Wil Reynolds, founder and CEO of Seer Interactive, is challenging SEOs to rethink what success looks like in a world increasingly shaped by AI.
In his SEO Week session, “SEO is a performance channel, GEO isn’t. How do you pivot?”, Reynolds said many marketers are focused on the wrong outcomes — and producing work that people don’t believe.
Marketing isn’t just about being seen
Reynolds opened by pushing back on the idea that visibility alone is the goal of marketing.
“Marketing was never just to be seen or be visible,” he said. “You had to turn that visibility into something — believing something about your brand… And then they ultimately have to choose you.”
He described a progression that marketers need to focus on: being seen, being believed and being chosen.
“It’s how you take your time with people, and turn them from seeing you, into believing something about you,” he said.
“I got the ranking, job finished,” he added. “Job’s not finished.”
Reynolds also questioned the value of surface-level success metrics.
“I got a lot more followers, but they don’t pay you,” he said.
Low-quality marketing is everywhere
Reynolds pointed to common marketing tactics — including automated outreach — as examples of work that doesn’t create value.
“That’s not marketing,” he said, referring to spam-like SMS messages.
Those tactics made him reflect on his own past work, he said.
“I started looking at the stuff that I used to do… was that really marketing?” he said.
“Some of us are strategists. Some of us are loopholists,” he said. “You’ve got to make a decision today.”
The industry is producing ‘zombie content’
Reynolds criticized the widespread use of scaled, templated content designed primarily to rank.
He used broad listicle-style pages as an example.
“Why would you write content saying best restaurants in Minnesota when nobody that’s a human looks for the best restaurant in Minnesota?” he said.
He described this type of content as “zombie content.”
“That’s what we do,” he said, describing how marketers repeat what already ranks instead of doing something different.
He also described how many marketers approach content creation.
“I’m going to look at the top 10 and look at what they did slightly wrong… and I’m only going to do it slightly better,” he said.
Short-term tactics vs. long-term brand building
Reynolds contrasted short-term SEO tactics with long-term brand building.
“Some people like to win in decades,” he said. “Other people like to win quarter to quarter.”
He described how many teams focus on immediate results.
“What works this quarter to get my boss off my back long enough so I can survive the next quarter?” he said.
That approach leads to work that people don’t actually want, he said.
“You will never produce a thing that anyone wants if you continue to play that,” he said.
SEO success doesn’t translate to AI visibility
Reynolds shared an example involving “ethical jeans” to show how SEO and AI results can differ.
One brand ranked well in Google without being known for ethical practices, while another brand that invested in ethical production ranked much lower.
In AI-generated answers, that outcome changed.
“If that worked, if it was the same, that brand would be showing up in AI models,” he said. “And they showed up in none.”
He connected this to credibility.
“Nobody believed them,” he said. “Nobody chose them.”
Visibility without belief doesn’t lead to outcomes
Visibility alone isn’t enough, Reynolds said.
“If you have all the visibility in the world and people don’t believe you or trust you, then you’re not going to get chosen,” he said.
Visibility is only part of the process, he said.
“This visibility is just an opportunity,” he said. “That’s all it is. … Iit is not the job to be done.”
What people say matters
Reynolds suggested looking at platforms like Reddit to understand how people actually talk about brands.
“Go to Reddit… look at all the brands,” he said. “You find out that humans don’t believe you. And they have to pay you for you to stay in business.
He contrasted that with how brands present themselves in content.
“Not only did they not think you’re number one — they don’t think you’re number 100,” he said.
The wrong metrics are being measured
Marketers often focus on metrics that are easy to track rather than meaningful, Reynolds said.
“We’re measuring the easy stuff to measure,” he said. “The real work is in the hard-to-measure stuff.”
He encouraged comparing visibility metrics with signals tied to outcomes.
“If your visibility is skyrocketing and your pipeline is flat, that’s bad,” he said.
Watching real users changes the picture
Reynolds described research his team conducted by observing real people using AI tools.
“When you actually watch people do the job… your eyes open so much wider,” he said.
One person typed four words, while another typed more than 100 words for the same task, he said.
He also noted that AI tools often suggest additional steps or actions beyond what users ask for, and people frequently accept those suggestions, he said.
Start with your brand
Marketers should focus on how their brand appears in AI-generated answers, especially for branded queries, Reynolds said.
“You spend all this money trying to get people to know your brand… and then you don’t want to make sure that answer’s right?” he said.
AI can shape your brand narrative
Reynolds shared an example where AI-generated responses surfaced incorrect information about his company.
“So now it’s showing up everywhere,” he said.
He described responding by publishing content to address the claim directly.
“If it’s false, then I’ve got to fight that,” he said.
There is too much content
“There’s too much content out there,” he said.
He described shifting his approach.
“I’m trying to become a curator,” he said.
Rethinking performance
Reynolds shared examples of how different traffic sources perform.
“My direct converts 1.5 times better than my SEO,” he said. “My social, five times better.”
A final question for marketers
Reynolds ended by asking marketers to rethink their priorities:
“Are you willing to sacrifice a little bit of this visibility game to be more believable?”
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One of the most dependable ways to grow organic visibility was to publish more content. Expanding into the long tail and creating pages around different variations of a topic often led to steady traffic growth.
Many SEO teams still operate with this mindset. Content calendars are built around search volume targets, and growth is often equated with how much new content is produced. The problem is the results no longer reflect the effort.
In many cases, adding more pages doesn’t lead to increased visibility and can even dilute overall performance. Large content libraries are harder to maintain, compete internally, and often result in fewer pages surfacing in search results.
The challenge is no longer producing more content, but understanding why much of it fails to contribute to visibility.
Why content volume worked for SEO
For a long time, increasing content volume was a rational and effective strategy. Search engines relied heavily on keyword matching and topical coverage, which meant expanding into the long tail created more opportunities to capture demand.
Competition was also significantly lower, and many queries had limited high-quality results, so publishing across a wide range of keyword variations often led to quick visibility gains. In this environment, covering more topics translated directly into increased traffic.
Publishing frequency also helped strengthen domain authority. Sites that consistently added new content signaled freshness and relevance, which improved their ability to compete in search results.
This approach was further amplified by programmatic SEO. By creating scalable templates and targeting large keyword sets, companies generated thousands of pages and captured traffic at scale.
Most importantly, this strategy worked because it aligned with how search engines evaluated content at the time. Expanding coverage increased the likelihood of ranking, and more pages meant more opportunities to be discovered.
However, the conditions that made this approach effective have changed. As search ecosystems have evolved and competition has increased, the relationship between content volume and visibility has become less predictable.
