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.
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.
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.
When I think about auditing an agency to find a genuine growth partner, I am often reminded of how many agencies sound the same at first glance. Yet, when we dig deeper, the real differences can be stark, particularly in their methods of optimization, measurement, and scaling.
As a seasoned performance marketing head at an agency, I frequently encounter agencies offering account audits during their sales pitch. Their goal is usually twofold: to deliver immediate value and to showcase their expertise.
But, in my experience, brand marketers seldom reverse roles to audit these agencies during the Request for Proposal (RFP) process. Over the years, I’ve noticed many brands settling for mediocrity simply because they aren’t equipped with the right questions to unearth the weaknesses in a potential partner’s strategy.
If I were a brand, eager to secure a true growth partner, these are the questions I’d make sure to ask.
1. What are your key services, and what percentage of your clients utilize each? I’ve seen many agencies claim they offer ‘full service,’ but true execution excellence is rare. I’d scrutinize where they truly focus their time and efforts. This not only includes channel proficiency but how their strengths align with our brand’s needs.
2. How are you approaching AI-driven account optimization and platform automation? Gone are the days when manual controls set us apart as high-performing marketers. Understanding how an agency balances AI automation without over-reliance is crucial.
3. What is your reporting process, and what KPIs do you focus on for the majority of your clients? A mere sample report won’t do. I need to comprehend their data philosophy, especially if it centers around revenue and ROAS metrics.
4. What’s the average industry tenure of the team on my account? A common query, yet crucial for understanding their ability to retain experienced professionals who leverage AI tools adeptly.
5. How is your team using AI on client accounts? Striking a balance in AI usage is essential. I prefer teams that use AI wisely for operational efficiency without sacrificing strategic insights and creativity.
6. When you take over an account, what are the first things you do to save budget without affecting growth? This is a litmus test of their technical proficiency, focusing on identifying and eliminating budget waste efficiently.
Ultimately, to distinguish a true growth partner from others, I focus on their service utilization rates, tactical AI applications, and budget efficiency approaches. These considerations help identify a partner ready to deliver genuine performance rather than just manage our budget.
I’ve recently discovered some exciting news from Google that’s perfect for those of us who rely on their ad tools and measurement resources. Google has just launched a developer hub that’s set to make our tech-driven advertising tasks a lot smoother.
The new Developer Hub centralizes everything into one easy-to-navigate destination, which promises to simplify our experience when building, automating, and scaling ad campaigns.
What’s Happening. Google is introducing the Advertising and Measurement Developers Hub. This centralized site is designed to give us seamless access to an array of tools and resources across their ad ecosystem. Say goodbye to hunting for documentation in multiple places!
The Hub organizes resources for products like the Google Ads API, Google Analytics, and publisher tools such as AdMob and Google Ad Manager into convenient categories including advertising, tagging, and measurement.
How It Works. It features a streamlined homepage where I can quickly access documentation, blog updates, and community channels. Plus, there are dedicated sections to explore products, connect with support, and engage with Google’s developer relations team.
Why We Care. For anyone deep into using Google’s tools, like me, this is a game-changer. The ease of access to advanced tools for automation, tracking, and optimizing campaigns can really boost efficiency. This new hub makes it nearly effortless to take advantage of Google’s robust ad tech ecosystem.
The Big Picture. As our advertising efforts increasingly lean on automation and APIs, Google is bolstering the infrastructure to support developers and technical users managing complex integrations.
Zoom In. New features I think are worth noting include a ‘meet the team’ section, a centralized support page with links to Discord and GitHub resources, and a media hub featuring content like Ads DevCast.
What to Watch. It’ll be interesting to see if this hub becomes the go-to entry point for developers across Google’s ad products, especially as more AI and measurement tools roll out.
Bottom Line. Google is betting big on developer support with this hub, anticipating that it will drive innovation and adoption within its ad tech ecosystem.
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.
As someone who manages ad campaigns across various platforms, I’m thrilled to share that Meta has launched a new template for Google Tag Manager! This makes setting up the Pixel incredibly simple, ensuring smoother cross-platform tracking with more consistency for advertisers like us.
Meta Platforms is committed to reducing the technical challenges we face, especially when juggling campaigns on different platforms. This new update is a step towards minimizing those hurdles.
