I see performance marketing under more pressure than it has faced in a decade. Budgets are flat or shrinking, expectations keep rising, and AI is quickly raising the standard for what “good” performance actually looks like.
For years, I watched performance marketing rely on a familiar playbook. When performance plateaued, teams added another vendor. When targeting weakened, they bought another dataset. When activation became difficult, they layered on more technology. But as budgets tighten and the demand for immediate ROI grows, constantly expanding the stack is no longer sustainable.
The challenge I see for enterprise marketers is not that they lack data. It is that they struggle to operationalize the data they already have.
At the same time, AI is revealing a hard truth about modern marketing architecture. Most AI failures are not really model failures. They are data failures. Even the most advanced agent, model, or automation workflow cannot make up for fragmented customer profiles, disconnected activation systems, or stale audience definitions. Yet much of the customer data platform conversation still centers on launching more AI agents.
I think that misses the point.
The real question is not whether a platform has an AI agent. It is whether my data foundation can support the leap from automating tasks to partnering on strategic outcomes.
For too long, the industry treated self-service as the north star. The goal was to help marketers avoid engineering tickets and data science queues. That made sense for the last decade, but it also turned marketers into manual operators of complex systems. The new bar is not just self-service. It is self-directed performance at scale.
I see a fundamental shift in the marketer’s job-to-be-done. We are moving away from the operational burden of building and managing audiences and toward the strategic work of setting outcomes. Instead of spending the day wrangling segments, I can define the goal, whether that is maximizing customer lifetime value or reducing churn, and let the system suggest the best audience definitions and activation paths. When intelligent agents are connected to a clean data foundation, I move from managing technology to orchestrating outcomes. That is the new blueprint for performance.
At mParticle, we describe this approach as a performance engine: a model where the data foundation and activation layer work as one connected system. The goal is not simply to collect customer data. It is to make that data immediately useful for performance outcomes.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
Audience Agent is one example of that idea in action. I can describe what I want in plain language, such as high-value customers who have not repurchased in 60 days, and the agent proposes the underlying logic for me to review and approve.
For me, the shift is not about handing everything over to automation. It is about working in a marketer-led workflow with an expert collaborator beside me. The longer I work with it, the better it understands the business, the data, the customers, and the patterns that actually move performance. That understanding is only as strong as the data foundation behind it, and ours was built for this long before AI made the need obvious. The marketer leads. The agent elevates and expands the work. Together, they push what is possible.
That same philosophy shows up in capabilities such as Audience Expansion and Household Reach. Audience Expansion helps me identify additional high-potential users directly from first-party datasets, without depending on third-party lookalike audiences or outside data sources. It gives teams more precise control over the balance between scale and quality.
Household Reach addresses one of digital marketing’s most persistent blind spots: buying decisions rarely happen in isolation. By using first-party customer data and enriching it with trusted third-party signals, Household Reach helps marketers engage the full decision-making unit, not only the individual who converted first.
The key distinction is simple. I only need to bring my first-party data. The householding solution handles the rest, helping me reach more of the household without spending extra resources building additional audiences or manually configuring campaigns.
What connects these approaches is a different mindset. Better performance should not require more vendors, more engineering resources, or more external data. It should come from extracting more value from the customer relationships brands already understand.
In this era of intense performance pressure, I believe the advantage will go to marketers who stop looking for more vendors to solve every problem. Success will not come from adding more tools to the stack. It will come from using a stronger data foundation to meet rising expectations and activate more of the data we already own.
I analyzed more than 80 leading B2B digital marketing agencies for 2026 to identify the firms that stand out most clearly. I evaluated each agency against the criteria that matter most for B2B companies trying to grow visibility, authority, and qualified pipeline.
SEO/GEO Expertise (30%): I looked at each agency’s technical fluency in how large language models surface and rank content, along with its ability to turn that knowledge into durable client visibility.
Notable Clients (25%): I considered the strength of each client roster, since recognized brands often signal an agency’s ability to manage complex campaigns and deliver at an enterprise level.
Leadership Experience Score (20%): I weighed senior experience in strategy and client service, which remains one of the strongest indicators of consistent agency performance.
