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Tag: Ad Optimization

Revamp Your Google Ads Strategy for Better Results

I’ve noticed that Google Ads tends to produce the same results repeatedly, no matter how much money I invest. This pattern stems from the system being trained by my consistent actions over time.

Previously, achieving success in paid searches was all about optimizing. I would adjust bids, restructure campaigns, refine match types, and add negatives, directly impacting performance.

While this method remains standard for many, during audits, these accounts often appear well-managed on paper—active management, matched targets, proper ROAS. Yet, their performance seems stuck.

Google Ads now builds upon the signals I’ve reinforced. Hearing phrases like “That didn’t work” usually indicates that minor changes didn’t override the ingrained patterns.

What many advertisers call optimization is actually training, and if I’m not careful, I might teach it the wrong lessons.

Why Isolated Optimizations Don’t Work Anymore

The current environment features Smart Bidding, Performance Max, and modeled conversions. These systems learn cumulatively rather than resetting at each change.

If I change my ROAS target today, it won’t wipe away months of established patterns. Shutting down a new campaign prematurely can mark such volatility as something to avoid.

It’s about optimizing for survival—behaviors that get funded, hit targets, and aren’t paused are what the platform focuses on.

When accounts plateau, especially under strong management, it often indicates that the system has been trained to avoid unpredictability—while that’s precisely where growth occurs.

What Training Looks Like in Google Ads

On the backend, Google Ads consistently evaluates the concept of success based on factors like conversion inclusion, valuation, and how I handle volatility.

Over time, these become the signals shaping its behavior, influencing queries, audience priorities, auction strategies, and demand exploration.

For example, if repeat customers easily hit ROAS targets but prospecting fluctuates, the system learns to prioritize what’s safe over what’s incremental.

Common Mistakes in Google Ads Training

These errors often pass for good management, but recognizing them is crucial. Here are a few I’ve noticed:

Mistake 1: Leaning on Easiest Revenue

Encouraging branded searches and repeat customers seems logical, but Google learns that predictable revenue is the ideal.

Shouldering this strategy makes incremental demand suffer as the account conservatively emphasizes what works, causing stagnation.

Mistake 2: Punishing Volatility

Responding to short-term inefficiency quickly by tightening targets or pulling budgets can send a message that exploration isn’t allowed.

This results in prioritizing stability, which eventually limits expansion and innovation, as the account simply recycles existing demand.

Mistake 3: Treating All Purchases the Same

Not all purchases are equal. When everything sends the same signal, Google defaults to what’s easiest to replicate—typically repeat purchases.

This can hinder new customer acquisition, a vital component of sustainable growth.

Intentional Training for Optimal Google Ads

Aligning Google Ads with business goals rather than just ROAS is key. Here’s my approach to intentional training that I’ve found effective:

Maintaining Efficiency Lanes

These are my accounts’ baseline revenue protectors. They include brand campaigns and high-intent terms with stable performance. These are not my growth engines.

Building Growth Lanes

Growth campaigns have broader match types and looser targets, aimed at demand expansion and new customer acquisition.

By separating growth lanes with realistic expectations, I allow them to learn even when fluctuations arise.

Changing Signals Slowly

Constantly adjusting ROAS targets can disrupt the system. I avoid weekly changes to let the data compound for broader query expansion and improved share.

Overall, it’s about accepting gradual growth rather than seeking overnight success.

Managing a Trained Google Ads System

Reflect on your management approach. If you’ve answered “yes” to questions about tightening targets quickly or pausing exploratory campaigns, it indicates your system is merely following the training it’s received.

The focus should shift from speed to thoughtful teaching, constantly evaluating what behaviors I’m reinforcing and how they align with my bigger picture goals.

Inspired by this post on Search Engine Land.

