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Category: Google Ads

Is Your Brand Campaign Truly Ready for AI’s Prime Time?

Not too long ago, I remember broad match being hailed as the future of paid search. Today, AI Max has taken on that mantle.

Over recent months, I’ve received plenty of suggestions to activate AI Max on brand campaigns, even when these campaigns are performing just as I want them to.

The reality is, many accounts still aren’t equipped with the essentials AI Max requires for optimum function. Conversion tracking issues, the lack of offline conversion imports, and budget-constrained generic campaigns are common hurdles.

AI Max thrives on robust conversion signals, adequate volume, and enough variation for effective learning. I often find that brand campaigns provide most of these signals.

However, applying AI Max to brand campaigns means layering additional automation over our most efficient and predictable traffic source.

The promise and limitations of AI Max

AI Max can broaden search targeting beyond your key phrases by using keywords, landing pages, and site content as signals instead of specific targeting criteria.

Much like dynamic search ads (DSA), AI Max can align with queries you didn’t explicitly target, and it ventures even further by transcending the intent limits set by your keyword arsenal.

Google portrays AI Max as the future of Search automation, preparing to merge DSA, automatically created assets, and broad match settings into AI Max this September.

With controls like brand exclusions, URL exclusions, text guidelines, and location targeting, AI Max might tap into growth opportunities in accounts rich with strong conversion signals and enough search volume.

Yet, many accounts haven’t reached that point.

With Google’s AI Surface eligibility expanding, it’s tempting to dive headfirst into AI Max. But it’s essential to focus on account fundamentals—measurement accuracy, conversion integrity, and solid account structures—before relying solely on AI Max.

Why AI surface eligibility isn’t reason enough to rush into AI Max

The growing interest in AI Max is fueled by Google’s push toward AI-powered search experiences. AI Overviews now engage approximately 2.5 billion users monthly, presenting ads in 25.6% of AI Overview results, according to Semrush data.

While maintaining visibility in these surprising new fields is advisable, rushing to apply AI Max without assessing your campaign structure and conversion strategies can be detrimental.

Typically, Google Ads representatives pitch AI Max for brand campaigns to ensure their eligibility in AI Mode and associated AI Overviews. However, this isn’t always the truth.

Ginny Marvin, a Google Ads liaison, confirmed that three campaign types are eligible for AI Overviews: broad match with Smart Bidding, Performance Max (PMax), and AI Max for Search. Meanwhile, exact match keywords never qualify for AI Overviews.

Thus, PMax and AI Max generally serve the same purpose concerning AI surface eligibility. Running PMax brand campaigns already gives you the necessary coverage, without the need for adding another layer of automation.

Before adding AI Max into your mix, examine whether it’s genuinely necessary over addressing your account’s foundational needs.

Test data doesn’t fully endorse Google’s AI Max assertions

Google claims that enabling AI Max could increase conversions by 14%, and those employing exact and phrase matches might experience a 27% increase. Nevertheless, independent tests have yielded a wide array of results.

The evidence for AI Max remains mixed

In tests covering 600 accounts, Smarter Ecommerce observed AI Max produced 35% lower ROAS than traditional match types. This outcome aligns with intentional budget minimization by advertisers.

Through a four-month examination, Xavier Mantica discovered AI Max resulted in the priciest conversions compared to phrase and exact match. While Mantica noted $100.37 per conversion with AI Max, phrase match was at $43.97, and exact match was at $52.69.

Moreover, 99% of impressions during Ezra Sackett’s 30,000 search term analysis returned zero conversions under AI Max.

Significantly, none of this data is brand-focused. AI Max may provide benefits in certain settings, but a successful, exact match defensive brand campaign may not be the right candidate for testing new automation.

If your brand is still the standout performer in your account, you may want to question why the rest of your campaigns haven’t met similar standards.

What to consider before testing AI Max on brand

Ask yourself these critical questions before branching AI Max into your brand campaigns:

1. Are the conversion signals trustworthy?

Does your setup cleanly distinguish between macro and micro conversions? Are offline imports running smoothly? Does the lead quality feedback enhance platform optimization?

