I’m thrilled to share that Google Ad Grants has introduced a new feature allowing nonprofits to focus on increasing actual foot traffic to their locations. By setting ‘shop visits’ as a goal, we can drive more visitors through our doors and witness our campaigns translate into real-world impact.
Driving the news. Before this update, attempting to set shop visits as a goal in Ad Grants would result in an error. It’s exciting to see this barrier removed, enabling eligible accounts to now include store visit conversions in their primary goal settings.
The update empowers us, as nonprofits and local organizations, to better use bidding and optimization strategies that align with increased in-person visits — a valuable enhancement for appearing in Maps and location-based searches.
Why we care. For organizations like museums, community centers, and places of worship, having a tool that marries digital engagement with physical impact is invaluable. Optimizing for shop visits ensures our ad success is directly linked to actual visitor numbers.
Between the lines. Google’s emphasis on local search intent and Maps-based discovery makes this feature even more critical for nonprofits. It shifts our focus from simply generating clicks to driving actionable visits, which can significantly enhance local community engagement.
What to do. If you’re using Ad Grants, I recommend reviewing your account-level goals to ensure shop visits are activated where applicable. This focus on foot traffic can significantly uplift local impact, especially for organizations heavily reliant on face-to-face interactions.
Spotted by: This advancement was highlighted by Google Ads Expert Jason King, who shared it on LinkedIn.
As an advertiser reaching out to Google Ads support, I’ve discovered there’s a new step involved in the process. Now, I must authorize any support-led changes to my account while still being accountable for the outcomes.
When I contact Google Ads support, I encounter a beta AI chat first. If I choose to fill out a support form instead, I need to check an ‘Authorization’ box. This allows a Google Ads specialist to access my account and deal directly with the issues by making necessary changes.
The fine print makes it clear that while Google may assist, they don’t guarantee any specific results. Any alterations are at my own risk, meaning I am fully responsible for any impact on my campaigns’ performance and costs.
Why do we care about this change? The new requirement places more responsibility on us, the advertisers. Even in an era of automation and AI, if any changes are applied by support, I still bear the risks associated with campaign performance and spending adjustments.
This situation presents a dilemma for people like me, as it offers a trade-off between speed and control. Allowing access can quicken the troubleshooting process, but it also means potential changes at the account level that might affect live campaigns without a guarantee of better results.
The bottom line? To obtain support, I might now have to temporarily hand over control, but I still need to remain accountable for my account’s future performance.
This change was first observed by PPC specialist Arpan Banerjee, who shared the message on LinkedIn.
I’ve discovered that shifting toward Demand Gen in Google Ads transforms the focus from simple keyword targeting to more visually-driven advertising. Relying on outdated methods not only wastes money but also limits the potential of what Demand Gen can achieve. To thrive, I need to see things like a social advertiser rather than just a search advertiser.
At SMX Next, Jack Hepp from Industrious Marketing shared valuable insights on why many businesses, particularly in the B2B sector and lead generation, find demand gen campaigns challenging, while also providing strategies that are applicable to ecommerce.
In transitioning to Demand Gen, I see Google’s move from intent-driven to discovery-focused campaigns. This involves reaching users casually browsing on platforms like YouTube, Gmail, or Discovery feeds rather than those actively searching for my offerings. This approach means that visual assets now play the role that keywords once did.
Aligning campaign strategies to fit this model requires abandoning old tactics. Here’s what I need to avoid:
Expecting bottom-of-funnel CPAs from mid-funnel traffic.
Employing imprecise, broad targeting.
Running dull, uninspired creative.
Lack of optimization know-how without negative keywords.
Seeing success demands that I adopt a mindset similar to social advertising.
Demand Gen structure consists of campaigns governed by broad parameters (like bidding strategies and conversion goals) and ad groups that dictate audience specifics. Each ad group learns independently, which allows for finely tuned audience segmentation.
When crafting interruption-based creative, my goal is to catch attention in the first 3-4 seconds. It’s about highlighting a specific pain point and offering a solution in a way that turns casual browsers into engaged prospects.
Ensuring my visual content aligns with the customer journey is crucial:
Cold audiences benefit from educational material.
Warm audiences engage with case studies and webinars.
Hot audiences are ready for demos or purchase offers.
When my creative addresses specific problems with bold visuals and compelling headlines, the engagement naturally increases. For instance, targeting specific challenges like cybersecurity for small businesses makes my ads stand out.
