I’ve recently delved into Google’s exciting release of Ads API version 24.1, and it’s packed with valuable updates for advertisers. This version brings us advanced reporting capabilities, expanded AI campaign testing, and improved security measures.
In this update, Google has prepared us for their upcoming data retention policy changes, which will commence next year—something I believe every developer should be ready for.
Why we care. The latest release highlights three crucial areas: performance visibility, creative control, and testing automation, which are becoming vital for advertisers like me.
What’s more, brands now have greater control over creative displays in Demand Gen campaigns, overcoming the typical limits imposed by automation. It’s a significant update that I’m excited to explore further.
Those of us who lean heavily on reporting infrastructures should also be mindful of Google’s impending 37-month data retention limit, set to impact historical performance analysis come 2026.
Mobile reporting gets more granular. One of the features I’m most thrilled about is the new mobile device platform segment that allows for reporting by operating system.
With the new segments.mobile_device_platform field, I’m able to differentiate performance across iOS and Android, a game-changer for app marketers and ecommerce advertisers alike.
Demand Gen adds classic image support. I love how Google is providing us with more creative control in Demand Gen campaigns, specifically through the classic_display_images field.
This new field allows us to upload and display static image ads exactly as designed, which is perfect for maintaining branding consistency without AI alterations.
Passkeys come to Google Ads. Security is always a top concern of mine, so I’m pleased to see the inclusion of the passkey_enabled field to boost account security through passwordless authentication.
Experiment support expands. I’ve noticed that Google has significantly enhanced the support for Experiments, allowing us to run and analyze tests across AI Max, Video, Demand Gen, and Performance Max campaigns.
This update also enables us to view metrics such as clicks and conversions more transparently, making experiment analysis straightforward and insightful.
A major data retention change is coming. From June 1st, Google Ads and related APIs will enforce a 37-month data retention limit, something I must prepare for to avoid disruptions in performance analytics.
The release includes a new error code: DateRangeError.REQUESTED_DATE_GRANULARITY_NOT_SUPPORTED, and it’s essential that I update reporting workflows accordingly.
What’s next. I’ve already checked out the updated client libraries and code samples for v24.1, and I plan to participate in Google’s live walkthrough on Discord, YouTube Live, and LinkedIn Live for additional insights.
I recently came across some fascinating insights regarding ChatGPT ads. The initial data indicates these ads are showing strong engagement, particularly with queries demonstrating intent, like Mother’s Day inquiries, which are driving more ad exposure.
According to SimilarWeb, ChatGPT ads are currently outperforming traditional benchmarks in engagement. However, the existing ad inventory is quite limited, and testing has been conducted on a relatively small scale, so it’s too early to label this as a long-term trend.
What’s Happening: The early analysis highlights that ads in ChatGPT conversations generate impressive click-through rates, surpassing Display and Podcast channels, likely due to high-intent user queries and the natural integration of ads within conversational responses.
These ad placements are uniquely integrated into conversational answers, making them feel more like part of the content instead of being disruptive.
Why We Care: If these CTRs can be sustained on a larger scale, ChatGPT may evolve into a powerful performance channel, particularly valuable for advertisers aiming to connect with users during moments of high intent.
However, there’s a catch to be mindful of: the ad inventory is still restricted, and early performance metrics often appear more positive before broader rollouts introduce competition and variability.
Between the Lines: Although high CTRs are promising, they don’t automatically translate into high performance. The ultimate factors will be conversion quality, cost efficiency, and scalability to determine if ChatGPT ads can compete with established platforms like Google Ads.
Furthermore, the novelty of the format might lead to higher user engagement simply because it’s a new experience.
Zoom In: Some ad categories are showing stronger results than others. For instance, prompts related to Mother’s Day are triggering ads about three times more often than average, showcasing a strong intent to purchase. Brands like Etsy and Nordstrom are already experiencing notable visibility in this space.
What to Watch:
Whether CTRs maintain their levels as the ad inventory grows
Comparisons of conversion rates to other platforms like search and social media
Evolution of pricing models beyond the initial testing phases
Bottom Line: Although ChatGPT ads have shown strong initial engagement, advertisers should remain cautious. Until more comprehensive data regarding scale, costs, and conversions are available, it’s wise to view ChatGPT as a promising test channel rather than a stable, established one.
