After notifying advertisers about the upcoming change last week, Google has now officially confirmed on the Google Ads Developer Blog that Local Inventory Ads (LIA) will be enabled by default for all Shopping campaigns beginning August 31st.

Why we care. Advertisers using Shopping campaigns will no longer be able to disable Local Inventory Ads through the Google Ads API. If your workflows or campaign structure rely on keeping local inventory separate from online inventory, you’ll need to update both your code and campaign configuration before the change takes effect.

What’s changing. Starting Aug. 31, Google will automatically enable the “Local products” setting for every Shopping campaign.

Previously, advertisers had to explicitly set the enable_local field to true in a campaign’s ShoppingSetting to serve products from their Local Inventory Ads feed.

After the update:

• Shopping campaigns will always have Local Inventory Ads enabled.
• The Campaign.ShoppingSetting.enable_local field will be ignored.
• Google will automatically treat the setting as true, regardless of the value submitted.

The change aligns Shopping campaigns with Performance Max for Retail, where Local Inventory Ads are already enabled by default.

What developers need to do. Developers using Google Ads API v25.1 or later should stop attempting to set enable_local to false.

Doing so will return:

• ContextError.OPERATION_NOT_PERMITTED_FOR_CONTEXT

For API versions earlier than v25.1, existing code will continue to work, but any attempts to disable Local Inventory Ads will simply be ignored, with Google treating the value as true.

How to prevent local products from serving. Advertisers that previously disabled Local Inventory Ads for specific Shopping campaigns now have two alternatives:

• Create a listing scope using CampaignCriterionService with product_channel set to ONLINE.
• Use the Inventory filter in the Google Ads campaign settings to exclude local inventory and separate budgets between online and local products.

In other words, Google is shifting inventory control away from a simple campaign setting and toward inventory filtering.

Not changing. The update applies only to Shopping campaigns.

The enable_local field will continue to function normally for other supported campaign types, including:

• Performance Max
• Demand Gen

Bottom line. Beginning Aug. 31, Local Inventory Ads will automatically be enabled for every Shopping campaign. Advertisers that currently rely on disabling local inventory should update their API integrations and inventory filtering strategy before the rollout.

Google has quietly updated its documentation for Lead Form assets, removing one of the biggest barriers that prevented smaller advertisers from using the format.

Why we care. The long-standing requirement for advertisers to spend more than $50,000 in Google Ads before becoming eligible for certain Lead Form experiences has disappeared from the Help documentation. If this reflects a product change—and not just a documentation update—it could make Google-hosted lead generation available to significantly more advertisers.

What’s changed. The $50,000 spend threshold is gone.

Previously, advertisers needed to meet one of two requirements to use Lead Forms in certain formats:

• Spend more than $50,000 USD in Google Ads.
• Or qualify as a reputable advertiser spending more than $1,000 per account (or $15,000 across accounts) and complete Advertiser Verification.

The updated documentation removes the $50,000 lifetime spend requirement entirely. The only eligibility path now mentioned is the advertiser reputation and verification requirement.

That is the most significant change in the update and substantially lowers the documented barrier to entry.

Campaign support has changed. Google has also simplified where Lead Form assets can be used.

Previously:

• Search
• Performance Max
• Display
• Video (beta)

Now:

• Search
• Performance Max

References to Video campaigns have been removed from the overview, while Display is no longer listed as a supported campaign type. Interestingly, the requirements section still references Display campaigns, suggesting the documentation may not yet be fully aligned across all Help pages.

More lead delivery options. Google expanded the documented methods for receiving leads.

Newly documented options include:

• Email notifications
• Zapier integration

These join existing delivery methods:

• CSV download
• Webhooks
• Google Ads API

The addition of Zapier gives advertisers a no-code way to automatically send leads into thousands of CRM platforms and business applications.

Better clarification on lead retention. Google also clarified how long lead data is stored.

The updated documentation now explicitly states:

• Manual CSV downloads are available for 30 days.
• Google stores lead data for 60 days.
• API exports can access up to 60 days of lead data.

Previously, the 60-day retention period wasn’t clearly explained alongside manual downloads.

New positioning. Google also refreshed how it describes Lead Form assets.

The previous documentation focused on generating leads and increasing conversions.

The updated version emphasizes:

• Higher-quality leads
• Higher lead volume
• Easier implementation
• OTP verification for improving lead quality

Expanded country availability. Google added more than two dozen additional countries where Lead Form assets are eligible, including Bahrain, Croatia, Estonia, Jordan, Kuwait, Morocco, Qatar, Serbia, Slovenia and Tunisia.

Bottom line. Google’s latest Lead Form documentation refresh includes several usability improvements, expanded integrations and broader geographic availability. But the standout update is the disappearance of the $50,000 Google Ads spend requirement—potentially opening one of Google’s most valuable lead-generation formats to far more advertisers.

Early in my career, I was responsible for reporting metrics for a company’s homepage. A member of the usability team wanted to know how much traffic a particular widget was getting. When we pulled the numbers, the result was underwhelming: About 2.5% of visitors actually used it.

But that’s not the number that ended up in the report. Instead, the widget’s usage was reframed as “a couple thousand visits per month.” That was technically true. It also told a completely different story from 2.5%.

That moment taught me something I’ve carried throughout my paid search career: Data isn’t black and white, and the person presenting it has a responsibility to tell an accurate story, not just a flattering one.

Data doesn’t lie, but PPC practitioners sometimes do. In PPC, we have more opportunities to blur that line than most people realize. Here’s where it happens and how to make sure your reporting holds up so you stay honest and ethical.

Conversions aren’t just conversions

If there’s one number that gets flattened most often in paid search reporting, it’s conversions.

A “conversion” can mean something completely different depending on what’s actually being counted. A form fill isn’t a marketing qualified lead (MQL). An MQL isn’t a sale.

For example, a phone call, a chat initiation, and a user watching 50% of a video are all conversion actions I’ve seen tracked in the same account, sometimes rolled into the same headline number and reported as “conversions.”

When you tell a client or stakeholder, “We got an excellent number of conversions,” without specifying what that conversion actually was, that’s not reporting. That’s editorializing, and it’s not a solid foundation for PPC strategy decisions.

