I’m seeing OpenAI continue to build out ChatGPT Ads with a new round of updates for advertisers. In an email, ChatGPT Ads announced changes across ChatGPT Ads Manager and the broader ad experience, including custom audiences, a new overview tab, suggested ad drafts, a refreshed static ad card format, and expanded availability in Japan and South Korea.
Here is what stands out to me from the latest update.
Custom audiences: I can now upload audience lists with 25,000 or more users to include or suppress audiences from campaigns. OpenAI is also allowing bid multipliers for audiences at the ad group level, which gives advertisers more control over how aggressively they want to reach specific segments.
Overview tab: The new overview tab gives me a more centralized place to monitor account health, review recommended tasks that may improve campaign performance, and analyze key performance metrics in a larger, more flexible trend chart.
A before-and-after look at ChatGPT's refreshed static ad card, turning a small sponsored grocery prompt into a cleaner, more readable format with larger visuals and a clear Ad badge.
Suggested ad drafts: If a campaign needs broader content coverage to improve delivery, I may see an option to select “Add new ad” from the campaign view. This feature uses existing website metadata to prefill an ad draft with an image, title, and description, which I can then review, edit, and assign to a campaign and ad group. Importantly, OpenAI says this does not generate new copy or imagery with AI.
Japan and South Korea expansion: ChatGPT Ads are now live in Japan and South Korea. That means campaigns can target users in both markets, giving advertisers more reach if they do business there.
Refreshed static ad card format: OpenAI is also rolling out a refreshed static ad card across web and mobile. I see this as a cleaner, more compact format designed to be easier to read while giving visuals more prominence. This format had already started appearing in late June.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Why I care: ChatGPT Ads are still new, and OpenAI is clearly moving quickly. New targeting tools, reporting views, draft workflows, market expansion, and format tests all point to a platform that is still taking shape.
My takeaway is simple: I need to keep watching these changes closely, test them as they become available, and continue refining ad creative, audience strategy, and campaign structure as ChatGPT Ads matures.
I am seeing OpenAI roll out a new feature that lets ChatGPT Ads generate ads for advertisers, and I suspect AI is doing the heavy lifting behind it. The option appears under “Add new ad” and includes a prompt to “generate ads for you.”
From there, I can choose to let ChatGPT create the ad, then review it, edit it, and approve it before it goes live on the ChatGPT Ads platform.
ChatGPT Ads Manager preview highlights OpenAI's generated ad workflow, where marketers can review an AI-created variation before activating it for a campaign.
What it looks like. Anthony Higman posted a screenshot of the feature on X, showing how the ad creation flow appears inside the platform.
A ChatGPT Ads dropdown highlights the quick Duplicate Ad option, pointing marketers to a faster way to copy an existing ad for review, edits, and reuse.
In the screenshot, the interface says, “We generated an ad variation based on your website and campaign settings. Review, edit as needed, and activate when you’re ready.” I can then move forward by selecting “Review and create.”
Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.
I also noticed that Higman spotted a quick duplicate ad option, which could make it easier to create variations faster.
Why I care. It makes sense to me that OpenAI would use AI to help advertisers create ads more quickly. If the tool reduces friction, it could lead to more ads being created, submitted, and activated on ChatGPT Ads, which would also help OpenAI generate more revenue from ChatGPT.
As a marketer, I would still be careful with AI-generated ads. I would review every version closely to make sure the messaging fits the brand, supports the campaign strategy, and aligns with performance goals, including ROI.
I’m watching a small but meaningful Google Search ads experiment that could change how people notice paid results. Google is testing labels that call out the ads it believes are most relevant to a user’s search query, which could affect both user trust and advertiser performance.
What’s happening. Google has started testing new Search ads labels such as “Strongest match” and “Strong match” on select ads in search results. Google Ads Liaison Ginny Marvin confirmed the experiment and said the labels are meant to help users quickly spot ads that closely match their search intent.
For now, I see this as a limited test. Google says it is only appearing for a small percentage of users in the U.S., so most advertisers may not notice it in the wild yet.
Why I care. This kind of visual signal could influence which ads users view as the most relevant and trustworthy. If Google expands the experiment, advertisers with stronger relevance and quality signals may gain more attention, while weaker or less aligned ads could become easier to ignore.
How it works. According to Google, these labels rely on the same ad quality and relevance signals already used inside its advertising systems. In other words, Google is not introducing a new ranking factor here. It is making its relevance assessment more visible directly in the Search results interface.
I see the goal as fairly straightforward: help users identify the ads most likely to answer what they were searching for, without making them interpret relevance entirely on their own.
