Tag: PPC

Semantic PPC and SEO Tactics That Still Win With AI

Why advanced semantic techniques still matter in PPC and SEO

Now that I can use AI to generate keywords and launch a paid search campaign in minutes, it is tempting to think the hardest part of PPC and SEO work has already been handled.

But I still need more than fast keyword output if I want structured, scalable performance. I need to understand how search actually works, how people phrase intent, and how noisy search term data can distort a campaign if I do not organize it properly.

That is where semantic techniques such as n-grams, Levenshtein distance, and Jaccard similarity continue to matter. I use them to interpret messy data, apply real client context, and build frameworks that AI alone cannot reliably produce.

What I learn from n-grams in PPC and SEO analysis

I think of n-grams as the “n” words that make up a keyword. In the search term “private caregiver nearby,” I can break the phrase into smaller pieces that are easier to analyze.

3 unigrams (one word): “private,” “caregiver,” and “nearby”
2 bigrams (two consecutive words): “private caregiver” and “caregiver nearby”
1 trigram (three consecutive words): “private caregiver nearby”

I use n-grams because they simplify large keyword lists without stripping away the patterns that matter.

For example, I recently restructured several campaigns that had more than 100,000 search terms. By using n-grams, I reduced those lists into much more workable sets.

~6,000 unigrams.
~23,000 bigrams.
~27,000 trigrams.

Once I have those smaller sets, I can spot patterns quickly. If every keyword containing the “free” unigram performs poorly, I can exclude “free” as a broad match negative.

On the other hand, if I see that “nearby” performs especially well, I may test more local variations, build location-specific landing pages, or adjust campaign structure around that intent.

I still have to respect the limits of this method.

I need a large volume of search terms, so this approach usually works best for accounts with bigger budgets.
As “n” gets larger, the output becomes less useful because the data expands again. At that point, I usually need more advanced methods such as Levenshtein distance or Jaccard similarity.

How I cluster keywords with n-grams

When I analyze SEO and PPC data, I often deal with huge volumes of long-tail search terms. Many appear only once and carry very little standalone data.

N-grams help me turn that chaotic long-tail data into clearer, more manageable intelligence.

That intelligence helps me reduce wasted spend, find new opportunities, and build a structure that can scale.

I start by exporting search term data. In PPC, that includes cost, impressions, clicks, conversions, and conversion value by search term.
For each n-gram, I sum cost, impressions, clicks, conversions, and conversion value.
Then I calculate CPA, ROAS, CTR, CVR, and any other metrics that matter for the account.

With a shorter and more digestible dataset, I can rank the top-spending n-grams that do not convert, which often become negatives, and the ones that do convert, which become positives.

From there, I build ad groups around recurring n-grams that consistently drive performance.

For example, I may find that emergency-related n-grams such as “24/7,” “same day,” or “urgent” deliver higher conversion rates. I would segment those terms so I can control budget, bidding, and messaging more precisely.

Bottom line: I use n-grams to isolate themes that deserve special attention.

Once I have identified those themes, it becomes much easier to build advanced paid search structures around high-impact n-grams and improve ROI.

Dig deeper: How to uncover hidden gems in your paid search accounts

How I use Levenshtein distance to improve keyword quality

Levenshtein distance measures the minimum number of single-symbol edits, including insertions, deletions, or substitutions, needed to turn one string into another.

That may sound complicated, but the idea is simple once I put it into practice.

The Levenshtein distance between “cat” and “cats” is 1 because I only need to add the “s.” Between “cat” and “dog,” the distance is 3.

One common PPC use case is finding brand and competitor misspellings inside search term reports.

For example, “uber” and “uver” have a Levenshtein distance of 1, so I would feel confident excluding the misspelled version from non-brand campaigns.

I can apply the same logic to keyword relevance.

If the distance between a keyword and the search terms it matches is too high, such as 10 or more, those terms probably have very little in common with the keyword and deserve review.

A low distance usually tells me those queries are close enough to be safe and do not need the same level of manual inspection.

How I consolidate PPC keywords with Levenshtein distance

After I use n-grams to create initial keyword clusters, I may still have thousands of search terms to organize into a practical campaign structure.

Manually sorting through 6,000 unigrams is not realistic. This is where Levenshtein distance becomes especially useful.