Before you present a conversion number, ask yourself:

• What action is actually being counted? A form fill, a call, a chat, a video view, or a purchase?
• How far is that action from an actual business outcome? A lead and a closed sale aren’t the same type of win.
• Would the person reading this report make a different decision if they knew what was behind the number?

If the answer to the third question is yes, you owe them that context, and you should include it when you deliver the report.

Your CTR benchmark is probably a decade out of date

I still hear practitioners say a campaign is performing well because its click-through rate is “over 2%.” That benchmark comes from an era of PPC that doesn’t exist anymore.

Today’s bidding algorithms are far more sophisticated at finding users who resemble your existing converters. That alone pushes click-through rate (CTR) up across the board, independent of anything you did strategically.

A 2% CTR benchmark from 10 years ago tells you almost nothing about whether a modern, algorithmically targeted campaign is actually healthy and meeting its goals.

Reporting that “CTR is above benchmark” without acknowledging what’s driving that lift, whether it’s better targeting, better creative, or simply a more capable algorithm finding easier audiences, is another way data gets presented as good news without earning that designation.

I don’t think there’s a legitimate universal benchmark left to point to. The algorithm has gotten too good at finding easy clicks for a single number to mean the same thing across accounts, industries, or even campaigns within the same account.

Stakeholders will always ask the fundamental question: “Are these numbers good or bad?”

As subject matter experts, our job isn’t to hand them a legacy benchmark to check off, especially one that doesn’t really exist in any meaningful form.

True expertise means redefining success and shifting the conversation away from vanity metrics that the algorithm inflates for us. It means anchoring our reports in the business outcomes we were hired to drive.

It’s also important to explain how modern bid strategies affect the metrics you’re reporting.

Dig deeper: Why a lower CTR can be better for your PPC campaigns

Raw numbers and percentages tell different stories — use both

The widget story I opened with is really a story about raw numbers versus percentages, and that same tension shows up constantly in paid search reporting.

When you’re breaking down conversions by type, for example, showing that phone calls make up 40% of conversions versus leads at 60% tells a very different story than saying “142 calls, 213 leads.” Neither version is wrong. But presenting only one of them, especially the one that happens to look better, is a choice. It isn’t neutral reporting.

It’s something we have to be conscious of whenever we present data.

The fix isn’t complicated: Show the data in more than one way.

By presenting raw counts and percentages together, you give whoever reads the report enough context to understand what actually happened, instead of what you want them to take away from the report. Percentages add context to the data.

What you choose to focus on in a report is itself a form of manipulation

This kind of manipulation by omission is something I think about most often because I’ve seen it cost advertisers real money and create confusion.

For example, I once took over an account from another practitioner who had been telling the business that its low cost per click (CPC) was a sign of success.

If a low CPC were actually the goal, it’d be easy to hit that number by running everything through the Display Network or another upper-funnel campaign. But that campaign type may not align with what the brand actually needs.

In my example, because the business had been told for months that a low CPC meant good performance, it had bought into the wrong metric entirely.

In reality, a higher CPC often drives better business outcomes and can even result in a lower cost per acquisition because you’re paying more to reach higher-intent, higher-value users instead of optimizing for cheap clicks that don’t convert. For my client, that turned out to be true.

Focusing a stakeholder’s attention on the metric that makes your work look best instead of the one that reflects their actual goals is one of the quietest ways data gets weaponized in this industry. We should be client-first when it comes to reporting data.

Attribution can hide whether your spend is doing anything at all

Even accurate reporting on conversions, CTR, and CPC can still mask a bigger question: Would those conversions have happened anyway?

Attribution models give credit for conversions across touchpoints, but it’s important to remember that credit isn’t causation. A branded search campaign can show a huge volume of “conversions” that would’ve happened through organic or direct traffic, regardless of whether the ad ever ran. The report looks great, but the incremental business impact may be close to zero.

This doesn’t mean we wouldn’t run a brand campaign. It means the data needs more context and nuance.

Incrementality testing — whether that’s using a holdout group, running a geo experiment, or conducting a conversion lift study — is the only real way to answer whether your media spend is creating new business or simply claiming credit for outcomes that would’ve happened anyway.

Reporting conversion volume without ever addressing incrementality is one of the most common and most defensible-sounding ways paid search data tells an incomplete story.

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

3 manipulation tactics worth naming directly

Most of the metric issues above happen without anyone intending to mislead. But a few specific patterns are worth calling out because once you see them, you can’t unsee them in your own reporting.

• Conversion stacking: Counting multiple actions from a single user journey — say, a chat, then a call, then a form fill from the same person — as three separate wins instead of one.
• Cherry-picked date ranges: Comparing this month to a deliberately slow month last quarter, or quietly excluding the week your account had an outage or tracking issue. A date range chosen against a weak baseline can make almost any account look like it’s improving.
• Vanity metric substitution: Leading with a metric that looks good — perhaps impressions, clicks, or “reach” – when the metric that actually matters — qualified leads, revenue, or CPA – tells a less flattering story.

I’m not saying every practitioner who uses these patterns is acting in bad faith. Most of the time, it’s habit, not deception. But habits are exactly what need to be questioned if we’re serious about reporting data honestly.

Paid search doesn’t have a governing board. That’s exactly why this matters

Unlike many licensed professions, paid search practitioners don’t answer to a regulatory body. We have platform certifications, not an ethics board. That means the standard for how honestly we present data to clients and stakeholders is largely self-imposed.

I don’t think that gets talked about enough in our industry. It’s easy to frame a number in the best possible light, especially when your job security or a client relationship depends on the story that number tells.

But contextualizing conversions accurately, using current benchmarks, showing both raw numbers and percentages, and focusing on the metrics that actually reflect business outcomes isn’t just good practice. It’s the ethical baseline for PPC.

If we don’t hold ourselves to that standard, no one else will.

Google is making it easier to implement Google Ads purchase conversion tracking by introducing a Guided Setup experience in Google Tag Manager, reducing the manual work required to configure tags.

What’s happening. A new “Guided Setup” card is appearing in Google Tag Manager for Google Ads Purchase Conversions.

The beta feature automatically creates the required:

• Tags
• Triggers
• Variables

Instead of requiring users to configure each component manually.

Why we care. Conversion tracking is essential for campaign optimisation, but implementation can be complex—particularly for advertisers new to Google Tag Manager. Guided Setup aims to simplify the process, reducing setup errors and helping advertisers start measuring purchases more quickly.

