Looking to take the next step in your search marketing career?
Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.
Job Description Content Marketing Manager Location: El Segundo, California, USA (HQ) Reports To: Director of Marketing Role Type: Full-Time, On-Site Compensation: $75,000-$90,000 annually About QuikStor QuikStor is the leading SaaS facility management platform for the self-storage industry, delivering a purpose-built, scalable system that serves as the foundation for intelligent automation and modern facility operations. We […]
(Hybrid) Description We are expanding our marketing team and seeking a Content Marketing Specialist to play a key role in executing our marketing strategy. You will own the creation and publication of content across blogs, social media, email campaigns, and the company website, helping bring DMC’s brand to life across digital channels. A portfolio of […]
Job Description Position Title: E-commerce and SEO Specialist Compensation Range: $55,000 – $75,000 Location: Hybrid / On-site – Englewood, CO About GOLFTEC Enterprises: GOLFTEC Enterprises is a dynamic, technology-driven leader in the golf industry, uniting two premier brands—GOLFTEC and SKYTRAK—with a shared mission: to help people play better golf. GOLFTEC, the world leader in golf […]
The Role Wpromote is seeking a Senior Technical SEO Manager dedicated exclusively to the Southwest Airlines account. This isn’t a typical SEO role — it’s an opportunity to shape how a leading travel company competes in a transforming search landscape. You’ll be a key player focused on organic discoverability, cross-channel collaboration, and measurable revenue impact. […]
Our digital marketing agency helps multi-location home service brands generate leads across dozens of local markets. Our flagship client is a PE-backed home services company operating 40+ locations across the U.S. and Canada under several brands, and we are expanding our SEO team to support rapid growth. We are looking for a hands-on SEO Manager […]
Job Description Salary: $45,000-$50,000 DOE Position Summary We are looking for a creative and motivated Content Marketing Specialist to join our team in the roofing industry. This role is ideal for someone with at least one year of marketing experience who thrives in a fast-paced environment and enjoys creating visually engaging, results-driven content. You will […]
Job Description Title: Director of Digital Marketing Reports To: President/CEO Location: Bellingham, WA or Waynesboro, TN (negotiable) Department: Marketing Salary: $115K-$135K annual base salary for the initial six months, with transition to an attractive incentive-based compensation package designed to reward performance and contribution. About Us Seeking Health is a fast-growing nutritional supplement company with $50M […]
The SEO Strategist is responsible for owning and executing comprehensive SEO strategies across their assigned portfolio of clients. This includes analyzing performance, creating actionable roadmaps, managing content and optimization planning, and resolving client-specific SEO issues. Strategists play a critical cross-functional role in ensuring SEO is aligned with the broader goals of client success, advertising, content, […]
We are seeking a “full-stack” technical SEO to join our growing team of talent. In the role, you’ll own technical SEO delivery for an amazing pool of clients in markets as diverse as fashion and finance to automotive and travel. You will be advising clients and colleagues on architecture, performance, and technical best practices and […]
Job Description Director of Digital Marketing Healthcare is increasingly unaffordable for many Americans. For those who can afford it, they are in a health insurance system that has become more confusing, restrictive, and lower value with each passing year. Here at WeShare our mission is to bring better healthcare to America at a better price. […]
Benefits 401(k) Bonus based on performance Competitive salary Dental insurance Free food & snacks Health insurance Opportunity for advancement Paid time off Training & development Vision insurance Are you passionate about supporting business owners and helping them grow? Do you enjoy being the go-to partner who helps clients show up at the right moment and […]
Overview Want to join an agency where your hard work will be recognized? An agency where you’ll have abundant opportunities to learn and grow while significantly impacting your skillset and your client’s bottom line? If so, hi, we’re Socium Media! We’re a performance marketing agency recently awarded as one of Adweek’s Fastest Growing Agencies in […]
About Greenlane Marketing Greenlane Marketing is not your average digital marketing agency. We were founded to be a true alternative to the standard agency model, one that prioritizes partnership, transparency, and data-driven results. We are a team of passionate, curious, and dedicated experts who are genuinely excited to tackle complex challenges and create custom strategies […]
ABOUT THE JOB Tapcheck is looking for a Sr. Performance Marketing Contractor to own and optimize our paid search programs across Google Ads and Microsoft Ads, with a strong emphasis on hands‑on execution and optimization. This is a 6-month part‑time contract role (approximately 15–20 hours per week) with opportunity for additional hours and transition to […]
Overview This role requires a hybrid schedule and will be based in our Fort Mill, SC Headquarters (Monday through Thursday) and work fully remotely on Fridays each week. This role is not open to visa sponsorship or transfer of visa sponsorship including those on H1-B, F-1, OPT, STEM-OPT, or TN visa, nor is it available […]
Design and execute inbound-led outbound campaigns—reaching prospects who’ve shown intent (visited pricing page, downloaded resources, engaged with content) at precisely the right moment
Build and optimize Apollo sequences, LinkedIn outreach, and multi-touch campaigns that book qualified demos for AEs
Pandemic-driven lockdowns exposed many people in India to PCs for the first time, expanding the country's PC userbase and creating demand for upgrades as those devices age.
Google is reaching out directly to advertisers via email, requiring them to confirm whether their campaigns contain EU political ads — with a hard deadline of March 31st.
Why we care. This isn’t optional. EU regulation now requires Google to verify political ad status across all active campaigns, and advertisers who don’t act before the deadline could face compliance issues.
What’s happening. Google is asking every advertiser to declare whether their existing campaigns include EU political ads. The requirement applies to all current campaigns and must be completed by March 31, 2026.
How to comply: Google has outlined three ways to submit the confirmation:
Campaign level — Go to Campaign Settings and select “EU political ads” to confirm individual campaigns.
Multiple campaigns — Go to the Campaigns tab and use the “EU political ads” option to confirm several at once.
Account level — Confirm for all new and existing campaigns in one go. Selecting “No” at account level automatically applies that answer to every campaign, including future ones. You can still override this for individual campaigns at any time.
Between the lines. The account-level option is the most efficient route for most advertisers who are confident none of their campaigns fall under the EU political ads definition. Google has made it straightforward to reverse or adjust the selection at any point, so there’s no risk in acting early.
The bottom line. Check your inbox — Google is contacting advertisers directly. If you run campaigns targeting EU audiences, log in and complete the confirmation before March 31st to stay compliant.
First seen. This update was spotted by Paid Search expert, Arpan Banerjee, who shared the details of the comms on Linkedin.
Do you think you’re able to answer the question every marketing leader dreads hearing from leadership: “Why isn’t our marketing effort doing more?”
How do you even go about answering that?
Let’s look at what I mean using a fictional location analytics company we’ll call Acme Area Analytics.
The Acme team reviews its reports. Nothing appears broken. Campaigns are running, leads are still coming in, and performance metrics are mostly stable. Yet sales momentum isn’t clearly accelerating, and it’s hard to pinpoint why.
Insights are scattered across site analytics, brand monitoring and SEO tools, CRM systems, and paid media dashboards. Each platform reflects part of the story, but none shows the full picture.
That fragmentation is exactly how well-intentioned “data-driven decisions” can go wrong. Let’s look at how that happens and how Acme, and you, can fix it.
When the data points in the wrong direction
In global, multi-channel campaigns like Acme Area Analytics’, the hardest moments are when nothing is obviously underperforming. Digital channels are running. Leads are coming in, and metrics are mostly stable, yet sales momentum is stalled and it’s unclear which lever to pull next.
At the same time, subtle signals raise concerns. Non-brand CPCs are creeping upward, and a competitor — Spotter Intelligence — is suddenly appearing more frequently in branded search.
Let’s say you’re part of the Acme marketing team. You go back to your reports and ask the question most marketers ask in this situation: Which tactic is underperforming?
When diving into the platform data, you uncover what looks like a clear answer: remarketing performance for your API has softened, conversion rates have dipped slightly, and efficiency has begun to decline.
On the surface, you have your answer. Spend should be pulled back to match demand because audiences have likely seen the creative too many times.
That decision could certainly make sense, and it’s what many teams actually end up doing. But it’s also often wrong. Why? Because you haven’t yet asked the right question.
The more useful question is harder to answer: “Is demand actually declining, or are we failing to create new interest upstream?”
The real issue becomes clear when you look beyond a single channel. The location analytics market still had strong growth potential, but your product was encountering a shortage of engaged audiences receptive to the message. That disconnect became clearer when you looked beyond paid media.
Site engagement trends in analytics and brand search behavior in Search Console suggested interest in your type of location AI wasn’t disappearing. It just wasn’t converting yet.
The focus had shifted from reach to engaged awareness, with a priority on attention and engagement, not just exposure. So your Acme team decided to introduce additional campaign layers, including new content designed to build relevance and trust.
Crucially, you didn’t see any improvement right away. Cost-per-lead efficiency continued to decline, and it looked worse after increased upper-funnel investment. From a platform-only view, this looked like the time to pull back.
But looking across systems changed how performance was interpreted. Engagement from awareness activity began feeding remarketing pools, but the impact wouldn’t surface immediately for a product with long sales cycles like your API.
During that gap, the Acme team maintained confidence in its strategy by sharing early signs of upstream momentum. Only later did results begin to show up. Remarketing efficiency improved and higher sales volumes of the API were confirmed from integrated CRM data.
The takeaway for the Acme Area Analytics marketing team wasn’t just that “remarketing worked again,” or that upper funnel activity drives demand. It’s that the hardest marketing decisions are the ones you have to make — and hold — before success shows up in the metrics leadership typically trusts.
In our Acme example, each dashboard told a technically accurate story, but no single dashboard could fully articulate the whole picture.
Paid media dashboards reflected efficiency trends.
Analytics and Search Console showed shifts in engagement and demand.
CRM data lagged behind decisions by weeks or months.
Looking at any of those in a silo wouldn’t have allowed Acme’s marketing team to fully understand what was happening.
But we know that the insight didn’t live in any single view. When the question the team asked itself shifted to whether demand was moving effectively through the funnel, and dashboards were evaluated together in context, the decision changed.
This is what unsiloed analytics looks like in practice. It’s not about teams fighting over which touch led to the result, but recognizing that each part of a marketing plan plays a distinct and important role in creating momentum that grows demand and lifts sales.
Leadership wants proof. Pipeline and revenue might feel like the safest validation. But in complex, multi-channel programs, those are often lagging indicators of solid performance.
By the time pipeline clearly reflects demand creation, teams have often already pulled back awareness investment, cut channels that looked inefficient in isolation, and shifted budget toward short-term demand capture.
In the example above, waiting for proof would have meant that Acme reduced awareness and remarketing spend and possibly exited a market that would later show great promise.
Integrated data didn’t eliminate the risk of shifting investment from lead generation to awareness-building in a market that had declining metrics. Instead, it added credibility to the case for doing so.