Bidding in Demand Gen focuses on campaign-specific goals. To gather the necessary data, I aim for significant monthly conversions and budget accordingly to enable optimal performance.
Even small budgets can work if strategically planned. By directing efforts at mid-funnel activities, I can achieve the necessary conversions for meaningful insights.
In building the right audiences, it’s about balance. I avoid extremes of too broad or too narrow segments and focus on custom segments complemented by lookalike data, optimizing as success dictates.
Aligning the messaging of my creative with the buyer’s stage ensures Google effectively targets potential customers. This strategy steering focuses more on creative, audience, and the offer itself.
Using targeted exclusions efficiently helps me concentrate effort on engaging users without overly restricting potential reach. It’s a strategic rather than blanket approach.
Optimization in Demand Gen focuses on creatively testing different formats and refining audience targeting. I continually test offers to match audience readiness and optimize post-click experiences to enhance campaign effectiveness.
In a real-world application, a telecommunications company achieved impressive outcomes by clearly defining its offer, targeting, and creative messages. The results highlighted the critical importance of aligning these elements for Demand Gen success.
Here are the key takeaways for any campaign I plan next:
Align creative content with my target customer’s stage in their journey.
Identify and target audiences at appropriate points in their journey.
Continuously test and refine both creative elements and offers to amplify impact.
I recently discovered that Google is changing how it attributes app campaign conversions. Instead of relying on the date when someone clicks on an ad, Google now ties the conversion to the actual install date of the app.
What’s Changing: Previously, Google linked conversions to the ad interaction date. Now, they’ll match the day of the app installation, aligning more closely with Mobile Measurement Partners (MMPs) like AppsFlyer and Adjust.
Why This Helps:
– This change reduces discrepancies between Google Ads and MMP dashboards, making life easier for mobile marketers who often deal with mismatched data.
– With Google’s old 30-day attribution window, many conversions were reported too late, hindering Smart Bidding’s access to the timely signals necessary for effective learning.
– By using the install date for attribution, Google’s algorithms will receive fresher, more accurate data, which could speed up optimization cycles and stabilize performance.
Why We Care: While it might seem technical, this change significantly affects how Google’s machine learning optimizes campaigns. The previous 30-day gap between ad clicks and conversion credit was a bottleneck. Now, Google’s machine learning gets the conversion data just when it needs it—right with the app install.
This shift should lead to smarter bidding and faster campaign optimization, helping to resolve the frustrating discrepancies between Google Ads and MMP reports. If you’ve ever been puzzled by inconsistencies between Google and platforms like AppsFlyer or Adjust, this update directly addresses that problem.
Between the Lines: Most advertisers don’t adjust their attribution window settings, leaving Google’s default 30-day window as is. Unfortunately, this was delaying crucial conversion signals that machine learning needs for improved bidding.
The Bottom Line: This seemingly minor tweak in attribution logic could have a significant impact on app campaign performance. I encourage mobile advertisers to monitor their data in the coming weeks for any shifts in conversion reports and optimization behaviors.
First Spotted: This update was first noticed by David Vargas, who shared a message about it on LinkedIn.
I’ve recently discovered an intriguing feature in Google Ads that provides advertisers, like myself, with enhanced visibility into how our landing page images can be automatically converted into ad creatives in Performance Max (PMax) campaigns. It’s fascinating to see the potential of these visuals beyond their traditional use.
Imagine having the ability to transform your website’s visuals into dynamic ads. By opting into this feature, Google can extract images from your landing pages and present them as ads. As I set up my campaigns, I can preview these automated creations before they go live, which grants me significant control over my advertising strategy.
Why this matters to us. With PMax, our website isn’t just a storefront but a vital component of our ad strategy. Any image—from banners to product visuals—can appear across platforms like Search, Display, YouTube, or Discover. This update offers a clear understanding of how our landing page images could become part of these campaigns, helping us visualize our potential reach.
I no longer have to speculate how Google might utilize my site’s visuals. Now, I can foresee, scrutinize, and regulate what content is utilized in my ads. This feature enables me to refine my landing pages and align them with my campaigns, minimizing surprises.
Between the lines: While automation is growing, so is the need for careful creative oversight. This update serves as a crucial tool for advertisers, ensuring we’re informed about what content goes live before it happens.