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Why this model is breaking down
Content saturation
Most commercially relevant topics now have dozens of established pages competing for the same queries, many with years of accumulated links and behavioral data.
A new page enters this environment at a disadvantage because the keyword spaces it targets are already consolidated around results with existing authority and signal history.
Diminishing returns
As sites expand into adjacent keyword variations, search engines increasingly route similar queries to the same URL rather than distributing traffic across multiple pages.
This shows up in Google Search Console as two or three URLs splitting impressions on identical queries — neither ranking strongly because neither has consolidated authority. The intent overlap that content teams treat as coverage, Google treats as redundancy.
Changes in search experience
AI Overviews now appear across a significant and growing share of informational queries. Google has confirmed continued expansion of the feature across search types and markets. Informational content is the most affected by this shift, and it’s also the type most volume strategies produce.
A site with a large number of blog articles is therefore more exposed than one focused on a smaller set of transactional pages. More ranked pages don’t produce proportional traffic when an increasing share of visible positions no longer generate a click.
Indexing limits
Google’s budget documentation states directly that low-value URLs drain crawl activity away from pages that matter. At scale, thin or redundant content is deprioritized — meaning a significant percentage of a site’s published pages may never meaningfully enter search competition regardless of how much continues to be added.
What’s less understood is how content libraries behave at scale. These are system-level problems that compound over time and are difficult to reverse.
Content debt
Every page published creates an ongoing obligation. It needs to be monitored for ranking decay, updated when information changes, evaluated periodically for pruning or consolidation, and factored into crawl allocation. These costs are rarely accounted for at the point of creation.
At low volumes, this is manageable. At scale, it becomes a compounding liability. A site with 2,000 articles isn’t sitting on 2,000 assets, it’s managing 2,000 maintenance commitments that depreciate at different rates.
Editorial resources that could strengthen existing high-performing pages are instead absorbed by keeping a growing library from becoming a liability.
The true cost of a volume-driven content strategy only becomes visible 18 to 24 months after the investment, when maintenance demands begin to outpace the capacity to meet them.
Crawl inefficiency and cannibalization
Google allocates a finite crawl budget to each domain. When a site scales content volume without proportional gains in quality or authority, Googlebot distributes that budget across a larger number of pages, many of which offer limited signal value. The result is that high-value pages are crawled less frequently, indexed less reliably, and are slower to reflect updates.
This creates a compounding problem for sites with important transactional or evergreen pages that depend on frequent re-crawling to stay current and competitive. Beyond crawl distribution, similar pages targeting overlapping intent compete for the same ranking positions internally.
Search engines consolidate these signals rather than rewarding each page individually, meaning two pages targeting near-identical queries often perform worse combined than one authoritative page targeting both would perform alone.
Topical authority dilution
Search engines evaluate whether a site is a genuinely deep and trustworthy resource within a defined topic space. Expanding into a wide range of loosely related subtopics can erode this signal rather than strengthen it.
A site with 40 tightly interconnected, substantive pieces on a specific topic will consistently outperform one with 400 surface-level articles spread across adjacent themes. The depth and coherence of coverage within a defined area are what build the authority signal that drives durable rankings.
Pursuing breadth at the expense of depth fragments that signal, making it harder for search engines to assign clear expertise to the domain on any individual topic, even the ones the site knows best.
Weak content and behavioral signals
Search engines use behavioral data such as dwell time, return-to-search rates, and click-through rates as quality signals at both the page and domain levels.
When a site publishes high volumes of content that users engage with poorly, those signals accumulate and begin to affect how search engines evaluate the domain as a whole. This creates a negative reinforcement loop that’s difficult to detect and slow to reverse.
Weak pages actively contribute to lower domain-level quality assessments, affecting the performance of pages that would otherwise rank well. More mediocre content compounds. Each low-engagement publish incrementally reduces the baseline trust that search engines extend to the domain’s better work.
The goal of SEO has traditionally been to rank. Increasingly, the more valuable outcome is to be cited or referenced in AI-generated summaries, pulled into knowledge panels, or sourced by other publishers as a primary reference. These two outcomes require fundamentally different content strategies.
LLMs and AI Overviews are selective about which sources they draw from. The selection is weighted toward pages with strong E-E-A-T signals, high specificity, and clear authoritativeness within a defined domain.
A site that has published hundreds of generic articles covering a topic broadly is less likely to be treated as a primary source than a site that has published fewer, more definitive pieces with clear depth and original perspective.
Volume doesn’t increase citation probability — it may actively reduce it by signaling that the domain is a generalist content producer rather than a reliable primary reference.
The long tail is saturated
The accessible long tail that drove content volume strategies for the better part of a decade no longer exists in the same form. Between 2010 and 2020, there were genuinely underserved keyword opportunities across most industries.
Today, in most commercial verticals, every remotely valuable query has multiple established pages competing for it, especially from high-authority domains with years of accumulated signals.
New content entering this environment doesn’t find open space. It enters a war of attrition against incumbents with advantages it can’t easily overcome. The marginal SEO return on a new article targeting a long-tail keyword is a fraction of what it was five years ago.
The economics only justify creation when there’s a genuinely differentiated angle, a proprietary data point, or a perspective that exists on your page that other pages can’t offer. A keyword existing is no longer a sufficient reason to publish.
At scale, these factors turn content growth into diminishing returns rather than compounding gains. The library becomes harder to maintain, harder for search engines to evaluate clearly, and harder to extract meaningful visibility from — regardless of how much is added to it.
The implication is to change what publishing is for.
Volume targets made sense when more pages meant more opportunities. In the current environment, they measure the wrong thing. The more useful question isn’t how much content a team is producing, but how much of what already exists is actively contributing to visibility, and what is quietly working against it.
For most sites, that audit reveals the same pattern. A relatively small number of pages generate the majority of organic traffic. A larger number generates little to none, and a significant portion actively drains crawl allocation, fragments topical authority, or dilutes the behavioral signals that stronger pages depend on.
You need to move from expansion to consolidation. Existing pages that cover overlapping intent are stronger merged than competing. Thin pages that rank for nothing and engage no one are more valuable removed than retained.
The energy going into producing new content at volume is often better spent deepening the pages that already have authority and signal history behind them.
New content earns its place when it:
Addresses something genuinely unaddressed.
Offers a perspective that existing pages can’t.
Targets an intent the site currently lacks.
In practice, this means retiring a few default assumptions:
That publishing for every keyword variation is coverage.
That indexing is the same as performance.
That output volume is a proxy for strategic progress.
None of these were ever true measures of content effectiveness. They were convenient ones.