This dynamic isn’t limited to complex, multi-channel programs. You can see it even within a single platform when multiple tactics work together.
Let’s look at a scenario where Acme’s brand search impression volume increased by roughly 50% year over year while Share of Voice remained flat. That means more people have been searching for Acme as the company has invested across out-of-home and other digital campaigns. Acme’s Google campaign then harvested the demand created by other channels.
If Acme’s brand search had been evaluated only in terms of its media plan efficiency, this signal of growing demand would have been easy to miss. In context, it confirmed that Acme’s awareness efforts were working, even though attribution couldn’t perfectly assign credit to individual channels.
What changes when data is integrated
In these examples, integrated data — unsiloed data — shifted the conversation.
Instead of Acme’s marketing teams debating budget cuts, they could monitor signs of early momentum, including longer time on site and rising brand search volume. Over time, that interest could be seen in the CRM as higher-quality leads that converted more frequently into closed deals.
The good news is that this doesn’t require new tools or perfectly stitched together data. It simply requires stepping back during planning and asking better questions about how potential customers signal interest as they consider your product.
In my experience, the most valuable marketing insights come from understanding how different data points relate.
Unsiloing your data isn’t about proving causality or winning attribution debates. Instead, it’s about recognizing opportunity early enough to act on it and identifying which metrics suggest that demand is quietly being built in the background.
The teams that win aren’t only better at reporting results. They’re better at seeing momentum while it’s still forming and acting on it early.
If I hear “always be testing” one more time, I might scream. It was great advice in 2016. In 2026, it’s a great way to light your budget on fire.
That mantra made sense when budgets were loose and platforms forgave a lot of chaos. Launch five audience tests simultaneously? Sure, why not! Swap out three creative variables at once? Go for it!
But the rules have changed. Our new reality has tighter budgets, longer learning phases, and signal fragmentation everywhere. One poorly structured test can distort your performance for weeks, not days. That performance hit compounds fast.
Modern experimentation is expensive and risky. Why pay that price when we have the power of agentic AI to help? And by help, I don’t mean slapping AI onto our existing process and asking it to generate more ad variants. That would just be an expedient way to light our budgets on fire.
Instead, it’s time to use agentic AI to design smarter experimentation systems.
The real cost of unstructured testing
In an “always be testing” era, it was all too easy to throw things to test at the scale Oprah gives out cars or Taylor Swift fills auditoriums. It often led to unstructured testing where we launched ideas on a Monday and checked results on Friday hoping for a lift. There was nary a risk model, overlap detection, or strategic sequencing in sight.
The costs of that approach are now exponentially higher. Take platform disruption. Algorithms crave stability. Industry benchmarks show ad sets stuck in learning phases often see CPAs 20-40% higher than stable sets.
Every time you significantly change creative, audience, or budget, you risk resetting that learning. If you’re running three overlapping tests that each trigger resets, you’re voluntarily paying a volatility tax on your entire media spend.
Then there’s waste. The majority of A/B tests deliver no statistically significant lift. If you aren’t ruthless about what deserves to run, you’re burning budget to prove most ideas don’t matter. “Always be testing” without guardrails turns into “always be destabilizing.”
From random tests to a real experimentation engine
The shift looks like this. Old approach: “AI, write me 10 new headlines.” New approach: “AI, design the smartest next experiment within our budget, risk tolerance, and current learning state.”
The reframe from creative generation to experimentation architecture is where real leverage lives.
Here’s a practical seven-step framework to turn testing from a tactical habit into strategic infrastructure.
Step 1: Set hard guardrails (humans draw the lines)
Before you let any AI near your experiments, lock in constraints. Without them, AI lacks proper context. With them, AI becomes a disciplined strategic partner.
Define and document five hard boundaries.
Budget allocation: Reserve a fixed percentage (e.g., 10%) explicitly for testing.
Maximum volatility: “No test can increase CPA by more than 15% for more than 5 days.”
Learning phase sensitivity: Document reset thresholds per platform.
Leading indicators: Use early signals (CTR, engagement drop-offs) to kill bad tests before they damage pipeline.
Brand risk: Define off-limits positioning (e.g., no discount-heavy testing in enterprise segments).
Document this in a single file (e.g., experimentation-guardrails.md) to teach AI the constraints that make ideas viable. Your AI agent must reference this before proposing any test.
Step 2: Let AI audit your experiment history
Most teams have the data sitting in spreadsheets, but never extract the lessons. Feed your last six months of test results into an AI agent and have it analyze variables changed, duration, performance delta, statistical confidence, and platform resets.
Ask it to find patterns, such as:
Over-tested variables: CTA buttons tested eight times with zero meaningful lift? That’s not a lever.
False failures: Many tests are declared losers simply because they never reached statistical significance. An AI agent can quickly assess statistical power and flag inconclusive results.
Volatility patterns: Often, your worst CPA weeks weren’t market shifts or a single bad creative, but rather the weeks where you launched three overlapping tests.
This is how AI becomes a true analytical partner.
Step 3: Write real hypotheses
Rather than jumping straight from idea to launch, use AI to help you enforce hypothesis discipline.
Weak: “Let’s test a new headline.”
Strong: “If we emphasize ‘faster time-to-value’ over ‘ease of use,’ we expect a 10-5% lift in demo requests from mid-market companies because win/loss analysis shows speed is their top decision criterion.”
Structured hypotheses create institutional memory. Six months later, when someone suggests testing “speed messaging” again, you’ll know exactly who it worked for and why. Yes, it feels like paperwork, but this discipline can protect your budget from algorithm chaos.
Step 4: Risk-score every proposed test
Budget isn’t infinite and neither is algorithm stability. Your AI agent should evaluate each proposed test across five dimensions and assign a risk score.
Budget impact (e.g., <5% vs >15%).
Algorithm disruption level (minor refresh vs new campaign).
Audience overlap.
Brand sensitivity.
Learning value.
High risk + low learning = Kill it. Low risk + high insight = Green light.
Example: Testing a radical new enterprise positioning statement is high risk in a paid conversion campaign. Instead, your AI agent might suggest validating it first via organic LinkedIn content or low-budget audience polling. Low risk. High signal.
This is one of the most underused applications of AI in experimentation. Synthetic testing means simulating how different personas may react to messaging before spending media dollars, and the data backs it up.
A study involving researchers from Stanford and Google DeepMind found that digital agents trained on interview data matched human survey responses with 85% accuracy and mimicked social behavior with 98% correlation.
This makes synthetic audiences surprisingly useful for early-stage signal gathering. While they don’t replace real-world data (at least not yet), they can act as creative QA.
Here’s how it works. Define psychographic archetypes.
The Skeptical CMO (burned by vendors, risk-sensitive).
The Growth VP (speed-obsessed).
The CFO (margin-focused).
Feed your proposed messaging into your AI system and ask, “How would the Skeptical CMO react to this?”
You might get feedback like: “The phrase ‘All-in-One’ triggers skepticism. It signals feature bloat. Consider reframing as ‘Integrated’ or ‘Modular.’”
That kind of signal costs pennies in API calls instead of thousands in paid testing.
Step 6: Sequence tests, don’t stack them
Changing audience, creative, and landing page in the same week teaches you almost nothing. Your AI agent should act like air traffic control: scan active campaigns, flag conflicts, and recommend sequencing.
A better flow:
Week 1-2: Audience test.
Week 3-4: Creative test on the winning audience.
If overlap is unavoidable, enforce clean holdout groups so you always have a source of truth.
Step 7: Build a living knowledge base
Treat tests like disposable experiments and you lose the compounding value. Have your AI auto-summarize every completed test:
Why did it win?
Who did it win with?
How durable was the lift?
What variables interacted?
Over time, this database becomes your moat. Everyone can buy the same targeting. Few teams have 100+ validated customer truths at their fingertips.
“Always be testing” was a growth-era mindset. In 2026, the winning mindset is “always be compounding intelligence.”
Rather than more tests, build your competitive advantage through structured, risk-aware, insight-driven experimentation that protects algorithm stability and ties experimentation directly to revenue.
The next time your stakeholder asks why you aren’t testing more, show them your experimentation architecture and say, “We’re not just running experiments. We’re building an intelligence engine.”
Video advertising has never been easier to distribute. Platforms can deliver impressions and views at an enormous scale across YouTube, paid social, short-form video, and connected TV.
But distribution isn’t the same as effectiveness. Many campaigns generate impressive platform metrics while producing little measurable business impact.
The problem usually isn’t targeting, budget, or platform choice. It’s a deeper strategic issue: campaigns are optimized for outputs like views and impressions rather than outcomes like attention, persuasion, and action.
Most video ads fail because they misunderstand attention
Poor targeting, limited budgets, and platform choice are rarely the real problem. The bigger issue is that many video ads are still produced as if they’re television commercials.
In the early days of online video, distribution was the challenge. Getting a video seen at all felt like a win. Today, distribution is abundant. Attention isn’t.
Every major platform — YouTube, paid social, short-form video, connected TV — competes for fragments of cognitive bandwidth. Users arrive with intent, habits, and expectations that have nothing to do with your campaign. We plan for reach, while viewers respond to relevance.
I’ve sat in many meetings where success was defined by impressions delivered or views accrued. But when you look downstream — search lift, site engagement, conversion — the connection often disappears.
Platforms will reliably deliver impressions. Turning those impressions into memory, persuasion, or action requires a fundamentally different mindset.
Skippable formats changed video advertising permanently, but many advertisers still haven’t adjusted creatively.
Early in my career, I believed strongly in branding up front. Logos, product shots, music cues — everything that signaled professionalism. Those ads looked great in presentations. They underperformed in market.
A clear pattern emerged over time. Ads that opened with a recognizable problem, a provocative statement, or an unexpected visual held attention longer — even when branding appeared later. Ads that opened with branding signals were skipped almost reflexively.
View-through rate isn’t persuasion. A “view” simply means the platform’s minimum threshold was met. It doesn’t mean the message landed, the brand registered, or the viewer cared.
In multiple brand lift analyses, most measurable impact occurred before the skip button appeared. If the opening didn’t earn attention, the rest of the ad didn’t matter.
What works: treat the opening frame like a headline, not a preamble. Lead with tension, a question, or a familiar problem. Design for sound-off environments. If the first frame wouldn’t stop a scroll, nothing that follows will matter.
Higher production value often correlates with lower performance
One of the most counterintuitive lessons in modern video advertising: polished ads frequently underperform scrappier ones.
I’ve seen simple, phone-shot videos outperform meticulously produced studio spots across YouTube, paid social, and short-form platforms. Not because quality doesn’t matter — but because perceived authenticity matters more.
Audiences are exceptionally good at identifying advertising. When something looks like an ad, they disengage. When it looks like content, they give it a chance.