Bottom line: Our websites have transcended their roles as mere landing pages; they’re now integral to our ad engines, driving our marketing efforts forward.
First seen. Digital Marketer Thomas Eccel was among the first to highlight this development on LinkedIn, showcasing a practical example.
I’ve just discovered an incredibly beneficial update from Google Ads that I’m excited to share. Now, we can see precisely where our Performance Max campaigns are running through the “Where ads showed” report. This change opens up a new world of clarity and optimization possibilities that were previously inaccessible.
What’s New? This update allows me to see exactly where my PMax ads are appearing across Google’s network, including search partners, display, and other placements. By tracking impressions by placement type and network, I can now understand the detailed performance of my campaigns like never before.
Why It Matters to Me This is a game-changer for anyone managing PMax campaigns. It brings much-needed visibility into where ads are appearing, including Google Search Partners and beyond. With access to placement, type, and impression data, I can optimize budgets and make informed decisions rather than relying on guesswork. It transforms previously opaque reporting into actionable insights.
User Reaction Digital marketer Thomas Eccel shared his experience on LinkedIn, expressing that the report was once a blank page but now displays real data.
“I finally see where and how PMax is being displayed,” he wrote, highlighting the significance of this update for clarity.
He also noted how Google Search Partners are now no longer a “blurry grey zone.”
The Bottom Line For me, and many other marketers, this update offers actionable visibility into PMax campaigns, helping us understand placement performance, optimize spend, and pinpoint which networks are yielding results — all within one comprehensive report.
I’ve recently discovered that Google Ads has introduced an impressive new Results tab within their Recommendations section. It’s designed to help advertisers like you and me see the actual performance impact of applied suggestions, especially when it comes to bid and budget adjustments.
After applying any bid or budget recommendation, Google analyzes the campaign’s performance one week later. It then compares the results against a baseline estimate, showing us the incremental lift such as additional conversions from raising a budget or tweaking targets. It’s a fantastic update for those of us wanting concrete data on recommendation outcomes.
Wondering where to find this information? You can spot the impact reporting right in your account’s Recommendations area. There’s a handy summary callout with recent results on the main page, plus a dedicated Results tab providing a detailed breakdown categorized by Budget and Target recommendations, with helpful filters.
Why is this an important update? As an advertiser, I’m thrilled because this lets us see whether Google’s automated recommendations truly deliver incremental results, not just predicted boosts. This is crucial for assessing the real business value of these platform suggestions.
But what should we expect going forward? The Results tab reports a seven-day rolling average, measured over 28 days following a recommendation. It zeroes in on the campaign’s primary bidding objective, be it conversions, conversion value, or clicks.
This feature introduces an added layer of accountability to automated recommendations, especially as we’re relying more on platform-driven optimizations. I find it reassuring to know there’s now more transparency.
Interestingly, this was first shared by Hana Kobzová, founder of PPCNewsFeed, who took to LinkedIn with a screenshot of the help doc.
Although there isn’t a live help doc yet, a Google spokesperson mentioned they’re running an early pilot. It’s exciting to be on the cutting edge of these developments!
I’ve noticed some exciting changes coming to Google Demand Gen campaigns. Starting in March 2026, Lookalike audiences will no longer be the rigid framework we’re used to. Instead, they’ll serve as optimization signals, ushering in a new era of AI-driven campaign enhancements.
Google is updating its Help documentation to reflect this transformation where Lookalike segments shift from strict targeting to a more flexible, AI-enhanced recommendation model.
Understanding the Transition. Previously, I would choose a specific similarity tier (narrow, balanced, or broad) to dictate exactly who my campaigns targeted. That’s changing.
Now, Google will use these tiers as signals. The system will intelligently expand its reach beyond my chosen Lookalike lists to engage users predicted to convert.
Behind the Change. This transition turns Lookalikes from a barrier into an enabling tool. It allows Google’s automation to use intent signals to explore audience performance well beyond predefined limits.
Interaction with Optimized Targeting. The new Lookalike-as-signal approach resembles Optimized Targeting but doesn’t replace it. When they’re layered, Google mentions it could further expand my reach.
In practice, this means multiple automation signals will be at play, providing the algorithm more freedom to either reduce CPA or boost conversion rates.
Opting Out. If I prefer the traditional Lookalike approach, I can opt out via a dedicated form, preserving the old targeting behavior. Absent that, campaigns automatically switch to the new format.