Venn diagram showing sets A and B with their overlapping intersection labeled A&B, illustrating Jaccard similarity for SEO and PPC keywords. — A simple Venn diagram visualizes how Jaccard similarity measures the shared overlap between keyword sets A and B in semantic PPC and SEO analysis.

My goal is to merge ad groups that target nearly identical keywords so I do not end up with an overly granular, SKAG-like structure.

Too much granularity makes reporting and account management harder. It can also create inefficient bidding and wasted spend.

Using the same dataset, I calculate the Levenshtein distance between queries across different ad groups.

Then I identify the closest keyword and ad group using a predefined threshold. A threshold of 3, for example, gives me a high degree of accuracy.

This helps me consolidate keywords and ad groups with confidence. If I use a looser threshold, such as 6, I can also group or name ad groups by broader similarity or intent.

Here is a simple example showing why these three keywords can be grouped together:

Levenshtein distance	24/7 plumber	24 7 plumber	247 plumber
24/7 plumber	0	1	1
24 7 plumber	1	0	1
247 plumber	1	1	0

Dig deeper: How to use negative keywords in PPC to maximize targeting and optimize ad spend

How I go further with Jaccard similarity

In PPC, I use Jaccard similarity as a practical proxy for understanding the overlap between two sets of n-grams.

The calculation is straightforward: I divide the number of shared unigrams between two sets by the total number of unique unigrams across both sets.

It sounds technical, but I visualize it simply:

Jaccard similarity = Red / Green

Here are a couple of concrete examples I use to explain the concept:

“new york plumber” and “plumber new york” = 1 because all three unigrams appear in both sets, just in a different order.
“new york plumber” and “NYC plumber” = 0.25 because only “plumber” is shared, and there are four unigrams in total.

Jaccard similarity is a helpful first step for deduplicating similar keywords. I see it as a bridge between old phrase match logic and broad match modified logic.

But it has an important limitation: it does not understand meaning.

In the example above, “new york” and “NYC” should be treated as equivalent, but the Jaccard calculation sees them as different.

To handle that kind of nuance, I need more advanced techniques, which I would treat as the next layer of analysis.

How I combine Jaccard similarity and Levenshtein distance

Consider a cybersecurity course campaign with the following top 10 keywords:

Keyword	Semrush average monthly searches in the U.S.
cybersecurity courses	5,400
cybersecurity online course	1,900
free cybersecurity courses	1,300
online cybersecurity courses	1,300
cybersecurity course	1,000
cybersecurity courses online	880
google cybersecurity course	880
cybersecurity courses free	720
cybersecurity free courses	590
cybersecurity online courses	480

By combining singular and plural versions, along with reordered versions of the same idea, I can reduce that top 10 into a more actionable top four.

“Cybersecurity courses.”
“Cybersecurity courses online.”
“Free cybersecurity courses.”
“Google cybersecurity course.”

I could use n-grams to do this, but scaling n-gram analysis across thousands of keywords can quickly become overwhelming.

A more efficient approach is to use both similarity metrics in sequence.

First, I apply Levenshtein distance to consolidate very similar queries.
Then I use Jaccard similarity to deduplicate reordered variants.
At each step, I sum the usual KPIs, including cost, conversions, and other performance metrics, so the analysis stays actionable.

The result is a clear, compressed structure that can hold up even as search term volume grows.

How I restructure paid search campaigns with semantic techniques

With the right semantic techniques, I can restructure massive keyword sets quickly and still produce consistent, high-quality results.

AI can absolutely help me create an initial summary, but I do not rely on it entirely.

Otherwise, I run into the classic problem of “garbage in, garbage out.”

Broad match can be powerful, but it also introduces more noise. These techniques help me verify that the queries I am matching stay aligned with campaign goals.

I use n-grams, Levenshtein distance, and Jaccard similarity to apply client context to raw search data and build a stable structure around real intent.

If the process feels overwhelming at first, I use this summary to decide which technique fits the job:

Scenario	Best technique	Why
Identify high-intent patterns in huge search-term exports	n-grams	Surfaces themes fast; reduces dimensionality
Clean duplicate / near-duplicate keywords at scale	Levenshtein distance	Captures spelling + structural similarity
Deduplicate reordered or slightly varied keyword strings	Jaccard similarity	Order-insensitive token-based comparison
Create scalable clusters for campaign rebuilds	Combo: Levenshtein → Jaccard → n-gram	Sequence gives accuracy + compression

For me, the main lesson is simple: AI can accelerate PPC and SEO work, but semantic analysis gives that work structure, signal quality, and strategic control.

