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?”
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/04/seen-believed-chosen-c3vJ59.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-28 14:33:442026-04-28 14:33:44SEO isn’t just about being seen — it’s about being believed and chosen
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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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.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-28 11:00:002026-04-28 11:00:00Where PPC and SEO teams lose control in branded search by Bluepear
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.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-27 21:46:022026-04-27 21:46:02Ginny Marvin on AI in search, PPC trends, and Google Ads evolution
On PPC Live The Podcast, I spoke with Peter Bowen, a Google Ads specialist with nearly 20 years of experience and a strong focus on B2B lead generation.
Pete shared two major lessons from his career: always check the basics, and never assume the systems around your ads are working just because the campaigns look fine.
The currency mistake that cost 10 times the budget
Pete Bowen shared an early mistake where a South African client’s account was set up in the UK, defaulting the currency to pounds instead of rand. That simple oversight led to spending roughly 10 times the intended budget, delivering great results at first — but ultimately setting unrealistic expectations and losing the client.
Why checklists protect PPC teams
The takeaway from that mistake was to formalise learning into process. Adding something as simple as a currency check to a setup checklist ensures that once a mistake is made, it doesn’t happen again — turning painful lessons into repeatable safeguards.
The bigger problem: system decay
Beyond setup errors, Pete highlighted a more subtle but common issue he calls “system decay” — where the infrastructure connecting ads, tracking tools, CRMs and sales processes gradually breaks down without anyone noticing.
Why conversion data failures hurt performance
When conversion data stops flowing properly, Google’s algorithms lose the feedback they rely on to optimise. This can lead to reduced spend, poor performance or campaigns that suddenly stop delivering — even if nothing appears wrong inside the platform.
PPC managers need to look beyond the interface
One of the biggest mistakes advertisers make is focusing only on what happens inside Google Ads. Strong performance depends on the entire journey, from click to conversion to revenue, and any break in that chain can undermine results.
What to do when conversion tracking breaks
When tracking fails, the priority is to fix the root issue quickly and, where possible, use data exclusions to prevent bad data from influencing optimisation. Longer term, building monitoring systems that flag issues early is essential to avoid repeat problems.
The danger of optimising for clicks
Pete also pointed to a common but damaging mistake: optimising campaigns for clicks rather than outcomes. Without proper conversion tracking, advertisers can end up driving large volumes of traffic that never turn into leads or sales.
Why Performance Max needs strong tracking
Automation like Performance Max can amplify this issue, as it will follow whatever signals it receives. Without accurate conversion data, it can scale irrelevant traffic quickly, making strong tracking a prerequisite before leaning into automation.
Why bid strategies need guardrails
Google’s bidding systems are powerful but literal — they optimise toward whatever you define as success. That means advertisers need clear goals, reliable data and sensible guardrails, such as CPC limits, to avoid extreme or inefficient outcomes.
Testing AI features carefully
With newer tools like AI Max, the risk isn’t testing too early — it’s testing without a clear definition of success. Metrics like impressions and clicks are not enough; advertisers need to measure impact on qualified leads, sales and revenue.
The problem with “always be testing”
Peter also challenged the idea that everything should be constantly tested. Many accounts simply don’t have enough data to make small tests meaningful, meaning time is often better spent improving fundamentals rather than chasing marginal gains.
The key takeaway
The overarching lesson is straightforward: mistakes are part of the process, but only if they lead to better systems. Every error should result in a checklist, a monitoring process or a safeguard — ensuring it doesn’t happen again.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-27 18:42:012026-04-27 18:42:01Pete Bowen talks about why Google Ads is not just about clicks
Google is upgrading Google Ads call campaign measurement with a new AI-qualified call leads feature, designed to optimize for lead quality — not just call length.
What’s new. AI-qualified call leads use machine learning to analyze calls and determine whether they represent meaningful business opportunities. The system then feeds that higher-quality data into bidding and reporting.