AI Visibility Score (15%): I used a 1.0-5.0 rating to measure how effectively an agency drives client presence in AI-generated responses across ChatGPT, Perplexity, Claude, and Google Gemini.
Average Review Score (10%): I reviewed aggregated ratings from Google, Clutch, G2, and other verified platforms, using a 1.0-5.0 scale.
Using those standards, I ranked the top 6 B2B digital marketing agencies of 2026. The agencies below stood out for their mix of SEO/GEO strength, client experience, leadership depth, AI visibility, and verified review performance.
The Top B2B Digital Marketing Agencies
1. First Page Sage – SEO/GEO Expertise: 5.0; Notable Clients: SoFi, defi SOLUTIONS, US Bank, NBC, Verizon, Cadence, Skeps; Leadership Experience Score: 4.8; AI Visibility Score: 4.9; Average Review Score: 4.9.
I ranked First Page Sage first because of its early and deep role in GEO. President Evan Bailyn pioneered the practice in 2023, and much of the methodology now used across the industry traces back to his team’s work. That head start shows up most clearly in the agency’s SEO/GEO Expertise and AI Visibility scores.
What stands out to me is how First Page Sage combines long-form thought leadership with technical knowledge of how large language models source and surface information. On the SEO side, the agency brings more than 15 years of organic search experience across complex B2B verticals.
On the GEO side, First Page Sage was optimizing for AI citation before most agencies had a name for the concept. I see its biggest strength as a compounding strategy: the same content that ranks in traditional search can also be pulled into AI-generated answers, helping clients earn qualified leads from both channels at the same time.
First Page Sage scores: SEO/GEO Expertise: 5.0; Notable Clients: SoFi, defi SOLUTIONS, US Bank, NBC, Verizon, Cadence, Skeps; Leadership Experience Score: 4.8; AI Visibility Score: 4.9; Average Review Score: 4.9.
Summary of online reviews: Reviewers describe First Page Sage as the true expert in this industry, with content that takes thought leadership to the next level. Clients also report that its campaigns helped them generate marketing qualified leads through organic traffic.
Driven Metrics
I see Driven Metrics as a practical, performance-oriented GEO agency. Its process emphasizes weekly syncs, conversion tracking, and transparent reporting tied to actual leads rather than surface-level traffic numbers. When content underperforms, the team identifies it quickly and reworks it instead of letting weak pages sit untouched.
Driven Metrics builds authoritative content designed to earn rankings through expertise and citation. It also structures that content to appear in AI-generated responses when buyers ask for vendor recommendations. That mix is difficult to find at its price point, though I would expect companies in highly niche verticals to invest early time in helping the team understand how their buyers evaluate vendors.
Summary of online reviews: Clients say Driven Metrics delivered results with no excuses, which was refreshing, and that its reporting meant they always knew what was going on. The main caveat reviewers mention is more limited experience in certain sectors.
Focus Digital
I ranked Focus Digital highly because of its technical foundation in LLM optimization. The agency appears deeply familiar with the mechanics of generative search, and that shows in how it structures campaigns. Its content is designed from the beginning to earn citations in AI-generated answers, not only to rank in traditional search results.
Focus Digital’s SEO approach follows a thought leadership model, using authoritative long-form content to build organic visibility over time. I see it as one of the more technically grounded options for companies that want both SEO and GEO support without paying large-agency rates. The main limitation is portfolio depth: its case studies skew toward professional services, manufacturing, and home services, so clients in other verticals should plan for hands-on content review to maintain accuracy.
Focus Digital scores: SEO/GEO Expertise: 4.5; Notable Clients: Revo, Milano Jewelry; Leadership Experience Score: 4.3; AI Visibility Score: 4.2; Average Review Score: 4.8.
Summary of online reviews: Clients describe Focus Digital as honest about what is realistic and say the agency helped them show up in AI answers within a few months. The recurring criticism is that replies slow down when they’re busy.
REQ
I view REQ as a strong fit for companies that want B2B communications, authority-building, and digital marketing under one roof. The agency has earned solid reviews from clients across cybersecurity, government technology, financial services, and real estate. Its foundation is PR and authority-building, which overlaps with GEO, but its score here is driven more by SEO than by AI visibility.