Between the lines. The update reflects Google’s broader push to lower the technical barriers to measurement as advertisers increasingly rely on automated bidding strategies that depend on accurate conversion data. By automating much of the configuration, Google could improve tracking adoption and data quality across Google Ads accounts.

What we’re watching. The feature is currently in beta, meaning it isn’t yet available to all Google Tag Manager users. Google has not announced when a wider rollout will begin.

The bottom line. Google Tag Manager’s new Guided Setup streamlines Google Ads purchase conversion tracking by automatically configuring key measurement components, making implementation easier for both new and experienced advertisers.

First spotted. The beta feature was shared by Paid Search expert,Vivek Gupta on LinkedIn.

Google is rolling out a new Enhanced Brand Lift study to more advertisers, giving brands a more sensitive way to measure campaign impact—at the cost of a significantly larger budget.

What’s happening. Advertisers can now choose between two Brand Lift Study options in Google Ads:

• Standard Brand Lift, which measures lifts of 2% or higher.
• Enhanced Brand Lift, which can detect lifts as low as 1.2%.

Google says the enhanced option requires approximately three times the budget, but increases the likelihood of detecting a positive lift by 60%.

Why we care. Measuring the effectiveness of brand campaigns has long been challenging, particularly when lift is modest. The enhanced study gives advertisers greater confidence in detecting smaller changes in awareness, consideration and other brand metrics, helping validate upper-funnel investment.

Between the lines. The introduction of Enhanced Brand Lift reflects Google’s broader investment in brand measurement as advertisers increasingly demand stronger evidence that awareness campaigns drive measurable outcomes. The higher budget requirement (x3), however, means the feature is likely to be most useful for larger campaigns with sufficient scale.

The bottom line. Google is giving advertisers a more precise way to measure brand campaign effectiveness, but the additional sensitivity comes with a significantly higher media investment.

First spotted. The update was first identified by Google Ads specialist, Thomas Eccel who shared the update on LinkedIn.

The same search no longer guarantees the same answer. In 2026, the biggest change in digital discovery isn’t simply that AI generates answers. It’s that those answers are personalized for individual users.

Traditional search engines ranked webpages primarily based on relevance, authority, and popularity. Today’s AI-powered search experiences, including Google AI Overviews, Google AI Mode, Claude, ChatGPT, Perplexity, and other large language model interfaces, are designed to understand the searcher as much as the search query itself. 

Instead of asking, “What is the best answer?” Search systems are asking, “What is the best answer for this particular individual?”

Understanding how AI personalizes search experiences is the first step toward adapting your SEO strategy.

The roots of personalized search

For much of SEO’s history, search professionals have talked about “ranking No. 1” as if everyone saw the same search results. In reality, that was never entirely true.

Google has personalized search for years using signals such as location, language, device type, search history, and geographic intent. A user searching for “coffee shop” in Seattle naturally received different results than someone in Miami. 

Mobile users also had different experiences from desktop users. Returning users encountered recommendations influenced by previous searches and browsing behavior. Personalization has been part of modern search for well over a decade.

What’s changed is the scope of that personalization. Instead of adapting results based primarily on location, language, or search history, AI systems tailor responses to the individual behind the query.

The shift from universal rankings to individual recommendations

Traditional search engines primarily ranked webpages. The underlying question was, “Which page best answers this query?”

Modern AI-powered search asks a different question: “Which answer is most helpful for this specific person at this exact moment?”

Large language models synthesize information from across the web while incorporating an expanding set of contextual signals. 

As a result, two people can ask the exact same question and receive noticeably different answers, not because one result is objectively “better,” but because each answer is adapted to the individual’s context.

Search and social are converging

A common misconception is that search and social remain separate disciplines. They’re becoming part of the same discovery ecosystem.

Historically:

• Search answered specific questions.
• Social platforms created awareness.
• Websites served as the primary destination.

Today, those boundaries are fading.

AI systems learn from and reference information published across multiple platforms, including:

• YouTube.
• Reddit.
• LinkedIn.
• X.
• TikTok.
• Instagram.
• Threads.
• Podcasts.
• Public forums.
• Community discussions.

At the same time, social platforms are becoming search engines in their own right:

• People search TikTok for restaurant recommendations.
• People seek out real-world reviews on Instagram.
• People use YouTube as a how-to engine.
• People browse Reddit before making purchases.
• LinkedIn has become a destination for expertise and professional credibility.

Recently, this dynamic was amplified further with the introduction of social platform reporting in Google Search Console. If you conduct news searches during tentpole events, you may see an X carousel at the top of the results page. That same page may also feature emoji reaction buttons.

The connection between search and social continues to strengthen. Smart brands are responding by integrating their search and social teams into a cohesive function rather than operating as separate, siloed groups.

The modern customer journey no longer follows a straight line from Google to a website. Instead, discovery happens across an interconnected network of search engines, AI assistants, social platforms, creator communities, and recommendation systems.

AI draws from the entire digital ecosystem

Large language models don’t think in terms of channels or landing on a single resource with the “best” answer. Instead, they synthesize information from an diverse range of sources.

An AI-generated answer might simultaneously incorporate:

• Your website.
• Your YouTube videos.
• Your LinkedIn articles.
• Customer reviews.
• Interviews.
• Reddit discussions.
• Local business profiles.
• News coverage.
• Structured business information.

Your digital reputation functions as an interconnected knowledge graph rather than a collection of isolated marketing channels. As a result, many digital strategists are shifting from traditional SEO toward overall visibility, brand mentions, and discoverability. 

Personalization makes brand signals more important than ever

As AI systems become more personalized, they also become more selective. They’re less interested in pages that simply target keywords and more interested in identifying brands that consistently demonstrate expertise across multiple environments. 

As LLM platforms continue to evolve, with ongoing inconsistencies and misinformation, Google’s E-E-A-T framework has taken on a broader scope and greater influence.

Your visibility depends on whether AI can answer questions such as:

• Is this organization credible?
• Is this information consistently supported elsewhere?
• Do experts reference this brand?
• Does this company publish original insights?
• Is this brand active across the places people seek information?

These are reputation questions, not just ranking questions.

How deep does the personalization rabbit hole go? 