The replacement for volume isn’t simply better content. It’s a different definition of what content is trying to achieve.
Depth over breadth
Focus coverage on a smaller number of topics and develop them thoroughly. A single piece that addresses a topic with specificity, original perspective, and clear authorial expertise will outperform multiple pieces covering adjacent variations of the same theme.
Depth is what builds authority signals, drives engagement, and increases citation potential. Prioritize what the site can say with the most credibility.
Distribution as a multiplier
Allocate more effort to distribution. Publishing less creates capacity to deliver strong content to the right audiences. Distribution is a core part of SEO performance in a citation-driven environment.
Being citation-worthy
Create content that can serve as a primary source. Focus on clear points of view, verifiable expertise, and specific insights that other pages can’t replicate.
The goal is to be referenced in AI-generated summaries, cited by other publishers, and included in the knowledge systems search engines rely on.
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The uncomfortable truth
Sites that rely on frequency and broad coverage are being outperformed by sites that are clearly authoritative on a defined topic, consistently useful to a specific audience, and structured in a way that search systems can evaluate with confidence.
Prioritize depth, clarity of expertise, and consistency within a focused topic area. Treat each published page as a long-term asset that requires ongoing maintenance, evaluation, and improvement.
The content factory model is no longer effective. The approach that replaces it requires more effort, stronger editorial standards, and a higher bar for what gets published.
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If your paid social campaigns aren’t converting, you may be undervaluing their impact. Your brand’s exposure on social media can influence other parts of your marketing that platform metrics don’t capture.
Here’s how to design and measure a test to understand how paid social influences your other marketing channels, including PPC.
Step 1: Determine your hypothesis
Start with what you want to learn, then define a hypothesis you can realistically evaluate with your data.
For example, this is a common hypothesis for measuring paid search lift from social traffic:
Search lift hypothesis: Increasing spend on social media will increase brand search volume and overall PPC CTRs.
Logic:
Social ads build brand awareness. As more people become familiar with our brand, they will search for it more often when making research and purchase decisions.
As more people are exposed to our brand, they will increasingly click on our PPC ads regardless of their search term (i.e., increasing non-brand and brand CTRs).
People exposed multiple times to our brand will have a higher trust factor in our products, and therefore, our conversion rates will increase.
Measurement:
Impression and click volume for our branded terms.
CTR changes for brand and non-brand terms.
Conversion rate changes for brand and non-brand terms.
Your hypothesis could have a different scope, such as measuring paid and organic lift from social spend or an increase in direct traffic.
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Step 2: The test
The next step is to set up the test parameters. Generally, measuring before and after a change is a mistake, as seasonality or other factors can affect your test results.
The most common test setup is a geographic split. In this test, we’ll increase social spend for only a set of geographies. Then we’ll examine the PPC data for the geographies where we ran the test and compare them with areas where we did not.
As you choose geographies, you’ll want to control for other variables that may affect your test. Here are some common issues that companies have run into and need to control for in their tests and measurements:
You sponsor a sports team, and they’re playing during your test.
If the game is regionally televised, this can dramatically affect your test results.
You’re running TV commercials in only certain regions.
You choose experimental geographies with many out-of-region commuters, such as New York City, and include New Jersey and Connecticut in your control group.
In these instances, grouping a region and its surrounding commuter areas together, and placing other cities with similar characteristics, such as Chicago and Philadelphia, in a different group, can help balance these tests. (Note: in this example, we’re splitting New Jersey in half.)
Seasonal or local events. Large conferences, festivals, or major weather events can affect your data.
Your control and experimental groups should be statistically similar across factors such as income levels, and urban versus rural regions.
As you set up and measure your test, consider your budget. If you increase social spend and expect higher clicks and conversions for your PPC campaigns, ensure you have the budget to capture the increased demand.
Examine your impression share and impression share lost to budget before and after the test to ensure budget limits won’t severely impact your results.
Measurement can go from very simple to extremely complex.
At a simple level, you can compare platform data to see how your data changed. In this case, a Google Ads report shows how pausing social spending and influencer campaigns across all social platforms (TikTok, LinkedIn, Facebook, YouTube, etc.) affects performance.
For this test, pausing social spending yielded mixed results for conversion rates. As brand searches decreased, conversion rates in some regions increased, while in others they fell.
However, what was consistent was a dramatic drop in conversions.
You can get more sophisticated in your testing. Depending on your analytics setup, some companies want to measure touchpoint differences for their conversions. Others will want to measure overlap rates between social and paid search visitors, or examine attribution touchpoints and models.
Before you set up your test, ensure you have the measurement capabilities needed to understand and interpret the results.
As you run various tests, you want to measure the results against your hypothesis. However, it’s useful to list other variables worth evaluating beyond your test criteria.
This is where search consoles, analytics tools, CRM, internal data, and even the paid and organic report can come into play.
In one example, a company was running a test to see whether pausing several advertising channels, from social media to TV ads, would dramatically change its brand search volume. They hypothesized that their brand was so well known in the marketplace that they could cut back on several forms of brand advertising and reallocate that budget to other channels and non-brand advertising.
While the simple paid and organic report in Google Ads won’t tell you the full story about in-store revenue and direct traffic changes, it can serve as a signal to form an overall picture of a very complex test.
They had recently launched a new product line, and that line continued to see a large increase in traffic during the test. However, their most common brand terms saw significant declines from the test. This was a year-over-year comparison across a set of geographies, rather than a period-to-period comparison, to help correct for the increase in holiday traffic that would have occurred during the previous period.
The results were by far the most dramatic I’ve ever seen in this type of test, to the point it was clear other variables had to be in play that could affect the test.
This takes you to the sniff test. Rely on your experience with data to make common sense adjustments. If you look at the data and it just doesn’t seem right, ask yourself whether this makes sense, if it’s a math quirk (common with low data), or if other unforeseen variables are in play.
In this example, no one believed the results should be this dramatic. The company stopped running the test and began an internal evaluation of its organic presence, including Google’s recent updates, changes to AI Overviews, AI engagement, and other factors affecting its web presence beyond its usual marketing channels.
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What to do with your social impact tests
The test setup is simple:
Determine your hypothesis.
Decide how you will test. The easiest setup is a geographic split.
Make sure you can measure the results.
Launch the tests.
Evaluate the metrics for your hypothesis.
Examine other metrics for insight or additional testing ideas.
For some companies, Facebook and other social channels are their top conversion channels, and these tests won’t be applicable. For others, social media advertising results often look poor when evaluated in isolation.
In these examples, the companies were already running many social media campaigns, so the test was to reduce social media spend. If you don’t run much social media, your test will be to increase your social media spend to see how it affects your data.