If the underlying signals falter, AI Max will simply magnify those issues.

2. Have you already explored generic growth?

In the accounts I review, problems like budget constraints, misaligned landing pages, outdated queries, and suboptimal structure frequently hinder generic campaign growth.

Real growth is often found within these issues, rather than an already strong brand campaign.

3. Can the account provide AI with sufficient learning data?

Remember, AI Max is not some sorcery; it mirrors the quality of the signals it receives.

Relying heavily on brand conversions will only amplify these markers and obstruct other growth pathways.

4. Are brand + modifier searches already structured properly?

Search variations like “Brand + pricing” or “Brand + reviews” ought to be treated as separate strategic campaigns. AI Max should not substitute for robust account architecture.

5. Do you have a strategic reason to expand the brand campaign?

Consider testing strategically through experiments, rather than viewing AI Max as a straightforward switch to augment visibility.

AI Max only works as efficiently as the signals guiding it

AI Max might develop into a truly beneficial tool over time, much like PMax did. Automation effective at any level still requires strong foundational signals for success.

The existing issue remains with insufficient solid foundations supporting the automation. Improved conversions, precise measurement, sound account structures, and comprehensive feedback loops are vital to making automation wiser.

Above all, don’t conflate Google’s automation agenda with your campaign objectives.

Inspired by this post on Search Engine Land.

June 9, 2026
Google’s July Update: Transforming Local Services Ads for Clarity

I’m intrigued by Google’s decision to update its Local Services Ads on July 6. This change isn’t just a simple update—they’re renaming policies as “requirements” and aligning everything with a recent badge system overhaul.

So, what’s going on? Google is working to refine the rules governing Local Services Ads. They’re not just updating the language; they’re also aligning advertiser requirements with their new verification standards.

One key change is the renaming of “Local Services platform policies” to “Local Services Ads requirements.” It might sound administrative, but these adjustments suggest a more straightforward way for businesses to comply and earn those coveted Google Guarantee badges.

For those of us in advertising, these updates are vital. They not only promise clarity but hint at the possibility that compliance will tie directly to badge status. Agencies and local businesses must stay vigilant and ensure their credentials and standards are spot-on.

What does this mean in the grand scheme of things? Google aims to make the advertiser requirements crystal clear, aligning them with the new badge framework while simplifying the guidance on compliance.

To be clear, Google isn’t cracking down hard on policy. Instead, they’re focused on clarity and modernization, simplifying how businesses access these requirements.

In summary, Google is refreshing its Local Services Ads policies. The shift is towards “requirements,” backed by a badge-driven approach, enhancing trust and eligibility for businesses.

Inspired by this post on Search Engine Land.

June 8, 2026
Optimize Your Google Ads with New Performance Max Tools
Hey there! I’ve got exciting news for advertisers like me who are constantly looking for better ways to fine-tune our Google Ads campaigns. Google has just introduced new Performance Max asset testing tools that make it easier to analyze creative performance and make data-driven decisions.

Google’s latest update is all about expanding our ability to experiment with Performance Max. Now, I can test creative assets and measure campaign performance more effectively before committing to large-scale changes.

What’s new? Google is enhancing how I run asset experiments in Performance Max campaigns. This update lets me test different creative assets to see which ones drive the best results.

The new feature allows me to compare entirely new asset groups, assess the impact of adding individual assets, or even measure how seasonal content stacks up against evergreen creatives.

I can also test assets generated through Google’s Asset Studio, opening up even more possibilities for creative experiments.

The bigger picture. While Performance Max has automated many aspects of campaign optimization across Google’s inventory, the real challenge has been understanding how creative changes impact results.

The new experiments provide a more controlled environment for evaluating creative decisions before rolling them out across all my campaigns.

Cutting through the noise. With an additional success metric, I can balance multiple objectives—like maximizing conversions and maintaining efficiency targets—by evaluating broader campaign performance rather than relying on a single KPI.