Algorithms reinforce this: they reward watch time, retention, rewatches, and shares. They do not reward lighting setups or production budgets.
I’ve seen brands “upgrade” social video to look more premium, only to watch performance decline. The creative looked better. The results were worse.
The goal isn’t to look amateurish. It’s to look like you belong.
Match the platform’s visual grammar. Prioritize clarity over polish. Use real people and authentic voices whenever possible.
Ads that feel native get watched. Ads that feel inserted get skipped.
Length is a creative decision, not a media constraint
“Shorter is better” is one of the most persistent — and misleading — rules in video advertising.
Six-second ads can work. So can 60-second ads. I’ve seen both exceed expectations, and I’ve seen both fail badly. The difference was never duration — it was justification.
Some messages can be delivered instantly. Others require context, proof, or emotional buildup. Forcing every idea into the same runtime produces predictable results: safe, bland, forgettable ads.
I’ve reviewed retention graphs where a 45-second ad held viewers longer than a 15-second version, because the story justified its length. I’ve also seen six-second ads lose half their audience in the first two seconds because they wasted the opening.
Test multiple edits, not just multiple lengths. Watch retention curves, not averages. Build modular narratives: hook, then value, then proof, then action.
The “right” length is however long it takes to make the viewer feel their time was respected.
Metrics are signals
Platforms provide more data than ever. The problem isn’t a lack of metrics. It’s confusing metrics with outcomes.
I’ve seen campaigns praised for high completion rates that produced no measurable business impact. Strong engagement coexisting with low conversion. Impressive view counts that delivered zero lift.
This happens because platforms optimize for their success metrics, not yours. If your goal is to maximize views, the platform can do that easily. If your goal is to influence consideration, preference, or action, things get more complicated.
One uncomfortable question I’ve learned to ask early: what would failure look like here? If the answer is vague, the campaign is already at risk.
Define success in business terms before launch. Tie video metrics to downstream behavior wherever possible. Use lift studies, holdouts, or assisted conversions when they’re available. If you’re running a brand-building campaign, measure brand lift. If you’re running a performance campaign, measure conversions.
Creative is often blamed when video ads underperform. In reality, creative usually does exactly what it was asked to do. The problem is the brief.
Vague objectives produce generic ads. “Brand awareness” without context leads to unfocused messaging. “Make it engaging” isn’t a strategy.
Strong video ads almost always begin with clear answers to three questions:
Who is this really for?
What do they care about right now?
What should they think, feel, or do differently after watching?
When those answers are clear, creative decisions become easier. When they aren’t, the work is compromised before production begins.
The deeper diagnostic questions are worth keeping close:
Are viewers actually paying attention, or just passively present?
What are they feeling — and which specific creative choices are driving that response?
Will they remember the brand once the ad ends?
What will they do next — share it, recommend it, search for the product, or buy?
I’ve seen entire campaigns improve simply because the brief forced alignment around audience insight rather than assumptions.
Distribution strategy is part of the creative
Another common mistake is treating creative and distribution as separate decisions. They aren’t.
The way an ad is consumed — fullscreen versus feed, sound-on versus sound-off, lean-back versus lean-forward — should shape how it’s made.
A video designed for connected TV shouldn’t simply be resized for mobile. A short-form ad shouldn’t be a truncated long-form story without rethinking the hook entirely.
I’ve seen strong ideas underperform because the creative didn’t match the placement. The concept wasn’t wrong. The context was.
Design with placement in mind from the start. Create platform-specific versions, not one-size-fits-all assets.
Accept that “reuse” often means “rethink,” not “repurpose.” Distribution constraints aren’t limitations — they’re creative inputs.
Testing should answer questions, not just generate variants
Testing is indispensable. It’s also frequently misunderstood.
Running endless A/B tests without a hypothesis rarely produces insight. It produces noise.
The most effective testing focuses on variables that materially affect attention and comprehension: opening frames, narrative structure, on-screen text versus voiceover, proof points versus emotional appeals.
It’s also important to recognize what testing can’t do. Algorithms are excellent at optimizing toward measurable signals. They don’t understand brand equity, long-term memory, or cumulative effect. Testing should inform judgment — not replace it.
Ultimately, the only thing that matters for creative effectiveness tools is whether their predictions actually correlate to real media and sales outcomes — reliably enough to inform strategy and media decisions.
The question worth asking of any such tool is simple: How often does what it predicts will happen actually happen?
For example, I frequently cite data from DAIVID, an AI-driven creative effectiveness platform. Why? Because in independent testing, DAIVID’s predictions aligned with real-world outcomes more than 80% of the time — a meaningful foundation for making creative decisions with greater confidence before a campaign goes live.
Platforms will change. Formats will evolve. Algorithms will shift in opaque and sometimes frustrating ways. But attention, curiosity, and trust remain stubbornly human.
The best video ads I’ve worked on weren’t optimized for view counts or completion rates. They were optimized for relevance. They respected the viewer’s time. They said something worth hearing.
Video ads don’t succeed because they follow platform rules. They succeed because they understand people. And that principle outlasts every algorithm update.
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Xbox CEO Asha Sharma has revealed that the next-generation Xbox is codenamed Project Helix and says it will “lead in performance,” with rumors pointing to a powerful new console arriving in 2027.
Google AI Max drives revenue but at a higher cost, according to Smarter Ecommerce’s Mike Ryan, who analyzed 250+ campaigns. Outcomes vary, and much more testing is still needed.
Why we care. AI Max isn’t a minor update. It’s Google’s most significant reimagining of Search campaigns in years, shifting away from keyword syntax toward pure intent matching. For you, that’s both an opportunity (possible growth) and a risk (an efficiency tradeoff).
By the numbers. The result of the analysis:
Median revenue: +13%
Median CPA: +16%
ROAS range: +42% to -35%
Advertisers who activate AI Max typically see 14% more conversions or conversion value at a similar CPA or ROAS, rising to 27% for campaigns still relying on exact and phrase match keywords, Google says.
Turning on AI Max is essentially a coin toss: you may see a lift, but efficiency likely won’t follow, Ryan concluded
What AI Max actually is. Rather than forcing Search campaigns into Performance Max, Google went the other direction — bringing PMax-style automation into classic Search. The result is three core features:
Search Term Matching (broad match expansion plus keywordless targeting),
Text Customization (dynamic ad copy), and
Final URL Expansion (automated landing page selection).
Four pitfalls Smarter Ecommerce identified:
Broad match cannibalization: Up to 63% of the time, recycling existing coverage rather than finding new queries.
Competitor hijacking: In one account, AI Max scaled so aggressively into competitor brand terms that it consumed 69% of total Search impressions.
Reporting overload: Search term and ad combination reports can run to tens of thousands of rows, making manual auditing nearly impossible without automation.
Search Partner Network blowouts: One campaign saw half a million monthly impressions land on SPN at a 0.07% conversion rate, versus 3.04% on standard Google Search.
Between the lines. Google’s 14% uplift stat conspicuously excludes retail — an omission Ryan flags as significant for ecommerce advertisers. There’s also a deeper irony: you’re most likely to adopt AI Max if you’re already running Broad Match, DSA, and PMax — yet Google says those accounts will see the lowest incremental benefit.
What’s next. In a conversation with Ryan, Google Ads Liaison Ginny Marvin confirmed that Google plans to deprecate Dynamic Search Ads and migrate the technology into AI Max for Search. No firm timeline was given, though past Google deprecations often run about a year from announcement.
Ryan recommends activating AI Max’s keywordless features in your existing Search campaigns now and beginning to wind down DSA — not migrating it to PMax.
Ryan’s verdict is cautious optimism. About 16% of advertisers are testing AI Max, and few have gone all in. Start small, audit aggressively, and don’t let FOMO around AI Overviews drive your decision.
A routine Windows 11 update locked my Xbox Ally handheld in recovery mode, forcing a full cloud restore that took more than 12 hours. Highlighting just how uncertain I am about the next Xbox being a Windows PC.
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Google is investigating a disruption affecting Google Ad Manager, according to an update posted on the Google Ads Status Dashboard.
The incident began at 13:49 UTC on March 4. By 13:54 UTC, Google said it was reviewing reports that some users could access Ad Manager but weren’t seeing the most up-to-date data.
What’s happening. The issue appears to impact reporting consistency. Specifically, Ad Exchange match rate and Ad Exchange request values are not aligning between Ad Manager’s interactive reports and the legacy reporting query tool (now deprecated).
Why we care. Reporting discrepancies in Google Ad Manager can directly impact how you evaluate performance and optimize campaigns. If Ad Exchange match rates and request data don’t align across reporting tools, it becomes harder to trust the numbers driving pacing, forecasting and revenue decisions.
What it means. Users can still log into Ad Manager, but reporting discrepancies may affect data accuracy — at least temporarily. There’s no indication yet of a full outage, but for publishers and advertisers relying on real-time reporting, mismatched metrics could complicate performance monitoring and optimization decisions.
What’s next. Google says it’s actively investigating and will provide further updates. In the meantime, affected users are advised to monitor the status dashboard and contact support if they’re experiencing issues not listed there.
Google introduced a new availability value in Google Merchant Center — built specifically for vehicle sellers who don’t carry every model on the lot. The new attribute, “build to order,” lets dealers flag vehicles that aren’t physically in inventory but can be customized and ordered by customers.
What needs to change. Sellers must update two areas: their structured data (set availability to BuildToOrder) and their Merchant Center feed (set availability to build to order). Consistency between structured data and feed submissions is critical to avoid disapprovals.
Instruction on when to use the availability [availability] attribute in GMC
Why we care. Until now, sellers had limited ways to signal that a vehicle wasn’t available for immediate pickup. The new value better reflects how many modern automakers operate — especially direct-to-consumer brands like Tesla and Rivian, where buyers configure features before production. For dealers offering factory orders or custom builds, this means clearer expectations for shoppers — and cleaner data for Google.
The fine print Vehicles marked “build to order” must have the condition attribute set to “new.” If a listing is marked “used,” it will be disapproved — Google considers build-to-order vehicles to be newly configured, not pre-owned.
Bottom line If you sell customizable or factory-order vehicles, this update gives you a more accurate way to reflect availability — but only if your feed, structured data and condition fields are properly aligned.
First spotted. This update was shared by Google Shopping specialist Emmanuel Flossie, where he shared how to implement this update on his blog.
PPC platforms are asset-hungry. What began as simple text ads and keyword bidding has evolved into an AI-driven ecosystem.
Tools inside Google Ads can now remove backgrounds, generate lifestyle scenes, and even create synthetic humans in minutes. But just because the technology allows it doesn’t mean every brand should use it.
That shift forces PPC advertisers to confront difficult questions:
Are you willing to trade efficiency for authenticity?
How far up the stack should your brand let AI operate?