I’ve seen a lot of these tests, and the results are highly inconsistent across companies. Many companies will increase their social media spend and see little change in their data. Others will increase their spend and see a nice lift in overall performance. These are tests you need to run yourself, as your results will vary by company.
Running geographic split tests in your social media campaigns and then measuring the results on paid or organic search traffic can give you insights into how to leverage social media campaigns for other marketing channels.
Google announced they are testing a new “conversational search experience to complement how you already search on YouTube.” It is called “Ask YouTube” and it lets you “dive deeper into the topics you’re curious about in a more interactive way,” Dave from YouTube wrote.
What it looks like. Here is a GIF of it in action:
How can I try it. If you want to try it out, you can go to youtube.com/new and try to opt into it.
This experiment is currently available for YouTube Premium members 18+ in the US who opt-in. Google is working on expanding the experiment to non-Premium users in the future.
What it does. Dave from YouTube posted this example:
“If you’re in the experiment, you can try it out by selecting “Ask YouTube” in the search bar. For example, you can ask for help planning a 3-day road trip from San Francisco to Santa Barbara, and you’ll get a structured, step-by-step itinerary instead of a list of videos. The response will bring together a new mix of long-form videos, Shorts, and informative text featuring local tips and must-see stops. You can ask follow-up questions like, “where can I find good coffee?” to explore local spots along your route. We’ll surface videos and relevant video segments, accompanied by their titles and channel details, to make it easy to discover new creators and jump into the most helpful content from your search.”
Why we care. AI search is creeping into every search interface across Google’s properties. YouTube is no exception. Expect more and more AI search experiences in more Google surfaces and expect them to change and adapt over time.
You can find more coverage of this across Techmeme.
Understanding the ins and outs of paid media can seem like an overwhelming process when you’re first entering the field. As AI has rapidly changed ad platforms in recent years, keeping up can feel challenging.
Thankfully, you’re not alone. You’re part of a supportive industry with a wealth of content and knowledge to share. Here are seven tips to help you learn and become a more confident PPC manager.
1. Be curious
Curiosity is foundational to growth in PPC. You’ll learn best by taking initiative to understand ad platforms, how campaigns are structured, and what options are available on the backend. Of course, be careful about tweaking settings you’re not familiar with, but don’t be afraid to dig in on your own.
If you’re part of a team, ask your colleagues why they use a particular setup. If you’re not familiar with a platform and have a team member who frequently uses it, ask if they can walk you through it.
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2. Absorb content and find community
There are countless industry professionals producing content to teach PPC. Whether you learn best from reading, listening to podcasts, or watching videos, you’ll find options that fit your style. Looking up the authors of articles on this site is a great starting point to build a list to follow.
Block out time in your schedule for education. Even setting aside a couple of hours a week helps you gain perspective from others in the industry and keep up with constant platform updates.
The PPC industry has long been known for its welcoming, supportive community. Seek out individuals and organizations who are actively sharing, and don’t be afraid to engage with them on social media. Conferences are also a great way to network with other PPC professionals and sometimes discuss their approaches in a more informal setting.
A brief word of caution: Vet recommendations you see from others against your own experience in ad accounts. Just because a “best practice” worked for one account doesn’t mean it’ll work for every account. Depending on the tactic, you may want to test it as an experiment to measure impact, or compare results before and after.
3. Take industry certifications with a grain of salt
While ad platform certifications can serve as a starting point for demonstrating basic functionality, be cautious about relying on them as the end-all proof of PPC expertise.
Certifications often lean heavily on platform-recommended best practices, which may conflict with tactics that align with a brand’s goals. Academic knowledge can’t match the insight gained from practical, hands-on experience in accounts.
4. Don’t chase what’s new and shiny
While I’d encourage staying aware of ad platform updates and current tactics, I’d discourage implementing a new campaign type or expanding into a new platform just because it’s new. Make sure you have sufficient budget and a clear reason to test.
Additionally, avoid making adjustments without a rationale. If campaigns are performing and driving qualified leads or sales, keeping the status quo may be best.
Basic marketing principles still apply, such as knowing your target audience, addressing their problem with a solution, and presenting a clear call to action. Focus on aligning your channel choices with these goals, and the rest will follow.
As you become more embedded in PPC, you may naturally use industry terms and acronyms such as CTR, CPC, ROAS, and CPA. However, these metrics are often meaningless to stakeholders who aren’t immersed in your world. One of the most vital skills for a paid media professional is translating abstract metrics into language that connects with what stakeholders care about.
For instance, I often default to “conversions,” even though the term can be ambiguous in reports. Referencing the actual action being tracked (such as account open, form fill, or purchase) is more concrete and ties directly to what stakeholders are tasked with driving.
6. Use AI, but don’t neglect the human touch
AI is an inevitable part of a future-forward career, and ignoring it will be detrimental to career development. However, don’t lose the human oversight that sets a seasoned PPC practitioner apart.
When writing ad copy, LLMs can offer a strong starting point and help refine wording. But don’t rely on AI to produce all your copy, as it may pull irrelevant content from your site (or elsewhere), and may not reflect your brand’s voice and perspective. Also, learn where AI can save time on “busy work” tasks, such as reviewing search terms and placements for exclusions, while still reviewing the output for accuracy.
While most ad platforms default to automated campaign setups and encourage a hands-off approach, a standout PPC manager understands the levers they can pull to maintain control when needed. Examples include:
Setting target bids or cost caps.
Excluding irrelevant keywords, placements, and audiences.
Pinning headlines and descriptions in responsive search ads.
Restricting geographic targeting to avoid unwanted locations.
7. Don’t change things for the sake of showing activity
One common temptation for both new and seasoned paid media practitioners is to make changes just to appear busy. The motivation may be valid, as you want to prove to your client or boss that you’re attentive to PPC account management.
However, particularly with campaigns that rely heavily on data to drive automated bidding, too many changes in a short period are often detrimental. Be sure to allow for data significance and enough time before pausing ads and keywords or tweaking bid targets.
If you can show positive performance trends and provide readouts on which campaigns and channels are driving those results, you can validate your decisions to take or not take action when presenting to stakeholders.
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Keep learning, start sharing
Becoming a confident PPC manager requires mastering a blend of technical, interpersonal, and marketing skills. As you build your knowledge, look for opportunities to share what you’re learning with peers. It’s one of the fastest ways to reinforce what you know and keep improving.
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Search is changing fast – make sure you’re not falling behind.
Sign up for the next SEO Update by Yoast and get expert-led clarity on what’s happening in SEO right now and what it means for your strategy.