What to look out for:
- All experiments, including conversion lift studies, are centralized under one Experiments page.
- More experiment and measurement capabilities are on the way.
- Support for manager accounts (MCCs) and the Google Ads API will start rolling out soon.
Why it matters. Creative assets are crucial in Performance Max campaigns, but testing new assets always carries some risk. With these new tools, I can validate my creative decisions using data before fully committing any budget.

Stay ahead of the curve. As Google continues to invest in automation and AI-generated creative, asset testing becomes even more vital. Being able to compare human-crafted, seasonal, and AI-generated assets provides deeper insights into what excels in Performance Max campaigns.

The takeaway. Google is empowering Performance Max advertisers like myself with sophisticated testing capabilities. I find it easier than ever to evaluate creative changes, measure results across multiple KPIs, and manage experiments from one place.

First sighted by. This update was first spotted by PPC News Feed.

Inspired by this post on Search Engine Land.
June 8, 2026
Unlock Your Google Ads Potential with Customer Match

Every time I run Google Ads campaigns, one thing I never skip is conversion tracking. It’s essential for measuring success. But here’s a question: why would I ever run ads without uploading my customer list? That’s a key part of gaining an edge in today’s digital landscape.

With third-party cookies fading away and privacy regulations tightening, I’ve noticed how much of the traditional tracking capabilities we relied on are becoming less effective. That’s where my own first-party data comes in, standing strong as the best tool I have to guide Google’s automation processes.

Think about it with me: if everybody has the same access to Google’s Smart Bidding and AI algorithms, relying on the same shared data won’t set me apart. The real advantage is in offering unique data that I alone hold—my customer list.

The $50,000 Threshold Myth for Customer Match

Let’s tackle the primary hurdle first. To leverage Customer Match for direct campaign targeting or exclusions, Google asks for a few things: good account standing, at least 90 days of spending history, and a lifetime spend of US$50,000.

If my account hasn’t reached that point, it doesn’t mean Customer Match is off the table for me. I still upload my customer list into Google Ads right away. Here’s why: even without direct targeting, that list becomes a crucial AI signal. Google Ads then uses it to enhance Smart Bidding and optimized targeting efforts by learning from my customer base’s traits and identifying similar high-converting prospects.

Plus, uploading a list gives me access to Audience Insights in Audience Manager. It’s amazing! I can dig into demographic data to see which Google audience segments my customers belong to—at no cost. This insight sparks new ideas for Demand Gen audience targeting and creative ad strategies, such as adjusting landing pages or ad creatives.

Customer Match Campaign Compatibility

I’ve observed that once my account surpasses the lifetime spend threshold, Customer Match becomes a natural fit for campaigns on Search, Shopping, Gmail, YouTube, and Display. It allows me to seamlessly apply my customer list for targeting or exclusion across various campaign types.

Though Performance Max lacks audience targeting capabilities, my strategy involves excluding data segments, including my customer list. This way, I achieve similar benefits via Customer Lifecycle goals.

Customer Match Unlocks Customer Lifecycle Goals

In my experience, Customer Lifecycle Goals have been invaluable in Search, Shopping, and Performance Max campaigns. It allows me to better prioritize different user segments according to campaign needs.

For instance, with “New Customer Only” mode, the customer list acts as a strict exclusion so I focus solely on acquiring new clients. Meanwhile, the “Customer Retention” mode does the opposite, concentrating only on my customer list to promote repeat purchases. There are other modes too, like New Customer Value and High Value Customers, all made possible through Customer Match.

Now, you may wonder when to prefer this over direct targeting or exclusion. Here’s my 1% Rule for lifecycle goals: if my active customer list doesn’t represent 1% of my target geographical location’s population, using lifecycle goals may not be necessary. For instance, in the US with its 340 million population, I’d need around 3.4 million users for these goals to be impactful, according to my rule.