If clients knew exactly where and how you were using AI, would they trust you, or would they question you?
A brand integrity hierarchy offers a way to navigate those decisions — a four-level framework that helps determine how much AI manipulation your brand, industry, and audience can tolerate.
Why PPC needs its own AI ethics framework
Generic AI ethics guidelines don’t account for the operational realities of paid search. PPC isn’t a brand storytelling channel. It’s a high-volume, high-velocity system that demands constant image production across dozens of audiences, formats, and placements.
You must generate fresh lifestyle imagery at a pace traditional creative workflows can’t sustain.
At the same time, Google and Bing enforce strict policies around accurate product representation, especially in Merchant Center, where even minor visual inaccuracies can trigger disapprovals or account risk.
Layer on top of that the platform pressure. Google Ads added Nano Banana Pro, turning Asset Studio into an AI co-creation environment. Performance Max actively pushes you toward AI-generated backgrounds, variations, and lifestyle images to improve performance. Demand Gen and Merchant Center also now have capabilities to change product images at scale.
Most brands can’t afford the photoshoots required to keep up with this demand, yet the volume and placement of images across channels make them unavoidable if you want to compete.
This combination of policy risk, creative pressure, and platform-promoted tools is unique to PPC — which is exactly why the industry needs its own AI ethics framework.
PPC context: This level is fully compliant with Google and Microsoft’s “accurate representation” policies. Merchant Center explicitly permits technical edits that don’t alter the product itself. This is the safest zone for regulated industries such as finance, healthcare, legal services, and brands with strict authenticity standards.
Client talk-track: “We’re using AI to make your reality look its best on every screen size. We aren’t changing what the product is, only how it’s displayed.”
Risk assessment: Zero brand risk. Zero policy risk. Maximum consumer trust.
I think about Level 1 the same way I think about working with a graphic designer in Photoshop. You’re not changing the product, the setting, or the truth — you’re simply cleaning up what already exists.
This level is about technical refinement, not creative invention. It’s the equivalent of adjusting lighting, removing dust, fixing a crooked crop, or correcting color balance. Nothing about the image becomes “untrue.” You’re enhancing reality, not altering it.
Level 2 – The inner ring (low risk): Contextual narrative
Definition: AI-generated environment, not AI-generated product.
Permitted activities:
Generative backgrounds (e.g., placing a watch on a mountain backdrop).
Removing visual distractions (e.g., power lines, litter, unrelated objects).
Seasonal or thematic settings (e.g., holiday scenes, office environments).
Generic commodity generation (e.g., coffee beans, grain, raw materials, not branded products).
Google Ads context: Performance Max’s AI background generation is designed for this level. Google allows contextual enhancement as long as the product remains unchanged. This approach is useful for scaling creative variations without expensive location shoots or studio rentals.
Risks:
Cultural mismatch. AI-generated settings may not reflect the target audience’s reality.
Unrealistic or off-brand environments.
Requires human review for brand consistency.
Client talk-track: “We’re using AI to build a world for your product to live in. The product the customer receives is identical to the one in the ad.”
Level 2 sits in an odd psychological space. The manipulations themselves are still low-risk. You’re creating scenes, composites, or enhanced environments the same way a graphic designer would in Photoshop.
Brands have been doing this manually for decades. But the moment AI performs the same task, something shifts. To customers, and even to some advertisers, the exact same edit can feel more artificial simply because an algorithm did it instead of a human.
That perception gap matters.
Even when the output is identical, AI-assisted scene creation can trigger a sense of “this looks fake” that traditional Photoshop work never did. It’s irrational, but it’s real and worth acknowledging at this second tier. The actual risk is still low, but the emotional risk is higher than Level 1.
Level 3 – The outer ring (high risk): Subject augmentation
Definition: Altering the “hero” — the product or the person.
Activities:
Beautification filters on models.
Slimming or reshaping human subjects.
Altering food textures to appear more appealing.
Removing “imperfections” from products.
Making products appear more premium than they are.
PPC industry context: The platforms prohibit misleading or manipulated product imagery. Merchant Center disapprovals often occur at this level. High sensitivity exists in beauty, apparel, food, and health categories, where consumer expectations are tied directly to visual accuracy.
Recent consumer trust studies show that users feel deceived when they discover product images have been significantly altered. This is a policy concern, more so a brand reputation issue.
Half of U.S. adults (51%) believe AI-generated and edited content needs better labeling, CNET reports. One in five (21%) believe AI content should be prohibited on social media with no exceptions.
Risks:
High PR risk (e.g., press call-out moments).
High policy risk (e.g., disapprovals, account suspension).
High consumer trust risk (e.g., returns, negative reviews).
Client talk-track: “This is where we risk the ‘press call-out.’ If we remove a model’s birthmark or make a burger look like a 3D render, we aren’t optimizing — we’re fabricating.”
Risk assessment: High brand risk. High policy risk. Potential for long-term damage to consumer trust.
Level 3 moves into territory where the image no longer reflects the real person or product. And yes, brands have been doing this in Photoshop for years, and they’ve been called out for it just as long. There’s precedent, and there’s backlash.
What changes at Level 3 is scale. AI lets you make edits instantly, repeatedly, and across entire product catalogs or campaigns. The ethical risk isn’t new, but the volume and speed at which AI enables these distortions make the consequences far bigger. A single questionable Photoshop edit is one thing. Hundreds of AI-altered images pushed across every channel is something else entirely.
This is where the risk stops being theoretical and starts becoming reputational — and where paid search teams need a clarified stance.
Level 4 – The edge (critical risk): Full fabrication
Definition: Synthetic humans, synthetic products, or fully AI-generated scenes.
Activities:
AI-generated models.
Virtual influencers.
Products that don’t exist.
Entirely fabricated lifestyle scenes with no real-world basis.
PPC context: Synthetic humans are allowed in some formats with proper disclosure, but Merchant Center prohibits listing products that don’t exist. There is a high risk of disapproval for “inaccurate representation.” This level may be acceptable for creative testing or conceptual campaigns, but it’s dangerous as a primary brand identity.
Legal precedents regarding copyright protection for non-human-authored creative works remain murky. Using fully synthetic assets may cause challenges if ownership disputes arise or if synthetic models are mistaken for real individuals without proper disclosure.
Risks:
Maximum brand risk.
Maximum policy risk.
Maximum consumer trust risk.
Potential long-term damage to “trust equity.”
Client talk-track: “This is for high-speed testing or fringe creative. If we use this for our main brand identity, we must be prepared for the ‘inauthentic’ label.”
Risk assessment: Critical brand risk. Critical policy risk. Use with extreme caution and full disclosure.
Level 4 is where AI stops enhancing reality and starts inventing it. The image becomes a construction. While I haven’t personally worked with brands operating at this tier, it’s absolutely where the industry could be headed, and it deserves serious consideration.
Fully fabricated imagery can mislead customers, violate platform policies, and erode trust at scale. When AI creates people, products, or environments from scratch, the line between creative expression and consumer deception becomes razor-thin. The reputational fallout from getting this wrong is far greater than anything in Levels 1 through 3.
This is the highest-risk tier because it asks a fundamental question: Are you still advertising your product or an AI-generated fiction of it?
Brand alignment: Defining your North Star
Not every brand should operate at the same level of the brand integrity scale. Your acceptable AI usage depends on four factors.
1. Define your non-negotiables
Every brand must choose its acceptable level(s) on the scale and document it in a brand AI manifesto for PPC.
Examples:
Dove (authenticity-driven beauty brand): Level 1 only.
Tech-forward DTC brand: Levels 2-3 acceptable with clear disclosure.
Ecommerce aggregator: Levels 1-2 for product listings, Level 3 for lifestyle content.
Action: Create a PPC brand AI manifesto in collaboration with creative, legal, and executive leadership.
2. The press test vs. the policy test
Two critical questions should guide every AI decision:
Policy test: “Will the platform approve this?”
Press test: “Would we be proud if The Verge covered this?”
The press test is the real guardrail. Google’s policies change. Public perception is permanent.
3. Human-in-the-loop protocol
Every AI-assisted asset must be checked for:
Material deception: Does this misrepresent the product or service?
Identity erasure: Does this erase diversity or cultural authenticity?
Cultural hallucinations: Does this AI-generated scene reflect reality or stereotype?
Product accuracy: Does the ad show what the customer will actually receive?
Automated AI generation should never bypass human review, especially in regulated verticals.
4. Align with your customer base
Different audiences have different tolerances for AI manipulation:
Gen Z: Values “perfectly imperfect” authenticity. Responds negatively to over-polished imagery.
B2B: Prioritizes clarity and utility. AI-generated backgrounds are acceptable. Synthetic humans less so.
Retail: Authenticity directly impacts conversion rates. Product accuracy is non-negotiable.
Operationalizing the brand integrity circle inside PPC ads
Creative workflow
Implement a pre-flight checklist for AI-generated assets:
Identify the level: Core, inner ring, outer ring, or edge
Apply the press test: Would we defend this publicly?
Check for bias: Does this asset represent your audience accurately?
Verify product accuracy: Is this what the customer will receive?
Document disclosure: If synthetic humans are used, is this disclosed?
Media workflow
Safe placements for AI-generated assets
Performance Max (with contextual backgrounds).
Demand Gen (lifestyle scenes).
YouTube thumbnails (conceptual creative).
Unsafe placements
Merchant Center product images (Level 1 only).
Regulated verticals (finance, healthcare, legal).
Sensitive categories (beauty, weight loss, medical devices).
Legal workflow
Legal teams should:
Review synthetic human usage for disclosure compliance.
Validate product accuracy claims.
Approve the brand AI manifesto.
Maintain documentation for regulatory audits.
Industry standards and emerging frameworks, such as the Coalition for Content Provenance and Authenticity (C2PA), are establishing transparency protocols for AI-generated media. Monitor these developments and align your practices accordingly.
What the PPC community thinks
Some PPC professionals are already experimenting with the tools discussed in this framework.
Ameet Khabra, owner of Hop Skip Media, tested Nano Banana when it first appeared inside the Google Ads interface. She found the tool useful for ideation and quick edits, but noted that strong results often required highly specific prompts.
That level of prompt detail may be realistic for experienced advertisers, but it’s less likely for many SMBs experimenting with AI-generated assets.
“I think it’s a great tool to use for ideation and potentially quick edits,” Khabra said. “But I would still have a graphic designer creating the final product.”
Even when AI imagery is available, some advertisers remain skeptical of how it appears to audiences.
Julie Friedman Bacchini, owner of Neptune Moon, says AI-generated images often look noticeably artificial.
“I don’t like AI images because they look like AI and that’s off-putting to me,” Bacchini said. “It can be hard to avoid. Even when you’re trying to use stock photos, there are so many AI images on those sites too.”
To understand how people outside the industry view these changes, I also polled the community on Threads.