Join Carolyn Shelby and Alex Moss as they unpack the most important SEO news, algorithm shifts, and industry developments – so you can focus on what actually moves the needle.
Who should sign up?
This update is ideal if you:
Want expert insight into recent SEO changes and trends
Need help refining or validating your SEO strategy
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Branded search is often treated as predictable and easy to manage. In practice, it isn’t.
PPC teams see rising CPC on brand terms. SEO teams see declining branded CTR, even when rankings hold. These issues are usually investigated separately, with different dashboards, hypotheses, and fixes.
Both signals often stem from changes within a single SERP. What look like two separate problems are, in reality, one shared environment reacting to shifts in competition and visibility.
The issue isn’t a lack of data. Most teams already have basic reports and brand monitoring tools, including PPC and SEO platforms. The problem is how the data is used.
To understand what’s happening in branded search, teams must manually piece signals together. This takes time, doesn’t scale, and delays decisions.
Here’s why that fragmentation is harmful and what to do about it.
What’s actually happening in branded search
Branded search is often described in terms of channels — paid and organic. For users, that distinction doesn’t exist.
A single SERP brings together multiple layers:
PPC ads
Competitor ads or comparison pages
Organic results, including brand-owned pages
Affiliate listings promoting the same brand
Review platforms and aggregators
All of these elements appear at once, within the same decision-making space.
From a SERP analysis perspective, this isn’t a set of isolated placements. It’s a dynamic environment where each element influences the others. A competitor ad above your organic result can reduce CTR. An affiliate listing can compete with your paid campaign. A review page can shift user intent before a click.
In practice, this creates a mismatch.
For users, branded search is a single page. Inside the company, it’s split across workflows and handled by different functions.
PPC focuses on bids and efficiency. SEO focuses on rankings and organic traffic. Affiliate activity is often tracked separately, if at all. Competitor tracking may exist, but usually within a single channel. The result is a fragmented view of what is, in practice, a shared space.
Understanding what’s happening in branded search often requires manual effort. The data is there, but building a complete, up-to-date view of the SERP on a regular basis is time-consuming and hard to scale. That makes it difficult to understand how these elements interact — and even harder to respond to changes as they happen.
What PPC teams see (and often miss)
From a PPC perspective, teams focus on these signals:
Brand CPC starts to rise.
More players appear in the auction.
Branded campaigns become less efficient over time.
At first glance, this suggests increased competition. The typical response is to adjust bids, defend impression share, or refine targeting. All of it makes sense within paid media.
But this is where context changes everything.
What PPC teams don’t always see is who’s driving that competition.
Not every new entrant in the auction is a direct competitor. Often, it’s affiliate activity — partners bidding on branded terms outside agreed-upon rules. Without deeper competitor tracking, these cases can look identical while requiring different actions.
There’s also the organic layer. Changes in SERP structure — more ads, different layouts, stronger third-party rankings — can directly affect paid performance. Even if the campaign setup stays the same, the environment shifts. Without ongoing SERP analysis, these changes are easy to miss.
In many cases, brands aren’t just competing with others — they’re competing with themselves. Over 40% of advertised pages already rank #1 organically (Ahrefs, 2025).
PPC teams rarely see the full page in context. They see auction data, metrics, and reports — but not always how their ads appear alongside organic results, affiliates, and other placements in real time.
But beyond missing context, there’s a more practical limitation.
Ad platform reporting rarely explains what changed. It shows performance shifts — but not how the SERP looked to users, who appeared alongside the ad, or how placements were arranged.
This creates a gap.
Competitor tracking without context doesn’t explain the situation — it only signals change. Without broader SERP-level brand monitoring, PPC teams often optimize on partial visibility, reacting to symptoms while the root cause must be reconstructed manually.
What SEO teams see (and often miss)
From the SEO side, branded search issues tend to surface differently.
The most common signals look like this:
Branded CTR starts to decline.
Rankings remain stable, often still in top positions.
SERP appearance shifts — new elements, richer features, or different page layouts.
On the surface, it looks like an SEO problem. The natural response is to review snippets, adjust metadata, or check for technical or content issues.
But in many cases, performance drops aren’t driven solely by SEO factors.
SEO teams generally know that paid activity, competitors, and affiliates can influence branded search. The challenge isn’t awareness — it’s consistent visibility over time.
To understand what changed, teams need to see how the SERP looked at a specific moment:
Which ads appeared and where.
Whether competitors or affiliates were present.
How organic results were positioned in context.
This isn’t what standard SEO workflows are built for. Teams often have to manually check results, compare snapshots across tools, or rely on incomplete data.
Then there’s the SERP itself. Modern branded SERPs aren’t static. Layout changes, added modules, and mixed result types can significantly affect click behavior.
Without consistent SERP analysis, it’s hard to isolate the cause. As a result, SEO teams may keep optimizing — and see no stable results.
Why PPC and SEO issues are actually connected
At a glance, PPC and SEO issues in branded search may look unrelated — different metrics, dashboards, and teams. But when you look at the SERP as a whole, the connection is hard to ignore.
Studies show this overlap isn’t an edge case. Nearly 38% of websites advertise on keywords where they already rank in the top 10 organically (Ahrefs, 2025). In branded search, the overlap is even higher.
That means both channels operate in the same environment — and compete for the same user attention.
Changes within that environment rarely affect just one side:
Increased ad presence can push organic listings lower or draw clicks away.
Aggressive bidding (from competitors or affiliates) can raise CPC while also reducing organic search visibility.
New entrants in the SERP can affect both paid efficiency and organic CTR simultaneously.
In this context, it’s not unusual for PPC performance to decline while SEO metrics shift in parallel. These aren’t isolated issues — they’re different reflections of the same underlying change. Yet they’re rarely analyzed together.
The real problem isn’t visibility — it’s fragmentation.
Most teams already have access to data. Specialized tools make SERP analysis, competitor tracking, and brand monitoring possible. The limitation isn’t what can be seen, but how it’s used.
PPC and SEO operate in separate systems — different platforms and reporting environments, KPIs, and workflows. To understand what changed in branded search, teams must align manually by comparing reports, checking SERPs, validating assumptions, and sharing findings across functions.
As a result, insights are delayed, alignment lags behind SERP changes, and decisions are made with incomplete or outdated context.
How to improve branded search performance
Most teams don’t miss the signals — a spike in CPC, a drop in CTR, unexpected competitors in the auction. These changes rarely go unnoticed. The challenge comes next: confirming what happened and deciding how to respond.