Inspired by this post on Search Engine Land.

July 3, 2026

Medical PPC Ads: My Guide to Safer, Stronger Results

PPC advertising for medical and mental health services comes with more restrictions than many other industries, but I still see it as one of the most effective ways to keep a steady flow of new patients and clients coming into a practice.

Whether I am managing campaigns for a client, promoting my own practice, or building a campaign from scratch, I focus on the same fundamentals: the right keywords, compliant messaging, clear landing pages, and lead-quality tracking.

Choosing keywords for medical and mental health advertising

When I choose keywords for medical or mental health advertising, I start by thinking about how real patients search. In most cases, their searches fall into three main groups.

First, some people search by symptoms or treatment options. They may not know which professional they need yet, so they search for phrases like “treatment options for depression” or “why does my ankle hurt when I run.” I do not ignore these searches, because they can still turn into new patients or clients.

Second, people often search for what they think the service is called. They may use simplified or incorrect terms, such as “therapist to manage bipolar medications” or “foot pain doctor.” These searches still show intent, even if the language is not medically precise.

Third, some searchers use the correct term because they already know what they need and are ready to contact a professional. They may search for “psychiatrist” or “endodontist near me.” Even then, I watch for confusion between similar roles, such as therapist, psychologist, and counselor.

Most of my budget usually goes toward the second and third groups, where searchers are closer to taking action and starting treatment.

If I have a larger budget, I may also test broader symptom-based or informational searches that could convert later. These can work, but I treat them carefully because informational searchers may or may not be ready to book.

I also rely heavily on negative keywords. They help me block searches for services the practice does not provide, which protects the budget and improves lead quality.

Dig deeper: A guide to Google Ads for regulated and sensitive categories

Staying compliant with ad copy

With medical and mental health ad copy, I have to be careful. I need the ad to make it clear that help is available, but I cannot write in a way that feels too direct, too personal, or too aggressive.

I expect some trial and error. An ad rejection does not automatically mean an account is in trouble. It usually means the ad was not approved, so I adjust the wording or request a manual review when appropriate.

Blunt language is often where problems happen. Instead of making strong claims, I test softer, more compliant language that still communicates the value of the service.

To stand out from competitors, I focus on practical benefits such as accepted insurance, payment options, specialized treatments, or distinctions like being family-owned, local, award-winning, certified, or licensed.

I avoid terms like “cure” and other language that implies guaranteed results. Google and Meta both have ad policies that restrict how medical, mental health, and wellness services can be promoted.

When an ad gets rejected, I rewrite it so it still explains the value of the practice without crossing policy lines.

For some psychiatrists, doctors, and other medical service providers, Google Ads may also require a LegitScript.com listing, especially for addiction treatment services.

Google Ads support or its documentation will explain whether that requirement applies to a specific practice.

Building effective landing pages

When I build landing or service pages, I start with the information the front office already gives to patients. That is often the clearest and most useful material available.

I pull details from pamphlets, office materials, and common intake conversations. Then I highlight key points such as accepted insurance, cash payment options, payment plans, financing, and specialized treatments.

I also answer the questions patients regularly ask in person or over the phone. A strong landing page should keep improving as new questions come up.

Those questions might include whether the practice works with children, accepts Medicare, offers phone or virtual sessions, or provides a specific treatment.

I make the next step obvious. That may mean booking an appointment, scheduling an initial consultation, requesting a free phone consultation, filling out a form or questionnaire, submitting a contact request, or calling with questions.

I avoid vague forms and generic phone numbers with no instructions. Instead, I explain the process clearly from pre-treatment to treatment to post-treatment.

I also like to include a FAQ section that answers questions such as “what is the process?” and “how does treatment work?” The more uncertainty I remove, the easier it is for a patient or client to take action.

Choosing the best campaign types

For medical and mental health services, I usually build the strategy around Search campaigns.

Automated or audience-based campaign types, including Performance Max and Demand Gen, can run into privacy and targeting limits. Depending on the service, the ads may not be approved.

Remarketing is typically restricted for the same reason. Video campaigns may be possible, but targeting limits often make them better suited for local branding than direct response.

Search campaigns work well because people are actively looking for answers, treatment, or a specific type of provider. They are typing in the exact services they need.