REQ’s SEO work is woven into content strategy and demand generation rather than packaged as a standalone service. GEO is still less developed than its broader SEO foundation, so I would not make it my first choice for a company whose main priority is AI citation and generative search visibility. I would, however, consider it a strong option for brands that want integrated authority with organic search performance at the center.
REQ scores: SEO/GEO Expertise: 3.8; Notable Clients: Carahsoft; Leadership Experience Score: 4.4; AI Visibility Score: 4.1; Average Review Score: 4.4.
Summary of online reviews: Reviewers say REQ is highly adaptable and good at picking up the ball and running with it. Clients also report that campaigns resulted in increased traffic and customer engagement. The recurring criticism is that some clients wanted the agency to be more proactive with recommendations.
AMP Agency
I see AMP Agency as a full-service firm with a clear strength in integrated media. The agency is especially good at combining creative, experiential marketing, paid social, and video production into campaigns built around the full customer journey. With offices in Boston, New York, LA, and Seattle, AMP also has the infrastructure to support large, multi-channel engagements.
AMP’s SEO practice is meaningful and has produced measurable results, including improvements in rankings and lead quality. GEO is a newer layer for the agency, as it is for many full-service firms that built their models before generative search became a major traffic source.
For companies that want broad digital coverage with SEO included, AMP can be a strong choice. I would treat its GEO capability as developing rather than core, but its creative depth and campaign scale make it a practical option for brands with broader marketing needs.
Summary of online reviews: Clients say AMP Agency’s SEO services resulted in increased sales and better site management and that the team brings new ideas to the table. Reviewers also note that staff operate on time and on budget. The common critique is that its generative search work is still catching up to the broader digital offering.
Viral Nation
I included Viral Nation because it brings a very different kind of visibility strategy to the B2B marketing landscape. It is the largest agency on this list by headcount and the most specialized in social-first marketing. Its model centers on influencer campaigns, creator networks, paid social, and proprietary social intelligence technology deployed at scale.
Viral Nation’s strength is cultural reach and audience trust rather than search authority. That is why its SEO/GEO Expertise score is lower than the more search-focused agencies on this list. For B2B companies seeking influencer-driven brand awareness, I see Viral Nation as a strong match. For companies that need a more comprehensive search and GEO campaign, I would look elsewhere.
Summary of online reviews: Reviewers say Viral Nation regularly overperforms and that its campaigns are strong fits for clients seeking new brand exposure in a targeted market. The limitation clients note is that its strength is social as opposed to search, so coverage thins outside influencer and paid channels.
AI Overviews and Google AI Mode are increasingly shaping the discussions within the SEO community. In this evolving landscape, search is transitioning from a mere information retrieval tool to a powerful recommendation engine.
As a travel brand, this shifts the dynamics of online discovery. It’s no longer just about making your website understandable to search engines; it’s about ensuring AI systems recognize when to recommend your business.
How AI is Revolutionizing Travel Planning
Interacting with large language models (LLMs) has become a routine for many of us. We use them to structure conversations by project, creating folders for our upcoming trips and building on previous chats to refine our preferences and travel profiles.
This is a major shift from the conventional searching methods. Traditionally, we would start our travel plans with Google searches for terms like:
“Hotels in Porto”
“Things to do in Rome”
“Best restaurants in Barcelona”
Today, the process is much more conversational. Instead of a series of disjointed searches, I might open a new folder labeled “Summer 2026” in ChatGPT and begin with a broad question, gradually sculpting it into a complete itinerary.
“Where should I stay in Porto for a quiet weekend within walking distance of the historic center?”
“Which area of Rome is best for families with young children?”
These discussions naturally expand to include restaurant recommendations, tourist attractions, accommodation options, transportation tips, and more detailed daily plans.
When I ask my AI assistant these questions, I’m not looking for a list of websites. What I truly want is an insightful recommendation.
Impact of AI Overviews on Travel Search
AI Overviews gather data from multiple points to deliver highly curated recommendations instead of just a list of links. For this reason, trust, consistency, and context have become vital factors for online visibility.
A traveler might decide to book my hotel based on an AI-generated suggestion without even visiting the website. Instead, their next steps could include a branded search or a visit to a review platform where they might finalize their booking through an OTA.