Several technologies have converged to make search fundamentally more personal.

AI systems now understand:

• Previous conversations.
• Search history.
• Location.
• Device content.
• Current activity.
• Images.
• Voice.
• Preferences.
• Calendar events.
• Gmail (when permission is granted).
• Shopping behavior.
• App usage.
• Multimodal inputs.

Google has publicly stated that search is evolving into a more intelligent, agentic experience that uses personal context to provide more useful answers and even complete tasks on a user’s behalf.

Rather than producing a universal ranking, search generates individualized recommendations.

Personalization doesn’t exist without multimodal search

One of the biggest reasons search feels much more personal in 2026 is that AI systems are no longer limited to understanding written text.

Modern search is multimodal, meaning it can interpret and combine multiple forms of information simultaneously, including text, images, audio, video, voice, documents, and, , live context.

For brands, this means discoverability is no longer confined to webpages. Every digital asset can become part of the search experience.

Examples: 

• A product photo may appear in Google Lens results.
• A YouTube video transcript may be cited in an AI-generated answer.
• A podcast interview can reinforce your expertise.
• A LinkedIn article can help establish topical authority.
• An Instagram Reel demonstrating a process may answer a user’s visual search.

Search has evolved from retrieving documents to understanding information regardless of format. 

Every piece of content becomes searchable

Historically, SEO focused on optimizing HTML pages because search engines primarily indexed webpages. 

Today, AI systems understand:

• Images and infographics.
• Short-form videos.
• Long-form video transcripts.
• Podcasts and audio.
• PDFs and presentations.
• Product photography.
• Maps and local business information.
• Social posts.
• Customer reviews.
• Structured data.
• User-generated content. 

In many cases, these assets are no longer supporting content. They’re the content being discovered.

The practical implication is that brands should think beyond “content marketing” and instead manage a portfolio of searchable assets that can be surfaced across AI-powered experiences.

Search is becoming ambient

Multimodal search is also changing when people search. For decades, search was an intentional activity. Users opened a browser, typed a query, and reviewed a list of links.

Today, search is woven into everyday moments:

• People search by speaking into their earbuds while walking.
• People search by taking pictures of products in a store.
• People search by asking follow-up questions without restarting the conversation.

AI assistants retain conversational context, allowing discovery to unfold naturally rather than as a series of disconnected keyword searches. Search is becoming less of a destination and more of a continuous, interactive layer that helps people interpret the world around them.

Why this matters for your brand

This evolution fundamentally changes how you should think about optimization. Every digital touchpoint contributes to discoverability.

A strong multimodal strategy includes:

• Descriptive alt text and accessible imagery.
• Video transcripts with clear speaker attribution.
• Original charts, diagrams, and infographics.
• Structured data that identifies people, organizations, products, and events.
• Consistent branding across websites, social profiles, podcasts, and video channels.
• High-quality visual assets that image search systems can interpret.
• Documents and downloadable resources with searchable text rather than image-only PDFs.
• Original research, case studies, and data visualizations that AI systems can reference and cite.

The new goal is to become a recognized brand

Although SERP position still matters, AI-driven discovery rewards recognizable brands, not just highly ranked pages. Brands should become recognized entities that AI systems understand, trust, and confidently recommend.

That objective requires building authority not only on your website, but also across the broader digital ecosystem, where search, social, video, local listings, reviews, and AI overlap. 

Success is becoming less about winning a single ranking and more about building a trusted, visible, and connected brand wherever people and AI systems look for answers. This expands SEO into a broader practice of helping brands earn recognition and visibility across an personalized, AI-mediated discovery ecosystem.

What other tactics can brands use to stand out in this new era of personalized audience engagement?

1. Give users a reason to make you a Preferred Source

Google’s Preferred Sources feature in Top Stories allows signed-in users to prioritize news publishers they trust.

While brands can’t force inclusion, they can encourage loyal audiences to favorite them through consistent, high-quality content and clear calls to action:

• Educate your audience by publishing a standalone article explaining how to set up Preferred Sources, or add a button at the top of your articles to simplify the process.
• Include related messaging in newsletters and social posts for loyal followers.
• Invest in recurring coverage that gives users a reason to return.
• Build recognizable editorial voices rather than anonymous content.

Google previously stated that “people are twice as likely to click through to a Preferred Source.” As publishers experience lower click-through rates from AI Overviews, that potential advantage is difficult to ignore. 

In May, Google announced that Preferred Sources would expand to AI Overviews and AI Mode, giving brands another opportunity to increase their visibility.

Similarly, the Follow feature in Google Discover allows signed-in users to prioritize publishers and creators in their feeds. After following a publisher or creator, users see more of that content in Discover. This rollout coincides with Google’s increased emphasis on social content in Discover, further blurring the line between search and social. 

These newer features complement longstanding ways users curate what they see:

• “Not interested”
• “Hide this source”
• “More like this”
• Site search (site:example.com “search term”)
• SERP navigation tabs, including “News,” “Images,” and “Videos”

The future of search is shaped by user preferences alongside algorithmic rankings. 

2. Build an audience, not just organic traffic

AI systems recognize brands with direct relationships with users.

Encourage visitors to:

• Subscribe to newsletters.
• Download apps.
• Opt in to notifications.
• Follow social channels.
• Create accounts.
• Subscribe on YouTube.
• Save Google Business Profiles.
• Join community groups.
• Follow authors.

Publishing updates more frequently can also encourage your audience to rely on your content.

Maintain:

• Breaking news coverage.
• Quarterly updates.
• Annual refreshes.
• Seasonal explainers.
• Trend analyses.

These touchpoints meet timely audience needs and strengthen brand familiarity across multiple platforms. 

3. Encourage repeat visits 

Returning users send stronger trust signals than one-time visitors.

Brands should create:

• Recurring columns.
• Weekly insights.
• Ongoing video series.
• Interactive tools.

The objective isn’t simply to attract traffic. It’s to become part of an individual’s regular routine.

4. Publish across multiple platforms

Modern discovery happens everywhere. Extend your editorial strategy beyond your website.

Create complementary content on:

• LinkedIn.
• YouTube.
• Reddit.
• TikTok.
• Facebook.
• Instagram and Threads.
• Podcasts.
• Industry newsletters.