Many providers also use directories like Psychology Today or ZocDoc for lead generation. I still like supplementing those channels with Google or Microsoft Search campaigns because they send traffic directly to the practice’s own site and give more control over patient or client flow.

My usual approach is to target very specific terms for people who are ready to hire a professional, then test broader symptom or research-related terms when the budget allows.

Meta Ads can also be useful, but privacy laws limit targeting. I also have to be careful with ad copy, images, and landing pages so the campaign stays compliant.

I review Meta’s ad policies before launching campaigns to reduce avoidable disapprovals. Meta can support larger budgets, but for most medical and mental health marketing, Google Search remains the most reliable starting point.

Dig deeper: How to prevent Meta Ads restrictions on health and wellness campaigns

Tracking lead quality

With any online advertising, and especially with medical and mental health services, I need to know more than how many leads came in. I need to know which leads became real patients or clients.

A simple CRM, whether generic or built for the industry, can track incoming leads and show which ones converted.

Google Ads, Microsoft Ads, and Meta Ads all offer built-in CRM connections. I can also use a tool like Zapier to connect systems without needing a programmer.

Beyond website form submissions, I also track inbound calls generated by marketing campaigns. Phone calls often represent high-intent leads, so leaving them out can distort ROI.

Call tracking tools such as CallTrackingMetrics, CallRail, and WhatConverts can integrate with CRMs and major ad platforms to measure lead quality.

They also offer call recording and are HIPAA-compliant, which matters when tracking performance in healthcare-related campaigns.

Keeping medical and mental health ads effective

To keep medical and mental health ads effective, I focus on four things: targeting the right searches, writing compliant ads, improving landing pages, and tracking lead quality.

When those pieces work together, I can build campaigns that attract the right patients and clients more consistently.

A steady, well-structured approach is what helps a practice maintain or expand its patient flow without creating unnecessary compliance risk.

Inspired by this post on Search Engine Land.

July 3, 2026
Google Ads Adopts CPM Billing for Discover’s Demand Gen Campaigns

I recently came across some notable updates from Google Ads that could impact a number of advertisers like me. From July 15, Google is making a big shift in how it charges for Demand Gen campaigns on Discover, specifically those aimed at view-through conversions (VTC). Instead of the traditional cost-per-click (CPC) model, we’ll be billed on a cost-per-thousand impressions (CPM) basis.

What happened. Google Ads informed me, along with other advertisers, that this shift will directly affect campaigns using VTC optimization. If you’re like me and use this optimization, be prepared for the billing change. This only impacts campaigns with VTC enabled, so if you’re not using it, you’re in the clear.

Luckily, no action is required on my part for this transition to take place; it’s automatic.

Why we care. For those of us focused on efficiency in Demand Gen campaigns, this switch could mean we’ll need to closely monitor changes in spend, impressions, and reporting metrics since the basis for billing is changing from clicks to impressions.

This shift in billing might prompt some of us, who primarily look for click-driven performance, to reassess if VTC optimization aligns with our goals.

Why Google is making the change. According to Google, aligning billing with campaign objectives is key. View-through conversions rely heavily on ad impressions. Thus, billing on a CPM basis could more accurately reflect the actual value generated from these campaigns.

Moreover, Google believes this shift will enhance the system’s ability to optimize for VTC goals more effectively.

Opt-out option. If the new billing structure doesn’t suit you, there’s an opt-out. Disabling VTC optimization in campaign settings will prevent this change from affecting your campaigns.

The bottom line. With Google tying payments more closely to the behaviors Demand Gen campaigns are crafted to optimize, those of us leveraging VTC will now focus on impressions rather than clicks for billing and optimization on Discover.

First spotted. This update first came to my attention through Adsquire founder, Anthony Higman, who shared details on X.

Inspired by this post on Search Engine Land.

June 16, 2026
Discover Microsoft’s Product Explorer for Enhanced Ad Performance

I’m excited to share Microsoft Ads’ latest tool—Product Explorer. It’s a remarkable addition that helps advertisers like us quickly spot catalog issues that might be hindering ad performance.

The introduction of Product Explorer represents Microsoft’s effort to create a central hub where advertisers can effortlessly monitor product catalog health and performance. Navah Hopkins, the Microsoft Product Liaison, highlighted its potential to revolutionize how we handle large product feeds.

Managing these expansive feeds often means struggling to pinpoint which items are ready to serve, which are capturing impressions, or which are missing vital data. Product Explorer steps in to make this task significantly more manageable.