April 16, 2026
Mastering PPC: Correcting Automation Drift for Better Conversions

I’m excited to invite you to our upcoming event on May 6, where I’ll be part of SMX Now for the second time. Join me as Ameet Khabra reveals insights on identifying and preventing PPC drift before it impacts your campaign’s performance.
It’s essential to remember that automation doesn’t inherently fail—it just executes what it’s programmed to do. The issue arises when Google Ads receives signals that are incomplete, misaligned, or too broad, which can lead to optimization for the wrong outcomes, catching advertisers off guard.
During the second edition of SMX Now, our breakthrough monthly series, Ameet Khabra from Hop Skip Media will dive into a real-life account. She will showcase a scenario where a 417% surge in conversions wasn’t the success it seemed. Through this case study, she’ll explain how automation drift manifests in four critical areas: signal drift, query drift, inventory drift, and creative drift.
You’ll gain a practical framework to identify drift early on, comprehend the importance of human oversight, and manage automation with intent. The goal is to ensure automation aligns with actual business objectives rather than just the successes platforms report.
Make sure to join us on May 6 at noon ET to learn more.
Save your spot

Inspired by this post on Search Engine Land.

April 15, 2026
Maximize ROAS by Slashing Paid Media Waste

I used to think hitting revenue targets with the same PPC budgets was challenging, but with rising platform costs, it’s like facing an invisible budget cut. It’s time to rethink our approach.

Data shows that average CPCs are up by as much as 40%, according to Wordstream, leaving teams grappling with flat marketing budgets at 7.7% of company revenue, as Gartner points out.

In my experience, 20-30% of accounts’ spend underperforms, which highlights a pervasive inefficiency in paid media as we know it in 2026. But all is not lost! Efficiency is about strategic spending, not just cutting costs. Let me walk you through discovering waste and optimizing for maximum returns.

The focus on efficiency has escalated as paid media automation obscures crucial data. Simultaneously, businesses are freezing budgets but still targeting growth, facing inflation that increases CPCs annually by about 10% in my observations.

With AI automation pushing us into smart bidding, managing rising CPCs requires skill in adjusting the right strategies. Customers’ attention is now scattered across multiple platforms, often leading to simultaneous double-screening.

A hard look at where every dollar goes is essential, shifting the fundamental business question from “how do we spend more?” to “how do we maximize our returns?”

Upon auditing accounts, I apply the 20-30% rule to identify inefficiencies. Whether it’s a product consuming too much budget or search term reports revealing spend on irrelevant queries, these are the typical culprits.

Common waste zones involve zero-conversion products, low ROAS/CPL outliers, and high spend with low returns. To address these, I apply impression, clicks, and spend thresholds to verify data adequacy.

When budgeting, I prioritize full-funnel tactics. Conversion-focused spending should be safeguarded, ensuring high-intent, high-return segments retain funding.

Creative assets are no longer just nice additions but essential to campaign performance. Platforms need continuous variations to function optimally.

I integrate AI-driven tools for analytics, but human direction remains crucial in areas where strategic insight is required. Automation should enhance decision making, not replace it entirely.

The bid strategies I select depend on conversion data and my ROAS goals. From Target CPA to Maximize Clicks, choosing wisely is key to success.

My advice is to conduct waste audits regularly, protect lower-funnel budgets, refresh creatives frequently, shift to blended measurement practices, and automate responsively. With these steps, efficiency isn’t just possible; it becomes a competitive advantage.

Inspired by this post on Search Engine Land.

April 10, 2026
Google Ads Shifts Focus: Performance Planner Changes

As someone deeply invested in the world of digital advertising, I’ve noticed that Google is making a significant change. They’re moving away from impression-based planning and encouraging us to adopt more conversion-focused strategies.

Recently, I learned that Google’s Performance Planner tool has refined its scope. They’re now emphasizing conversion-focused campaign types, leaving behind the traditional impression-based planning style.

What’s happening? Last month, Performance Planner stopped supporting planning for Display and Video campaigns. This adjustment also means that metrics like impression share, top impression share, or absolute top impression share are no longer viable on their platform.