Social platforms appear within search experiences. These modules can uncover content gaps and inspire new articles.

Editorial teams should optimize:

• Captions.
• Hashtags.
• Alt text.
• Spoken keywords.
• On-screen text.
• Video descriptions.

User behavior tells AI systems which creators and platforms are trustworthy and worth citing.

5. Invest in author recognition

Personalization happens around people as much as brands.

Feature:

• Real authors.
• Executive thought leadership.
• Subject matter experts.
• Interviews.
• Conference presentations.

Further amplify the reach of in-house contributors through:

• Original research.
• Proprietary data.
• “Boots-on-the-ground” videos.
• Benchmark reports.
• Downloadable resources.
• Expert commentary.
• Visual explainers.

People often follow individuals before organizations. Strong author credentials improve discoverability across AI search.

6. Make every asset searchable

Don’t hide valuable expertise inside formats AI can’t easily understand.

Include:

• Transcripts for videos.
• Alt text for images.
• Captions for social posts.
• Structured data.
• Descriptive filenames.
• Searchable PDFs.

The more formats AI can interpret, the more opportunities you create for personalized discovery.

7. Build strong internal linking based on user journeys

Organize internal links around logical next steps rather than related keywords alone.

For example, an explainer on “best hiking clothes” can link to:

• Beginner hiking guide
• Hiking checklist
• Trail safety FAQ
• Best national parks ranking
• Backpack review roundup

This approach mirrors how users naturally progress through topical research.

8. Optimize for follow-up questions

Personalized AI search is conversational and often driven by questions backed by specific intent.

Starting with a general topic, you can build a content strategy shaped by personal preferences, ultimately providing information tailored to the individual user.

General query: “What’s a good dinner recipe?”

Follow-up questions:

• What would you recommend for a vegetarian meal? (Dietary preference)
• Which recipes would work best for two people? (Social preference)
• Which dishes can I make in 30 minutes or less? (Time restriction)
• Which entrees can be made with common kitchen ingredients? (Resource restriction)
• Which recipes are most popular right now? (Trendy or seasonal)

Additional conversational context:

• Clarifies intent.
• Eliminates repetitive input.
• Refines recommendations.
• Maintains continuity.
• Improves personalization.

You aren’t competing to answer the first question. You’re competing to stay useful throughout an entire AI-assisted conversation. Every follow-up question creates another opportunity to increase brand visibility.

You can also answer questions before they’re asked by conducting exploratory research in:

• Reddit discussions.
• Community forums.
• Social comments.

User-generated content has become influential. Tapping into related spaces can help brands turn recurring questions into editorial content before competitors strike. 

9. Create content for different experience levels

Personalization means beginners and experts often receive different responses.

Develop content for:

• Beginners.
• Intermediate users.
• Advanced professionals.
• Executives.
• Educators.
• Students.

Interactive content can also help brands meet the needs of different experience levels.

Develop:

• Calculators.
• Quizzes.
• Assessments.
• Recommendation tools.
• Interactive maps.

Different audiences need different applications, imagery, FAQs, and insights. The broader your content offering, the more user intent levels you can satisfy. 

10. Strengthen your entity across the web

AI recommendations depend on understanding who your organization is.

Maintain consistent information across your:

• Website.
• Google Business Profile.
• LinkedIn.
• Email communications.
• Industry associations
• Conference speaker pages.
• Podcast appearances.

Entity consistency helps AI connect your digital signals and build trust with your audience. 

11. Increase localized editorial production

Personalization relies on location signals.

Create:

• Neighborhood guides.
• City pages.
• Regional comparisons.
• Local event coverage.
• Location-specific FAQs.

Even national brands can capitalize on local search intent. Reflect how people talk and search locally to create a more personal user experience that fosters long-term loyalty.

12. Measure relationship metrics, not just rankings 

Traditional SEO reporting emphasized:

• Rankings
• Impressions
• Clicks

Modern discoverability should also track:

• AI citation frequency.
• AI Overview appearances.
• Discover visibility.
• Google Top Stories inclusion.
• Social search impressions.
• YouTube search traffic.
• Referral traffic from LLMs.
• Branded search growth.
• Return visitor rate.

These metrics better reflect whether you’re building an ongoing relationship with your audience.

Personalized search rewards lasting relationships

In 2026 and beyond, brands also need to convince people they’re worth following, subscribing to, and choosing repeatedly. Those direct relationships shape the personalized experiences people receive from search engines, AI assistants, and social platforms.

The brands that thrive in this digital landscape won’t just publish content. They’ll cultivate loyal audiences whose preferences become signals AI systems can recognize and amplify.

]]> best-yoga-classes-in-Los-Angeles Screenshot Screenshot Screenshot Screenshot Screenshot Screenshot Screenshot Screenshot AP USA Today Wimbledon Emmy nominations Screenshot Screenshot https://searchengineland.com/local-services-ads-come-to-google-ads-via-performance-max-482692 Mon, 20 Jul 2026 13:14:35 +0000 https://searchengineland.com/?p=482692 Advertisers will gain a unified campaign management experience with automatic Google Business Profile syncing.]]>

Google is moving Local Services Ads (LSAs) into the Google Ads interface, replacing the standalone LSA dashboard with a new Performance Max for pay-per-lead goals campaign designed specifically for local service businesses.

What’s happening. Beginning with a phased rollout in August 2026, eligible advertisers will manage Local Services Ads directly within Google Ads.

The new campaign type is built exclusively for Local Services advertisers and retains the core characteristics of LSAs:

• Ads continue to appear only on Search and Maps.
• Campaigns remain keywordless, using information from the advertiser’s Google Business Profile (GBP).
• Advertisers continue to pay only for valid leads—such as calls, messages and bookings—not clicks.

Despite the Performance Max branding, these campaigns do not expand to Google’s other advertising channels.

What’s new:

• Centralised campaign management: Advertisers will manage campaigns, leads and calls from within Google Ads instead of a separate Local Services Ads dashboard.
• Real-time Google Business Profile syncing: Changes made to a Google Business Profile will automatically update campaigns, including business details and photos, reducing manual maintenance.

Why we care. The integration simplifies campaign management by bringing Local Services Ads into the same platform advertisers already use for Search, Shopping and Performance Max. It also introduces tighter Google Business Profile integration, helping ensure ads stay up to date automatically.