I’ve been closely following the fascinating world of ChatGPT ads, and I’m thrilled to share what I’ve learned. Adthena is tracking over 50,000 daily ad placements from more than 600 advertisers. Let’s dive into what this means for our campaigns.
The ChatGPT ad trial is currently live in the U.S., and it’s moving along faster than I expected. Launched on February 9 for users on Free and Go tiers, it now boasts participation from over 600 advertisers.
With a massive user base of 800 million active weekly users, it’s only a matter of time before ChatGPT ads see a global rollout.
I’ve heard from OpenAI that the next wave will expand to Australia, New Zealand, and Canada. From our trialists’ feedback, it looks like the UK might see ads by mid-May.
Tracking these ad placements since their rollout has given me a front-row seat to their evolution. With an index capturing 50,000+ daily placements across sectors like B2B software, ecommerce, fintech, and consumer verticals, here’s what I’ve discovered.
Here’s what ChatGPT ads look like: They appear inline within conversation responses. Imagine asking about the “best weekend getaway” or “top running shoes under $100,” and a sponsored result labeled “Sponsored” pops up alongside the AI’s answer.
Unlike a search bar, ChatGPT is a conversation. Users come already engaged and researching, often on the brink of a decision. The format is more concise than traditional search — just a headline, brief body, and a destination, with no sitelinks or extensions.
Here’s a surprising discovery: what I term the “Double Parked” phenomenon, where a single brand like New Balance appears twice in one ChatGPT response. This unique visibility aspect opens discussions on frequency and owning conversations on the platform.
This is a unique moment with new formats and a largely untapped landscape, presenting data that most competitors don’t yet have. Here’s what I’ve found from over 50,000 daily placements:
Headlines often follow a “Brand: Benefit” formula. Think “Betterment: 5.25% APY Cash Account.” This is a common thread among top performers.
Most ads begin with the brand name. This reflects the deeper intent of users already engaged in a conversation.
Headlines average just 30 characters, peaking at 36. This forces brief, impactful messaging.
Body copy uses around 19 words. Usually two precise sentences: one proof point, one call to action.
Context mirroring is key. Top ads reflect the user’s query directly, enhancing relevance.
The $ symbol boosts conversions. Specific financial figures and rates outperform vague promises.
Brands thriving in this space don’t just reuse their Google ad copy. They tailor ads for an engaged, decision-oriented audience. Leading with the brand, anchoring offers in specifics, and minimizing friction are essential.
The biggest consideration? Visibility. If competitors are in these conversations and you’re not, you miss more than clicks — you miss part of the dialogue itself.
Adthena’s ChatGPT Ads Intelligence offers a solution. It goes beyond impressions and clicks, giving insight into your ad presence, competitors, and performance. Here’s what to expect:
The Ads Performance tab provides live snapshots of your activity, while Topics and Keywords Analysis offers tactical recommendations for improving your ad presence.
Understand competitor strategies through Competitor Creative Analysis, and never miss shifts in the competitive landscape using the Ads Benchmarking tab. This is how you find gaps before your competitors do.
As OpenAI expands, scaling up your ChatGPT presence can provide a head start in a rapidly changing competitive field. Don’t let competitors win the first prompt. Join Adthena’s waitlist or use their free ChatGPT AdBridge to optimize your campaigns and seize the opportunity now.
I’ve recently discovered a new tool that could significantly streamline how I manage my ad campaigns. Google has rolled out a feature that adds more precision to policy appeal processes, potentially saving time and reducing the chance of resubmitting outdated ads.
Driving the news. With this update, Google now allows me to select ads from specific campaigns when requesting a re-review. This is part of Google’s effort to simplify ad appeals, reducing the bulk of unnecessary submissions that can bog down the process.
Before this change, I often found myself resubmitting all eligible ads across an account, including those from older campaigns that were not relevant to current policies.
This was not only time-consuming but also cluttered the review process with ads that hadn’t been updated yet.
