I recently came across some intriguing Adobe data that sheds light on how AI-driven traffic is making waves in U.S. retail. AI traffic isn’t just increasing; it’s actually outperforming traditional channels like paid search in terms of conversion rates!
In the first quarter, AI-generated traffic surged by an impressive 393% compared to the previous year, with a 269% rise just in March alone. What’s even more exciting is that AI traffic is converting significantly better than it did last year.
By the numbers, AI-driven visits converted 42% better than their non-AI counterparts in March. Just a year prior, these AI visits were actually 38% less likely to lead to a purchase, showcasing a remarkable turnaround.
Consumers are truly engaging with AI-driven platforms, as indicated by a 12% increase in engagement, 48% more time spent on site, and a 13% uptick in pages viewed per visit. Adobe’s consumer survey further reveals that 39% have tried AI for shopping, and out of those, 85% felt it enhanced their experience. Additionally, 66% of users believe that AI tools deliver accurate results.
What they’re saying, Vivek Pandya, the director of Adobe Digital Insights, emphasizes, “Notably, AI traffic continues to outperform non-AI traffic in conversions, which includes other channels like paid search and email marketing.”
Yes, but, despite this upward trend in adoption and positive metrics, Adobe points out that many retail sites still haven’t optimized their platforms for AI visibility, particularly on product pages.
Why we care: The debate around whether AI traffic is superior to organic search traffic has been continuous. However, this latest analysis suggests that AI’s capacity for conversion is growing, and much like generative AI, it’s expected to become an even more valuable channel.
About the data: Adobe’s insights are derived from analyzing direct transaction data from over one trillion visits to U.S. retail websites, supplemented by a survey involving over 5,000 U.S. consumers to gauge their AI shopping behaviors.
The report: For more details, check out the Adobe report on the AI-driven traffic surge and its impact on U.S. retail sites.
Dig deeper: Explore related studies that discuss various aspects of AI traffic and conversions in retail.
I’ve recently discovered that Google’s latest update to Chrome now offers an ingenious AI Mode, designed to make my browsing experience more streamlined and efficient. With this new enhancement, I can dive deeper into searches with fewer tabs, making my workflow smoother than ever before.
What’s new? Let me walk you through the three exciting features in Chrome’s AI Mode. First up is the ability to search side-by-side. Now, when I click on a link in AI Mode on my desktop, the related webpage opens right next to it. This setup allows me to easily compare details, visit relevant sites, and ask follow-up questions without losing the context of my search. Here’s how it looks:
Another fantastic addition is the ability to search across my tabs. Whether on desktop or mobile, I can now tap the new “plus” menu on the New Tab page or within AI Mode to incorporate recent tabs into my search. This feature helps AI Mode provide more customized responses and suggest additional sites worth exploring.
Lastly, there’s the multi-input and easy tool access feature. I can mix and match various tabs, images, or files such as PDFs, and bring that context directly into AI Mode. Plus, tools like Canvas and image creation are readily accessible wherever I see the new plus menu in Chrome.
Understanding why this matters to us as users is crucial. These Chrome-specific features launched initially for U.S. English users unlock greater AI Mode capabilities. While currently limited to Chrome users, they clearly indicate Google’s forward-thinking direction in AI integration.
ChatGPT citations prioritize ranking and precision, not length. I recently came across an intriguing study conducted by AirOps that examined how ChatGPT assigns citations. It revealed that pages with precise, narrow answers are favored over lengthy, broad content.
After reviewing 16,851 queries, AirOps found that pages with well-matched headings and focused content rank higher in citations. Impressively, the top retrieval result was cited 58% of the time, indicating a strong preference for relevance over mere volume.
Why this matters to us. These findings are crucial if we’re aiming to earn more ChatGPT citations. To succeed, we need to prioritize winning retrieval spots, mirroring queries in our headings, and providing highly precise answers.
Key insights. The study emphasized retrieval ranking as a pivotal factor. Top-ranking pages were cited 58.4% of the time, compared to only 14.2% for pages positioned tenth. This highlights the significant impact of retrieval rank on citation frequency.