Rollout details. The transition will begin with a small group of U.S. advertisers across pet care, home services, wellness and education in early August 2026. Google says the rollout will continue in phases through 2027, with advertisers receiving advance notice before their campaigns migrate.

Existing budgets, settings and creative assets will transfer automatically, although historical performance reports will not migrate to Google Ads. Google is encouraging advertisers to download past reporting data before their accounts transition.

The bottom line. Google is folding Local Services Ads into Google Ads without changing how the product works, giving local businesses a more unified campaign management experience while keeping the familiar Search, Maps and pay-per-lead model intact.

If you manage Google Ads, you’re responsible for creative that runs across surfaces and formats that never used to exist.

Not long ago, an ad was a single headline and two short descriptions. Today, a single Google ad can include multiple responsive text assets, videos, and images, generating tens of thousands of permutations.

We’re often told that when it comes to ad creative, we should just “let the data decide.” So mediocre assets enter rotation, the platform reports on performance, and nobody can explain why an ad works or doesn’t. 

Part of your job is to champion good ideas and push back on bad ones. Creative direction isn’t about overriding your tests. It’s about improving the quality of the ideas that enter them.

That takes creative judgment, and it’s a skill you can build. At SMX Advanced 2026, I shared the framework I use to evaluate ads like a creative director. It’s called MOCA, which stands for magnetic, obvious, congruent, and actionable.

Let’s walk through each pillar using real ads.

Magnetic ads qualify the click

Magnets are selectively attractive. They pull some things in and actively push others away.

That’s exactly how great ads work. You want to attract buyers and repel tire kickers because every unqualified click is budget you’ll never get back.

Look at these two ads running on the same “investing” keyword:

• Get Started with $1
• $500 Min Investment

Robinhood says you can start investing with just $1. That’s clearly for beginners.

Meanwhile, Percent promotes a $500 minimum investment for accredited investors. That’s clearly for high-net-worth individuals.

Both ads qualify the click before the visit ever happens.

Here are two more examples of magnetic ads:

• Find ISO 27001 Gaps Before Auditors
• Crazy Comfortable 4-Way Stretch

If you’re preparing for an ISO 27001 audit, the first ad grabs your attention. If you’re not, you scroll right past it.

If you like stretchy jeans, Mugsy’s shopping ad earns your click immediately. If you’re not in the market, there’s no reason to click.

Your ad should make it obvious whether someone should click or skip.

In a broad match world, qualifying the click with creative is the difference between funding your growth and funding Google’s.

Magnetic ads qualify the click, so your budget doesn’t have to.

How magnetic is your ad?

Does it tell viewers whether to click or skip?

• Attract: Does your target audience want what’s being offered?
• Repel: Would your anti-audience self-select out before clicking?

Obvious ads don’t make you think

An obvious ad is self-evident. Someone with zero context — your aunt, let’s say — should immediately understand what’s being offered without decoding anything. You’d be surprised how many ads fail this test.

Non-obvious ads usually fall into two categories: the “mystery ad” and the “information dump.” Here are examples of each:

• Start for Free. How will you use it?
• Measure what matters: lasting change

The first ad is a mystery ad. The image is cryptic, and the headline asks, “How will you use it?” Use what, exactly? It’s not clear what the offer even is.

The second ad is an information dump. Three scorecards and a wall of tiny text fill the image, yet it still takes effort to understand what the ad is actually about.

These next two ads both promote apps, but one is immediately obvious, and the other is confusing:

• Earn While You Walk! Walk and earn coins daily
• Discover the joy of cheerful Pikmin planting flowers around you

“Earn while you walk” is an obvious headline for a step-tracking app. “Discover the joy of cheerful Pikmin planting” isn’t, and the image does nothing to clarify it.

The paradox of ‘magnetic’ vs. ‘obvious’

At first glance, “magnetic” and “obvious” seem to contradict each other. Magnetic ads narrow the audience. Obvious ads should be universally understood.

So how can an ad be both? How do you write something that’s only for your target audience and still instantly clear to everyone?

The answer is simpler than it sounds. I don’t have to want what you offer, but I do have to know what category it belongs to.

Take another look at the ISO 27001 audit service ad. I don’t know what ISO 27001 is. But I know with absolute certainty that I’m not preparing for one. So when I see the ad, I know I can safely skip it. The ad doesn’t need to explain ISO 27001. It just needs to be obvious enough that I immediately recognize, “This isn’t for me.”

That’s the real job of an obvious ad: not to persuade everyone, but to make the sorting happen instantly.

How obvious is your ad?

Would your aunt know what it’s about?

• Defined offer: Is the offer and its payoff clear? Do people understand what it is and what they’ll get?
• Reinforcement: Does every asset (text, image, and video) reinforce the same idea?

Dig deeper: A quiet Google Ads setting could change your creative

Congruent ads meet the user where they actually are

Most of us think “congruence” means message match: the ad should reflect the landing page. That matters, but it’s only part of the equation. The more important aspect of congruence is matching the offer to user intent.

Here are two ads for handheld devices:

• Try it risk-free. Free shipping, free returns
• Accept Payments Anywhere. Sell faster…

The first ad doesn’t connect the product to the prospect’s actual goal. Nobody wakes up hoping to get free returns on a mystery device.

Contrast that with Square’s “Accept Payments Anywhere. Sell faster.” You immediately understand what it does and whether it solves your problem.

These next two ads for schools offer an even starker contrast:

• HBS Online Business Strategy. Build an understanding of AI
• You are In. Now Deposit. Your college journey begins when you deposit

The HBS ad describes the program. The second jumps straight to payment options in a prospecting ad. It’s the embodiment of the advice, “Don’t ask for marriage on the first date.”

How congruent is your ad?

Does it meet your user where they actually are?

• Mind matching: Does it match what’s on your user’s mind, not just the keyword?
• Ad-to-landing-page alignment: Does the promise match the landing page?

Actionable ads drive the next step

You want your audience saying, “Here, take my money,” because they’re eager for what’s on the other side of the click. Clicking should feel like a no-brainer.

One of these ads tells you what you’ll get after you click. The other doesn’t.