This is where branded search performance slows. Teams dig through separate reports, trying to reconstruct what the SERP looked like at a specific moment. By the time the picture is clear — if it ever is — the window to react has already passed.
Improving performance here isn’t about adding more data. It’s about changing how it’s collected and used.
With the right setup, SERP analysis becomes continuous instead of manual. Changes in branded search are captured automatically, including competitor and affiliate activity that might otherwise require manual checks, post-fact validation, or go unnoticed.
Tools for branded search monitoring such as Bluepear provide:
Unified look on SERP in a specific moment.
Automated alerts when meaningful changes occur.
Pre-collected, timestamped evidence that removes the need to manually gather screenshots or reconstruct past states.
Instead of spending time collecting screenshots, comparing reports, and reconstructing what happened, the information is already structured.
This shifts the process from reactive to operational. Instead of investigating issues after the fact, teams receive a clear signal or a complete case.
This creates a reliable record of what actually happened:
When a new player entered the SERP.
How placements shifted over time.
Where potential violations or conflicts appeared.
Instead of scattered evidence and manual reconstruction, teams get structured, ready-to-use context.
Reporting becomes simpler. Insights can be shared across PPC, SEO, and affiliate teams without rebuilding context each time, reducing internal alignment time. Most importantly, decisions can be made faster.
With Bluepear, brand monitoring and competitor tracking become continuous. Teams receive structured signals instead of raw fragments and can act without rebuilding the situation from scratch.
To see how Bluepear can improve your workflow, create an account and start your free trial.
Final takeaways
PPC and SEO teams don’t lack data — they interpret different signals from the same SERP. But these signals are connected. They’re shaped by the same changes in the search environment, even if they appear in different reports.
When SERP analysis is fragmented, it’s harder to see the full picture — and even harder to act quickly.
What makes the difference is not more data, but better coordination:
Continuous brand monitoring instead of occasional checks.
Shared visibility across PPC, SEO, and affiliate teams.
A consistent view of the SERP, not separate channel reports.
When branded search is managed holistically, teams don’t just react to performance changes — they understand what drives them and respond with clarity.
To simplify how your team tracks and responds to branded search changes, start using Bluepear to automate monitoring, capture SERP changes, and centralize evidence in one place.
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From today, your AI tools, dashboards, and automated workflows can now talk directly to Yoast SEO, thanks to the new Abilities API, built to work hand in hand with WordPress 6.9 .As WordPress evolves, we evolve with it, and the release of the Yoast SEO Abilities API is an extension of these new capabilities.
What does that mean in plain English?
If you use AI assistants, automated workflows, or custom dashboards, they can now automatically find and read your Yoast content scores, without anyone needing to build a custom connection or dig through documentation. It just works.
What can these tools see?
Once connected, any compatible tool can instantly pull the following from your most recent posts:
SEO scores and focus keyphrases
Readability scores
Inclusive language scores
What can you do with this?
Here are a few examples of what’s now possible:
Ask an AI assistant “How is my SEO health looking this week?” and get a real answer based on your actual posts
Set up a fully autonomous AI workflow, where agents can flag trends in your recent posts.
Pull your content scores into an external dashboard or reporting tool, with no manual exports needed
In short, Yoast SEO is ready to plug straight into your workflow, whatever that looks like. As WordPress continues to open up new capabilities, you can expect Yoast to be right there alongside it.
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In our Rethinking SEO in the age of AI article, we briefly explored how AI might move beyond simple prompt-and-response interactions. One emerging direction is agentic AI. Systems that can take action, not just generate answers. While this space is still evolving, we’re already seeing early signs of tools that can identify gaps, suggest improvements, and adapt to changing trends with minimal input. If these capabilities continue to develop, they could reshape how we think about maintaining continuous discoverability in SEO.
Agentic AI for SEO represents a shift from traditional visibility and ranking to being trusted and understood by AI systems
The web’s structure remains stable, but interaction through AI agents changes how content is accessed and consumed
SEO must evolve to focus on being structured, reliable, and adaptable for AI interpretation
Challenges include data quality, integration complexity, and balancing automation with human judgment
The future of discoverability in an agent-driven web emphasizes collaboration between AI and human insight, expanding SEO’s role beyond just ranking
Understanding the coexistence of web and AI agents
Before understanding agentic SEO, let’s first look at the role of AI in shaping the web. Is it staying the same, or quietly changing?
For a long time, the web has been more than just a collection of pages. It has functioned as an interconnected graph of entities. Websites representing people, businesses, ideas, and concepts, all linked together through content, context, and trust. This structure, often referred to as the open web, has remained relatively stable for decades. Humans created content, users discovered it through search or links, and meaning was formed through exploration.
What seems to be shifting now is not the structure itself, but how that web is accessed and consumed.
Earlier, discovery was largely a direct interaction between humans and websites. You searched, clicked, read, compared, and formed your own conclusions. Today, AI systems are increasingly stepping into that journey. They sit between the user and the web, interpreting, summarizing, and sometimes even deciding which information to surface.
This is where the idea of AI agents begins to emerge. Not just as tools that generate responses, but as systems that can navigate the web, retrieve information, and potentially act on it. Early examples, such as experiments in natural language interfaces like NLWeb, hint at a web that can be interacted with more conversationally, without losing its openness and interconnectedness.
Some refer to this shift as the beginning of an “agentic web.” But it’s important to see it less as a complete transformation and more as a layer forming on top of the existing web. The open web still exists, content is still created by people, and links still matter. What’s evolving is how that content is discovered, interpreted, and used.
And that shift in interaction is where things start to get interesting for SEO.
If AI agents are starting to reshape how people interact with the web, it naturally raises a follow-up question: where does that leave SEO?
For years, SEO has largely been about helping users find your content. You optimized for rankings, improved visibility on search engines, and relied on users to click, read, and navigate. But if AI agents begin to mediate that journey, not just retrieving information but interpreting and acting on it, then SEO may need to expand its role.
Not necessarily replace what exists, but build on top of it.
From ranking pages to being selected by systems
In a more agent-driven environment, discoverability may no longer depend solely on where you rank, but also on whether your content is selected, trusted, and used by AI systems.
That introduces a subtle but important shift:
It’s not just about being visible
It’s about being understandable, reliable, and usable by machines
AI agents don’t browse the web the way humans do. They:
Parse structured and unstructured data
Look for clear signals of authority and accuracy
Combine information from multiple sources before presenting it
So instead of optimizing only for clicks, SEO may also involve optimizing for inclusion in AI-generated responses and workflows.
What stays, what evolves, what gets added
Let’s ground this a bit. Traditional SEO doesn’t disappear. Many of its fundamentals still apply, but their role may shift.