The sentiment was strikingly consistent: while the industry focuses on efficiency, the public is increasingly wary of fantasy versus reality.
One commenter wrote:
“False advertising. That seems like a pretty big concern. As a consumer, I actually would like to see the real thing I’d be buying.”
Another described the issue more bluntly:
“Bait and switch. Fantasy versus reality. Falsehood versus the truth.”
AI isn’t inherently deceptive. Nor is it inherently transparent. It’s a tool. Like all tools, its ethical impact depends on how it’s used. As PPC experts with access to these technologies and advisory roles with brands, we need a clear point of view to guide these decisions.
The brand integrity scale outlined above provides a structured approach to AI use in PPC, helping you navigate the tension between automation and authenticity. By defining your brand’s position on this spectrum today, you ensure tomorrow’s campaigns are remembered for their resonance.
Adopt ethical AI standards — define your brand AI manifesto, implement the press test, and ensure every AI-generated asset passes human review before it reaches your audience. Your brand’s integrity depends on it.
Google is communicating that starting April 1st, Customer Match uploads through the Google Ads API will stop working for certain users, in a message sent to API developers.
Specifically, developers who haven’t uploaded Customer Match data in the past 180 days using their developer token will no longer be able to do so via the Ads API.
What’s changing. If you fall into that inactive bucket, any attempt to upload Customer Match lists through the Google Ads API after April 1 will fail. Instead, Google wants you to move those workflows to the Data Manager API. The change applies only to Customer Match uploads — all other campaign management and reporting tasks should continue as normal in the Google Ads API.
Why Google says it’s doing this. Google positions the Data Manager API as a more modern, unified data ingestion solution across its platforms, with stronger security protocols. It also includes features not available in the Ads API, such as confidential matching and enhanced encryption — signaling a push to centralize and better secure audience data handling.
Why we care. If you or your developers haven’t touched Customer Match uploads in the last six months, this could catch you off guard. After April 1, 2026, the old workflow simply won’t work — and errors will replace uploads.
The takeaway. Check whether your developer token has been used for Customer Match recently and plan a migration to the Data Manager API now, before Google flips the switch.
First spotted. This announcement was shared by Paid Search specialist Arpan Banerjee who shared the message he got from Google on LinkedIn.
Google has long been considered the gold standard for ad spend compared to social platforms. But scale doesn’t equal immunity. Click fraud remains a persistent risk, and the safety of your budget depends entirely on where your ads are running.
While Google Ads offers immense reach, its campaigns aren’t created equal. Some are significantly more exposed to malicious activity than others. To protect your margins, you must understand what constitutes click fraud, where it originates, and how to shield your campaigns.
What are invalid clicks?
Invalid clicks are interactions that lack legitimate consumer intent. Because they aren’t driven by real human interest, they skew performance data and drain budgets without any possibility of conversion. These clicks generally originate from four primary sources:
Botnets: Networks of hijacked devices controlled by a “botmaster.” They generate massive volumes of automated traffic that mimic human behavior to inflate metrics or carry out DDoS attacks.
Click farms: Large groups of low-paid workers or automated scripts tasked with manually clicking ads. They create an illusion of high engagement, misleading brands into believing a campaign is more effective than it truly is.
Ad injection and malware: Malicious software that “injects” unauthorized ads into websites or forcibly redirects users. This hijacks legitimate revenue and erodes consumer trust.
Pixel stuffing and ad stacking: “Invisible” fraud in which ads are served but never seen. Pixel stuffing compresses an ad into a single 1×1 invisible pixel, while ad stacking layers multiple ads on top of each other in a single slot. You pay for thousands of impressions that have zero chance of being viewed.
The average invalid click rate across Google Ads is 11.4%, a recent study by Fraud Blocker found. The figure is climbing.
That upward trend becomes clearer over time. In 2010, the average invalid click rate sat at 5.9%. By 2024, that number jumped to 12.3%. This doubling of fraud is likely driven by the increased sophistication of AI-powered bots and malware that can more effectively bypass basic security filters.
Invalid click rates fluctuate based on your campaign setup. Three key factors typically drive these numbers:
Industry competition: High CPC industries like legal services, insurance, and real estate are primary targets. Competitors or bots may intentionally target these campaigns to exhaust your daily budget.
Targeting parameters: Overly broad keywords or targeting regions known for high botnet activity can inadvertently invite “junk” traffic.
Refinement tools: Negative keywords and audience exclusions act as a shield, reducing the frequency of unintentional clicks.
Campaign hierarchy: Which are the biggest violators?
Not all Google Ads inventory carries the same level of risk. Here’s how campaign types stack up from highest to lowest exposure.
The biggest risk: Google Video Partners
Video Partners show the highest levels of invalid traffic since they reach beyond YouTube to Google’s network of third-party sites.
Many of these sites offer zero control, racking up views from bots or “muted” placements in tiny windows where real people never see them.
Display campaigns: Highly vulnerable
Display ads are often plagued by low-quality “made-for-ads” or AI-built sites.
In some cases, more than half of the clicks on a specific site can be invalid.
While major publishers are safer, the risk across the wider network is uneven and requires constant monitoring.
Shopping and Demand Gen: The automation tax
These campaigns face less malicious fraud from price-checking tools, scrapers, and automated bots.
While not always intended to deplete your budget, these clicks still drain spend and skew optimization data, which is particularly damaging for low-margin businesses.
Performance Max: Hidden exposure
While PMax scales inventory across all Google properties, it spreads risk across the entire ecosystem.
Because it’s difficult to see exactly where traffic originates, even a low percentage of invalid clicks can result in significant wasted spend.
Search: The safest bet
Traditional Search campaigns remain the most secure.
It’s much harder for bots to accurately mimic a human searching for a specific solution.
However, in high CPC industries, even a 2% fraud rate can be financially painful.
How to mitigate the risks
Across the diverse range of industries my clients serve, I’ve identified specific patterns in how fraud manifests across different sectors. As a result, the best prescription is proactive. Address these vulnerabilities by shifting from broad, automated settings to a more refined, high-intent strategy.
The following table highlights the specific patterns we monitor to lower invalid click rates:
Factor
Higher risk (Aggressive)
Lower risk (Strict)
Location
Global or “Presence or Interest”
“Presence Only” (User is physically there)
Keywords
Broad match / Generic terms
Exact match / Long-tail phrases
Networks
Including “Search Partners” and “Display”
Google Search Network only
Exclusions
No negative keywords or placement lists
Robust negative lists and app exclusions
Scheduling
24/7 (Bots often spike at night)
Custom schedules aligned with business hours
Here are proactive steps you can take to reduce your exposure to fraud.
Audit placement data: Regularly review where your ads appear. Immediately exclude low-quality apps or sites that show high click-through rates but zero conversions.
Limit AI Max overreliance: Automation is powerful, but set and forget is a recipe for wasted spend. Maintain manual guardrails on your automated campaigns.
Review refunds: Google does issue refunds for detected fraud, but subtle cases often slip through. Compare your internal lead data against Google’s click data to find discrepancies.
Google is far from a uniform entity. It’s a diverse ecosystem of distinct environments where risk levels can vary by as much as 400%.
Prioritizing high-quality traffic results in superior data integrity, more precise optimization, and reduced acquisition costs. In today’s market, the strategic structure of your campaigns is just as vital to your success as the size of your budget.
One of the most profitable Google Ads targeting tactics is retargeting: showing ads to people who are already familiar with your business. But if you still think that “retargeting” means a Display campaign chasing users around the web with banner ads, you’re missing out on how “Your data segments” actually function today.
Let’s explore how you can leverage your proprietary audience data in new ways, and what mistakes to avoid in 2026 and beyond.
What are “Your data segments” in Google Ads?
Retargeting means showing ads to people who are already familiar with your business. Google uses the euphemistic name “Your data segments” to refer to all the retargeting lists in your account.
What types of retargeting can you do in Google Ads?
A variety of different retargeting methods are available in Google Ads. They mirror what you’ll find on other ad platforms like Meta, LinkedIn, or TikTok. I find it helpful to group them into four categories:
Website Visitors: This is the standard one — people who have visited your website. You collect this data using Google Tag Manager or Google Analytics.
App Users: If you have a mobile app, you can pull data from Firebase or other third-party analytics tools into Google Ads for retargeting.
Customer Match: This is the “holy grail” of retargeting. You take your business’s first-party data (email addresses, phone numbers, etc.) and upload it directly to Google Ads, so that Google can find those same users across its platforms.
Content Engagers: People who have interacted with your content on Google-owned properties. Examples include a segment of users who have watched your YouTube videos, or a segment of users who have clicked through to your site from search results (this is called the Google Engaged Audience, which we explored in another article).
Should you upload “your data segments” if you’re not planning to do retargeting?
Many practitioners overlook this detail: your data segments aren’t just about ad targeting.
Even if you don’t have a single retargeting campaign running, the mere existence of these lists in your account provides a vital signal for Smart Bidding and Optimized Targeting.
For example, when you upload a customer list, you’re telling Google, “These are the people who actually buy from me.” Even if you never add that list to your audience signal in Performance Max, Google will still use it to understand likely converters and adjust bidding/targeting accordingly.
Similarly, let’s say you only run Search and Shopping campaigns, and you use Target ROAS bidding. When Google is trying to set the right bid for the right user at the right time, their presence (or lack thereof) on a “your data segment” list is one of many signals incorporated into that bidding calculation.
How can you use retargeting lists in Google Ads?
Different campaign types handle audience data differently. It’s important to know the distinction so you can plan your targeting strategy accordingly.
Search, Shopping, Display: In these campaigns, you have three options with Your data segments: Targeting, Observation, Exclusion.
Targeting means your ads will only show if the user is a member of your data segment
Observation allows you to see your campaign data segmented by list, without narrowing your reach
Exclusion means your ads will only show if the user is NOT a member of your data segments.
Performance Max and App Campaigns: In these AI-powered campaigns, you can include Your data segments as part of your audience signal. Performance Max recently added the ability to exclude Your data segments as well.
Demand Gen: In Demand Gen, you can Target and Exclude Your data segments, but there is no “Observation” option.
If you’re new to retargeting, I find Demand Gen the best place to start. It’s built for visual storytelling and works well with the Google Engaged Audience or basic website visitor lists.
If you have some experience with retargeting campaigns, you might want to try New Customer Acquisition or Customer Retention mode in PMax or Shopping, as these are powered by Your data segments.
What’s the biggest retargeting mistake to avoid?
Over-segmenting. I know it can be tempting to create 50 different lists: “People who visited the cart on a Tuesday,” or “People who looked at three pages but didn’t click the ‘About’ section.”
Unless you’re spending six figures or more every month, this level of granularity doesn’t help, and may actually hurt your campaigns. Google’s AI needs data density to learn. When you slice your audience into tiny slivers, you don’t have enough “matched records” for the system to optimize.