Zoom in. Advertisers will get AI-generated call summaries and tags, giving more transparency into what happened during each interaction. At the same time, smart bidding can prioritize higher-value leads based on these signals rather than simple time thresholds.
Why we care. Call campaigns have long relied on blunt metrics like duration to signal value. This update shifts optimization toward actual lead quality, filtering out low-value interactions like spam or robocalls. This should result in better ROI, less wasted spend, and clearer insight into which calls actually matter.
How it works. Call recording is turned on by default for most advertisers so AI can assess call quality, though industries like healthcare and financial services are excluded. Advertisers can still adjust call length thresholds or disable recording in account settings.
The fine print. The feature is currently limited to calls in the U.S. and Canada.
The industry has been building top-down for 30 years. Start with awareness, get in front of as many people as possible, and work them down through the acquisition funnel.
The logic made sense in the broadcast era, and it wasn’t entirely wrong in the search era.
In AI-driven environments, it’s simply wrong.
Search engines, assistive engines, and agents build their ability to recommend your brand from the bottom up. They need to understand who you are before they can evaluate whether you’re credible. They need to evaluate your credibility before they recommend you to anyone.
If you build from the top down, you’re wasting budget on awareness while the engines and agents have no foundation to attach it to.
Agential systems make the stakes absolute. An agent acting on behalf of a user evaluates your brand, your offers, and your credibility, then commits.
If the machine doesn’t understand who you are, what you offer, and whom you serve, the agent can’t act in your favor. If it understands you but doesn’t find you the most credible option, it selects your competitor.
This is the ultimate zero-sum moment in AI: the recommendation you never saw happening, to the prospect you never knew was considering.
The acquisition funnel runs simultaneously in opposite directions
The user experience of the acquisition funnel hasn’t changed. Someone hears about you, considers you, and decides whether to commit. That journey runs wide to narrow, top to bottom: awareness first, evaluation second, and decision at the bottom.
This is the familiar funnel. Elias St. Elmo Lewis formalized it in 1898. Every marketing model since has been built around it, and for 128 years, nothing fundamental has changed. The channels evolved, but the direction was always the same: reach first, relationship second, commitment third.
In 2002, my friend Philippe Lanceleur described the web perfectly for search: building a website and hoping people find it is like opening a shop in the middle of a field. Nobody passes by accident. You go where your audience hangs out, engage with them, and invite them to cross the field and visit your shop. Awareness was still the prerequisite, and your marketing had no chance of working without it.
The shift to entities changed the prerequisite. When Google introduced the Knowledge Graph in 2012, the machine began forming opinions about brands independently of what users were searching. The machine was drawing its own map and building roads for you.
Those machine-built roads are built from the shop outwards by the machines, which means brand understanding and reputation, not awareness, become the prerequisite. All my work since 2012 has been focused on brand understanding and reputation for exactly this reason.
AI makes the acquisition funnel flip more powerful still. Assistive engines and agents now actively direct users toward destinations they’ve assessed as credible. Lanceleur’s shop in the field is no longer a handicap if the machines know it’s there and believe it’s the best destination for their users: they provide the roads.
This is the first genuine structural break in how brands must think about marketing since 1898. The display funnel is unchanged: the user still travels from awareness to decision. What makes you a candidate at the top of that funnel in AI engines and agents is built by training the machine to bring users to you.
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How top-down and bottom-up coexist
The big takeaway is that the build funnel runs in the opposite direction.
The machine starts at the bottom. Does it know who you are?
It works up through credibility. Does it trust what you do?
Only then does it reach advocacy. Will it recommend you proactively?
The moment of commitment by the user stays the same: know-like-trust the brand, but the only way for the user to arrive at that moment in AI assistive engines is that the machine knows, likes, and trusts your brand.
The coexistence of the bi-directional funnel is real. You can build top-down in channels you control: paid media, broadcast, and direct outreach. You can still buy awareness and pull people to decision. In the engines themselves, the user still has the top-down experience.