Why this matters to us. This shift away from impression-focused planning affects how we forecast and optimize campaigns concentrated on brand awareness. Google’s push towards conversion-focused and automated strategies challenges us to rethink our approach to upper-funnel tactics.

The bigger picture. It’s evident that Google Ads is prioritizing automation and performance-driven results. They are aligning their tools more with campaign types like Search, Shopping, App, Demand Gen, Local, and Performance Max.

How it’s working now. We can continue using the Performance Planner for supported campaign types, but any plans that included Display or Video campaigns, based on impression share metrics, are no longer editable or viewable.

What I’m watching. I’m curious about how we’ll adapt our planning and forecasting strategies for upper-funnel channels like Display and Video now that they lack native support in Google’s tools.

Bottom line. Ultimately, Google’s focus on performance-driven planning means that impression-based strategies might soon be a thing of the past. It’s time to embrace the shift towards conversions.

Inspired by this post on Search Engine Land.

April 9, 2026
Boost Ad Campaigns with AI: Emotional Triggers & ROI Tips
I’ve discovered the power of turning AI into a strategic ad partner using prompts that dive deep into buyer emotions, target high-intent audiences, and tackle objections.

Many of us are already tapping into various generative AI tools to breathe life into our marketing ideas and boost the effectiveness of ad campaigns.

Using prompts isn’t just a solo brainstorming alternative; it’s a productivity booster that opens up a world of possibilities.

In this guide, I’ll share some of my favorite marketing prompts for ad campaigns, designed to spark creativity in crafting your own prompts.

Why Use Prompts for Online Ads?

Prompts are your fast track to brainstorming ad elements like triggers, emotions, actions, and your target audience.

The beauty of prompts is they’re versatile. You can tweak outputs across different channels and initiatives like ads, emails, and social media.

Getting closer to optimal campaigns from the outset means saving time, a real boon for low-budget efforts that are hungry for feedback.

The prompts themselves make all the difference. Craft strong questions to extract valuable insights from large language models (LLMs).

Feeling stuck? Ask AI tools for prompt recommendations or use mine. Here’s a selection I often use for online ads.

Emotional Trigger Prompt

Purchases are fueled by emotions, so it’s essential to tap into what makes your audience feel.

Try this prompt: “What are the top emotional triggers that would make X audience buy Y product?”

As an example, I explored what emotional triggers would prompt parents to purchase math learning software for their kids. The LLM highlighted key triggers alongside scarcity and urgency hooks:
- Fear of falling behind: Anxiety and a protective instinct. Example: “Ensure your child never falls behind in math.”
- Desire to give kids a competitive advantage: Ambition and pride. Example: “Equip your child with math skills that top students develop years ahead.”
- Relief from homework stress at home: Relief and peace of mind. Example: “Say goodbye to math homework battles at home.”
Purchase Intent Prompt

Explore these questions to identify who’s ready to buy your product or service now:
- Who is most likely to buy immediately?
- Who needs convincing?
- Who will never buy?
To prevent wasting ad spend, focus on audiences poised for purchase and steer clear of those unlikely to buy.

Keep probing which audiences are most likely to convert. Use the LLM’s feedback to get more specific with your ads.

In the math software scenario, the LLM advised that parents of struggling kids in math were the best converters due to high urgency and low friction.

The second-best group? Homeschooling parents, motivated by the need to manage the entire curriculum. This insight allowed us to craft ads and test conversions.

Overcoming Objections Prompt

Addressing objections is crucial for sealing the deal. Ask for three to five potential objections buyers might have about your product.

In our math software example, the LLM identified these objections:
- My child already has too much screen time.
- Will this actually improve my child’s math skills?
- It’s too expensive.
Next, craft a persuasive counter-argument for each using logic, emotion, and evidence. For “it’s too expensive,” consider:
- Logic: “Less than the cost of a tutor.” Establishes a higher anchor, making the price seem reasonable without calling it cheap.
- Emotion: “Don’t let your kids fall behind in math.”
- Proof: “80% of students improve by one letter grade in two months.”
Psychological Profile Prompt

Request a comprehensive psychological profile of your ideal customer from an LLM. Use questions like:
- What are your ideal customer’s fears?
- What are their frustrations?
- What do they envy?
- What do they pretend doesn’t bother them?
- What keeps them up at night?
In the math software scenario, I asked, “What or who do my ideal customers envy?”