Another crucial point I noted was the importance of heading relevance. Pages where the heading strongly matched the query were cited 41% of the time, significantly outperforming less matched options.
It also showed that narrowly focused pages outperform comprehensive guides, challenging the typical “ultimate guide” approach many of us might consider effective.
Factors driving citations. From what I gathered in the study, being well-ranked, using query-matching headings, and maintaining content focus are key to earning citations from ChatGPT.
Additional structural insights: While structure like JSON-LD markup offered a slight boost in citations, it wasn’t as critical as I initially thought. Pages with this markup had a citation rate of 38.5% versus 32.0% for those without. Interestingly, articles with 4 to 10 subheadings performed notably well.
Furthermore, content length had diminishing returns. Pages with 500 to 2,000 words performed best in citations, whereas those exceeding 5,000 words were cited less than even the briefest ones.
Freshness matters, but only to an extent. Content published within 30 to 89 days had the best performance in terms of citations, while newer content underperformed slightly, suggesting the need for time to build retrieval signals.
Older content, particularly those older than 2 years, struggled in citations, implying the potential benefits of refreshing existing content if it currently ranks well for target queries.
Understanding the data. AirOps examined 50,553 responses derived from 16,851 unique queries, each run three times. The exhaustive dataset encompassed 353,799 pages across various sectors and query types.
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.
When it comes to SEO, I’ve learned that topical authority is just the beginning. AI search systems take it a step further by assessing choices among entities, not just content. Understanding the nine-cell model is crucial for grasping how these selections truly happen.
The concept of topical authority is fundamental in SEO. I’ve realized it doesn’t fully explain how search and AI choose between different sources. The critical element is missing, lying in the selection signals that separate mere eligibility from being the chosen one.
Topical Authority: Understanding Content vs. Selection
In my journey, I see topical authority as foundational for both SEO and the evolving AEO and AAO. However, it’s not enough. The current framework accounts for semantics, content, and structure but falls short of explaining topical ownership — the real goal.
Topical authority reflects what I’ve built, while topical ownership is about whether AI systems prefer my content over others during the selection. This hinges on having content that surpasses mere existence and becomes preferred through the selection processes in AI pipelines.
My insights have been influenced greatly by Koray Tuğberk GÜBÜR’s work. His methodological approach to content architecture has consistently demonstrated how signaling genuine expertise results in notable outcomes.
GÜBÜR’s formula and framework, which include the temporal dimension, are crucial to expanding the cell model. His innovation in coining terms like “topical map” has provided the industry with structured guidance steeped in thorough research and understanding.
Row 1: Coverage as the Starting Line
I’ve come to see coverage as more than just ticking off content boxes. It means providing unmatched depth, comprehensive breadth, and offering unique insights. These elements together ensure that one’s presence is unmistakably their own.
While ensuring complete coverage is vital, presenting a new perspective is what keeps content relevant in the dynamic AI landscape. Original thought is my ticket to retaining repeated attention from AI systems, fostering recognition and engagement.
Row 2: The Foundation of Architecture
The architecture of content, from sentence clarity to strategic linking, is a cornerstone for effective communication. Starting with source context helps determine the identity and structure that align with my strategic goals.
Good architecture, as I’ve experienced, is not just about organizing content but about making it accessible and understandable for AI systems. It bridges what exists with how it is understood, a critical factor for effective communication.
Row 3: Position Decides the Game
Building a strong position requires more than content. It involves staking my claim as an entity of authority, ensuring recognition and relevance in my chosen topics. In AI, position is the differentiator that sets entities apart in a crowded digital landscape.
The effort I invest in establishing this position pays off when AI systems recognize and prioritize my contributions, setting me apart from others with similar coverage and architecture. This understanding underscores the significance of position in AI optimization strategies.
Through exploring these strategies, I have seen how each layer — coverage, architecture, and position — supports and enhances the other. Together, they create a robust framework that ensures my content stands out in competitive AI environments.
Have you ever felt like you’re living in an ‘AI Groundhog Day’? Despite the wealth of AI tools we can use, many of us find ourselves stuck in a loop, manually prompting AI again and again. If we aim to truly automate PPC tasks, we need to move beyond this cycle.