What’s new? Now, I can explore my entire product catalog through a searchable interface. This tool allows for filtering by SKU, title, GTIN, and product ID, helping to quickly identify active products that are delivering performance results.

What it does. Product Explorer is designed to highlight eligibility issues and metadata gaps, along with other elements that might prevent products from serving. Plus, it offers recommended actions and the option to export filtered product lists for deeper analysis.

Why we care. As advertisers, having diagnostics and performance reporting combined in one interface means we can move more products into a servable state while identifying underperforming inventory more efficiently.

From searchable catalog reporting to gaining product-level performance insights covering the last 30 days, this tool offers issue detection and actionable recommendations to enhance feed quality.

The big picture. As retail advertising becomes more automated, focusing on feed quality is increasingly essential. Accurate visibility into catalog issues can significantly impact the reach and performance of our campaigns.

Availability. According to Navah Hopkins, the tool is live and ready for use in our accounts.

Inspired by this post on Search Engine Land.

June 12, 2026
Decoding the New Dynamics of Attribution in PPC

When I dive into platform reports, I realize they tell only part of the story. It’s the incrementality, CRM data, and broader measurement insights that truly reveal the impact of our marketing efforts.

I recall a time when PPC attribution was never flawless. Now, with AI widening the gap, it’s even trickier to pinpoint what truly influences a conversion and what ends up receiving credit.

Imagine someone discovering a product on social media, watching a YouTube review, diving into Reddit opinions, using an AI tool to compare options, and then returning through a branded Google search ad days later.

While the PPC report might show a single conversion from branded search, I see a more complex journey that needs recognition beyond the final click.

AI is reshaping brand discovery, how purchases are researched, and how ad platforms decide who sees which ads. As a marketer, I find there’s now less visibility into these platform-driven decisions.

It’s clear to me that relying solely on platform attribution data doesn’t tell the whole story of my business’s truth.

AI is changing where the journey begins

Traditionally, the search journey starts well before an advertiser sees a measurable click. Recently, findings like those from Responsive’s 2025 research indicate that a significant portion of B2B buyers favor generative AI over traditional search when exploring vendor options.

For someone entrenched in the tech sector, I can’t ignore how 80% of tech buyers are now using generative AI at least as much as traditional search.

If AI-derived lists are excluding my brand from their answers, I’m instantly out of the buyer’s consideration set, which is disconcerting.

Google’s announcements about AI advancements reaching billions of users show how rapidly the landscape is evolving. This shift means that brands like mine need a strategy to ensure we’ll still be visible.

I can’t help but notice how Pew Research Center’s findings about declining clicks when AI summaries are present have personal and business implications for me.

I also realize the importance of brand recognition, even if initial interactions don’t result in a direct click-through.

The discovery phase deeply influences the eventual conversion, yet often, only the final touchpoint gets the credit.

Dig deeper: What 4 AI search experiments reveal about attribution and buying decisions

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Branded search often receives credit for demand generated elsewhere

Observing branded search, I frequently note it’s a classic case where attribution is mistaken for actual impact.

The efficiency portrayed by a branded search campaign can be misleading. Although such campaigns often perform well on metrics, primarily because they target users already familiar with the brand, they don’t always generate the initial demand.

A user might only search my brand due to exposure from other channels, such as social media, YouTube, or even an AI-generated suggestion.

Thus, distinguishing between demand capture and creation is vital. The real test is understanding whether certain conversions would have occurred absent of these campaigns.

AI-driven discovery creates a measurement blind spot

In client data, I’ve observed that direct traffic from AI platforms boasts a higher conversion rate compared to organic search, which piques my curiosity.

With these findings, I’m reminded of how much goes unmeasured. AI introduces complexities that create attribution challenges, as visible AI traffic might be just a small fraction of the journey.

Recognizing this, I understand the importance of viewing these interactions as part of a larger conversion narrative.

Ads are becoming part of AI-generated search journeys

With ads now interwoven in AI results, I face an added layer of complexity in correlating AI search with paid media.

Google’s policy of serving ads based on the commercial intent inferred from AI responses means my ads could surface earlier in the buyer’s research journey—a fact that fascinates me.

Despite these placements, I’m aware of the limited visibility and reporting challenges they present, which is both frustrating and intriguing to navigate.