The response indicated parents envy children in enrichment or advanced classes, seeking future educational opportunities.

Here’s a message for them: “Help your child stay ahead instead of playing catchup.”

The Lifetime Value Prompt

Sustain long-term success by focusing on customer lifetime value (LTV) instead of one-time sales.

Consider these questions:
- Why might your customers stick around?
- Why might they buy more?
- What retention strategies are effective?
For a luxury furniture brand, we turned these into a brief playbook to boost LTV. The LLM suggested shifting from a transactional relationship to a long-term design partnership.

For instance, segment your customer base and use direct mail for your highest-value group by sending a lookbook. Though it seems old-school, it can result in a higher LTV than general mailings.

Your clients deserve strategic thinking and clear priorities. AI tools help us achieve that, supporting both strategy and execution.

Fix Lagging Average Order Value Prompt

When performance dwindles, it’s tempting to ask sweeping questions about metrics like return on ad spend (ROAS).

But that’s a path well-trodden, often leading to generic, uninspired checklists.

We grapple with B2C and B2B search query overlaps. Focusing on B2B users is challenging but crucial for securing high-value, long-term customers.

We noticed a likely cause of a B2B client’s lagging ROAS: average order value (AOV) as reflected in Google Ads’ Value/Conv. Smart Bidding had shifted to high-converting but lower-quality sessions, impacting performance.

We enlisted an LLM to ascertain and address the issue.

With Ads Advisor (Gemini) in Google Ads, the initial response focused on trivial consumer scenarios, like holiday themes.

Upon refining the prompt, we received more targeted, actionable suggestions, saving valuable time.

We doubled down on audience targeting, emphasizing specific Google audience segments and first-party audiences with value rules.

AOV increased. While it didn’t promise higher order values, it honed focus on B2B intent and reduced low-priority consumer purchases.

Key performance metrics improved, guiding the path to growth and profitability.

Better Prompts Lead to Better Campaigns

Begin simply — incorporate one or two of these prompts into your next campaign, tweak the outcomes, and expand from there. Over time, you’ll establish a repeatable system where AI becomes integral to your marketing workflow.

Inspired by this post on Search Engine Land.
April 9, 2026
10 Years of PPC Insights: When Breaking Rules Pays Off

I’ve spent a decade delving into PPC strategies and what I’ve learned is that chasing ‘best practices’ often limits true performance potential. Real growth stems from daring to deviate and experiment with new methods.

PPC conversations frequently revolve around sticking to best practices. These mandates include maintaining clean account structures, controlling match types, scaling budgets incrementally, ensuring campaigns don’t overlap, and keeping everything logical and easy to explain.

While these fundamentals do promote consistency and prevent inefficiencies, they are not the secret to achieving significant gains.

Looking back, many of the most impactful improvements came from testing unorthodox ideas that didn’t neatly fit into the established frameworks, but instead aligned with how platforms like Google Ads and Meta actually operate. These platforms don’t optimize for best practices, but rather for signals, prompting a rethink in approach to performance.

Control Still Matters: Revisiting SKAGs

In several accounts, reintroducing Single Keyword Ad Groups (SKAGs) for high-intent, high-revenue keywords led to improved performance. Ad relevance shot up, conversions grew, and query matching became more precise. It’s not about reverting to old structures, but recognizing where control adds value.

The narrative that machine learning abolishes the need for such control is overly simplistic. My experience shows that precision matters, but only in contexts where the intent justifies it.

Harnessing Broad Match with Control

Historically, broad match has been met with skepticism due to its expansive nature. However, combining broad match with aggressive negative keyword management allows Google to explore broadly while you shape the output through strategic query mining.