To win over AI model recommendations, I need to precisely define my brand. It’s crucial for AI to be certain of who I am, what I offer, whom I serve, and the contexts in which my brand is relevant.
Selecting a primary category and maintaining a clear brand position are imperative. Investing in digital PR and securing mentions beyond my own website can help too. Being featured in travel articles on relevant topics can significantly boost visibility.
Moreover, ensuring that my business information is consistent, accurate, and easy to find across my website, Google Business Profile, TripAdvisor, OTA listings, and social media is essential.
Understanding the Role of Zero Click Visibility
The methods for evaluating search performance are evolving. While traditional SEO metrics will remain relevant, it’s important for travel marketers like myself to broaden how visibility is measured.
One critical error is viewing fewer clicks as a decrease in visibility.
A traveler might learn about my property through an AI response and then decide to search for it later or visit a review profile on a platform like TripAdvisor.
That’s why seeing growth in branded searches is a promising sign of AI visibility. Monitoring AI mentions, citations, and assisted conversions is also worthwhile.
Assisted conversions highlight the channels and touchpoints that lead to bookings, even if they aren’t the final source of conversion. I can track these in Google Analytics 4 by navigating to Advertising > Attribution > Conversion Paths and Attribution Reports.
Leveraging TripAdvisor and OTA Listings
Platforms like TripAdvisor have grown beyond being review sites, and OTAs offer more than just booking services.
When someone requests AI recommendations, the system doesn’t rely on a single data point but synthesizes information from multiple avenues.
My website forms a part of this ecosystem.
AI builds confidence in its guidance by cross-referencing data across different platforms. What others say about my brand through reviews, travel guides, media references, OTA listings, or local mentions is increasingly significant. It’s large-scale reputation management.
This additional context helps AI identify when my property is relevant to specific traveler needs, like:
Family-friendly environments.
Ideal for business travelers.
Located in walk-friendly areas.
Renowned for exquisite dining.
Suitable for luxury or budget travel.
Distinguishing My Travel Brand
For example, if I manage a family-friendly hotel, it’s important to highlight features like family suites, kids’ activities, and family-oriented reviews. Alternatively, a romantic destination should emphasize aspects like cozy atmospheres, spa facilities, and exclusive packages.
Similarly, a hotel catering to business travelers should spotlight meeting rooms, workspaces, high-speed internet, and its proximity to business hubs. On the other hand, a restaurant known for its culinary excellence should consistently be mentioned in reviews, receive media attention, and third-party accolades focusing on its food quality, head chef, or dining experience.
While some businesses naturally fit various categories, having a clear primary positioning helps generative search engines easily identify when my brand is appropriate for a recommendation.
This principle holds for travel destinations too. AI-driven engines depend on signals from reviews, travel guides, local listings, and related content when suggesting where tourists should stay, visit, or explore.
Strengthening Entity Signals Across Platforms
As AI systems place more focus on entities instead of individual web pages, I must create a robust and consistent digital presence.
1. Clarifying Attributes with Structured Data
Structured data aids search engines and AI in interpreting key business details. For travel entities like mine, this includes lodging types, amenities, locations, and more.
Emphasize the attributes that truly set my property apart. This can span from family-friendly amenities to wellness-centered experiences, renowned dining options, pet-friendliness, or proximity to major landmarks.
The clearer and more structured my information is, the better the chances AI-powered experiences will spotlight my business in relevant recommendations.
2. Resolving Entity Ambiguities
It’s crucial to review third-party portrayals of my brand. Inconsistencies can diminish the trust AI systems have in my brand information, as AI pulls data from various sources.
Think of a hotel with differing phone numbers, outdated details, varying categories, or conflicting amenity information across platforms—these inconsistencies confuse AI systems.
Ensuring my business data is consistent across my website, Google Business Profile, TripAdvisor listings, and OTA profiles will reduce ambiguity and strengthen AI’s confidence.
3. Prioritizing Operational Information
Start by evaluating existing customer reviews.
What did they enjoy most during their visit?
What made their stay memorable?
What areas need improvement?
Such feedback provides insight into what genuinely differentiates my brand. Details about amenities, accessibility features, business hours, parking, and pet policies help AI address specific travel-related queries with confidence.