What’s happening. Meta has unveiled an official Pixel template within Google Tag Manager. This effectively replaces the need to rely on third-party or community-generated solutions.
How it works. This template takes advantage of our existing GA4 dataLayer, allowing us to utilize pre-configured events for Google Analytics 4 without needing to rebuild our tracking systems. It also makes mapping enhanced e-commerce events automatic, such as purchases and add-to-cart actions, which means we don’t have to worry about redundant tagging.
Why we care. The simplified setup reduces the time we spend implementing these systems while lowering the risk of tracking errors. This ensures our campaigns run smoothly across Google and Meta platforms.
What to watch. I’m curious to see if this user-friendly setup encourages more advertisers to adopt Meta Pixel tracking and whether it will lead to similar integrations in the future.
Bottom line. By removing one of the biggest pain points in ad tracking, Meta is making it quicker and simpler for us to gain reliable insights across various platforms.
First seen. This update was discovered by Paid Media expert Thomas Eccel, who highlighted it on LinkedIn.
I’m thrilled to share that Google has launched a groundbreaking onboarding guide for its Universal Commerce Protocol (UCP). This new system marks a significant shift towards integrating seamless checkout experiences directly within search. It’s a game-changer for advertisers and merchants alike.
Google is setting the stage for what they call ‘agentic commerce,’ where I can see purchases happening right in the AI-driven search moments. It’s all about making the buying process smoother and more intuitive for users like me.
What’s happening. Google has unveiled a detailed onboarding guide for the Universal Commerce Protocol (UCP) in Merchant Center. This guide shows merchants how to integrate with UCP, which allows checkout directly from product listings in AI Mode and Gemini. I find this incredibly useful in streamlining my customer journey.
The big picture. With AI search evolving into transaction facilitation, Google aims to keep users like me engaged by embedding shopping and checkout into conversational experiences. It’s all about keeping us within their ecosystem.
How it works. Before jumping in, merchants need to complete a technical integration and submit an interest form. After getting approval, they can access onboarding tools in Google Merchant Center. This includes a testing sandbox, identity linking, and checkout APIs — tools that I find essential for successful integration.
Why we care. Google’s move of aligning search closer to transactions means that I, as a user, might complete my purchases directly inside AI interactions rather than visiting separate websites. This could redefine how we measure, attribute, and optimize our advertising performance. Early adopters of the Universal Commerce Protocol could gain a competitive advantage as shopping becomes more integrated into AI tools like Gemini.
Zoom in. The protocol acts as an open standard, connecting product data, user identity, and payment flows. I’m excited about making seamless purchases without any redirection to external sites.
What to watch: The rollout is gradual and currently limited to the U.S. I should keep an eye out for a dedicated UCP integration tab appearing in Merchant Center accounts in the coming months.
Bottom line. If widely adopted, the Universal Commerce Protocol could transform online shopping, making search a complete, AI-powered checkout experience. I hope to see this fully integrated soon.
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:
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.
I’m excited to share some fantastic news for advertisers using Google Ads! They’ve introduced a new feature that lets us scale AI-generated ads quickly while keeping our brand’s voice consistent and under our creative control.
Google is granting us more influence over AI-generated ad copy, paving the way for us to expand our campaigns efficiently without compromising our brand consistency.
What’s happening: Google Ads is testing a beta feature where we can reuse text guidelines from existing campaigns. This means we don’t have to start from scratch each time, simplifying the process of maintaining brand rules.
How it works: With just one click, I can apply the approved tone, style, and messaging rules from one campaign to another, keeping AI-generated ads on-brand and cutting down on setup time.
Why we care: This feature is a game-changer, allowing me to launch campaigns faster while ensuring brand consistency across various accounts with multiple campaigns running at once.
Between the lines: It’s clear there’s an increasing demand among us marketers to “train” AI systems. This shift allows us to turn brand guidelines into reusable inputs, steering automation with more precision.
Bottom line: AI is accelerating the ad creation process, but what sets us apart is maintaining control, and Google is starting to return more of that control to us advertisers.
First spotted: This update first came to my attention through Paid Media expert Arpan Banerjee, who shared his find on LinkedIn.