By continuously refining query inputs, broad match can expand reach without compromising relevance, redefining how control is applied.

When Visibility Trumps Efficiency: Target Impression Share

Target Impression Share often supports defensive strategies, but applying it to high-value, non-branded terms can boost SERP dominance even at the cost of efficiency. In such cases, ensuring visibility can outweigh concerns over cost efficiency, especially when aiming for market dominance rather than mere competition.

Focusing on Conversion Quality: Weighting Over Tracking

Most lead generation accounts capture multiple conversion actions, but treating them equally can lead to suboptimal interpretations. In one instance, assigning different values based on conversion likelihood—like prioritizing phone calls—shifted optimization to improve conversion quality rather than volume.

This approach emphasizes what’s truly valuable, ensuring platforms optimize effectively based on input.

Competitor Bidding: Leveraging Existing Intent

Despite their reputation for inefficiency, competitor campaigns succeed by capturing existing intent. Users searching for competitor brands often convert thanks to their advanced position in the decision process, proving crucial when strategically managed with clear positioning and relevant landing pages.

Rethinking Top-of-Funnel Keywords

Although often removed for low conversion rates, top-of-funnel keywords can indirectly enhance account performance by strengthening remarketing pools and audience signals, thus supporting high-intent campaign efficiency.

These queries play an unseen but vital role in driving conversions across the account.

Trusting the Data Over Assumptions

Initial audience hypotheses frequently miss the mark, whereas data often pinpoints the most efficient converters. By trusting data and adjusting strategies accordingly, accounts can improve performance by aligning with audience realities.

Revisiting Account Structure’s Role

While clean setups simplify management, they’re not always the most effective. Controlled overlaps between campaigns can leverage shared signals for better auction outcomes, challenging the notion that rigid structures lead to optimal performance.

Treating Product Feeds as Dynamic

In Shopping campaigns, product feeds are often overlooked. Yet, revisiting and adjusting feed details—like product titles and attributes—can significantly enhance product visibility and click-through rates, underscoring their strategic importance.

Retargeting: A Hub for Testing Strategy

Retargeting is not just about conversions; it’s ideal for testing variations in messaging and creative content due to its high-intent audience. Successful test results can then be confidently scaled, reframing retargeting as a strategic testing ground.

The Real Secret Behind Top Account Success

Over the years, I’ve realized that outperformance doesn’t stem from strictly adhering to playbooks, but from understanding and influencing platform signals and stepping beyond conventional boundaries to outperform beyond expectations.

Inspired by this post on Search Engine Land.

April 9, 2026
Enhance Your Data Strategy with Server-Side Tagging Solutions

I’ve been noticing the rapid transformation in how brands are tracking user behavior online. With privacy laws tightening and browser extensions increasingly blocking data, the demand for cleaner data from ad platforms is higher than ever. This change urged me to explore server-side tagging as a solution.

By implementing server-side tagging, I’ve managed to reduce data loss while collecting cleaner, privacy-compliant data. This approach is invaluable, especially considering the experiences I’ve had with providers like Elevar and Littledata.

So, what exactly is server-side tagging, and in which situations does it really shine? Let’s dive into the details!

What is server-side tagging?

Traditionally, tracking scripts ran directly in the browser. However, with server-side tagging, these scripts operate on a server I control, giving me more control over data processing.

Here’s how it works: instead of sending data straight to multiple third parties from the browser, events are sent to a first-party server endpoint, often using a Google Tag Manager server-side container. The server then processes, enriches, and forwards this data to tools like Meta and Google Analytics.

This setup provides benefits such as more data control, a cleaner page performance, and better compliance with privacy laws.

Moreover, server-side tagging grants me the flexibility to enrich and transform data before it reaches ad platforms, standardizing event names, filtering out low-quality events, and adding custom parameters for better audience segmentation.

Is server-side tagging right for you?