The difference is that within the engines for organic, you have to build from the bottom of the funnel (BOFU) up because that’s how the machines build the roads to your brand.
Every algorithm, assistive engine, and agent operates on entity and brand signals, not on how loudly you push. Reach on social media has always been influenced by brand recognition, engagement, and topic, and here too, brand understanding and trust are gaining increasing weight.
With AI, roads to your shop in the field are increasingly machine-built, and machine-built roads are built from brand understanding outwards to awareness.
The original 1898 funnel still describes what users experience. In AI assistive engines and agents, it no longer describes the strategy that gets you in front of them: for that, you need to flip the funnel.
In short, you can’t build your funnel in AI engines and agents top-down in a world where those machines are the mediators between you and your audience. The machine won’t recommend brands it doesn’t understand, and it will only advocate for brands it trusts. This is a mechanical fact.
AI infrastructure works like this, so you also must.
Understandability creates the entity node.
Credibility gives it preferential consideration.
Deliverability gives it visibility.
Foundation. Proof. Reach. Put like that, it really does seem obvious, unavoidable, and comfortable.
The user journey on Google used to be a series of single-composed SERPs that users navigated themselves. Search engines composed those pages cleverly (Google and Bing have run a whole page algorithm since universal search launched in 2007, Darwinistically pulling elements from across verticals and scoring the composition as the “product”), but the navigation across the funnel was the user’s job.
As an SEO, you optimized for a position in the composition, and the user carried themselves from awareness to consideration to decision by browsing, comparing, and choosing.
Over the last few years, the algorithmic trinity has fundamentally changed that dynamic. The LLM reasons about what the user is asking, decides whether to answer directly, ground, search, or fact-check via the knowledge graph, and runs fan-out queries to retrieve across multiple angles of the question.
Those fan-out queries (which I’ve also called cascading queries) help the assistive engine answer the question more completely and more accurately than a single query would. But the breadth of what it gathers also lets it do one more thing — and this is the mechanic that actually matters in the funnel that leads to the perfect click: it can anticipate what the user is likely to do next, and set the current answer up to flow toward it.
The explicit representation of the LLM’s prediction of “next step” is the follow-up questions you see in the results. But there’s an additional implicit side to this architecture you might have missed: the way it composes the current answer shapes what the user is likely to do next. The AI is, to a very large extent, defining the acquisition journey. It seems to me the user is less in control than they feel.
That means your job appears to be to fight for a slot in a sequence the machine has already built.
That’s fair. But I’d argue that the brand’s job is also to train the machine’s expectations about what a logical next step looks like, so that when the LLM composes, your content is the natural thing it reaches for.
You supply the ideas, you structure the follow-ups, you publish the logical bridges (“if you’re thinking about X, the next thing to consider is Y, and here’s the evidence”) in enough places, and with enough corroboration, that the machine treats those bridges as settled, not speculative. The machine then guides users toward you because your content is what its prediction landed on, because your framing is what made that prediction logical in the first place.
Now, is the AI thinking one step ahead? Or playing chess and planning several moves in advance? It depends. How far ahead the machine can usefully look depends on the territory.
On well-traveled ground, the paths are well-worn, and the branches are narrow, so the LLM can stage two, three, or more moves ahead. Think of this as established neurological synapses: your influence on the paths is limited here.
In unusual territory, the branches collapse the prediction horizon back to one, perhaps two steps. That’s an opportunity for a brand to create the synapses with your brand firmly anchored. Here’s yet another good reason to niche down, solve very specific problems, and have a very clear funnel pathway.
When defining the content I work on and terms I track, I use the concept of funnel pathway for exactly that reason — a top-of-funnel (TOFU) query that naturally leads to my brand at BOFU with a series of steps that are logical and relatively predictable.