Picture this: you open a chat window, carefully craft a prompt, and paste in your context. The result is fantastic! Yet, an hour later, the cycle repeats. If this sounds familiar, you’re still entrenched in manual work, albeit with a digital twist.
To harness AI effectively, I’ve realized we must transition from being doers to orchestrators. This means moving away from one-off prompts and starting to build robust systems. My book, “The AI Amplified Marketer,” delves deeper into how the human element remains crucial even as AI evolves rapidly.
Today, I’ll guide you on using Skills, an emerging AI capability, to enhance efficiency in managing PPC.
What’s a Claude Skill?
Many of us marketers have tried ChatGPT’s Custom Instructions—a broad directive for AI behavior. A Claude Skill, however, is more precise, dictating specific instructions to ensure consistent and predictable outcomes aligned with my expectations.
Recently, while rating search terms, I noticed AI’s inconsistency. One session yielded letter grades, another a percentage, and another, a numerical scale. This variability can disrupt workflows, confusing tools and team members alike.
A Skill eliminates this inconsistency, ensuring that every time, the results format remains unchanged. This evolution transforms AI from an unreliable assistant to a steadfast team member.
The latest capabilities in Claude allow a Skill to morph your comprehensive PPC strategy into an executable AI playbook, coordinating tasks among various tools and subagents efficiently.
Whether it’s auditing accounts or analyzing search query reports, Skills encapsulate your expertise into scalable systems for your team to deploy with AI seamlessly.
How to Build Your First AI Skill
Starting a new Skill might seem daunting, but it’s quite straightforward. In a chat with your AI, you can upload an audit checklist, a SOP, or a workflow blueprint, and instruct Claude to formulate it into a Skill.
Intriguingly, Claude employs a specialized protocol to construct Skills, guaranteeing outputs that are structured, adhere to best practices, and align with Anthropic’s architecture.
Technically, a Skill is stored as a Markdown (.md) file, serving as the playbook for the task at hand. Concerned about data privacy? You can save this locally or opt to share it in a cloud repository for easy team access and updates.
You don’t need to start from scratch. Platforms like GitHub offer pre-built Skills that you can experiment with and tailor to your needs.
How to Use a Skill in PPC
To get started with a Skill, make sure you have some available in your account.
Simply tell the AI the specific task you wish to accomplish. If a suitable Skill exists, the AI will apply those instructions to carry out the task.
Keep in mind, having competing skills could disrupt consistency. For instance, two skills performing Google Ads audits might randomly select different methodologies, thwarting the predictability.
PPC Skills Need Real-Time Data
While a Skill defines powerful logic, without real-time data, its application remains theoretical. Consider crafting an analysis to review search terms over the past 14 days—it’s great in concept, but without active data pulling from Google Ads, it remains incomplete.
Previously, this required manually downloading CSVs from interfaces. It worked, but was slow and the data became outdated immediately.
Enter the Model Context Protocol (MCP), bridging AI Skills to live data sources seamlessly. Using protocols like Optmyzr’s MCP, Skills can dynamically access and apply live Google Ads data, converting static instructions into an adaptive, responsive tool. (Disclosure: I’m the cofounder and CEO of Optmyzr.)
From Grunt Work to System Oversight
Integrating Skills with MCP transforms AI from assistantship into management. Tasks like search term analysis can shift from hands-on processes to automated oversight, with the AI undertaking everything from data pulling to implementing results.
Incorporating capable logic (Skills) with real-time data (tools) nurtures a practical system ready to shoulder routine tasks, enabling me to focus more on strategy orchestration.
4 PPC Skills You Can Build Today
Ready to jump into action? Here are four PPC Skills to inspire you:
1. Search Term Mining
This Skill guides AI in evaluating search query reports to target waste and opportunities.
Without tools, it requires manual CSV uploads and report implementation. However, with MCP, the necessary data is automatically sourced and applied directly in your Google Ads account.
2. Ad Copy Generation
Using a landing page and keywords, this Skill generates ad copy tailored to user intent and value propositions.
Manual editions involve copying assets, whereas MCP integrations can identify underperforming ads, generate new copy, and even initiate ad experiments autonomously.