While server-side tagging isn’t a one-size-fits-all solution, many brands find it essential, particularly if you:

You need to meet strict privacy or compliance requirements

Server-side setups allow for greater control over how data is processed and shared, supporting compliance with regulations like GDPR and CCPA.

You want faster website performance

In my experience, client-side tracking can slow your page down, but server-side tagging shifts data processing to the server, resulting in faster websites.

You want more accurate tracking (despite ad blockers)

Ad blockers can hinder client-side scripts, but server-side tagging circumvents many of these restrictions, making your data collection more reliable.

You’re investing heavily in paid media

For those heavily invested in platforms like Meta and Google Ads, achieving better data accuracy can significantly impact return on ad spend.

How to implement server-side tagging

When it comes to implementing server-side tagging, you have two main options: building it internally or using a service provider.

Option 1: Internal setup

Choosing an internal setup gives me complete control but requires technical expertise and ongoing maintenance. This involves setting up a GTM server-side container and adding logic for data processing.

Option 2: Use a server-side tagging service

Platforms like Elevar and Littledata offer turnkey solutions that integrate seamlessly with existing tools, allowing me to focus on strategy rather than technicalities.

Our direct experience: Littledata vs. Elevar

In my experience with Littledata and Elevar, each caters to different needs. Littledata is ideal for emerging brands with simpler tech stacks, while Elevar is suitable for those outgrowing entry-level solutions.

Investing in server-side tagging has transformed how I handle data, ensuring that I remain compliant with privacy laws while boosting site performance and data reliability across all my platforms.

Inspired by this post on Search Engine Land.

April 9, 2026
Explore Google’s New Swipeable Location Carousel in Ads

I recently stumbled upon an exciting development from Google that’s set to transform how we view local search ads. They’re experimenting with a swipeable location carousel, designed to make results more interactive and competitive, especially for advertisers with multiple locations.

The key change lies in how Google is planning to make local search ads more engaging. By grouping multiple business locations into a horizontal carousel, they allow users to swipe through different options right from the ad unit. Imagine being able to compare options without leaving the search results page. This feature could potentially change how advertisers capture nearby demand.

What’s Happening: This new format for Google Ads aims to consolidate business locations into a swipeable carousel. It promises a richer browsing experience for users, who can now view multiple locations directly within the ad.

How It Works: Instead of displaying each location separately, the carousel groups them together. Each location includes business details such as ratings and proximity, all easily accessible by swiping.

Zoom In: The move from static, stacked listings to a more dynamic experience is notable. It consolidates multiple location listings into one elegant, swipeable unit.

Why We Care: For advertisers, this could mean increased visibility in a single ad, while users enjoy a faster way to compare options nearby. It’s a win-win.

Between the Lines: While this could boost engagement with location-based ads, it might also heighten competition within the carousel as businesses compete for user attention.

What to Watch: I’m eager to see if this feature rolls out more widely and the impact it will have on click-through rates and overall local ad performance.

First Spotted: This intriguing update was first noticed by Anthony Higman, Founder of Adsquire, who shared his discovery on LinkedIn.

Inspired by this post on Search Engine Land.

April 8, 2026
Mastering Audience Engineering: Elevate Your Paid Media Strategy
Embrace audience engineering to influence AI decisions, manage ad spend wisely, and connect with high-value customers through creativity and data.

I’m witnessing a significant transformation in the paid media landscape as platforms shift from manual targeting to AI-driven audience discovery. This change is redefining how we approach advertising, with automation tools consolidating campaigns, obscuring data, and favoring prediction algorithms over manual selection.

This transition requires me to innovate by mastering the art of audience engineering. By doing so, I ensure I’m equipped with strategies to thrive in this evolving landscape.

The End of Manual Targeting as I Knew It

Previously, I depended on detailed keyword lists and demographic filters to pinpoint my ideal audience. I directed platforms about where to focus and paid to access the desired market.