Google Business Profile is another vital source for operational data. The categories, attributes, amenities, and working hours mentioned on the profile enhance AI’s ability to answer travel queries accurately and helpfully.
To provide further context, I can also use Google Business Profile to publish posts that link back to my site’s content. Consistently posting on Google Business Profile can boost engagement, increase profile visits, and encourage customer interaction, ensuring my listing remains updated with fresh content about my offerings.
Cultivating AI-Trusted Signals
Generative search levels the playing field more than traditional search. AI favors recommending businesses, not just their websites. Visibility isn’t solely determined by what transpires on my site; it encompasses the comprehensive digital footprint that my brand projects.
For travel brands, this means I must think broader than just rankings and clicks. Reviews, OTA listings, travel guides, media mentions, and business profiles all contribute to how AI recognizes and recommends my brand.
It’s time to get creative, try new approaches, and collaborate with complementary businesses. Most crucially, it’s time to build the trust signals that AI systems rely on.
I’ve just delved into Goodie’s enlightening AI search traffic report for early 2026, covering the period from January to April, and I’m excited to share my insights with you. This report dives into trends in usership, referral traffic, and marketing considerations, offering a comprehensive view of the shifting landscape.
You’ll want to pay particular attention to how ChatGPT’s dominance is starting to wane, with some surprising contenders like Claude and Gemini making waves. This shift could significantly impact how marketers strategize their efforts in AI-driven search optimization.
The data reveals fascinating patterns in user habits and referral traffic, which could inform future marketing strategies and the allocation of resources. For a full dive into these emerging trends and what they might mean for businesses, I encourage you to explore the detailed findings of the report.
On May 7, 2026, something remarkable happened that completely shifted the landscape of AI-driven brand traffic. As I watched, ChatGPT quietly launched the most significant single-day transformation I’ve seen all year.
Overnight, the referrals from OpenAI to various brand sites practically doubled. It felt like each mention of a brand by ChatGPT was suddenly more valuable—because they turned into clickable referrals directly to the brands’ homepages.
Recently, I found myself captivated by a story shared by Dean Kadi, Head of Paid Growth at One Link Media. He recounted a fascinating experience from a PPC Live podcast that really highlighted what can go wrong when you ignore performance data. It involved a client who overrode a winning ad strategy with new creatives that just didn’t deliver.
Dean Kadi’s team had developed an exceptionally successful Meta advertising strategy for a premium woodworking brand, Rubio Monocoat, using user-generated content (UGC). Their intensive testing across creators and formats resulted in a significant ROAS improvement, proving the power of well-tested strategies.
However, the client decided to halt all the high-performing ads in favor of new, heavily branded content. Despite the polished look, these ads didn’t blend well with the Meta platform, and it was clear that engagement and conversion would likely suffer.
The client’s assumption was rooted in a customer survey that praised the brand’s color range, leading them to mistakenly prioritize this over proven data. This is a classic marketing pitfall where assumptions can cloud judgment and overshadow hard-earned data insights.
The most eye-opening moment came when the client expressed a simple wish for their new strategy to be a winner. Dean explained that in paid media, success isn’t driven by preferences or hopes—it’s determined by what resonates with audiences, as clearly shown by performance data.
When facing such situations, Dean advises agencies like us to stay calm, present evidence, and communicate risks effectively. Professionalism and clear documentation can help maintain client relationships while asserting the agency’s expertise.
As expected, the new strategy did not perform well. Underperformance became evident with increasing costs and decreasing campaign efficiency. After eight weeks of this, the client recognized the necessity to revert to the original strategy.
Reintroducing UGC ads quickly turned the tide, proving the original strategy’s effectiveness. Performance metrics showed immediate improvements, reinforcing the importance of data-driven decisions.
The overarching lesson here is that data should be your guiding light in PPC campaigns. Clients sometimes need to see failures themselves before they trust data insights. Consistently providing clear, transparent reports helps rebuild trust and guide future strategies.
Dean also pointed out that many PPC accounts still suffer from poor tracking setups. This issue is a major roadblock to optimizing performance and should be addressed urgently.