This has created a split from a completely open web into two – the ‘human’ web and the ‘agentic’ web… SEOs will have to consider both sides of the web and how to serve both.
That framing makes the shift clearer.
Your content still needs to rank. But it also needs to work at a second layer of the web, where AI systems interpret, select, and sometimes act on information before a human ever sees it.
So now, your content needs to be:
Understood without ambiguity
Trusted enough to be referenced
Structured well enough to be reused
In that sense, SEO doesn’t disappear in an agentic web. It stretches.
From helping users find information…
to helping systems choose it.
Role of agentic AI in SEO
If the web is gradually being experienced through both humans and AI agents, then it’s worth asking what role these agents might begin to play in SEO itself. Not as a replacement for SEO teams, but as a new layer within how SEO work gets done.
What we’re starting to see is a shift from SEO as a set of periodic tasks to something more continuous, assisted, and adaptive. Some early tools already hint at this. They don’t just analyze data, they suggest actions. In some cases, they even implement changes. If this direction continues, agentic AI could become less of a tool you use and more of a system you collaborate with.
Let’s break down where this role might start to take shape.
How agentic AI may reshape SEO workflows
Shift
Traditional SEO approach (how it typically works today)
With agentic AI (emerging direction)
Audits → Always-on optimization
SEO teams run audits at set intervals (monthly, quarterly) using tools such as site crawlers.
Issues such as broken links, missing metadata, or slow pages are identified and then manually fixed over time.
Improvements often depend on when the audit is conducted.
Systems continuously monitor site performance, flag issues as they arise, and may suggest or implement fixes in real time.
Optimization becomes ongoing rather than dependent on manually scheduled audits.
Reacting → Anticipating
Actions are usually triggered by visible changes.
For example, a drop in rankings leads to an investigation, or an algorithm update prompts content revisions.
SEO is often a response to what has already happened.
AI systems analyze patterns in search behavior and performance data to detect early signals.
This could mean identifying emerging topics, shifting intent, or declining engagement before it significantly impacts performance.
Manual execution → Guided systems
Tasks such as keyword research, clustering, content optimization, and internal linking are performed manually or with tools.
SEO specialists interpret the data and execute changes step by step.
AI assists with these tasks by identifying keyword opportunities, grouping topics, suggesting optimizations, and even applying specific changes.
SEOs shift toward guiding strategy, reviewing outputs, and setting priorities.
Static content → Adaptive content
Content is created, published, and revisited occasionally.
Updates are often triggered by performance drops, outdated information, or scheduled content refresh cycles.
Content evolves more dynamically.
Systems can recommend updates based on performance, refine sections for clarity, or restructure content to better match user intent and AI consumption patterns.
Generic UX → Contextual journeys
Most users experience the same content and navigation structure.
Personalization is limited or rule-based, such as basic recommendations or segmented landing pages.
Experiences become more contextual.
Content, navigation, and recommendations can adapt based on user behavior, intent, or journey stage, creating more relevant and engaging interactions.
A quick example: structuring content for machines, not just humans
If agentic systems rely on structured, connected, and machine-readable content, then this isn’t entirely new territory for SEO.
In many ways, we’ve already been moving in this direction through structured data and schema. What’s changing is how important and foundational it may become.
For example, features like schema aggregation in Yoast SEO bring together different pieces of structured data across a site and connect them into a more unified graph. Instead of treating pages as isolated units, they help search engines better understand how entities, content types, and relationships fit together.
This might seem like a technical detail, but it reflects a broader shift.
If AI agents are parsing, combining, and interpreting content across multiple sources, then clarity and connection at the data level become more important. Not just for visibility in search results, but for how content is understood and reused.
So while agentic AI may feel like a new layer, some of the foundational work, like structuring content, defining entities, and building semantic relationships, is already part of modern SEO. It just becomes more critical in this context.
So, where does this leave SEO teams?
If there’s one pattern across all of this, it’s not replacement, but redistribution.
Agentic AI may take on:
Repetitive tasks
Data-heavy analysis
Continuous monitoring
Which leaves humans to focus more on brand-building aspects like:
Strategy and positioning
Editorial judgment and brand voice
Deciding what should be done, not just what can be done
In that sense, agentic AI doesn’t redefine SEO overnight. But it does start to reshape how it’s practiced.
Understanding the risks and challenges of agentic AI for SEO
So far, agentic AI might sound like a natural evolution of SEO. But, as with most shifts in technology, it may also come with trade-offs.
Not because the technology is inherently problematic, but because it introduces new dependencies, new layers of complexity, and new decisions for SEO teams to navigate. In that sense, adopting agentic AI isn’t just about adding a new capability. It may also involve rethinking how much control to delegate and where human judgment continues to play a critical role.
Here are some of the challenges that could emerge as this space evolves:
1. High technical and integration complexity
Agentic systems are unlikely to operate in isolation. They may need to connect with your CMS, analytics tools, and multiple data sources.
This could introduce challenges such as:
Managing integrations across platforms
Ensuring consistent and reliable data flow
Defining clear workflows across systems
For many teams, this might not be plug-and-play. It could require time, experimentation, and coordination across different roles.
2. Data quality and dependency
Agentic AI may be heavily dependent on the quality of data it receives. If the data is:
Outdated
Incomplete
Poorly structured
Then the outputs could reflect those gaps.
At scale, even small inconsistencies might influence multiple recommendations or decisions. Which is why maintaining clean, reliable data sources may become even more important in an agent-driven setup.
3. Risk amplification and the need for governance
One of the strengths of agentic AI is speed. But that same speed might also amplify unintended outcomes.
Without clear guardrails:
Content updates could introduce inaccuracies
Technical changes might lead to issues like broken links or indexing errors
Best practices may not always be consistently followed
This is where governance frameworks and approval checkpoints may become essential, not to slow things down, but to keep them aligned.
4. Hallucinations and accuracy considerations
AI systems can sometimes generate outputs that sound plausible but aren’t entirely accurate.
In an SEO context, this might look like:
Misinterpreted data
Inaccurate keyword insights
Fabricated or blended information
The challenge is that these outputs can be difficult to spot at a glance. This suggests that validation and source-checking may remain an ongoing part of the workflow.
5. Limited understanding of nuance
SEO often goes beyond data and structure. It includes tone, context, and intent. Agentic systems may not always fully capture:
Brand voice and positioning
Legal or compliance nuances
Subtle differences in user intent
This could result in outputs that are technically sound, but not always contextually aligned. Human input may still play a key role here.
6. Balancing automation with human judgment
A broader question that may arise is how much to automate.