3. Account Auditing
This Skill performs a checklist to spot issues like missing ad extensions or budget constraints.
Manually, it reports findings, but with MCP, it remedies problems directly, such as applying existing extensions to appropriate ad groups.
4. Budget Reallocation
Analyzing comparative data, this Skill identifies budget shifts to maximize returns.
Without tools, it suggests reallocations; with MCP, it dynamically analyzes and implements these changes, optimizing budgets promptly.
The Future of Your Role: From PPC Doer to PPC Designer
The fusion of Skills and tools allows us to depart from mere AI collaboration to AI-driven responsibilities. Instead of juggling tasks, our focus shifts to designing automated systems, crafting Skills, and setting the course for relentless efficiency.
As technology melds development and user-friendly interfaces, we’re at the cusp of a paradigm where non-developers design systems. It’s time to innovate and welcome AI as a genuine ally.
The End of Endless Prompting
The cyclical nature of endless prompting confines us to manual execution. By harnessing Claude Skills, we’re revolutionizing our approach to PPC—from mundane tasks to sophisticated system design. This transition embodies the essence of an AI-amplified marketer, fostering a dependable, efficient partner that channels our expertise into thriving systems.
The journey begins by viewing your daily routines through a designer’s lens. What process is ripe for crafting your inaugural Skill?
I keep hearing about AI search as if it’s become the norm for everyone—an inevitable shift in how we discover information. But in reality, it’s not so simple.
AI search is indeed on the rise, but it’s not being adopted equally. The real divide comes down to something rarely discussed: household income.
My agency started closely monitoring search behaviors back in early 2025. In our latest study, we took a closer look through the lens of household income.
The results? A significant divide emerged. While a general 27% of users claim to regularly use ChatGPT, income-specific data paints a different picture.
In essence, higher-income households are significantly more likely to use generative AI tools.
This major variation challenges the common assumption that AI adoption progresses uniformly across demographics.
We’re seeing a new layer of digital inequality in accessing information. This divide, visible across the UK, is adding to an existing digital skills gap.
AI adoption relies on more than just having the right tools. It’s also influenced by:
If you work in certain sectors like digital or corporate, you’re more likely to be encouraged to incorporate AI into your daily routines.
Capability plays a role, too. For some, using AI tools comes naturally. For others, it’s an intimidating process without proper guidance.
Then there’s confidence—trust in AI tools varies. In our research, users on platforms such as Perplexity report high levels of trust, but they remain niche.
These disparities mean that AI literacy is quickly becoming another possible layer of the digital divide, augmenting the advantage of the digitally savvy.
For businesses, this division has tangible implications. Different audiences are developing distinct behaviors:
This isn’t a minor shift. Making incorrect assumptions about user behavior could lead to strategic missteps, like over-investing in one area and neglecting another.
Yet, there’s an upside. Fast adopters of AI are often the very decision-makers and high-income consumers that brands value most.
These users are frequently termed “digital explorers” and see AI as an integral part of their decision-making process.
Behavior and confidence are intertwined, shaping how far users will go with AI.
To respond to these fragmented behaviors, brands need to:
A comprehensive understanding of AI’s role at every step of the customer journey becomes essential.
Ultimately, as AI weaves deeper into our lives, the human element remains paramount in determining the future of search.
Every year, I eagerly anticipate the release of Duane Brown’s PPC Salary Survey. It provides a revealing glimpse into what we’re really earning in this industry. The 2026 survey, which gathered input from 445 practitioners across over 50 countries, is particularly telling. What stands out this year is the growing divide in middle-career PPC salaries, as the extremes continue to pull away.
PPC salaries aren’t uniformly dropping. Instead, there’s an expanding gap between the high earners and those at the baseline. This divergence has never been clearer, or more concerning.
AI has certainly sped up this change, but the roots of this transformation have been deepening for years.