Platform automation can make attribution look better while making analysis harder

Within account platforms, the allure of automation promises efficiency, yet it can blur analytical clarity.

I reflect on how broader targeting can deliver impressive surface-level results, but the lack of granular insights into why certain ads perform complicates future decisions.

This dilemma emphasizes for me the critical balance between leveraging automation and maintaining rigorous scrutiny.

I see the trap of prioritizing metrics like reach and click-through rate over genuine business outcomes.

The challenges extend to future optimizations and highlight the importance of qualifying lead quality over sheer volume.

Bringing CRM data into PPC reporting brings everything full circle, ensuring the focus isn’t lost in translation between metrics and actual business value.

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Poor-quality traffic can affect future optimization

Generalized targeting can be a mixed bag. It’s beneficial when the platform’s conversion data is robust, but can yield low-quality traffic otherwise.

This traffic can skew future optimizations, making it crucial for me to pay close attention to lead quality over sheer volume.

The real question becomes, which leads convert into opportunities, and which don’t hold much promise?

Ultimately, I find that aligning PPC efforts with actual CRM outcomes leads to more meaningful insights and strategies.

Automation also creates a new layer of reporting risk

In my experience, the rise of automation has increased the need for vigilance over conversion settings and ad placements.

I remember when platform automation surprised us with inflated conversion numbers due to changes in reporting settings.

This taught me the importance of regularly reviewing each platform’s settings to ensure they align with my advertising goals.

Upper-funnel campaigns influence lower-funnel conversions

Assessing upper-funnel activities, I note that they can have sustained, profound impacts on lower-funnel metrics— a sentiment validated by research indicating significant long-term returns on initial media investments.

This insight reassures me of the need to invest in awareness and video campaigns that extend beyond immediate ROAS measurements.

Dig deeper: How to measure paid social’s impact on PPC

What PPC teams should report in 2026

A single ROAS figure no longer suffices. PPC reporting, in my view, must integrate platform attribution with broader business metrics and strategic experiments.

1. Separate demand creation from demand capture

I ensure campaigns are assessed by their unique objectives—demand creation versus demand capture.

2. Review attribution paths, not just final clicks

Using GA4’s paths report, I analyze the customer journey comprehensively to understand how channels influence conversions from start to finish.

3. Import deeper CRM outcomes

For me, importing qualified leads and sales data enriches platform optimization and aids strategic alignment.

4. Monitor the metrics sitting outside the PPC dashboard

I track various metrics—branded searches, AI-referred sessions, and lead quality, which together form a holistic view of the customer journey.

5. Test incrementality rather than assuming

Incrementality testing, such as Google’s Conversion Lift, helps me understand the genuine impact of my ads beyond the dashboard numbers.

6. Add regular human checks to automated accounts

Despite automation, I regularly review and ensure account settings and outcomes align with my overall business objectives.

Dig deeper: Why your B2B PPC metrics may be lying to you

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Stop searching for one perfect attribution model

I’ve learned there isn’t a single PPC attribution model to explain the fragmented, AI-influenced customer journey we see today.

Rather than abandoning attribution, I see the value in treating it as just one piece of the puzzle alongside analytics and CRM outcomes.

The most insightful question isn’t, “Which channel received the conversion credit?” but instead, “What would be different if this activity never happened?”

Inspired by this post on Search Engine Land.

June 12, 2026
Google Enhances Local Real Estate Ads Nationwide

I’ve got some exciting news to share! Google is expanding its enhanced Local Services Ads (LSAs) for Home Listings all across the U.S., and it’s set to revolutionize the home-buying process.

As someone who frequently turns to Google at the start of my own home-searching journey, I see this as a fantastic opportunity for connecting homebuyers like me with local agents earlier in the process.

What’s New: With the updated LSA experience, I’m thrilled to see that ads now include detailed property information, such as pricing, photos, and key home features, right within the ad itself.

This new functionality is made possible through a collaboration with HouseCanary, which provides the property data showcased in the ads.

Why It’s Important: For me, having access to actual property listings, including visuals, pricing, and details directly through Google’s Local Services Ads, means I can better evaluate homes and reach out to agents without ever leaving the search page. This could very well boost lead quality and conversion rates.

How It Works: If I’m in the market for a new home, I can contact agents directly from these ads, whether through a call, message, or by booking an appointment.