Conversion-Based Customer Lists: Another Customer Match Feature

When paired with Enhanced Conversions, Customer Match introduces another valuable feature: Conversion-Based Customer Lists. I’ve found that this bridges the gap between isolated conversion actions and ongoing data segment management.

While a conversion may be a momentary action, a data segment is a dynamic list of users—like a customer list or website remarketing list. Conversion-based lists automatically generate a list of users who’ve completed specific conversion actions like purchasing, making this process effortless and continuously updated.

Technical Execution: How to Upload Your Customer List

Securing my customer data in Google Ads is simple once I head to Tools > Data Manager for checking direct integrations. Platforms like Shopify, HubSpot, and Salesforce link directly, keeping my data synced effortlessly. Otherwise, I can always opt for a manual upload via CSV through Tools > Shared Library > Audience Manager.

The key is to keep this data fresh. One mistake I’d often seen is not updating lists, leaving them outdated. For those with regular leads or transactions, a daily update makes sense. In contrast, those with a slower pace might only need bi-weekly or monthly reminders to refresh data.

It’s crucial to remember that user consent is a must for uploading data on Google Ads. Using bought lists from third parties can breach Google’s policy and local privacy laws. My website’s privacy policy must clearly disclose sharing user data with third parties like Google for advertising.

The Exception: Who Shouldn’t Use Customer Match

If I operate within sensitive industries, such as healthcare or finance, unfortunately, Customer Match isn’t an option due to restrictions that prevent data misuse.

However, if my field is less sensitive, Customer Match is invaluable. My proprietary data is one of the most powerful competitive advantages, offering Google’s AI the precise framework it requires to identify my next top customer.

This entry is part of an ongoing series on Search Engine Land, ‘Everything You Need to Know About Google Ads in Under 3 Minutes.’ Through each installment, Jyll introduces a different Google Ads feature, delivering insights to maximize results in just three minutes.

Inspired by this post on Search Engine Land.

June 4, 2026
Unlocking AI Success with Google Display Exclusions

When I manage digital marketing campaigns, accidental clicks, bot traffic, and low-quality placements can really muddy the data. That’s why I rely on strategic exclusions to keep my optimization efforts on track.

Let me unpack how Google Display Network (GDN) placement exclusions have evolved from basic account hygiene to vital components in AI-driven optimization strategies.

Traditionally, blocking undesirable placements meant compiling extensive lists of unwanted URLs and mobile app categories. This helped safeguard brand integrity and ensured I wasn’t wasting my budget on traffic that wouldn’t convert.

In the past, ensuring our ads dodged clickbait blogs and mobile games was crucial. GDN exclusions have now taken on a more strategic role, influencing Google’s optimization signals for automated campaigns.

This shift means I can use placement exclusions not just for blocking but as a strategic tool to sidestep low-quality traffic and unreliable conversion signals. Here’s how it works.

In traditional PPC, placement exclusions served dual purposes: they protected brand safety and conserved my advertising budget.

No one wants their brand next to inappropriate or clickbait content. The GDN offers vast inventory, but much of it can be high-click and low-conversion, making exclusions essential.

Even high-profile sites could become budget drains without contributing to conversions. Thus, large exclusion lists and regular audits became routine practices to manage ad placements efficiently.

However, AI has changed how I approach this. With Smart Bidding algorithms like Target CPA and Target ROAS, optimization is more nuanced. Google’s AI actively seeks out the right audiences, and the data-quality matters significantly here.

Without strategic exclusions, AI might gravitate towards cheap, high-volume placements. I’ve seen how accidental clicks and low-quality sites appear promising due to high CTRs but ultimately fail to convert.

Strategic placement exclusions provide guidance, ensuring AI avoids these pitfalls by directing it towards more beneficial data signals.

By refining where the AI can operate, I reintroduce human intent into automated systems, steering campaigns with a strategic hand on the wheel.

For brand awareness, I allow ads on premium sites while excluding lesser-known directories. This ensures visibility on reputable platforms.