Upload your unique data to Google Ads, keep your strategy simple, and let the bidding algorithms do the heavy lifting in driving returning customers for you.
This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature, and what you need to know to get the best results from it – all in a quick 3-minute read.
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Google will begin enforcing a minimum daily budget for Demand Gen campaigns starting April 1, 2026.
What’s happening: The Google Ads API will require a minimum daily budget of $5 USD (or local equivalent) for all Demand Gen campaigns. The change is designed to help campaigns move through the “cold start” phase with enough spend for Google’s models to learn and optimize effectively. The update will roll out as an unversioned API change, applying across all buying paths.
Technical details:
In API v21 and above, campaigns set below the threshold will trigger a BUDGET_BELOW_DAILY_MINIMUM error, with additional details available in the error metadata.
In API v20, advertisers will receive a generic UNKNOWN error, with the specific validation failure referenced in the unpublished error code field.
The rule applies when modifying budgets, start dates, or end dates in ways that push daily spend below the $5 floor — covering both daily and flighted budgets.
Impact on existing campaigns. Current Demand Gen campaigns running below the minimum will continue serving. However, any future edits to budgets or scheduling will require compliance with the new floor.
Why we care. For advertisers and developers, this adds a new compliance layer to campaign management workflows. Systems will need updating to catch and handle the new validation errors before deployment.
The bottom line. Google is standardizing a minimum investment threshold for Demand Gen — prioritizing performance stability, while requiring advertisers to adjust budgets and automation accordingly.
To help you prevent a single policy issue from snowballing into a full account suspension, here’s how Google’s three-strike system works and what you should do at every stage to keep your ads running.
Case study: Appealing a Google Ads strike
Over the past 10+ years, I’ve helped thousands of advertisers identify and resolve Google’s policy concerns so that their businesses can resume running ads. One such situation involved helping a business that sells ceremonial swords for military dress uniforms.
Google’s Other Weapons policy prohibits advertising swords intended for combat. However, that same policy permits the advertising of non-sharpened, ceremonial swords, which is what this business sells. Even though this business was properly advertising its products within Google’s ad policy parameters, Google issued them a warning for violating the Other Weapons policy.
After the warning, we documented for Google that the business wasn’t violating Google’s policy. We also added specific disclaimers to the business’s sword product pages, noting that the swords were only ceremonial. Frustratingly, Google decided to issue a first strike to the business anyway.
We appealed the strike because the business wasn’t violating Google’s policy. But Google quickly denied that appeal. We tried appealing again, and Google denied the second appeal. The ad account remained on hold with no ads serving, and the business was losing revenue.
Ultimately, we had to “acknowledge” the strike to Google (I’ll explain what that means later) so that the ads would resume serving. We then worked with Google to craft more precise disclaimer language, stating that the swords for sale were ceremonial blades and not sharpened for use as weapons. This disclaimer was added to the business’s website footer so that both Google’s robots and human reviewers could see it on every single page (regardless of whether swords were for sale on a particular page).
Because of all these changes, Google’s concerns were satisfied and the business has never received any subsequent warnings or strikes. The end result was a success, even though technically there should never have been a warning or strike issued because an actual policy violation never occurred.
Key takeaway: Google will sometimes incorrectly issue warnings and strikes, and even reject appeals, and will often require excessive website disclaimers to convince them that all is well.
Understanding Google’s strikes system can save your ads account from suspension. The search giant adheres to a system that begins with an initial warning and is followed by a “three strikes and you’re out” protocol.
The warning: Your ‘mulligan’ opportunity
Before issuing your ad account an initial strike, Google will first send you a warning notification.
This warning informs you that there’s a problem and allows you to address and resolve Google’s concern before your account is penalized with an official strike.
The penalty: None (yet). Your ads can continue to run.
What to do: Appeal any ad/asset disapprovals if you’re confident Google made a mistake, or identify the issue and replace the disapproved ads/assets with fully compliant versions
Treat warnings seriously — ignoring them likely ensures your account will begin receiving strikes.
Strike 1: At least three days without ads
If Google decides that the same policy violation still exists after a warning was issued, your ad account will receive its first official strike.
The penalty: All ads will stop serving for three full days.
What to do: Acknowledge or appeal the strike.
Acknowledge the strike
This is your fastest path back to serving ads. But Google counts strikes as cumulative over a 90-day period.
If you acknowledge the strike rather than successfully appeal it, you’ve started the clock on the possibility of three strikes and a permanent suspension. Deciding which approach is best is a case-by-case determination.
To acknowledge the strike, you must:
Remove all ads/assets that violate Google’s cited policy
You understand the policy Google says you violated.
You have removed all violations.
You will comply with Google’s policies from now on.
After you acknowledge the strike and the three-day hold ends, your ads will resume serving.
Appeal the strike
Submit this appeal form and explain why your ads aren’t violating Google’s policy. Keep in mind:
Your account remains on hold during Google’s review.
Reviews typically take 5+ business days, so be patient.
If Google accepts your appeal, they will remove the hold and your ads will resume serving.
If Google rejects your appeal, your account will stay on hold and no ads will serve.
After a rejected appeal, you can attempt appealing again or acknowledge the strike.
Appealing is often justified, but it costs time and success isn’t guaranteed (even if you’re in the right, as the earlier case study shows).
Strike 2: At least seven days without ads
If Google decides there’s been another policy violation within 90 days of resolving your first strike, or if your original violation was unresolved during those 90 days, your account will receive a second strike.
The penalty: All ads will stop serving for seven full days.
What to do: Your options are the same as for Strike 1: acknowledge or appeal the strike.
Strike 3: Your account is suspended
If Google decides there’s been another policy violation within 90 days of resolving your second strike, or if your previous violation was unresolved during those 90 days, your account will receive a third strike.
The penalty: Your account is suspended, and you may not run any ads or create a new ad account.
What to do: Your only recourse now is to appeal the suspension.
Successfully appealing a suspension is definitely possible. But the process is often a nightmare, and the results are never guaranteed.
Important: Once suspended, you’re unable to make any changes to your ad account.
Google is sometimes inconsistent at following their own rules. Here are two examples I’ve seen first-hand.
Successfully appealing a strike doesn’t always reset the 90-day clock
I have a client who acknowledged a first strike on June 25. They received a second strike on July 26, which they successfully appealed. You would think that should reset the 90-day counter back to June 25.
However, Google gave them another second strike on October 16, far beyond 90 days from the date of the first strike, but within 90 days from the date of the “first” second strike, which they successfully appealed.
Google sometimes automatically returns your account to ‘warning’ status after a first strike expires
I have a client who received a warning on August 7, followed by a first strike on September 7. They acknowledged the first strike, and that strike expired on December 6, 90 days after it was issued.
However, the account immediately reentered “warning” status, with a new 90-day clock starting from when the first strike expired. There was no new email notification about this warning, and the warning didn’t appear on the Strike history tab.
Look for a notification at the top of your Google Ads account.
Check the Policy manager page in your Google Ads account.
How do I see my history of strikes?
Go to the Strike historytab on the Policy manager page in your Google Ads account.
Can you get a strike without having ad disapprovals?
Yes. Google can issue strikes even if no ads are formally disapproved.
How are Google’s three- and seven-day ad holds calculated?
Google counts full days. For example, if you receive and acknowledge a first strike (a three-day hold) on January 1, your ads won’t be eligible to resume serving until January 4th.
Are account strikes worse than ad disapprovals?
Yes, account strikes are significantly worse than individual ad disapprovals. A strike prevents all your account’s ads from serving and can easily escalate to a full account suspension.
Which Google policies have the three-strikes rule?
Enabling dishonest behavior.
Unapproved substances.
Guns, gun parts, and related products.
Explosives.
Other weapons.
Tobacco.
Compensated sexual acts.
Mail-order brides.
Clickbait.
Misleading ad design.
Bail bond services.
Call directories, forwarding services, and recording services.
Credit repair services.
Binary options.
Personal loans.
Important: If you violate one of Google’s many other policies not listed above, you could find your ad account suspended immediately, with no warning or three-strikes system.
What you can do to prevent and navigate Google Ads strikes
Follow these best practices and tips to minimize the chances of receiving a Google Ads strike:
Read the Google Ads policies that apply to your industry so that you know what to do and what not to do.
Delete old ads and assets you no longer need, so they can’t trigger strikes unexpectedly.
Add clear and comprehensive disclaimers to your website that will help Google understand you’re complying with any ad policies you think they might otherwise decide you aren’t.
Save copies of any appeals you submit because Google won’t show them to you after they’re submitted.
If you receive an account strike, closely monitor the 90-day clock so you know when you’re safely out of the previous “strike” window.
Google understandably cares deeply about its reputation and the safety of its users. That’s why Google’s policy team often strictly enforces its advertising policies, and why they’re sometimes over-aggressive when interpreting and applying their own policy language.
To keep our Google Ads accounts in good health and our ads running, the best thing we can do as advertisers is to deeply understand Google’s advertising policies and requirements.
Always be ready to jump through hoops to explain your unique situations, and over-comply with Google’s edicts whenever feasible.
Meta is updating its ad measurement framework, aiming to simplify attribution in what it calls a “social-first” advertising world.
What’s happening. Meta is narrowing its definition of click-through attribution for website and in-store conversions. Going forward, only link clicks — not likes, shares, saves or other interactions — will count toward click-through attribution. The change is designed to reduce discrepancies between Meta Ads Manager and third-party tools like Google Analytics.
Between the lines. Social media has overtaken search as the world’s largest ad channel, according to WARC, but many attribution systems were built for search-era behaviors. On social platforms, engagement extends beyond link clicks. Historically, Meta counted all click types toward click-through conversions, while many third-party tools only counted link clicks — creating reporting misalignment.
What’s changing. Conversions previously attributed to non-link interactions will now fall under a renamed “engage-through attribution” (formerly engaged-view attribution). Meta is also shortening the video engaged-view window from 10 seconds to 5 seconds, reflecting faster conversion behavior — particularly on Reels. The company says 46% of Reels purchase conversions happen within the first two seconds of attention.
Why we care. This update makes it easier to see which actions actually drive conversions, reducing confusion between Meta reporting and third-party analytics like Google Analytics. By separating link clicks from other social interactions, marketers get a clearer view of campaign performance, while the new engage-through attribution captures the value of likes, shares, and saves.
This gives advertisers more confidence in their data and helps them make smarter, more impactful
Third-party tie-ins. Meta is partnering with analytics providers like Northbeam and Triple Whale to incorporate both clicks and views into attribution models, aiming to give advertisers a more complete performance picture.
The rollout. Changes will begin later this month for campaigns optimizing toward website or in-store conversions. Billing will not change, but reporting inside Ads Manager may shift as attribution definitions update.