Who Benefits: Existing LSA advertisers are automatically included in this enriched experience. Real estate professionals not yet using Local Services Ads have the chance to sign up and start receiving high-quality leads. Additionally, portal partners can sign up agents through Google’s managed partner program.

The Bottom Line: Google’s strategy, combining rich listing information with direct agent connections, seems designed to make Search a more beneficial starting point for homebuyers like myself. It’s poised to become a valuable resource for agents looking for high-intent leads.

Inspired by this post on Search Engine Land.

June 11, 2026
Unlocking AI Search and Ads: Insights from Ginny Marvin

After Google Marketing Live, I’m still left with a lot of questions, and I’m sure I’m not the only one. Thankfully, Ginny Marvin, Google Ads Liaison, joined a comprehensive Q&A with Julie Bacchini and the PPC Chat community to tackle big topics like AI Max, AI Search ads, first-party data, and more.

The discussion was enlightening, bringing clarity to AI Search eligibility, reporting challenges, and Google’s increasing focus on data quality.

AI Max: Not a Must-Have for AI Search Ads

A major revelation was that AI Max isn’t required for participating in AI-driven search experiences. This surprised many of us, as we’d assumed AI Max was crucial for tapping into Google’s AI search surfaces.

Ginny highlighted that campaigns with broad match keywords are still eligible for AI Overviews and AI Mode. Even so, AI Max does broaden possibilities by treating phrase and exact match keywords with broad match behavior and enabling keywordless matching.

This means there are still multiple avenues available for us to access AI Search inventory.

AI Search Reporting is Still on Hold

Many of us were eagerly hoping for detailed reporting on AI-powered search results. However, Ginny confirmed that current ads in AI Overviews and AI Mode are reported like other top-of-page ads, with no distinct breakdown. Google’s still figuring out what these reports should eventually look like.

This leaves us with limited insights into how much AI-driven traffic and performance we’re actually seeing.

Google’s AI Brief: A New Layer of Control

A significant part of the discussion circled around AI Brief, set to become the control layer for AI Max campaigns. Advertisers like me will soon be able to provide specific guidance such as “never mention prices” or define target audiences, message themes, and search intents to prioritize.

The rollout will start with English Search campaigns and eventually spread to Performance Max and Shopping campaigns.

For those of us worried about automation reducing our control, AI Brief offers a promising solution.

The Core of Effective Advertising: First-party Data

If there’s anything I walked away with, it’s the emphasis on data quality, particularly first-party data. Google’s focus is what they call “Data Strength,” and tools like Enhanced Conversions and Google Tag Gateway are pivotal.

It’s clear: better data enhances AI performance and outcomes.

Exploring New Metrics: Qualified Future Conversions

Another fascinating development is Qualified Future Conversions (QFC). This metric estimates potential conversions occurring within 180 days post-ad interaction. It’s especially useful if you’re in B2B or lead generation sectors with lengthy sales cycles.

Currently, it’s in testing with select advertisers, and I’m keen to see it roll out further later this year.

Key Areas of Excitement at Google

When asked about her personal highlights from GML, Ginny shared three areas: the new ad formats for AI Search, measurement innovations like QFC, and YouTube Creator Partnerships.

This truly illustrates where Google is investing: AI discovery, advanced measurement, and creator-driven advertising.

Putting It All Together

This Q&A has definitely filled in some gaps left by the GML presentations. I’ve realized that broad match terms still provide a pathway to AI Search, AI-specific reporting is evolving, and Google’s vision continues to be centered on automation, powered by first-party data.

Most importantly, it’s about balancing automation with new controls like AI Brief to shape Google’s AI systems to our advantage.

Inspired by this post on Search Engine Land.

June 10, 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
Shopify Outage Hits Merchants: Sales and Access Disrupted
Tuesday was quite a day as I experienced a significant Shopify disruption impacting essential commerce functions. Many merchants, including myself, found it challenging to manage our stores, while customers faced difficulties completing their purchases.

The big picture. Shopify confirmed that issues affected multiple services, such as storefronts, checkouts, the admin dashboard, and Retail POS. I’m sure other merchants felt the effect just as I did, struggling to maintain access to Shopify Support during this downtime.

What happened. Shopify first acknowledged the problem at 9:27 a.m. EDT. We were informed that merchants might face access issues with:
- Shopify Admin
- Retail POS
While dealing with my own frustrations, I realized customers may encounter issues with storefronts and checkouts, making the day particularly challenging for those relying on Shopify Support.