What Four Years of Salary Data Reveal
The salary survey has kept tabs on U.S. median pay by experience since 2018. When you lay out the data for four straight years, a distinct pattern emerges:
Experience
2022
2023
2024
2025
2026
3-5 years
$80,000
$80,016
$80,000
$75,000
$87,500
6-9 years
$100,000
$110,000
$108,000
$110,000
$100,000
10-15 years
$125,000
$150,000
$136,000
$133,500
$135,000
15+ years
$150,000
$134,000
$144,000
$140,000
$150,000
Two key insights stand out:
The salary for the 3-5 year band rebounded significantly in 2026 to $87,500 after a drop to $75,000 in 2025. This indicates junior-to-mid practitioners who secure roles are being compensated fairly.
However, the 6-9 year band slipped back to $100,000, and the 10-15 year group has stagnated between $133,500 and $136,000 for three years. For those with a decade of experience, pay has essentially stalled or decreased when adjusted for inflation.
The difference becomes even more pronounced at the extremes. Data from the U.S. survey shows top salaries exceeding $300,000 for the 10-15 years cohort. Freelancers with comparable experience have a median income of $202,895, compared to an agency median of $123,545. That’s a $79,000 premium for going independent, demonstrating the distinct advantage if you offer something valuable enough to justify it.
The Growing Divide: In-house vs. Agency
The 2026 survey highlights an increasing divergence in mid-career earnings between in-house and agency roles.
Experience
Agency (median)
In-house (median)
Difference
3-5 years
$80,000
$89,000
+$9,000
6-9 years
$90,000
$170,000
+$80,000
10-15 years
$123,545
$140,000
+$16,455
15+ years
$120,000
$140,000
+$20,000
Although the 6-9 year in-house statistic is somewhat inflated by outliers, the trend is clear: in-house professionals regularly out-earn their agency peers, sometimes by significant margins. For those with 10-15 years of experience, an in-house position could mean a $16,000 annual advantage.
This isn’t merely a question of individual skill development; it’s about the strategic role you play. Agency work, despite its diversity, doesn’t match up to in-house strategy roles in terms of financial reward. Automation of execution tasks makes it harder for agency workers to justify their billing rates, likely pushing salaries down.
Examining the Gender Pay Gap
The 2026 survey paints a complex picture of gender pay differences in our field.
For the 3-5 year experience band, women in the U.S. are actually earning more than men, with a median of $87,500 compared to $85,000. At the 10-15 year level, women also slightly surpass men with a median of $135,000 against $130,000. However, a chasm appears at senior levels, with men earning a median of $150,000 versus $120,000 for women—an alarming 25% gap.
This trend aligns with broader compensation research, where pay gaps tend to close at mid-career but widen at senior levels, a result of factors like negotiation skills and access to high-value client relationships. It’s crucial for the industry to address this discrepancy as we increasingly value strategic capabilities.
The U.K. and Europe: Stagnation at the Pinnacle
In the U.K., salary trends are worrying. The 5-year survey shows the 10-15 year median fluctuating between £48,800 and £60,000, finally settling at £50,000 in 2026, a drop from £60,000 in the previous year.
Conversely, European data shows a more positive trend at senior levels. The median for the 10-15 year experience range rose from €50,000 in 2024 to €65,625 in 2026. However, the 3-5 year band has fallen back to €37,200, less than it was in 2022, indicating entry-level and early-career pay isn’t keeping up with job demands.
In Berlin specifically, the 2026 survey reports a 10-15 year band median of around €76,000, significantly above the broader EU figure, showing that the Berlin market still values senior experience highly.
Beyond AI: The Real Power Shift
I want to assert that the shift in PPC salaries isn’t merely about having or lacking AI skills.
The State of PPC 2026 report notes AI has dropped to the third priority among professionals, not because its use declined, but because it has become standard. AI saves us around 5.2 hours per week; useful, but not a salary game-changer.
Payscale’s 2026 Compensation Best Practices Report reveals that 55% of companies offer no additional benefits for AI skills, even though 61% require them. AI fluency is now expected, not exceptional.
Top earners have shifted from being campaign operators to business outcome leaders. They:
Focus on revenue contributions and margin impacts rather than ROAS and CTR.
Position themselves closer to the CFO than to the media buyer.
Demonstrate their expertise through effective communication, meaningful frameworks, and insightful questions in board meetings.
While salary data indicates past trends, it’s your approach that determines where on the scale you land.