The bottom line. Meta is attempting to balance clearer, search-aligned click reporting with better visibility into uniquely social interactions — giving advertisers cleaner comparisons across platforms while still capturing the incremental impact of engagement-driven conversions.
What do conversion rate optimization (CRO) and findability look like for an AI agent versus a human, and how different do your strategies really need to be?
More and more marketers are embracing the agentic web, and discovery increasingly happens through AI-powered experiences. That raises a fair question: what does CRO and findability look like for an AI agent compared with a human?
Several considerations matter, but the core takeaway is clear: serving people supports AI findability. AI systems are designed to surface useful, grounded information for people. Technical mechanics still matter, but you don’t need entirely different strategies to be findable or to improve CRO for AI versus humans.
What CRO looks like beyond the website
If a consumer does business directly through an agent or an AI assistant, your business needs to make the right information available in a way that can be understood and used. Your products or services need to be represented through clean, well-structured data, with information formatted in ways that downstream systems can process reliably.
As more people explore doing business with AI assistants, part of the work involves making sure your products and services can connect cleanly. Standards, such as Model Context Protocol (MCP), can help by enabling agents to interact with shared sources of information.
In many cases, a human may still decide to engage directly on a brand’s site. In that context, content and formatting choices matter. Whether you focus on paid media or organic, ensuring your humans can take desired actions — and will want to — is important.
Old‑school SEO encouraged the idea that more keywords and larger walls of text would perform better. That approach no longer holds.
Wayfair does a great job using accessible fonts, a call to action when the user shifts to a transactional mindset, and easy-to-understand language.
Both humans and AI systems tend to work better with clearly structured, modular content. Large blocks of uninterrupted text can be harder for people to scan and understand. Clear sections, spacing, layout, and visual hierarchy help users quickly understand what they can do and how to accomplish the goal that brought them to the page.
There’s no fixed minimum or maximum amount of text that works best. You should use the amount of content needed to clearly explain what you offer, why it’s useful, and what sets it apart.
A technical topic will need more text, broken into smaller paragraphs. There are great calls to action as well.
A technical topic will need more text, broken into smaller paragraphs. There are great calls to action as well.
Visual components can be helpful when paired with useful alt text. Lead gen forms should be easy for humans to complete and regularly audited for spam or friction. Content that’s hard for people to use is also harder for automated systems to interpret as helpful or relevant.
Optimization 2: How are you communicating with your humans?
One of the best ways to communicate clearly to systems is to communicate clearly to people. Lean into what makes you an expert, but avoid unnecessary jargon or overly complex language. Descriptions should stay specific, accurate, and on-brand.
A simple gut check: if a 10-year-old couldn’t broadly understand what you do, why it matters, and how to engage with you, you’re probably making things harder than necessary. Even though AI systems are sophisticated, clarity still matters because the goal is ultimately to support a human outcome.
If you’re unsure, try putting your positioning copy into an AI assistant and asking it to critique its clarity. Ask for simplification and clearer explanations, not for new claims or embellishment.
Visual components matter here as well. Comparison tables can help when they genuinely support understanding, but they can hurt when they’re used as a gimmick rather than a guide. Accessibility principles matter, too. Color contrast, readable font sizes, and restrained font choices reduce the risk that someone can’t process your site.
IAMS has a thoughtful quiz to find the right dog breed and offers additional close matches. High-contrast color, easy-to-understand buttons, and high-quality photos help.
Images should be easy to understand and clearly connected to the surrounding text. Alt text helps people using assistive technologies and reinforces the relationship between visuals and written content.
A user comes to your site to do something. They might want to buy, request a quote, or speak with your team. That action should be clear.
When the intended action is unclear, it becomes harder for both people and automated systems to understand what your site enables.
Tarte Cosmetics does a great job of leaning into CRO principles, including inclusivity, accessibility, and social proof.
Shopping experiences tend to surface in conversations with shopping intent because assistants are trying to complete the task they were given. If it’s unclear how to add an item to a cart or complete a purchase, you make it harder for a human to do business with you. You also make it harder for systems to understand that you’re a transactional site rather than a catalog of items without a clear path forward.
Lead generation requires similar clarity. If the goal is to talk to your team, include a phone number that can be clicked to call. You might also include a form that submits directly into your lead system or a flow that opens an email client. Forcing users through multiple form pages often frustrates people and adds unnecessary complexity to the experience.
I cover technical considerations last for a reason. The most important work you can do is support the humans you serve. Technical improvements help, but they rarely succeed on their own.
Tips from the Microsoft AI guidebook. (Disclosure: I’m the Ads Liaison at Microsoft Advertising.)
Excessive imagery, low contrast between text and background, or unstable layouts can create challenges.
Make sure your site renders consistently and meaningfully. Large layout shifts after load, measured in cumulative layout shift (CLS), can frustrate users. Pages overloaded with ads or pop-ups can distract from the reason someone arrived in the first place and may introduce trust concerns.
Security matters as well. Malware warnings, broken rendering, or incomplete page loads can raise red flags for both users and automated systems.
Tools like IndexNow can help notify search systems of content changes more quickly. Microsoft Clarity is a free tool that shows how users behave on your site, surfacing friction you might otherwise miss. This includes Brand Agents that help your humans have more meaningful chatbot experiences.
One useful check is to review how your site appears when used as input for ad platforms or auto-generated creative tools, such as Performance Max campaigns or audience ads.
These can provide a helpful lens into how platforms interpret your content. When the resulting positioning and creative align with what you intend, you’re usually doing a good job serving both crawlers and people. When they don’t, it’s often a signal to revisit clarity, structure, or user flow.
Google is rolling out Video Reach Campaign (VRC) Non-Skip ads, expanding how brands reach connected TV audiences on YouTube.
What’s happening. VRC Non-Skips are now live globally in Google Ads and Display & Video 360. Built for the living room experience, they run as non-skippable placements optimized for connected TV (CTV) screens.
Why we care. YouTube has been the No. 1 streaming platform in the U.S. for three straight years, making the TV screen a critical battleground for your brand budget. With guaranteed, non-skippable delivery, you can ensure your full message reaches viewers in premium, lean-back environments.
AI in the mix. Google AI dynamically optimizes across 6-second bumper ads, 15-second standard spots, and 30-second CTV-only non-skippable formats. Instead of manually splitting your budget by format, you can rely on AI to allocate impressions for maximum reach and efficiency.
Bottom line. Advertisers now have a simpler way to secure guaranteed, full-message delivery on the biggest screen in the house — using AI to maximize reach and efficiency across non-skippable formats without manually managing the mix.
Google is expanding its recurring billing policy to allow certified U.S. online pharmacies to promote prescription drugs with subscriptions and bundled services.
What’s happening. Certified merchants can now offer:
Prescription drug subscriptions — recurring billing for prescription medications.
Prescription drug bundles — combining drugs with services like coaching or treatment programs, as long as the drug is the primary product.
Prescription drug consultation services — recurring consults to determine prescription eligibility, either standalone or bundled with medications.
Requirements for eligibility. Merchants must maintain certified status, submit subscription costs in Merchant Center using the [subscription_cost] attribute, include clear terms and transparent fees on landing pages, and comply with all existing Healthcare & Medicine and recurring billing policies. Accounts previously disapproved can request a review once requirements are met.
Why we care. The update opens new revenue opportunities for online pharmacies, letting them leverage recurring models and bundled services while staying compliant with Google policies.
The bottom line. Certified U.S. online pharmacies can now run recurring prescription and bundled offers, giving them more flexibility to reach patients and scale subscription-based services.
If you’re not actively managing your branded search campaigns, you’re leaving money on the table and your reputation in the hands of competitors, review aggregators, and affiliate marketers.
Brand protection through PPC isn’t just about bidding on your own name. It’s a strategy that spans defensive bidding, query monitoring, ad copy testing, and reputation management across the entire customer research journey.
Why brand search deserves more than basic defense
Most PPC managers treat brand campaigns as an afterthought. Set up a campaign, bid on the exact brand name, maybe add some close variants, and call it done.
But the reality is far more complex, especially when we’re talking about bigger, well-known brands. Your brand exists across dozens of query contexts, each representing a different stage of the customer journey and requiring a different strategic approach.
Consider what happens when someone searches for your brand. They’re not just typing your company name, they’re asking questions, seeking validation, comparing alternatives, and researching specific features.
If you’re only covering exact-match brand terms, you’re missing the majority of brand-related searches and leaving those high-intent users exposed to competitor messaging.
Third-party sites like review aggregators and affiliate comparison websites actively bid on your brand terms to capture traffic and redirect it to their comparison pages, where your competitors pay for prominence.
The cost? Your brand equity, customer trust, and ultimately, conversion rates.
4 categories of branded searches you need to cover
Based on user intent and competitive vulnerability, branded searches fall into four strategic categories. Each requires different bid strategies, ad copy approaches, and landing page experiences.
Let’s break down each category and the specific PPC tactics that can work.
Brand trust and reputation queries
“Is [Brand] good?”
“[Brand] reviews.”
“Is [Brand] legit?”
“Is [Brand] worth it?”
These searchers are in the validation phase. They’ve heard of your brand but want social proof before committing.
The competitive threat here comes from review aggregators and affiliate sites that will happily show your reviews alongside competitor CTAs.
PPC strategy
Bid aggressively — these are high-intent users who are close to converting.
Use review extensions and star ratings in your ads.
Highlight trust signals in ad copy (years in business, customer count, awards).
Send users to dedicated testimonial or case study landing pages, not your homepage.
Test callout extensions with specific proof points.
Product features queries
“What is [Brand] known for?”
“Pros and cons of [Brand].”
“Does [Brand] offer [feature]?”
Users searching for feature-specific information are evaluating whether your solution meets their requirements. Competitors often bid on these queries with ads suggesting they offer superior features.
PPC strategy
Create feature-specific ad groups with tailored ad copy.
Use sitelink extensions to direct users to specific feature pages.
Address the specific feature in headline 1, don’t waste space on your brand name.
Include feature demos or video on the landing page.
Test whether these queries warrant higher bids than core brand terms.
Comparison queries
“Alternatives to [Brand].”
“How does [Brand] compare?”
“Is [Brand] better than [Competitor]?”
“Is [Brand] right for [use case]?”
This is the most competitive category. Users are actively comparing you to alternatives, and both direct competitors and third-party comparison sites are bidding heavily. This is where you’re most vulnerable to losing customers who were already considering you.
PPC strategy
Bid at or above top-of-page estimates to maintain Position 1.
Create dedicated comparison landing pages for each major competitor.
Include pricing transparency if it’s a competitive advantage.
Monitor auction insights obsessively to identify new competitive threats.
Consider category-level comparison ads for “best [category] tools/products” searches.