Conversely, for direct response campaigns, I block costly broad-reach sites, pushing AI towards niche sites where conversion intent is high.

Blocking unwanted placements early in a campaign prevents unnecessary spending during the AI’s learning phase, allowing for more effective targeting from the get-go.

By excluding malicious bot-heavy sites, I prevent ‘signal poisoning,’ ensuring the AI optimizes based on genuine user interactions.

Advanced tactics involve running automated scripts to routinely exclude budget-draining placements and blocking mobile apps unless explicitly targeted. These strategies keep the AI focused on valuable data, minimizing waste.

Adopting these strategic exclusions enhances campaign performance significantly, transforming basic blocklists into a powerful performance edge.

Inspired by this post on Search Engine Land.

June 3, 2026
Maximize Lead Management with Google Ads’ New AI-Powered Dashboard
I’ve just discovered that Google Ads has introduced a built-in lead management dashboard, which is incredibly useful for advertisers like me who are keen on optimizing lead quality and enhancing bidding performance with AI insights.

Google has integrated lead management directly into Google Ads, which means I can now track, qualify, and manage leads from Google-hosted forms all in one place. This system not only streamlines the process but also feeds higher-quality conversion signals back to Google’s AI bidding technology.

What’s new. I’ve found that Google Ads now features a dedicated lead management interface designed specifically for organizing and acting on leads generated through Google-hosted forms, making my job as an advertiser so much easier.

The dashboard gives me a comprehensive view of lead activities, which include:
- Total leads
- New leads
- Qualified leads
- Lost leads
- Lead status and progression through the funnel
From a single interface, I can also review individual lead records, including contact information and lead stage, making it incredibly efficient to keep up with potential customers.

Why we care. This new feature allows me to sync lead-quality signals directly with Google Ads, which significantly helps Smart Bidding in identifying leads that are more likely to convert into actual customers, rather than merely increasing form submissions.

The dashboard simplifies my workflows by providing a centralized view of lead status, enabling my marketing and sales teams to prioritize high-value prospects and ultimately boost conversion rates.

Key benefits:

Centralized lead management
- Manage Google-hosted form leads all in one place.
- Reduce the risk of losing track of potential customers.
Better AI optimization
- Share lead-quality and conversion signals with Google Ads.
- Help bidding algorithms focus on high-value prospects.
Faster sales cycles
- Identify and prioritize qualified leads faster.
- Move prospects through the funnel more efficiently.
Simplified workflows
- Enjoy a lightweight, integrated CRM experience without having to leave Google Ads.
What to watch. The enhanced reporting features within the dashboard also offer greater insights into sales funnel performance, including numbers of qualified leads and conversion rates.

As Google continues expanding its AI-powered advertising tools, direct access to lead-quality data is becoming increasingly crucial for advertisers like me who aim to enhance lead volume and improve downstream revenue.

Inspired by this post on Search Engine Land.
May 29, 2026
Unlocking New Controls in Google AI Max for Branded Searches
I recently came across a fascinating development in Google Ads that’s really worth discussing. Google seems to be testing new branded search controls within AI Max campaigns, which might just give advertisers a better way to separate branded from non-branded traffic.

If you’re like me, you’ve probably faced challenges with AI Max campaigns capturing branded searches, especially since their launch. It seems Google might finally be addressing this common concern by offering more control over how these campaigns interact with branded queries.

What’s happening. Some advertisers have reported a fresh ‘Branded Searches’ control option within AI Max campaigns. This feature potentially allows us to dictate how the campaigns handle brand-associated searches.

The option includes three settings:
- Show ads on all relevant searches (default strategy)
- Manage branded searches via inclusions and exclusions
- Restrict ads to only appear on unbranded searches
Why we care. For those of us managing campaigns, one major critique of AI Max has been its tendency to capture branded traffic. This traffic is often already covered by dedicated brand campaigns, leading to complications.