However, these options are now outdated:
- Google has transitioned to Performance Max, which eliminates keyword-specific targeting in favor of more fluid groups and signals.
- Meta’s Advantage+ automates demographic focus, turning my role into that of a signal provider instead of an audience selector.
- Microsoft’s inclusion of this model confirms this is an industry-wide evolution.
While traditional targeting seems to have vanished, it has merely moved to the internal structures of the platforms where algorithms dictate the direction based on their indigenous data.

The Rise of Audience Engineering

My role shifts from targeting to engineering as it becomes more about guiding algorithms than manually selecting audiences.

From Targeting to Teaching

The distinction is crucial. Traditionally, targeting emphasized choosing audiences, but now it’s about educating AI with comprehensive conversion data, targeted creativity, and insightful first-party data.

Previously, I might have targeted CFOs with job filters, but now I feed the AI robust data (e.g., “deal closed” signals) to characterize valuable prospects and devise creative content tailored to their needs.

The New Competitive Discipline

Embracing this transformation gives me an edge. By finetuning conversion signals, honing creative content, and fortifying data systems, I ensure our performance remains robust.

The performance gap now relies on the quality of signals, making audience engineering pivotal for success.

The Three Levers that Now Drive Targeting

I focus on optimizing these three crucial AI inputs to ensure effective audience segmentation:

1. Conversion Signal Quality

By providing the algorithm with relevant business outcomes rather than superficial metrics, I encourage it to find results that truly matter.

Using tools like Offline Conversion Imports (OCI) and the Conversions API (CAPI), I ensure our data highlights genuine sales by leveraging value-based bidding techniques.

2. Creative as a Targeting Mechanism

With no demographic filters, my creative content now acts as the primary targeting tool, filtering users through its message.

If my creative targets niche pain points, the AI connects with users aligned with that perspective, even without traditional filters.

3. First-Party Data as Competitive Moat

Our customer lists and engagement signals become core learning elements for the algorithm, replacing third-party signals and offering a competitive edge.

Essentially, I’m arming the AI with a guide to discover the most profitable audiences.

How This Plays Out in Real Campaigns

The journey to AI-led targeting isn’t just theoretical. Within our agency, managing over $215 million in media spend annually, we have evaluated this approach across different platforms, witnessing its power firsthand.

Advantage+ Audiences in Practice

One long-standing client had a specific perception of their audience based on a vast history of accurate data. Initially, our campaigns ran with tightly controlled targeting to maintain efficiency.

Transitioning to Advantage+ allowed for data-driven optimization, revealing an unexpectedly lucrative older demographic, improving their click-through rates by 37% and conversion rates immensely.

Broader AI-optimized targeting cut costs and raised revenue — outperforming past manual methods.

By aligning goals with data and creative, we found valuable segments conventional targeting schemes previously overlooked.

Microsoft PMax Placement Transparency and Advanced Audience Signal Targeting

Another client benefited from a Microsoft PMax test, effectively targeting high-intent prospects using internal data across several Microsoft networks, seeing notable increases in performance metrics each month.

This trial highlighted the importance of combining strategic oversight with smart AI deployment, enhancing the algorithm’s reach while maintaining disciplined campaign direction.

The balance between scale and strategic input preserved efficiency and bolstered overall performance.

The Risks Nobody is Talking Enough About

While automated targeting offers significant advantages, it’s essential to understand its limitations. Here’s what I strive to avoid:

Garbage In, Garbage Out

Poorly defined conversion objectives, weak data quality, or junk data hinder performance and mislead the algorithm. Feeding it quality information and focused outcomes is crucial.

An overly broad goal without distinct signals results in quantity over quality, which doesn’t necessarily translate to business success.

The Self-Reinforcement Trap

If the seed data has biases, the AI will continuously optimize for those biases, possibly neglecting valuable audience segments.

These underrecognized biases present inherent risks in leveraging automated systems without mindfulness.

Automation Without Oversight

Platforms promote broad automation, but I recognize the need for continued oversight to realign campaigns with business goals.