Niche questions
“Is [Brand] expensive?”
“Does [Brand] offer discounts?”
“Is [Brand] secure?”
These queries reveal specific concerns or evaluation criteria. They’re often low-volume but extremely high-intent because they represent genuine decision-making criteria.
PPC strategy
Develop FAQ landing pages that address multiple related concerns.
Test lower bids — these queries often have less competition.
Use search query reports to identify emerging concerns and address them proactively.
The traditional single-brand campaign approach doesn’t give you enough control or insight at scale. Instead, structure your brand defense across four specialized campaigns, each targeting different intent signals and requiring distinct bid strategies.
Core brand defense
This covers exact-match brand terms and common misspellings with aggressive bidding to maintain 95%+ impression share and top positions. Never let this campaign be budget-limited.
Use multiple RSAs to test different value propositions. Monitor lost impression share due to rank as your primary competitive threat indicator.
Brand + category
Capture phrase-match queries like “[Brand] CRM” or “[Brand] for [use case],” where users are researching you within a specific product context.
Bid slightly lower than core brand terms, but ensure ad copy acknowledges the category and emphasizes your category leadership. Test whether category-specific landing pages outperform your homepage for these queries.
Brand reputation and reviews
Theseintercept validation-phase users searching “[Brand] reviews,” “[Brand] ratings,” or “is [Brand] good” before they click through to third-party aggregators. Bid aggressively here — these comparison-shopping clicks are worth more than core brand searches.
Use review extensions prominently, include specific social proof metrics in ad copy (4.8 stars, 10,000+ reviews), and send traffic to dedicated testimonial pages rather than your homepage. Test video testimonials on landing pages.
Competitive comparison defense
Control the narrative for queries like “[Brand] vs [Competitor],” “[Brand] alternative,” or “better than [Brand].” These are users you’re at risk of losing, so pay up to your maximum acceptable CPA.
Create unique landing pages for each major competitor with honest comparisons that emphasize your advantages, include side-by-side feature tables, and offer special conversion incentives like extended trials or migration assistance.
Defensive tactics against third-party aggregators
Sites like G2, Capterra, and other affiliate comparison sites actively bid on your brand terms without violating trademark policy because they legitimately have content about your brand.
But they’re siphoning off your traffic and often presenting biased or incomplete information. Your defense requires three coordinated approaches.
Bid aggressively on review keywords
Review aggregators bid heavily on “[Brand] reviews” and “[Brand] ratings” because these are their money keywords, so you need to bid even higher.
Run the math: If a review aggregator click costs you $3 but sends that user to a page where your competitor’s ad costs $50, you’re getting a deal at $10 per click on your own review keywords.
Calculate the lifetime value of a customer versus the cost of letting them click to a third-party site where competitors can advertise. Also, keep in mind it’s cheaper for you to bid on your own brand than for competitors to outbid you.
Claim and optimize your profiles on major review platforms you want to work with
Even if you can’t prevent them from bidding on your brand, ensure that when users click through, they see optimized content, strong ratings, and an active presence with responses to reviews.
Many review platforms offer advertising options — test running ads on your own profile pages to capture users who arrive via organic search or competitor ads.
Build dedicated testimonial and customer story pages
Make yours more compelling than third-party review aggregators. Include video testimonials, detailed case studies with metrics, filterable reviews by industry or use case, and verified customer badges.
Then use your PPC ads to drive users to these owned properties instead of letting them discover review aggregators organically.
Your brand campaign ad copy needs to do more than confirm your brand name. It needs to preempt objections, differentiate from competitors, and provide compelling reasons to click your ad instead of a competitor’s or third-party site. Three frameworks deliver results.
The preemptive strike
Identify the top 3-5 objections that come up in your sales process and address them directly in your ad copy before users encounter them on competitor or review sites.
If implementation time is a concern, use “Live in 5 days, not 5 months.”
If pricing is opaque, try “Transparent pricing, no hidden fees.”
If enterprise readiness is questioned, lead with “Trusted by 500+ enterprise customers.”
If ease of use is a concern, emphasize “No training required, start today.”
The competitive differentiator
Don’t just state features, state features your competitors don’t have or can’t match. This is especially critical for comparison queries where you know competitors are showing ads. Examples include:
“Only platform with native [unique integration].”
“Industry’s fastest performance, verified by [third party].”
If you can’t identify any unique features or USPs, that’s a signal to improve your product positioning or capabilities. Without clear differentiation, PPC alone won’t drive sustainable conversions.
Social proof stacking
Combine multiple types of social proof to build credibility quickly. Don’t just pick one element, stack them. Try
“4.8 stars from 10,000+ reviews. G2 leader 5 years running.”
“Join 50,000+ companies. Featured in Forbes and TechCrunch.”
“Winner: Best [category] 2025. 98% customer satisfaction.”
Sending all brand traffic to your homepage is a missed opportunity. Different branded queries represent different user intents and concerns, and your landing pages should address those specific intents.
Feature-specific pages
When users search “[Brand] + [feature],” send them to dedicated pages that explain the feature in detail, show it in action, and provide clear next steps.
Include a hero section explaining the feature in one sentence, a video demo or animated screenshot, technical specifications for enterprise buyers, integration details if relevant, and customer examples using this specific feature.
Comparison pages
Create dedicated comparison landing pages for each major competitor. Be honest about differences while emphasizing your advantages. Include side-by-side feature tables, pricing comparisons if advantageous, and customer testimonials from switchers.
Acknowledge competitor strengths without being dismissive, highlight 3-5 key differentiators where you excel, and offer migration assistance or switch incentives. Make your CTA clear and prominent, offering a trial or demo.
Trust and validation pages
For review and reputation queries, create dedicated pages that aggregate social proof rather than linking to your G2 profile or hoping users browse scattered testimonials.
Display aggregate ratings prominently (average of G2, Capterra, etc.), place video testimonials above the fold, show recent reviews with verified badges, make reviews filterable by industry, company size, and use case, include case studies with concrete metrics, and highlight third-party awards and recognition.
Monitoring and optimization: The ongoing battle
Brand protection isn’t a set-it-and-forget-it strategy. The competitive landscape constantly evolves, new competitors emerge, third-party sites adjust their strategies, and user search behavior shifts. You need systematic monitoring and rapid response capabilities across three time horizons.
Weekly monitoring
Review:
Search term reports to identify new query patterns.
Auction insights for increased competitor presence.
Impression share metrics to diagnose declining performance.
Lost impression share breakdowns by budget and rank.
Manual searches of your top 10 brand queries to see what ads are showing.
Quality score checks for brand keywords to diagnose landing page or ad relevance issues.
Monthly deep dives
Analyze conversion paths to understand how brand search fits into the broader customer journey.
Review assisted conversions since brand campaigns often contribute to non-brand conversions.
Audit landing pages for relevance and conversion performance.
Gather competitive intelligence on what landing pages competitors use for brand conquesting.
Test new ad copy variations focused on emerging objections or competitive threats.
Analyze search impression share by device and location to identify gaps.
Quarterly strategic reviews
Audit your complete branded query coverage to identify missing categories or query types.
Assess whether your coverage across the four query categories remains comprehensive.
Conduct competitive conquest analysis to determine which competitors most aggressively target your brand.
Evaluate ROI of different brand campaign types to optimize budget allocation.
Review third-party aggregator presence for new sites bidding on your brand.
Advanced tactics for sophisticated brand protection
Dynamic keyword insertion
For validation queries like “is [Brand] good” or “does [Brand] work,” use dynamic keyword insertion to echo the user’s specific question in your ad copy, creating higher relevance and click-through rates. Try headlines like “Yes, {KeyWord:[Brand]} Is Excellent” or “Absolutely, {KeyWord:[Brand]} Works.”
Geo-modified campaigns
If you have location-specific offerings or competitors vary by geography, create geo-modified brand campaigns. Users searching “[Brand] New York” or “[Brand] enterprise” may have different needs than general brand searchers.
Audience layering
Apply audience segments to brand campaigns to adjust bids based on user quality. Users who’ve visited your pricing page before should get higher bids on brand searches than first-time visitors. Similarly, prioritize users who match your ideal customer profile demographics.
Trademark enforcement
While Google generally allows competitors to bid on your brand terms, using your trademarked brand name in their ad copy is often prohibited.
Monitor competitor ads and file trademark complaints when they use your brand name in headlines or descriptions. This is particularly effective against smaller competitors and affiliates who may not realize they’re violating policy.
Problem/solution queries
Capture queries where users are researching whether your solution addresses a specific problem. These are often high-intent and represent clear use case alignment.
Target queries like:
“[Brand] for [problem].”
“How to [solve problem] with [Brand].”
“[Brand] [use case] solution.”
“Can [Brand] help with [challenge].”
Budget allocation and ROI considerations
How much should you invest in brand protection versus acquisition campaigns? The answer depends on three factors:
Competitive pressure.
Brand strength.
Customer lifetime value.
If you operate in a highly competitive category where multiple well-funded competitors actively bid on your brand terms, invest more in brand protection. Run auction insights weekly to monthly to quantify competitive presence.
If competitors show in 40% or more of your brand auctions, this is a high-threat environment requiring aggressive defense. Stronger brands with dominant organic presence can afford to spend less on core brand defense because their organic listings provide natural protection. This doesn’t apply to reputation and comparison queries where third-party sites rank organically.
High LTV businesses should invest more aggressively in brand protection because the cost of losing a customer to a competitor or having them influenced by negative review sites is substantial. If your average customer is worth $50,000 over their lifetime, paying $50 per click to defend against comparison queries is economically rational.
For most B2B SaaS and high-consideration products, allocate approximately 15-25% of total paid search budget to comprehensive brand protection. Within that allocation, dedicate 40% to core brand defense (exact match), 25% to competitive comparison defense, 20% to reputation and review queries, and 15% to feature and niche question queries.
Brand protection through PPC isn’t just defensive marketing. It’s a competitive moat. When you control the narrative across branded search contexts, you ensure high-intent users see accurate information instead of competitor ads or third-party pages monetizing your brand equity.
The brands that win treat this as strategy, not maintenance. They segment branded queries by intent, build landing pages to match, monitor threats continuously, and defend high-value search real estate aggressively.
Start with an audit using the four-category framework. Close coverage gaps, align campaigns and landing pages to intent, and commit to weekly monitoring, monthly optimization, and quarterly strategic reviews.
If you don’t own your branded searches, someone else will.
ASUS has released a new AMD graphics driver for the ROG Ally with the Ryzen Z1 Extreme, version 32.0.22029.13001. While the update appears to be a minor revision, it confirms that ASUS is still maintaining the handheld amid concerns about broader Z1 Extreme driver support.
Google Ads PMax placement reporting is now populating with data for more accounts, revealing Search Partner domains and impression counts for brand safety review.
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