Campaigns that pull in branded traffic can pose several issues:
- Increased costs for likely conversions
- Complexities in attribution across different types
- Diminished clarity on incremental gains
- Worries of AI Max overshadowing branded efforts
The ability to focus on purely unbranded searches, newly introduced, could help direct AI Max towards fresh demands and new prospects.

Between the lines. Up until this point, preventing AI Max from engaging in branded queries required exclusion lists. A native setting would simplify this and potentially offer more insight into brand intent handling.

The big picture. Google seems committed to adding more oversight to automated campaigns, reacting to our calls for greater transparency and control over AI.

If these controls are deployed widely, it could indicate Google’s acknowledgment of our traffic management concerns, as they forge ahead with AI automation.

What to watch. Whether this is a full release, a selective test, or just an experiment is still unclear. Keep an eye on your AI Max settings and stay alert for updates from Google regarding branded search controls.

Bottom line. This new control in AI Max might soon empower advertisers to distinctly separate branded and non-branded traffic—something many of us have long requested. But for now, it’s an observation rather than a confirmed rollout.

First spotted. This development was originally highlighted by Paid Search specialist Thomas Eccel, who shared his discovery on LinkedIn.

Inspired by this post on Search Engine Land.
May 29, 2026
Boost Your Google Ads Visibility in AI Overviews with These Strategies

I’ve discovered that AI Overviews are changing the way Google Search displays paid ads. Nowadays, it seems like there’s more pressure to get my ads to appear in AI-generated responses, as direct search results provide fewer opportunities for clicks.

Google suggests that Shopping, Performance Max, and AI Max for Search campaigns are best suited for this evolution. However, just choosing the right campaign isn’t enough. I need to ensure the quality of my feeds, optimize my landing pages, and use effective audience signals and creative content strategies to boost my ads’ chances.

Enable Google-Recommended Campaigns for AI Overviews

I’ve found that Google is quite clear about which campaign types are most likely to appear in AI Overviews. Interestingly, these opportunities are often overlooked by experienced marketers due lack of full control.

Despite this, I’ve come to understand that combining control with data and an understanding of search intent will benefit both me, as an advertiser, and the searcher. This involves strategizing beyond picking the right campaign types, focusing instead on fully optimized feed data and content alignment.

To boost my visibility in AI Overviews, I’ve enabled Google’s recommended campaigns to sync with the feature, particularly Shopping, Performance Max, and AI Max for Search, utilizing broad match keywords and smart bidding with final URL expansion.

Shopping Campaigns

Learning that the original keywordless campaign relies heavily on my data feed quality, I’ve focused on creating a well-built and optimized product data feed, using high-quality images, and ensuring my titles and descriptions are thorough.

I’ve realized how crucial the product data feed is in determining ad visibility for specific queries. When high-intent questions are asked, the AI Overview can feature a product carousel, enhancing the prominence of shopping results.

Performance Max Campaigns

In Performance Max, I’ve seen how keywordless campaigns utilize page content, data feeds, and audience insights to decide ad display. These inputs are key in determining ad visibility for queries.

Enabling Final URL expansion has allowed my ads to appear in more searches by leveraging page content for user query relevance.

AI Max for Search Campaigns

By using existing keywords as a starting point, AI Max for Search expands beyond to determine ad delivery strategies. This means keywords signal intent rather than dictate ad display.

I’ve noticed that AI Max uses search term matching and asset optimization to target queries unaddressed by traditional keyword targeting.

6 Best Practices for Ad Campaigns

To improve my chances of being featured in an AI Overview, I’ve optimized my campaigns by focusing on creative, copy, schema, and link-building techniques to reinforce brand authority.

1. Diversify Your Assets

With campaigns like AI Max and Performance Max, I’ve realized the importance of using varied creative assets. Incorporating informative headlines, descriptions, and visuals in multiple formats allows for diverse ad placements.

2. Use a Conversational Tone

Understanding Google’s approach, I’ve shifted from generic sales pitches to a conversational tone in my Responsive Search Ads, using language that assists the user rather than typical sales jargon.