• Private 5+ Acreage Near Nashville. Land & Cabin Combo $119,000
• Built Into The Culture Of Schindler. Explore Our Products

The private acreage ad is essentially one big call to action (CTA), telling you exactly what you’ll get after you click.

The second ad doesn’t even imply a call to action. It references company culture, not the product line. Urgency and exclusivity also strengthen CTAs. 

• 50% Off WSJ’s Best Budgeting App of 2025
• We Deliver Daily Dose Text. Text designed for busy people…

“50% off” is a real reason to click today. Meanwhile, “My Daily Dose” is trying to sell text-message subscriptions to busy people who already resent their notifications. There’s no path forward that feels good.

An actionable ad makes it clear what happens after the click and why it’s worth taking.

How actionable is your ad?

Does clicking feel like a no-brainer?

• CTA clarity: Is it clear what happens after you click?
• Reason to act now: Does your ad give people a reason to click now instead of later?

Dig deeper: How Google Ads’ AI tools fix creative bottlenecks, streamline asset creation

Score your ads with the MOCA matrix

To make this framework more practical, I vibecoded a free MOCA scoring matrix.

It won’t score ads for you. That would defeat the purpose of developing your own creative evaluation skills. Instead, you’ll evaluate each ad against the four MOCA pillars: magnetic, obvious, congruent, and actionable.

A high score doesn’t guarantee an ad will win. It means the ad is worth testing. MOCA is quality control before your creative enters testing.

Creative judgment is still yours. MOCA gives you a way to apply it and defend it in front of clients, CMOs, and data purists.

Technical SEO is full of recommendations, but not all of them deserve the same priority. A task that’s critical for one website may not be worth the effort on another.

Instead of chasing green audit scores or clearing every warning in your SEO tool, focus on the activities that will have the greatest impact. The best technical SEOs prioritize what’s needed over what’s most obvious.

Why prioritizing technical SEO is important

When you’re considering a technical SEO task, it’s important to prioritize it against the other work competing for your time. 

You have limited time

You have limited time and resources. It isn’t possible to tackle every task on your to-do list, so you need to identify which activities deserve the highest priority.

Focus on the activities that will deliver the biggest results for the time you invest. 

You have limited developer resources

Technical SEO tasks often require help from your development team. That means you’ll need to secure time in their sprint to implement them. Anyone who’s worked in SEO for any length of time knows how valuable that resource is.

Make sure every request is worth your developers’ time and the tradeoff of other work they won’t be able to complete.

You need to show impact

Every technical SEO task you take on should have a clear expected ROI. That isn’t always easy to quantify, but you should be able to explain why you’re doing it and how you expect it to move the needle.

Once you can answer those questions, you can start prioritizing technical SEO tasks against one another.

Dig deeper: Where to focus technical SEO when you can’t do it all

How to tell if you might be wasting time

Rather than labeling specific tasks as time-wasters, consider how to determine whether a task is worth your time on your website.

If you’re doing it to check a box

If your only reason for carrying out a technical SEO activity is to clear alerts in your SEO tool or because you saw it recommended on LinkedIn, stop. Very few, if any, technical SEO activities should be a top priority for every website.

If you don’t have a reason for doing it beyond “SEO best practice,” I’d argue it isn’t something you should dive into. Focus instead on activities that will improve your website’s performance for users and search bots. 

You can’t quantify the impact

This is perhaps the most important reason to park a technical SEO task: You don’t know what impact it will have.

If you want people to buy into your SEO plans, you need to show why they’re financially worthwhile.

That might mean improving revenue, reducing churn, or increasing qualified traffic.

For example, if you’re considering improving a page’s load speed, don’t make “reduce page load time” the goal. Instead, consider what faster load times could achieve for your organic performance and business goals.

If you work on an ecommerce site, you may know that faster load times can improve conversion rates. That’s a much stronger reason to take on the work than simply achieving a “good” page speed score. 

By making conversion rate improvement the goal, you can estimate the potential financial impact. For example, if a page converts at an average rate of X% and generates $X in annual revenue, increasing the conversion rate to X+1% could generate up to $Y in additional revenue.

You don’t know how to measure success

If you aren’t sure how you’ll measure the impact of an activity, that may be a sign it shouldn’t be a priority.

Examples of work that can be a waste of time

Any technical SEO activity can be a time-waster. Technical SEO is about optimizing your website to provide the best possible experience for users and search bots. That means what is a waste of time for one website may be a necessary activity for another.

That said, there are several technical SEO activities I’ve seen appear on countless checklists despite offering little discernible benefit. That doesn’t mean you should remove them from your to-do list. It means they should only be there if they’ll solve a problem your website actually has.

Core Web Vitals work when they aren’t that bad

Yes, I know Core Web Vitals scores are important. I just don’t think they’re so important that you should continually optimize them. As long as your CWVs are in the “good” range, pushing them further into that range is likely to deliver diminishing returns. 

I doubt spending developer time to shave a few milliseconds off your Largest Contentful Paint score will have much impact on rankings or user behavior.

Reserve Core Web Vitals optimization for pages that genuinely aren’t meeting targets. These are the pages users struggle to load or that negatively affect usability. 

Get your scores into the “good” range before investing time chasing incremental improvements.

Removing redirects when you don’t have chains

I understand the perfectionist approach to redirects. No one likes running a crawl and seeing 301 status codes reported. However, the real concern with redirects is how they affect search bots’ ability to crawl your website.

If you aren’t seeing redirect loops, chains with more than five hops, redirects to irrelevant pages, or enough redirects to affect bots’ ability to crawl most of your website, you can probably leave them alone. 

Sure, if you have a few spare minutes and you’re already in your CMS, you can update links so they point directly to the final destination instead of a redirecting URL. Realistically, though, there are only a few situations where this belongs near the top of your to-do list.

Leave redirect cleanup until it has a measurable impact on your site’s performance.

Optimizing bot crawling when your content is already crawlable

There’s a lot of misinformation about crawl budget. For most websites, it isn’t an issue. Large enterprise websites often need to optimize crawl paths, but smaller websites usually don’t.

If your website has developed spider traps or is allowing bots to crawl pages you don’t want indexed, that’s worth addressing. Otherwise, if Google Search Console shows Google is crawling your important pages, you can probably move crawl budget optimization lower on your priority list.

Fixing every 404 status

Like fixing every 301 redirect, eliminating every link to a 404 page is often more about tidying up than solving a meaningful problem.