So, track a set of terms that have a natural pathway to your brand at the zero-sum moment at the bottom of the funnel. Some start at TOFU and move through MOFU to BOFU. Others begin at MOFU with a clear path to BOFU, and some start (and end) at BOFU.
I’ll probably get pushback here. The number of possible paths is effectively infinite because conversations with AI can go anywhere. True. But this is a better system than chasing search volume or tracking the terms the boss likes: it forces you to think, focus, and prioritize — and it works.
Get your foot in the door, and keep it there
Strategically, you have to get a foot in the door as early as possible in the conversation, and ensure that you keep your foot there as the conversation evolves and the AI guides the user down the funnel.
The stronger your foot in the door, the more you shape the conversation the machine builds, the more that conversation thins the field of competitors the machine considers for the next step, and, by virtue of elimination, the more likely you are to get the perfect click at the zero-sum moment at the bottom of the funnel.
I’m advocating for educating the algorithms (remember, Google is a child?). The better you guide, the more the machine’s best-brand prediction converges on you step after step, because the path it’s following is the path you built into its brain.
Get in high, and the compounding works in your favor. Get in late, and your competitors’ bridges become the machine’s bridges, and every subsequent step is a fight to re-enter a sequence where your competitor is Top of Algorithmic Mind.
Display is where your acquisition funnel lives in the AI engine pipeline
The AI engine pipeline runs 10 gates from discovered to won.
Everything up to annotation (Gate 5) is infrastructure: can the machine access, store, and classify your content?
From recruitment (Gate 6) onward, the engine compares you to every alternative.
The understandability, credibility, and deliverability (UCD) layer is where the user sees the machine evaluation at display (Gate 8). Understandability is the key to won (Gate 9).
The three dimensions of brand visibility at display
Display is the moment when the machine can make or break your brand by being the most visible in the market at every touchpoint when your ideal customer profile (ICP) is having a conversation with the engine or agent.
It’s obvious that this is the key moment when you need the engine or agent to be absolutely convinced that you’re the best solution to the specific user’s problem at the exact moment they convert (see the 95/5 rule here).
Understandability(U) is the trusted partner/decision layer, without which nothing else will work long term. Does the machine know who you are, what you do, and who you do it for?
U is BOFU, which is both the moment of decision and (logically) the deepest trust layer for both the AI user and the human user. When someone searches your brand name or asks an AI assistant directly about you, the machine draws on its understanding of your entity.
If that understanding is weak, contradictory, or absent, the machine either hedges or stays silent. Typical failure modes show up in AI responses as “claims to be,” “appears to offer,” or “no idea who you are talking about.” The doubt tax — where prospects ready to buy get a hedge instead of a confirmation — is a U failure.
Credibility (C) is the recommender/consideration layer. Does the AI believe you’re genuinely better than your competitors at what you do?
C is MOFU, the comparison and evaluation layer. When someone asks an AI who is the best in market, the machine draws on its confidence in your N-E-E-A-T-T credibility and will exclude you if you haven’t built a rock-solid argument to be cited.
If AI confidence in you is weaker than its confidence in the credibility of your competitor, you lose the comparison. The ghost tax – absent from competitive evaluation and ignored in shortlists — is a C failure.
Deliverability (D) is the advocate/awareness layer. Does the AI surface your brand to people who aren’t searching for you, recommend you unprompted when they research the market, and treat you as the reference option in your category?
D is TOFU, the reach layer. When someone asks an AI about a problem, you solve without knowing your brand exists, the machine draws on its confidence that you are the right answer to put in front of them.
Advocacy only happens when the machine has first understood who you are (U), and judged you better than the alternatives (C). The invisibility tax — never mentioned to prospects researching the market — is a D failure.
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The business case for UCD: The three taxes
My untrained salesforce framing is super clear for a non-technical audience. Google, ChatGPT, Perplexity, Claude, Copilot, Siri, and Alexa are seven employees working 24/7, and they’re either selling for your brand or for your competitors. AAO can be defined as training AI assistive engines and agents to sell for you at the top, middle, and bottom of the funnel.