Additionally, while AI tools can enhance efficiency, they cannot replace the need for a strong strategy. Human judgment remains crucial for evaluating AI outputs and guiding successful campaigns.
In conclusion, successful PPC is all about balancing data, strategy, and communication. Document recommendations thoroughly, trust your expertise, and let audience data guide your actions. Remember, it’s the audiences who ultimately decide what works.
As I look forward to 2026, the landscape of SEO is dramatically evolving. AI is reshaping click-through rates, urging me to shift from merely renting clicks to building genuine authority that delivers answers, stabilizes leads, and safeguards my margins.
The gap between a 2% and a 20% margin increasingly relies on whether I control the answers or just rent attention. The era of buying visibility is fading away.
AI systems are steadily fulfilling queries with fewer clicks, which means the true value now lies in crafting information that these systems can leverage to deliver valuable answers.
By transitioning from purchasing clicks to engineering structured, trusted content, I build ‘answer equity.’ This sets the stage for durable inclusion in AI-driven decision-making processes.
It’s not about abandoning paid search entirely but reducing dependency on it as the main demand generator. Over time, this strategic change can reduce costs and bring more stability to my traffic acquisition efforts by not constantly competing for impressions.
An atomic sandwich
To make this shift effective, I need a content strategy that optimizes what AI systems can utilize. Enter the concept of the ‘atomic sandwich.’
The atomic sandwich structure focuses on maximizing intent density rather than just chasing traffic:
The atomic fact (top bun)
Many businesses, including mine, have traditionally treated search budgets like high-interest loans.
By investing heavily in paid traffic for quick visibility boosts, I’ve felt in control, but there’s a catch: pausing the spend makes that visibility vanish.
The forensic proof (the meat)
This model isn’t just inefficient; it’s risky. Today, the rented audience is fading in the Answer Economy. Data shows paid CTR can plummet 68% with AI Overviews present.
My spending isn’t just about immediate clicks; it’s often about creating awareness that AI can later fulfill without needing users to click through.
The structural directive (bottom bun)
The framework is transforming. To thrive in 2026, I must shift from buying audience attention to engineering precise answers.
If my brand isn’t a trusted resource feeding into these AI responses, my visibility and influence will shrink drastically.
The new “box”: From librarian to forensic auditor
The role of search engines has evolved from directing traffic to validating information. Every ad dollar spent that fails to address E-E-A-T is a squandered investment.
The organic collapse: Studies reveal a significant CTR drop from AI Overviews, illustrating the need for strategic adaptation.
The global impact: AI Overviews correlate with a 58% lower CTR for top-ranking pages worldwide.
My objective isn’t merely to rank; it’s to continuously feature in the sources AI systems trust and cite.
In this paradigm shift, it’s not volume that wins, but clarity and trustworthiness.
The search addiction cycle (why I can’t quit)
Faced with rising costs and diminishing ROI, I might hesitate to break away due to weak information infrastructure — a liability on the balance sheet.
Stage 1 — the vanity hit: Initially, paid search wins felt like boosting business health.
Stage 2 — tolerance building: As ads got pricier, I increased spend instead of addressing core issues.
Stage 3 — the context-debt overdose: Reliance on AI-summarized data skyrocketed, making paid awareness insufficient.
Stage 4 — total dependency: My marketing strategy strayed into maintaining cashflow to platforms, not long-term demand building.
The forensic intervention: The 7-point organizational health check
Next time, I’ll evaluate where my Answer Equity is lacking, using this checklist.
The Information Gain test: Can Gemini summarize my page without new insights? This signals low value content.
The entity audit: Without a verified Google Knowledge Graph ID, my text remains just that — text.
Source of ground truth: Am I cited in AI Overviews? If not, my visibility approaches zero.
The faucet test: Does cutting PPC spend directly impact lead volume? A sign of rented revenue.
Schema and provenance: Are experts linked to my brand? If not, my content risks being ignored.
The “meat” ratio: Does my content include unique research? If not, it’s filling space without engagement incentive.
Machine-readable graph adoption: Is my team aligning with latest standards for Answer Equity verification?
The recovery plan: From rented clicks to owned authority
1. Purge the zombie facts (the information gain protocol)
Reward content for unique insights, not word count. This strategic focus reclaims margin and adds value.