Constant monitoring is essential to ensure objectives are met, avoiding a passive management style.

Creative Complacency

As automation advances, creative strategy becomes a crucial differentiator and shouldn’t be neglected.

Crafting compelling creative that addresses core customer issues is vital in distinctively standing out.

How to Put Audience Engineering into Practice

Here’s how I integrate audience engineering into everyday operations:
- Audit Conversion Events: Ensure conversion signals mirror authentic business achievements, prioritizing revenues.
- Restructure Creative: Focus on intent signals, addressing what beliefs inspire conversion.
- Predefine Guardrails: Establish performance boundaries before unleashing the algorithm, allowing for better campaign control.
The Future Belongs to Audience Engineers

The era of manual targeting is closing, but precision remains crucial. Audience engineering acts as an invaluable skill, unlocking AI’s full potential to achieve maximum results in this dynamic landscape.

Inspired by this post on Search Engine Land.
April 8, 2026
How Clarity Beats Creativity in ChatGPT Ad Performance
I recently delved into an intriguing analysis by Adthena, which examined over 40,000 daily ChatGPT ad placements. What stood out to me was how these ads are evolving into a streamlined, high-intent messaging format, specifically tailored for users who are already deep in the decision-making process.

The big picture: ChatGPT ads are gravitating towards a style that’s concise, well-structured, and highly contextual. This approach emphasizes precision over persuasion, signaling a shift from traditional creative advertising to real-time, intent-driven assistance.

By the numbers:
- The average headline is just 30 characters long, consisting of about 5 words.
- Body copy averages 116 characters and roughly 19 words.
This makes it clear that every word needs to be purposeful, enhancing clarity or directly driving conversion.

What’s working: The dominant pattern I observed involves a “Brand: Benefit” headline structure, which clearly delineates the brand name from the value proposition. This works well because users in conversational settings prioritize immediate clarity over intrigue.

In this environment, brand recall is essential, especially as ads often start with the brand name—ideal for users evaluating rather than discovering options.

Headlines have become succinct, resembling functional labels more than traditional slogans. This brevity continues in the body copy, usually composed of two concise sentences: one proving a point and another offering a subtle prompt.

Context mirroring has emerged as a distinguishing feature. The best ads expertly reflect a user’s query or environment, suggesting real-time message tailoring—a level of AI-native targeting that transcends basic keyword matching.

Concrete value signals are vital. The dollar symbol and specific numerical claims, such as prices or performance metrics, significantly outperform generic promises. Numbers naturally instill credibility, which is crucial in a context where users are actively researching and comparing.

Low-friction offers—like trials or demos described with the word “free”—are the most effective conversion drivers. They lower the commitment threshold for users still exploring options.

Calls to action are direct and action-focused, using phrases like “Shop now,” “Compare,” or “Book,” steering away from generic prompts like “Learn more.”

The overall tone is calm, confident, and measured, with minimal punctuation like exclamation points or question marks. This aligns more with the voice of helpful guidance than traditional advertising hype, allowing ads to blend naturally into conversational contexts.

Why we care: ChatGPT ads target users with high intent, where clarity and relevance trump creativity or storytelling. In a conversational space, ads compete against genuinely helpful answers, so precise and value-driven copy truly stands out.

This brings advantages to early adopters as the format becomes standardized, rewarding those who use shorter, structured messaging.

Between the lines: While ChatGPT ads share characteristics with paid search—focused on intent and relevance—they must seamlessly fit into dialogues, respond to users with high intent, and present messages that feel supportive rather than disruptive.

The takeaway is that success in ChatGPT advertising increasingly relies on precision, relevance, and credibility over emotion or brand storytelling. Achieving this means perfectly integrating at the moment when users need clear, trustworthy information.

Dig deeper: Check out the complete infographic shared by Adthena CMO Alex Fletcher on LinkedIn.

Inspired by this post on Search Engine Land.
April 2, 2026

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