Ask Yourself the Right Questions
The PPC salary curve is not collapsing, yet it is branching.
The 3-5 years cohort remains competitive salary-wise.
U.S. freelancers with over 10 years of experience and strong positioning can earn $200,000+.
Senior in-house strategists see salaries ranging from $140,000 to $170,000.
What’s stagnating is the middle—the agency expert with 6 to 15 years of experience. While skilled at running campaigns, they lack the differentiated value that would push them to the next tier.
This group faces pressure from below, with automation taking over execution, and from above, where strategic roles demand more than just campaign prowess.
The question is—not just whether I’m using AI—but am I the go-to person when the AI report arrives?
If you find yourself unsure, it might not be about upgrading your tools, but rather a reevaluation of your positioning. Now is the time to make that change, before the salary gap widens further.
Hi there! I’m Maddie Lightening, Head of Paid Media at Hallam, and I’ve had my fair share of lessons and challenges in the fast-paced world of PPC. Over the last decade, I’ve navigated through search, social, and digital programmatic channels, and I’m excited to share some of these experiences with you.
The journey has been a mix of mistakes, insights, and shifts in mindset. Through it all, I’ve come to realize the importance of adaptability and a clear understanding of reporting metrics. Let’s dive into some notable points from my career.
The Misreported ROAS That Taught Me a Big Lesson
Early in my career, I encountered a significant reporting error due to different account currency settings. While working with an Australian billing system and reporting in GBP, conversion values were miscalculated, dramatically skewing performance results. This error only came to light when we compared with CRM data, revealing actual performance was double what had been reported. A reminder of how crucial it is to get technical setups right!
Outdated Account Structures: When Old Strategies Meet New Technologies
Managing legacy account structures can be tricky, especially in today’s AI-driven marketing world. I worked with a travel client who had an outdated setup, which used thousands of campaigns. While it worked in the past, this granular approach clashed with modern strategies, highlighting the necessity for streamlined data and advanced AI bidding tactics.
The Right Timing for Strategy Implementation
Another lesson learned was the importance of timing. We had an account restructure plan ready but delayed its implementation to avoid peak season disruption. This delay cost us when performance dipped in January, forcing us to make rapid changes. In hindsight, an earlier start could have mitigated these risks.
Navigating Real-Time Performance Pressure
Dealing with performance declines during critical periods is stressful, especially when clients are heavily invested in their peak seasons. This pressure-cooker environment underscores the importance of teamwork, maintaining composure, and focusing on solutions rather than succumbing to panic.
How a Max CPC Cap Helped Rebalance Performance
One effective strategy was implementing a max CPC cap within portfolio bidding, even while using automated systems. This tactic significantly reduced CPCs without impacting performance, proving that it’s possible to guide AI to deliver better outcomes with the right constraints.
Embracing AI: A Pathway to Growth
Rejecting AI in marketing is a mistake. During my time in an agency that opposed AI tools, I realized this limits potential. Embracing AI doesn’t mean losing control, but rather finding strategic ways to harness its power for growth.
The Power of Quality Prompts in AI
In my AI experience, detailed input is crucial for quality results. Vague prompts lead to weak outputs, whereas providing detailed context such as goals and target audience enhances outcomes. AI should augment our work, not replace it.
The Importance of Curiosity and Experimentation in PPC
Staying curious is key! I encourage ongoing experimentation, even if success isn’t guaranteed. My “test and learn” approach emphasizes that lessons learned from failures are as valuable as those from successes.
Learning from Small Mistakes
We all make mistakes—like sending the wrong client report—but the important part is how we handle them. Quick accountability and problem-solving maintain perspective and prevent minor slips from becoming major issues.
The Big Picture: Adaptability and Growth
Success in PPC comes down to adaptability and a learning mindset. Whether it’s tackling legacy systems or embracing AI, evolving our strategies is crucial for distinguishing strong teams from the rest.
Final Thoughts
Ultimately, my experiences illustrate that mistakes, when managed well, refine strategies and boost performance. Staying curious, proactive, and open to change is essential for thriving in the paid media landscape.
As I delve into the vast realm of AI, I’ve realized how integral Large Language Models (LLMs) are to virtually every aspect of our lives—be it work, leisure, shopping, or health. They are the ignition point for nearly everything we do.