Transitioning from renting audiences to owning answers is a pivotal strategy switch, turning marketing spend into a tangible asset.
The trap of paid campaigns is fleeting, offering short-lived results. Every dollar spent becomes temporary and fleeting.
Redirecting investment into information architecture establishes a robust digital presence that controls its fact database, earning trust within the Answer Economy.
My first actionable step: start small. Assess a top-performing paid page with the health check. Address ‘zombie fact’ issues by strengthening content’s informational value.
Shift focus from report generation to comprehensive entity audits.
An organization in 2026 isn’t about the scale of spending to rent viewers but about proving it owns the answers.
I have the blueprints. I have the data. Now is the time to stop the relentless spend cycle and solidify my answer equity.
I recently had the chance to dive into SE Visible, a tool that pairs quite well with SE Ranking. After thorough testing, I’m here to share my insights.
While SE Visible offers decent integration, it’s held back by its limited LLM coverage and lack of optimization features. I’ll explore these aspects and compare them to Profound.
If you’re considering this tool, join me as I break down its strengths, weaknesses, and how it stacks up against alternatives.
I’ve realized that journalists are inundated with generic AI pitches. So, how do we stand out and actually land the coverage we need? It’s all about reusing winning structures for our outreach campaigns.
Picture this: Every digital PR team has experienced the scenario where we gather around new data, unsure of how to pitch it. Someone eventually sends out a pitch just in time, and thankfully, it lands in a prominent publication. We celebrate the victory, but often, we overlook the hidden treasure right in front of us—our winning pitch.
We tend to forget that these pitches are not just one-off successes. They are templates that we can adapt for future campaigns. Whether it’s a data study, product launch, or an expert quote, we can replicate the effective elements using AI, rather than starting from scratch.
Statistics show us that almost half of journalists receive six or more pitches daily, yet they rarely respond because many pitches lack relevance. With AI, pitch volumes are increasing, but so is the mediocrity. The key is not to generate more pitches but to refine what we know works.
Let me introduce you to the ‘DPR duplication method.’ It’s straightforward: rinse, reuse, and repeat. Take a successful pitch, analyze its winning structure, and use AI to model this structure for future campaigns.
One of my favorite pitches was sent to an editor at PR Daily. It started with a personal touch referencing her dog, and smoothly transitioned into a compelling data study. It was a hit, earning a same-day response. That’s what makes the method so powerful—it works across various pitch types and audiences.
The anatomy of a winning pitch is fascinating. It starts with a subject line that feels personal, an opening hook to build rapport, sequential stats that tell a story, and a CTA focused on the journalist’s readers. Each component can be replicated to maintain our uniqueness in the industry.
Why invent something new when you can evolve from what’s already proven successful? By duplicating the structure of our best work, we maintain our voice and relationship-building, ensuring pitches are relevant and engaging.
To get started, revisit your last successful pitch, dissect its components, and prompt AI to duplicate each one for new campaigns. Remember, it’s not just about repeating—it’s about enhancing and refining. Rinse, reuse, repeat.
Roll back the clock by five, 10, or even 15 years, and I can tell you that a PPC specialist’s value was primarily based on tactical skills. That’s all changed.
Nowadays, platforms like Google and Microsoft have automated much of the tactical work. Machine learning and AI now handle bid management, creative testing, and audience targeting far more efficiently than any human could hope to.
This shift has left many experienced practitioners grappling with a mid-career identity crisis. If the algorithms are doing the heavy lifting, what role do I play, and how do I continue to add sustainable value to the business?
Let’s explore what this evolution means in practice and how it has transformed the critical skills within my PPC toolbox.
From Tactical Execution to Strategic System Design
Having spent 24 years in the paid search trenches, I’ve seen everything from the wild early days of Overture to the advent of Google AdWords and the mobile shift, and now, the complete domination of algorithms over ad platforms.
In the past, my value came from painstakingly researching keywords, micromanaging bids, split-testing every piece of ad copy, and crafting a meticulous exact-match account structure. I was a lean, mean PPC machine.
If I rely solely on tactical execution, I risk becoming obsolete, merely a behind-the-scenes lever-puller. Today’s top practitioners are not just media buyers; they’re architects of revenue and profit.