404s are a normal and expected part of a website. If they’re hurting the user experience, absolutely fix them. If you’re simply trying to clear the list of 404s in your crawling tool, it can probably wait until you have time. 

Removing obsolete metadata like ‘meta keywords’

I know no one is adding meta keywords anymore. That’s good. It also doesn’t mean you need to spend precious time removing them. Just leave them. 

Chances are they’re so old they aren’t even giving your competitors useful hints about the keywords you’re targeting. We’ve all moved on from meta keywords, so you can, too.

Routine disavow file maintenance

Many SEOs will tell you they don’t use disavow files at all. I agree their usefulness has diminished significantly. There may be times when you want to submit or update one. 

You may also want to audit it to make sure nothing inappropriate was submitted in the past. However, I struggle to think of a reason why routinely updating a disavow file on a scheduled basis would be productive.

Clearing audit tool issues

Not every issue your SEO tool flags is an emergency. Remember that SEO tools lack the context of your broader strategy and your website’s foundation. 

You may have intentionally blocked pages in your robots.txt file or returned 404 status codes for some pages. That doesn’t mean they’re problems or that you need to fix them.

Your auditing tools should support your technical SEO work, not dictate it. You don’t need to clear every issue they report to have a technically healthy website. Don’t let the red lights and warnings distract you from more meaningful technical SEO work. 

Dig deeper: How to make your SEO strategy more commercially aware

What to do instead

Instead of working through a technical SEO checklist, spend that time on projects that will have a greater impact.

How do you know which tasks are worth doing? Any technical SEO work should meet at least one of these three criteria.

It directly impacts revenue or business goals

It’s much easier to get buy-in and recognition for your technical SEO work when you can tie it directly to revenue or business goals.

That means drawing a clear line between the work you’re doing and a business outcome. For example, if you’re improving slow-loading pages, you can connect that work to higher conversion rates. That’s a much stronger indicator of business value than simply making a page load faster. 

It creates a foundation for work that supports business goals

Technical SEO is worth the investment when it enables other work that will move the needle. 

For example, it may be difficult to show how optimizing crawl paths on an enterprise website directly affects business goals. However, you can connect improved crawl paths to revenue-driving pages being discovered and crawled, ensuring SEO tests on those pages are found and processed.

Without crawl optimization, search bots may never see those SEO tests, making the work ineffective. Activities that enable other important SEO initiatives are valuable in their own right.

It prepares your website for AI-driven search

A final criterion for meaningful technical SEO work is whether it prepares your website for future changes. Some work may not deliver immediate returns but could pay off over time. Preparing your website for AI-powered search experiences is one example.

You may not be getting much traffic or many conversions from AI agents yet, but there’s a strong possibility that this is where the web is heading. Preparing for that shift can feel risky because it’s speculative. However, when done well, this work is also likely to improve your website for today’s search bots and users.

Avoid busywork, prioritize impact

Technical SEO work can be time-consuming, involve multiple teams and stakeholders, and ultimately make the difference between a well-performing website and a poor one.

The key is making sure your time and energy are spent on activities that produce meaningful results. Treat technical SEO like any other business investment: prioritize the work that’s most likely to produce meaningful results.

Dig deeper: Why proving technical SEO ROI is so difficult ]]> https://searchengineland.com/heather-robinson-talks-about-the-ppc-mistake-that-cost-1000-482637 Fri, 17 Jul 2026 18:53:01 +0000 https://searchengineland.com/?p=482637 A £50 Meta campaign that accidentally spent over £1,000 became a valuable lesson in transparency, process, and why honesty matters.]]>

During the latest episode of PPC Live the Podcast, Google Ads specialist Heather Robinson shared how a Meta campaign intended to spend just £50 over a weekend accidentally spent more than £1,000 after the budget was set as daily instead of lifetime. Because the campaign wasn’t revisited after launch, it continued running for three weeks before the mistake was discovered while preparing for a client meeting.

Why routine tasks can be the most dangerous

Heather explained that the mistake wasn’t caused by a lack of knowledge but by complacency. After setting up similar campaigns countless times, the process became second nature, making it easier to overlook a small but critical setting. Combined with a busy workload and the absence of a second pair of eyes, the campaign went live without the final checks that could have prevented the overspend.

Honest communication saved the relationship

Rather than looking for excuses or blaming the advertising platform, Heather chose to be transparent with the client. She addressed the mistake during their scheduled face-to-face meeting, accepted responsibility and committed to preventing it from happening again. Although the client was understandably unhappy, they appreciated the honesty and transparency, and nearly a decade later they remain one of her clients—a reminder that trust is often built through difficult conversations rather than perfect performance.

Checklists are better than confidence

The experience fundamentally changed Heather’s campaign launch process. Every Google Ads and Meta campaign now goes through a structured launch checklist before being published, regardless of how routine the task may seem. While she occasionally uses AI to provide a second opinion, she still relies on manual reviews because she believes a disciplined process is more reliable than assuming experience alone will catch mistakes.

Conversion tracking remains the biggest problem

Beyond her own experience, Heather said the most common issue she encounters when auditing new client accounts is incorrect conversion tracking. Many accounts are still suffering from mistakes made during the migration from Universal Analytics to GA4, with businesses unknowingly optimising campaigns towards actions that don’t generate revenue. In one example, an ecommerce account had spent an entire year optimising for visitors using the site’s search bar instead of completed purchases, forcing the account to effectively restart its machine learning once the tracking was corrected.

AI is a helpful assistant—not a replacement

Heather believes AI has become a valuable productivity tool, but only when it’s used to support experienced marketers rather than replace them. While she has seen too many advertisers rely on Google’s AI-generated ads without reviewing them, resulting in repetitive and low-quality messaging, she has successfully used AI to analyse search term reports, identify optimisation opportunities and reduce hours of manual work. The key, she says, is ensuring that human expertise remains responsible for the final decisions.

Learning comes from testing—and making mistakes

Reflecting on both her own experience and the pace of change within Google Ads, Heather encouraged PPC professionals to continue testing new features while accepting that not every experiment will succeed. She believes mistakes are an unavoidable part of developing expertise, provided they are accompanied by honest communication, thoughtful analysis and improved processes that reduce the chances of repeating them.