What’s new. Now, with the “Select eligible campaigns” option available on the Google Ads policy violations page, I can fine-tune my appeals. This means I can send only the ads that have been recently updated, while ignoring outdated campaigns.
Here’s how this benefits me:
Reduce unnecessary inclusions of old ads,
Simplify and expedite the appeal process,
Focus on solving current ad issues effectively.
Why we care. For those of us handling large accounts, being able to fine-tune bulk submissions by campaign makes managing widespread disapprovals or policy issues more efficient. It not only speeds up the process but minimizes confusion when dealing with multiple policy amendments at the same time.
The bottom line. While it might not be a groundbreaking product launch, this update is a workflow enhancement that many advertisers like myself have long been waiting for. It offers greater control and less hassle when addressing disapproved ads.
First spotted. Hana Kobzová at PPC News Feed was the first to notice this valuable update.
I’ve been closely following the dynamic world of digital advertising, and there’s a significant shift on the horizon. Meta Platforms is expected to surpass Google in global ad revenue by 2026. This marks a move towards platforms that emphasize automation and performance.
Driving the news. According to eMarketer, Meta is predicted to generate $243.46 billion in ad revenue globally this year, just edging out Google’s projected $239.54 billion.
Meta is anticipated to capture 26.8% of global ad spending, whereas Google is estimated to hold 26.4%. This would be the first time Google has ever lost its leading position in digital ad revenue.
Why we care. Meta’s growth indicates that brands are getting more value from automated tools focused on performance. This trend may influence how brands allocate budgets between Meta and Google, reminding us that platform dynamics are evolving rapidly and media strategies need to stay flexible.
Catch up quick: In the digital advertising realm, Google has long dominated with its Search and Display ads, as well as YouTube.
However, their primary ad business is growing slower than previous years.
On the other hand, Meta’s success stems from advancements in AI-driven ad automation, enhanced performance metrics, and its vast reach across Facebook, Instagram, and WhatsApp.
Why Meta is winning now. Advertisers are focusing more on platforms that offer substantial reach and a measurable return on investment.
Meta’s strength lies in its rapid automation of creative and targeting processes, campaign optimization with minimal manual input, and easy demonstration of ROI. This approach is particularly enticing in an economic climate where marketers need to achieve more with reduced budgets.
Yes, but. Google remains a colossal entity and continues to grow.
Its search business is one of the most profitable advertising engines globally, and YouTube consistently draws in brand budgets. However, the company faces challenges from AI search advancements, antitrust scrutiny, and slowing growth in traditional search advertising.
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.
In my extensive three-decade career, I’ve witnessed keywords dominate the landscape of paid search. However, in today’s world, they have become just a part of a larger puzzle. What truly drives performance now is strategy.
I remember spending weeks meticulously researching keywords, crafting strategies around them, and managing every aspect, from bid adjustments to audience targeting. It was the foundation of success in this industry.
We used to focus heavily on precise placements, structured URLs, and audience targeting, primarily with Google’s influence leading the charge. Our profession thrived on the tactical control this model offered.
We enjoyed the ability to identify which queries triggered ads and make informed decisions to optimize budgets accordingly. Sometimes we would even segment ad groups intricately to maximize returns.
What Changed Across Platforms
Now, advertising has embraced a significant shift: automation, driven by AI, has taken over critical tasks like bidding and creative assembly. While keywords remain relevant, they serve as just one of many signals that AI systems use.
With tools like AI Max for Search, Google has transformed keywords from being the focal point to just signals in guiding ad delivery. It’s fascinating how AI now uses elements like existing keywords and landing page content to enhance performance.
Advertisers employing AI Max often experience notable gains, with some campaigns seeing up to 27% more conversions. Integrating it with other tools like Performance Max can further amplify reach across various platforms.
When I mention strategy as the new keyword, I mean focusing on specific inputs shaping ad performance. These include conversion data quality, a critical factor for systems like Google’s Smart Bidding, which relies on quality data to optimize campaigns.
We now prioritize which conversions hold the most value. It’s a shift from purely manual adjustments to strategic evaluations that highlight what truly matters for campaign success.