Too much automation might: Reduce control over strategy or brand
Too little might: Limit efficiency and scalability
Most teams may find themselves balancing the two. Using agentic AI to extend their capabilities, while still guiding direction and decision-making.
7. High initial investment and learning curve
While agentic systems may offer long-term efficiency, getting started could take time. This might involve:
Learning how the systems work
Setting up workflows and integrations
Aligning outputs with business goals
There’s also a level of uncertainty here. The technology is still evolving, and so are the tools built around it. Which means costs, capabilities, and best practices may continue to shift.
For many teams, adoption may not be immediate. It could happen gradually, through testing, iteration, and figuring out what actually works in practice.
8. Zero-click experiences and shifting traffic patterns
As AI systems become more involved in surfacing information, zero-click experiences may become more common.
Users might:
Get answers directly within AI interfaces
Interact without visiting the original source
This doesn’t necessarily reduce the importance of SEO, but it may shift how success is measured. Visibility and influence could become just as relevant as traffic.
What discoverability might look like in an agent-driven web?
Agentic AI may open up new possibilities for how SEO is done. But alongside that, it may also introduce new considerations.
It could require:
Stronger data foundations
Clear governance and review processes
A thoughtful balance between automation and human input
In many ways, the goal may not be full automation. It may be a better collaboration.
Even if agents take on more execution, the responsibility for direction, accuracy, and trust is likely to remain human. And maybe that’s the more interesting shift here. Not whether AI agents will “take over” SEO, but how they might reshape what good SEO looks like.
If discoverability is no longer just about ranking, but also about being selected, interpreted, and reused by systems, then the role of SEO starts to expand. It becomes less about optimizing for a single interface and more about preparing content to exist across multiple layers of the web.
How do we design content that works for both humans and machines?
We don’t have all the answers yet. And maybe that’s okay.
Because this isn’t a fixed destination. It’s something that’s still taking shape.
And as it does, SEO may continue to evolve alongside it. Not disappearing, not being replaced, but adapting to a web that is becoming more dynamic, more layered, and a little less predictable.
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Ginny Marvin didn’t get into PPC because she had a grand plan.
She got into it because she was ready to start again.
After years working in print publishing and ad sales marketing, Marvin found herself at a career pivot point. A startup magazine she had helped launch folded, and she decided it was time to move fully into digital.
That meant going from marketing director to entry-level applicant.
“I don’t know what I’m doing, so I’ll start from the beginning,” she recalled.
That reset eventually led her into search marketing, Search Engine Land, and later Google, where she is now Google Ads Liaison.
In this interview, Marvin looks back at how paid search has changed, what marketers still misunderstand, and why the next phase of search will reward curiosity more than control.
PPC clicked faster than SEO
Marvin started on the SEO side at a small agency.
Then the paid search manager went on holiday.
She took over the campaigns temporarily — and immediately saw the appeal.
Coming from print, where measurement was slow or sometimes impossible, PPC felt almost instant. You could launch, spend, measure and see action quickly.
That speed changed everything.
For Marvin, PPC made the connection between marketing activity and business results much clearer than SEO did at the time.
Google won by moving faster
When Marvin entered the industry, Google wasn’t the only serious search player.
Yahoo was still a major force, and Microsoft was part of the mix. But over time, Google pulled ahead.
Marvin believes the difference was focus.
Google kept improving the product, launching new features and iterating faster than competitors. It became increasingly clear that Google was building around advertiser needs and pushing the industry forward.
Early PPC was painfully manual
Today’s PPC marketers may complain about manual work, but the early days were on another level.
Campaigns were built around huge keyword lists, endless permutations and highly granular structures. Advertisers spent hours creating keyword combinations and negative keyword lists.
It gave marketers a sense of control, but it also forced them to build campaigns around how the platform worked — not necessarily how the business worked.
That, Marvin said, is one of the biggest changes in paid search: campaigns now start more naturally with goals.
Search Engine Land became the industry’s newsroom
When Search Engine Land launched, Marvin was still early in her search career.
But it quickly became the place people went for search news, updates and expert analysis.
What made it valuable wasn’t just the reporting. It was the mix of fast news, contributed columns and practical insight from people doing the work.
For Marvin, Search Engine Land played a major role in professional growth across the industry because it made knowledge easier to share.
The search community has always been different
One thing Marvin repeatedly came back to was the generosity of the search community.
From the early days, practitioners shared what they were testing, what worked, what failed and what others should watch for.
That culture of learning helped define the industry.
It also shaped Marvin’s own career, both as a journalist at Search Engine Land and now in her role at Google.
AI is not as new as people think
Marvin believes one of the biggest misconceptions about AI in search is that it suddenly appeared.
Machine learning has been part of Google Ads for years, powering changes such as close variants, Smart Bidding and automation.
What changed recently was the speed of progress driven by large language models.
AI did not arrive overnight. But LLMs accelerated the shift dramatically.
Consumer behaviour is changing search
For Marvin, the biggest change is not just what Google can do.
It is how people search.
Queries are getting longer and more complex. People are searching through images, voice and multimodal inputs. Search can now understand intent without relying only on typed keywords.
That means advertisers need to think beyond the final conversion moment and understand the full customer journey.
Success still means business outcomes
Marvin does not think the definition of success in search has changed.
It still comes down to business outcomes.
What has changed is marketers’ ability to measure those outcomes and connect campaign activity to business goals.
That makes data, measurement and first-party signals more important than ever.
The next 20 years will reward curiosity
When asked what kind of marketer will succeed in the next phase of search, Marvin pointed to curiosity.
The best advertisers will be those who keep learning, watch how customers behave and adapt before they are forced to.
She compared it to mobile, where consumers moved faster than advertisers did.
The same thing is happening with AI.
PPC marketers say they love change — until it happens
Marvin’s reality check for the industry was simple.
PPC marketers often say they love change, but many resist every major shift when it arrives.
Her advice is to take a longer view.
Many of the changes that feel sudden have actually been building for years. Automation, AI, broader intent matching and full-funnel campaigns have all been moving in this direction for a long time.
Her advice: start experimenting
Marvin’s message is not that every new feature will work immediately.
It is that marketers should not write things off forever because they tested them once months or years ago.
Platforms evolve quickly. Capabilities improve. What failed before may work differently now.
For advertisers still holding tightly to old ways of working, the next phase of search will be harder.
What she is proudest of
Looking back, Marvin said she is proud of the search community itself.
Its willingness to share, learn and support each other has made the industry stronger.
She also sees her role, both at Search Engine Land and Google, as being a resource for marketers.
As she put it, communicating “by marketers, for marketers” has always mattered.
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