Why we care. It’s crucial to monitor storefronts and checkouts; their unavailability means paid traffic can’t convert to sales, risking wasted ad spend and misaligned campaign performance data. For those running ads on platforms like Google or TikTok, keeping a close eye on performance during such outages is vital in assessing campaign results.

Latest status. By 10:37 a.m. EDT, Shopify reported identifying the root cause, noting improvements. “We’ve identified the problem and are seeing recovery from our mitigation efforts,” Shopify updated us, pledging continued monitoring.

Earlier updates at 9:45 a.m. EDT mentioned Shopify actively investigating the situation. It’s a relief to see progress, but vigilance remains necessary.

Between the lines. Given Shopify’s vast reach, even brief interruptions can immediately affect merchants’ revenue, especially when checkouts are compromised. This outage was a stark reminder of how pivotal continuous platform availability is for businesses.

For anyone with ongoing promotions or high-traffic campaigns, disruptions translate into lost sales and frustrated customers, something we all dread as business owners.

What to watch. While Shopify mentioned recovering services, I, like many, will keep monitoring until the incident is declared entirely resolved. It highlights our dependence on core platform providers like Shopify for crucial ecommerce functions.

The outage serves as a potent reminder of how much ecommerce relies on a few key platforms. Ensuring diversifications and contingencies is more important than ever.

First spotted. A heads-up on this issue came from Senior Paid Media Manager Ayisha Yousef, who encountered an error message and shared it on LinkedIn. This alerts us of how even internal team members aid in monitoring ongoing situations.

Inspired by this post on Search Engine Land.
June 3, 2026
Don’t Be Fooled: The Truth About B2B PPC Metrics

More conversions and higher ROAS are not always indicative of increased pipeline or revenue. I’ve discovered how to measure incremental value more accurately, and I’m excited to share it with you.

As a B2B PPC advertiser, I now have more options than ever before to gauge success. Previously, all I had was form-fill data. Now, with offline conversion data, I can feed invaluable insights into Google Ads and Microsoft Ads.

I’ve realized that while it’s tempting to measure every possible metric, optimizing them all is impractical. If you chase everything, you might end up achieving nothing substantial.

Determining if I’ve driven true incremental value and identifying the right success metrics for B2B PPC campaigns was crucial. Often, the metrics that truly matter aren’t the ones I initially focused on.

I’ve witnessed advertisers integrate offline conversions and get thrilled with a spike in total conversions, only to be hit with disappointment when there’s no boost in the bottom line.

After incorporating numerous conversion actions and setting them all to primary, advertisers, including myself, saw conversion counts rise, but not their actual impact. We were essentially counting the same leads multiple times.

This led to inflated platform-reported ROAS. Attaching conversion values to each action, which is advisable, also resulted in false increases. Both scenarios result from faulty calculations.

Solely focusing on average CPA proved misleading. It can mask the marginal CPA, the cost of acquiring an additional conversion as marketing expenditure increases, potentially leading to overspending as the account scales.

Setting up conversion values is crucial for understanding offline conversions. But it’s easy to get stuck if the conversion value isn’t known until it processes further down the pipeline.

Even if using actual conversion values is impossible, assigning relative values is still beneficial. I learned this through a simplistic example scenario.

Here, whenever I employed arbitrary values, I made sure to validate them against real data to ensure bidding algorithms responded accurately. This adjustment improved the relative perceived value of MQLs and SQLs for better alignment with true business goals.

By doing this, within just a couple of weeks, we managed to significantly boost MQL and SQL volumes while keeping leads flat, ultimately delivering higher-quality leads at the same cost.

Experimenting with campaign-specific goals allowed Smart Bidding to focus strictly on down-funnel actions, which fine-tuned our optimization efforts.

However, if lower funnel actions yielded low volumes, I noted automation might struggle due to insufficient signals. Adjusting strategy with this understanding ensured clearer outcomes.

To measure success effectively, beyond traditional CPA and ROAS, I focused on incremental conversions, evaluating them against baselines to understand the financial sensibility of further investments.

The most reliable measure of incremental value was mapping CRM data back to actual paid search campaigns. This helped in identifying assets and campaigns that, while generating fewer leads, drove significant pipeline growth.

Understanding this dynamic was critical in recognizing diminishing returns and preventing unfounded overspending on non-cost-effective channels.

Inspired by this post on Search Engine Land.

May 29, 2026