Here’s the part most of the industry still hasn’t internalized: machines aren’t an alternative audience. They’re a mirror of how people process information, with the noise filtered out.
Optimizing for machines is optimizing for humans with less guesswork. A brand SERP is Google’s opinion of the world’s opinion of you, and Google’s opinion is built from the same signals that form human opinion, only weighted more consistently, and corroborated across millions of data points.
When you optimize to improve what Google believes about your brand, you’re not gaming an algorithm. You’re correcting and reinforcing what the world already believes about you, expressed with the precision humans rarely articulate. The algorithm is the clearest feedback loop marketing has ever had.
Each tax is a specific failure mode of that untrained salesforce.
The doubt tax is what you pay when they can’t confirm who you are to a prospect ready to buy.
The ghost tax is what you pay when they can’t argue your case against competitors in a shortlist.
The invisibility tax is what you pay when they don’t mention you at all to the prospect researching the market.
The fixes run in one order: U before C, C before D, because the taxes are mechanically ordered, and the remediation has to match.
Content was king in the keyword era, context took the throne around 2016, and confidence is king now. The AI engines don’t just store and retrieve. They stake their own credibility on the brands they recommend, and that staking runs on accumulated confidence at every layer.
Build U to retire the doubt tax. Build C to retire the ghost tax. Build D to retire the invisibility tax. Every tax retired is a recommendation earned, and every recommendation earned is revenue the machine now generates on your behalf instead of your competitor’s.
Strategy: Your brand SERP and AI résumé tell you where to begin
Brand SERP is what Google shows when someone searches your brand name. The AI résumé is the same object in conversational format. The agent dossier is the machine’s silent judgment during evaluation before any recommendation reaches a person.
All three are dual-function objects. They’re the machine’s output to every audience that asks about you, and your diagnostic instrument for reading the machine’s current confidence. That dual function is why they’re both the product and the audit.
Read all three as the machine’s understanding of you, its assessment of your credibility, and its confidence in you as a solution provider. The diagnostic triage is short.
If the machine gets things wrong, hedges facts, or the results don’t reflect your brand narrative, that’s an understandability problem. The entity record is inconsistent, weak, or contradictory, and the work is on your entity home: clean structured data, consistent descriptions, clear schema, and entity resolution that points to a single authoritative source.
If the results are unconvincing, unflattering, or don’t do you full justice, that’s a credibility problem. Your N-E-E-A-T-T is weak, and the work is offsite: third-party mentions, review platforms, earned media, and co-citations from sources the machine trusts.
If the results don’t reflect your digital marketing strategy, that’s a deliverability issue. The work is in content, both on your channels and on third-party properties, the type of material the machine treats as proof rather than a claim.
In every case, the diagnosis comes before the tactics. U before C, C before D, and the sequence isn’t optional.
Acquisition is one act in a 15-stage play
The acquisition funnel feels dominant because it’s where conversion happens. The funnel sits on the display gate, where UCD determines whether the machine recommends you.
Everything else, the work that lets display happen at all and the work that compounds afterward, runs across the nine gates before it and the five gates after it.
Those five gates after Won are where most of the money is made and most of the confidence is generated. Onboarded, performed, integrated, devoted, and codified — every client outcome feeds signals back into gate zero for the next prospect who has never heard of you.
The flywheel is the mechanism. Get it right, and every satisfied client strengthens the machine’s confidence in your brand for the next one. Get it wrong, and every neutral outcome decays it.
That’s more than just an acquisition strategy; it’s a business strategy, with the machine as a constant participant at every stage.
The final articles in this series will show you what happens after won: how every satisfied client either trains the machine to recommend you more confidently next time, or quietly erodes the confidence you’ve already built.
The funnel isn’t where the money is made, but it is the critical moment the flywheel feeds where the path to money is.
