seems almost too good to be true. But it isn’t just marketing fluff; it’s a utilization of innovative mathematical models.
This transformation is powered by a testing methodology that emphasizes probability and learning, rather than aiming for absolute certainty.
Understanding this new approach is crucial for accurately interpreting these incremental results and for enhancing our PPC strategies.
Before we delve deeper, let’s quickly revisit some key Bayesian terms that marketers often encounter.
Glossary: Bayesian terms for search marketers
Prior: What we assume before the test begins.
Posterior: Updated belief after analyzing the data.
Credible interval: It shows the likely range of the result.
P-value: Frequency-based probability indication.
Traditional A/B testing, which most PPC advertisers know even if unknowingly, follows frequentist statistics.
These conventional A/B tests use metrics like p-values and fixed sample sizes to evaluate if changes reach statistical significance, often restricting smaller-budget tests.
In contrast, Bayesian testing veers away from this binary framework, instead asking, “Given all we know, how likely is this result to be true?”
Let’s see how Google legitimately manages to make $5,000 tests work effectively by embracing priors combined with its extensive data resources.
Google’s strategy rests on informed priors, hierarchically modeling, and probability assessments based on extensive campaign history.
This enables a competent analysis even with modest budgets, thus transforming limited data insights into actionable intelligence without averaging noise across campaigns.
Bayesian methods provide flexibility and adapt as more data is gathered, making them ideal for dynamic marketing environments, unlike their frequentist counterparts.
As more data rolls in, Bayesian tests evolve, relying increasingly on real results rather than priors, ensuring refined decision-making from smaller experiments to large-scale trials.
Using Bayesian inference, Google allows advertisers to derive directional insights without needing enormous budgets, effectively bridging gaps where frequentist testing falls short.
Takeaways for advertisers interested in Bayesian testing include understanding the diminishing role of priors as data accumulates, needing a discerning approach to interpreting outcomes.
To conclude, this mathematical ingenuity leverages Google’s vast data resources, offering a practical perspective over traditional methods, empowering PPC campaigns with more cerebral decision-making.