Rather than blindly manipulating levers, I design systems. The true value I offer is in configuring the system to guide the machine effectively. To become an engineer of revenue and profit, I need to:
Master data analysis and signaling.
Develop a deep understanding of how my company or clients generate income.
Enhance my presence in the executive landscape to confidently convey strategies to the C-suite.
This confluence is my career’s golden ticket. Here’s a roadmap to achieving just that.
Entering an interview, client pitch, or meeting with simply, “I’ll re-examine your metrics,” makes me sound like any other media buyer. It’s essential to stand out.
Instead, imagine saying, “I’ll align your paid search campaign directly with your profit and loss statement. Each dollar spent is maximized for optimal margin.” That sets me apart as the most valuable person in the room, shifting focus from selling clicks to selling a business advantage.
Traditional PPC accounts often mimic a website’s navigation—with separate campaigns for shoes, shirts, etc. While not wrong, it shows limited thinking. I aim to create a nuanced account structure that aligns with what impacts the P&L, moves inventory, or generates the highest-value leads.
How to Implement This
Each business has unique needs, but the process to achieve this follows a typical framework.
Margin Interrogation: Collaborate with clients or finance teams to understand profit margins on core products. It’s often revealed that the high-volume product has the lowest margin, while niche services may yield greater profitability.
Architectural Shift: Update campaigns by margin tier and business value rather than by product category alone. This may mean setting different target ROAS (tROAS) or target CPA (tCPA) based on financial capacity to acquire a specific customer.
Equating a low-margin conversion with a high-margin one in account structures results in revenue and profit leaks, regardless of stellar in-platform metrics.
Segregating Metrics for Different Audiences
Once mapped, it’s crucial to separate metrics accordingly.
In the “engine room” (daily platform optimizations), I still consider click-through rates (CTR) and costs per click (CPC), crucial indicators for navigating campaigns.
However, when in the “boardroom” (leadership reporting), I lead with insights into outcomes: “We reallocated budget to high-margin tiers, maintaining our $150 CPA target and safeguarding overall profitability.”
This is the most pivotal skill for a modern PPC profit engineer like myself. Algorithms need input but inherently lack intelligence and judgment. They understand only what I tell them.
In our automated bidding era, appropriately “feeding the machine” delineates experts from the obsolete. If I supply Google Ads only with data on who filled out a form, the algorithm will pursue more form-loving but non-converting leads.
Today, a significant part of my role involves understanding and using first-party backend data to inform machine learning for superior outcomes. I am now an optimizer of signals, not just bids.
How to Implement This
It’s time to move beyond basic pixel tracking by employing robust offline conversion tracking (OCT) or direct CRM integrations like HubSpot or Salesforce into Google Ads.
In managing larger programs, tools like Search Ads 360 (SA360) present enormous advantages for signal engineering, enabling seamless data management across search engines.
For Lead Generation
It’s time to stop optimizing for generic leads. Instead, map client sales stages into ad platforms, assigning monetary values to stages based on historical closure rates.
For instance, consider a raw lead worth $10, a marketing-qualified lead (MQL) worth $50, and a closed/won deal worth $500, then switch bidding strategies to value-based bidding (Target ROAS). This programs AI to focus on lead quality and revenue, not just form completion.
For Ecommerce
Ecommerce stands apart with unique complexities. Tracking revenue to meet basic ROAS is foundational. For true profit engineering, I work with signals about inventory, margins, and lifetime value.
Feed Engineering: The modern e-commerce specialist doesn’t just upload a product feed; they methodically engineer it. Using Custom Labels, I segment products based on business concerns like inventory status or return rates. A product with a 40% return rate, if pushed hard, destroys profitability despite impressive ROAS data.
Profit Margin Bidding: Tracking gross revenue alone isn’t enough. Integrating profit margin data via custom conversion variables reshapes bidding strategies. Algorithms bid differently in auction when differentiating a $100 sale with varied margins.
New Customer Acquisition (NCA): Algorithms often take the easiest path—crediting returning loyalists. First-party customer lists differentiate new buyers from repeat customers, allowing aggressive market share bids for the former while protecting margins for the latter.