First-party data, enriched and well-structured, is paramount. It’s akin to the foundational keyword research of the past, vital for driving performance on today’s platforms.
Creative assets have evolved beyond mere deliverables; they’re now strategic signals that AI uses to target effectively. These visuals and messages have become an integral part of how we engage audiences.
The quality of landing pages and websites has also taken on new importance. AI determines relevance based on post-click experiences, emphasizing the need for seamless user journeys.
Our roles have adapted to these changes. It’s less about managing keywords or bids manually and more about creating strategic frameworks that guide AI systems effectively.
Subject-matter experts like us now focus on ensuring data quality, defining creative strategies, and identifying when human intervention is necessary.
We guide AI through a careful mix of conversion architecture, audience signal quality, and creative frameworks rather than traditional methods of keyword lists and bidding.
It’s crucial to understand how these advanced systems and platforms operate, as well as to emphasize the signals that matter most. Building strong first-party data and strategic frameworks will enhance AI capabilities and redefine the future.
Embracing this evolution, practitioners focusing on strategy over technical execution positions will find themselves best equipped to thrive in this changing landscape.
The keyword list remains, but our primary focus now is on strategy.
As I delve into the world of e-commerce, I’m constantly amazed by how paid search can transform business growth. Platforms like Google Shopping and Amazon Ads are game-changers, offering high conversion rates and efficient spending when campaigns are crafted thoughtfully.
These platforms are adept at capturing high-intent demand, providing the crucial data to expand my campaigns. They connect search queries directly to revenue streams, letting me pinpoint which terms are boosting sales so I can allocate my budget wisely.
However, the true test lies in organizing campaigns to effectively leverage this data.
Why does paid search excel in e-commerce? It’s all about intent and data. Google and Amazon thrive on search-driven environments. When someone seeks a product, they’re clearly expressing their needs. I don’t need to make inferences; I’m delivering precisely what customers want.
Moreover, Google Shopping and Amazon Ads offer unparalleled keyword-level revenue data. This insight helps me understand conversion rates and costs better. Amazon, in particular, shines with its granular product and category level revenue visibility.
Together, this data forms a powerful feedback loop. By analyzing which terms tie back to revenue, I can strategically shift my spending and enhance my return on ad spend (ROAS) over time. On Amazon, higher conversion rates even boost organic rankings, reducing future acquisition costs.
My success in search campaigns hinges on creating multi-funnel structures. While the concept remains consistent, execution varies based on campaign types, settings, and bidding strategies.
I implement campaign architectures that utilize wide-net, low-cost discovery initiatives to explore the search landscape. High-intent converters funnel into dedicated performance campaigns with strategic bidding. This approach not only strengthens ROAS but also enhances rankings and fosters scalable growth.
Embarking on Google Shopping, the priority sculpting method, inspired by Martin Roettgerding, is invaluable. Utilizing a three-layer campaign structure, I route keywords into distinct campaigns based on their performance.
This strategy optimizes spending on discovery keywords and directs investment toward high-performing, high-intent terms. The Google Shopping priority settings are pivotal; high-priority campaigns initially serve at lower bids.
Layer 1 focuses on capturing branded search traffic through a Performance Max campaign, maintaining an assetless format to focus on shopping inventory and avoid bleeding into other channels.
Layer 2, the catch-all, casts a wide net, experimenting with search terms to gather conversion data, while Layer 3 dedicates budget to best-performing terms, aligning with high-ROAS strategies.
Amazon’s multi-tier campaign structure offers its own set of advantages, like higher conversion rates and the intricate connection between ad spend and organic rankings. Campaigns are organized at the SKU level, employing research, ranking, and performance tiers.
Each tier serves a unique purpose, managed by differing advertising cost of sales (ACOS) targets, tailored for profitability. The research tier explores broad keyword possibilities, performance tiers maximize returns on proven converters, and ranking tiers drive organic positions aggressively.
Both Google Shopping and Amazon Ads offer unique opportunities in the e-commerce landscape. Whether aiming for short-term gains on Amazon or long-term brand building via Google, using these platforms synergistically can propel a business to new heights.