Why This Matters. This update affects the control I have over ad targeting in Google Demand Gen campaigns. Lookalike audiences will now guide rather than confine targeting, significantly influencing scale, CPA, and performance.
Additionally, it indicates an industry-wide move toward automation, similar to shifts driven by Meta Platforms. I’ll need to test thoroughly, rethink strategies, and decide whether to embrace the added reach or opt out for tighter targeting.
Industry Context. Google’s strategy echoes a broader trend toward AI-first audience expansion, aligned with similar adaptations from Meta in recent years. The advertising landscape is increasingly prioritizing machine-led optimization over detailed manual control.
The Reasoning. According to digital marketer Dario Zannoni, there are two main reasons for Google’s shift:
Stringent Lookalike targeting can limit scale and hinder performance in conversion-focused campaigns.
The complexity of maintaining high-quality similarity models makes automation a more viable option.
The Bottom Line. For performance marketers like me, this marks another step towards automation-centric strategies. Reduced control might be daunting, but similar platform changes have historically yielded performance gains. A fresh testing cycle is on the horizon as I examine the impact of expanded Lookalike signals on CPA, reach, and conversions.
Observed and Shared. Dario Zannoni initially highlighted this update on LinkedIn.
I’ve recently discovered an exciting development in Google Ads that’s set to revolutionize how we track and measure our advertising success. The platform is now testing a beta feature that allows us to link external data sources directly into the conversion action settings. This move aims to strengthen the bridge between our first-party data and campaign measurement.
How does this work, you might ask? In the conversion action details, a new section titled “Get deeper insights about your customers’ behavior to improve measurement” encourages us to connect our external databases to our Google tag, offering a seamless integration experience.
This integration supports platforms like BigQuery and MySQL, with the primary goal of enriching our conversion metrics and enhancing performance signals. Notably, this feature is highlighted within the data attribution settings and is gradually being rolled out in its Beta phase.
Why do we care? The ability to directly integrate these data sources reduces the hassle of syncing offline or backend data with ad measurements. This beta feature from Google Ads simplifies connecting first-party data to conversion tracking, improving our measurement accuracy and campaign optimization.
By harnessing the power of platforms like BigQuery or MySQL, we’re able to incorporate richer customer data into our signals, crucially offsetting any data loss resulting from recent privacy changes. In practical terms, this means smarter bidding, clearer attribution, and the potential for a stronger ROI.
Beneath the surface, embedding these data connections directly within conversion settings—rather than relying on separate pipelines—democratizes advanced measurement tactics, making them accessible not only to large enterprises but to advertisers like you and me.
As ad platforms compete for superior measurement accuracy, these native data integrations are emerging as a pivotal advantage, particularly for brands heavily investing in proprietary customer data.
I’ve just discovered an exciting development in the Google Ads world that’s sure to interest any advertiser looking to optimize their campaigns. Google Ads is experimenting with a new ROAS-based tool that automatically suggests conversion values, aiming to enhance how we bid for new customers without the need for manual estimates.
For those like me who are focused on campaigns that target new customer acquisition, this update is a game changer. It empowers us to bid more assertively to capture those elusive first-time buyers.
How it works. I enter my desired ROAS target for new customers, and Google Ads does the rest. It proposes a conversion value that aligns with the goal I’ve set, removing much of the guesswork that previously complicated bidding strategies.
Currently, this feature doesn’t customize at the auction, campaign, or product levels. Instead, we apply values at a broader setting; this means the system doesn’t yet allow variable bids based on different contexts.
Why we care. This new tool addresses a significant shortfall in performance bidding—assigning the correct value to new customers. Many of us have relied on flat manual values, which don’t always reflect true profitability or align with our long-term goals.
By linking conversion values to a target ROAS, the door is opened to more strategy-driven bidding, potentially enhancing our balance between growth and efficiency in acquisition campaigns.
What advertisers are saying. Initial feedback suggests this feature is a notable improvement over the static manual inputs we’ve been using. Andrew Lolk, Founder of Savvy Revenue, believes the next step could be auction-level intelligence that dynamically adjusts values based on campaign or product performance.
What to watch. If Google decides to expand this feature to support more granular adjustments, it could significantly reshape how we plan our acquisition strategies and value long-term customer growth.
For now, the tool provides a more structured approach to calculating the value of new customers.
First seen. This update was first spotted by Andrew Lolk, who shared the insight on LinkedIn.