3. Be Clear and Informative

By answering key questions succinctly, my ads now have a better chance of being highlighted in AI Overviews. A focus on information-rich landing pages has proven essential.

4. Check Schema Markup and Links

I ensure my schema markup is thorough and aligned with my content. Linking to reputable sources builds authority, and collaborating with my SEO team has enhanced these practices.

5. Guide Automation with Audience Signals

I recognize the lack of control in these campaigns, so I’ve guided automation using strong audience signals, exclusions, and negative keywords to refine my targeting strategies.

6. Regularly Monitor Campaigns

Regular monitoring is crucial for brand safety and profitability. Reviewing search terms, landing pages, and ad assets ensures my message remains consistent and aligned.

Adapt Your Approach for AI Overviews

Adapting to conversational AI Overviews requires me to focus on maximizing visibility on the SERP. Emphasizing data feed quality, content alignment, and creative diversity turns this shift into an opportunity for growth.

Inspired by this post on Search Engine Land.

May 27, 2026
Google’s Real-Time Ad Policy Review: Quick Approval Process

I’m excited to share that Google has introduced a new feature designed to streamline the ad approval process called Real-Time Policy Reviews. During the creation of campaigns, this system offers instant feedback, making it faster and easier to get ads up and running.

The feature is currently tailored for Responsive Search Ads, but Google has plans to expand it to other campaign types within the year. This means as I create ads within Google Ads, I receive immediate policy feedback, eliminating the need to wait in a post-submission review queue.

The real magic happens in two phases. First, as I draft my ad, the system flags any editorial issues instantly, like typos or errors with destination links, allowing me to correct these before finalizing my ad.

Once I’ve saved the ad, Google provides a policy decision immediately. Ads that pass without any issues can go live almost instantly, whereas those with more complicated violations are redirected to a post-save review page, detailing the problem and outlining possible solutions.

I find this update crucial because it reduces campaign launch delays, especially during promotions or product launches that demand immediate action and can’t afford postponements.

Google has segmented policy issues into two main categories: ‘editable,’ which are simple problems I can fix on the spot like formatting errors, and ‘complex,’ which need further certifications or appeals.

This aligns with Google’s ongoing mission to make campaign management smoother by integrating it into our day-to-day tasks, especially essential for those rapid-response campaigns.

As Real-Time Policy Reviews become available across more campaign types, I anticipate a faster transition from creation to delivery. However, it also emphasizes the importance of addressing compliance throughout my creative process.

Check out more about the updates on Real-Time Policy Reviews.

Inspired by this post on Search Engine Land.

May 27, 2026
Prepare for Google Ads Data Deletion: Secure Your Historical Insights Now
Starting in June, Google Ads will implement a policy that deletes any reporting data older than 37 months, unless we take action to export and preserve it.

As someone who heavily relies on historical data for reporting and forecasting, I recognize the urgency to revamp my data management strategies before access to older records is lost.

What’s Changing. From June 1st, only data from periods shorter than a month—such as hourly, daily, and weekly reports—will be accessible for 37 months. For longer spans like monthly, quarterly, and annual reports, we will enjoy access for up to 11 years.

Once those retention periods lapse, the data will no longer be available in the Google Ads interface or through APIs.

Nitty-gritty Details. Metrics that measure reach and frequency will have even shorter retention limits, staying available for just three years. These metrics include:
- unique users,
- average impression frequency per user,
- 7-day and 30-day average impression frequency,
- and frequency distribution metrics.
The Larger Impact. The policy change means I need to export and securely store historical Google Ads data soon, or it’ll become permanently inaccessible.

I acknowledge that long-term trend analysis and benchmarking depend heavily on years of granular data, which may no longer be directly accessible in Google Ads.

Looking Ahead. If I rely on external BI tools or customized reporting systems, I need to set up automated data export pipelines to maintain continuity before the new retention limits take effect in 2026.

For More Information. Read more about Google’s data retention changes on their official support page.

Inspired by this post on Search Engine Land.
May 27, 2026

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