But here’s something that often goes unnoticed: how these models wrap up their interactions. They don’t just stop; they subtly guide us forward, and that’s a game-changer.
It’s as if LLMs adopt a “no, you hang up first” approach, perpetually inviting us to continue. They ask things like, “Would you like me to draft that travel itinerary for you?” or, “Shall I compare the Nike and New Balance running shoes for your marathon?”
These gentle nudges make it incredibly easy to stay engaged. More often than not, I find myself responding with a simple “sure” or “sounds good,” eager to see what’s offered next.
Such nudges are pivotal in shaping consumer behavior. Where the LLMs lead us truly matters.
If you represent a premium brand and an LLM suggests a price comparison, it might not align with your strategy, but it’s vital to grasp and react appropriately.
We’ve delved into various LLMs to understand these nudges across different platforms, seeking patterns that shape user behavior and signaling what it means for brands aiming to steer the digital journey.
What LLM Nudges Look Like Across Platforms
Budget and Deals Dominate
Across the board, LLMs frequently suggest follow-ups related to budgets and deals, with about 45% of mentions falling into this category. Though not uniformly distributed, these elements are often default interests for consumers.
For instance, Perplexity and ChatGPT feature over 60% of budget-related suggestions, while Meta doesn’t lean as heavily into this assumption.
Comparisons Drive the Next Step
Product comparisons are the second most common type of suggestion. LLMs compare everything from retail products to financial services and health treatments, touching various industries.
Specs Play a Minor Role
While there’s a common belief that providing detailed specifications is vital, these comprise only a small fraction of the LLMs’ recommendations. That said, they do add ranking value, even if LLMs typically don’t extend conversations in this manner.
How Each Platform Uses Nudges Differently
In our research, we’ve noticed that each LLM has a unique style of extending conversations, offering insights into how these platforms subtly influence consumer behavior.
Platform
Dominant Nudge Style
Key Characteristic
ChatGPT
“If you want…”
Heavy commerce focus: Primarily nudges toward deals and product comparisons.
Microsoft Copilot
“If you tell me…”
Interactive/clarifying: Frequently asks for more user data to refine recommendations.
Google Gemini
“Would you like me…”
Polite and permission-based: Exclusively uses this formal invitation to continue helping.
Perplexity
“I can help…” / “If you’d like…”
Service-oriented: Uses varied phrasing to offer utility and assistance.
Meta AI
“Let me know…”
Casual and passive: Primarily nudges toward product comparisons and specs with a less aggressive tone.
What Actions to Take Based on AI Nudges
These nudges are not just to keep the dialogue open; they also push users to explore further, greatly influencing consumer behavior and the entire customer journey.
As data becomes more plentiful, we’ll better optimize for these nudges. For now, our insights are somewhat limited to individual interactions.
Here are three key actions to prioritize, largely tied to the content you create across various channels:
Capitalize on the “Support” Gap
Proactive nudges related to troubleshooting and support are significantly lower in frequency than commerce-driven themes.
Focus on owning the post-purchase “how-to” and technical support space to establish long-term authority where AI currently isn’t as assertive.
Strengthen “Product A vs. Product B” guides to capture AI’s primary next step.
Maximize the “Budget and Deals” Opportunity
Pricing and discounts are the top drivers of AI nudges, comprising 48% of all prompts.
Ensure your site maintains structured, real-time deal data to become a preferred destination for AI-driven commerce referrals.
As the LLM landscape rapidly evolves, these platforms will become the main touchpoints for consumer research and decision-making. Understanding how LLMs discuss your brand and how these conversational nudges affect users is essential.
By dissecting these automated cues across platforms like Gemini, ChatGPT, and Perplexity, we can see where consumers are being steered—whether towards budget-friendly alternatives, product comparisons, or technical specifications.
Recognizing these trends enables us to shift from mere observation to actionable strategies, ensuring our value proposition remains clear, even when an LLM reframes the conversation around cost or competitors.
Monitoring these shifts is key to maintaining brand authority as AI-driven interactions increasingly dictate the customer journey.