Why Google is testing it. Google says the experiment is designed to improve the Search ads experience for both consumers and advertisers.
For users, the label could act as another cue that a paid result may be especially useful for their query.
For advertisers, it could help highly relevant ads stand out in front of high-intent audiences, which may lead to stronger engagement and higher click-through rates if the feature performs well.
Reading between the lines. I view this test as part of Google’s broader push to make ad relevance more visible and more understandable to searchers.
Historically, relevance signals have mostly worked behind the scenes through auctions, quality systems, and ranking logic. By showing those signals more clearly, Google may be trying to build more trust in sponsored results while also rewarding advertisers that closely match their ads to search intent.
The timing also matters. Search platforms are under ongoing pressure to prove that their ad experiences are useful, high quality, and worth users’ attention. A label like this gives Google another way to frame certain ads as more helpful, not just more prominent.
What I’m watching next. Google has emphasized that this is an early-stage experiment and has not said whether “Strongest match” or “Strong match” labels will become permanent. For now, I would treat this as another reminder that ad relevance, landing page quality, and alignment with user intent remain central to Google’s direction for Search advertising.
When I first started looking at budget allocation, I was tempted to believe that every marketing channel followed the same path: spend a little, get a lot, but with diminishing returns.
Visually, it’s easy to assume all channels mimic this pattern.
The typical log-shaped curve illustrates that the first dollar you spend is often the most productive. With this mindset, spreading the budget across numerous channels seems like the go-to strategy.
However, I quickly learned not all channels conform to this model. Some require much more than just a sprinkle of funds to be effective. These channels start with a less efficient spend but eventually pay off if given time to warm up. This condition shifts away from the usual ‘test small, scale the winners’ strategy many marketers follow.
At the core of this difference lies a fundamental question: Is the response curve C-shaped or S-shaped?
Understanding the shape of the response curve can drastically change how I conduct channel testing and measurement, especially with Google’s increasing inclination towards S-shaped campaigns.
Let’s delve into what these two curves signify and why they are crucial.
Response curves plot conversions or revenue against spend. Typically, we encounter two main types in marketing.
A C-shaped curve means diminishing returns kick in from the first dollar spent. Meanwhile, an S-shaped curve starts slow, becomes steep at the inflection point, and finally leads to saturation.
This insight is crucial for allocation because the marginal curve—the derivative—guides budget decisions. Here, shapes diverge with significant implications.
For a C-shaped curve, the highest marginal return is from the first dollar, decreasing thereafter. Conversely, for an S-shaped curve, the initial return is low, increases up to a peak, and then declines.
This aspect of increasing marginal returns is pivotal. It’s what differentiates channels with productive small budgets from those that seem inefficient but could perform better when scaled correctly.
Mainstream marketing campaigns exhibit this principle clearly. For instance, if your CPA goal is $50, the way the S-shaped channel behaves under scaling tells a critical story.
A preliminary $10,000 test may misleadingly suggest failure, but at $20,000-$25,000, the channel might be your most cost-effective choice. Small trials in the warm-up phase mislead the eventual conclusion.
This common misconception arises as many automatically rely on ‘test small, scale what works’. Yet, without sufficient testing past the warm-up phase of an S-curve, we risk dismissing channels that could have been game-changers.
For allocation logic, in C-shaped channels, going wide is beneficial. One global optimum dictates that spreading your budget thinly across many channels generally works.
But with S-shaped channels, a small budget is inadequate. Either commit enough budget to surpass the inflection point or don’t invest at all. There is a true minimum budget to ensure viability.
In marketing, determining whether a channel requires breadth or depth is critical. Channels historically leaned towards a concave shape, although modern platform dynamics have blurred these lines.
The differences are increasingly relevant with AI-driven campaigns. For example, ‘AI Max’ necessitates sufficient conversion data to learn effectively, affirming the concave-to-sigmoid shift. Campaigns like PMax blend both response types, initially concealing inefficiencies through promising headline numbers.
The key is recognizing the harvest versus create dichotomy. Harvest channels, like branded searches, display fast saturation and diminishing returns. Still, creating new demand—especially through platforms like Meta or YouTube—demands investment beyond superficial trials for truly incremental growth.
In conclusion, understanding whether to expand broadly or concentrate deeply in a specific channel can transform the efficiency of a marketing strategy.
When it comes to PPC, some of the toughest lessons aren’t about bidding strategies or keywords. It’s about knowing when to walk away from a client. On a recent episode of PPC Live The Podcast, I, Laura Abreu, a performance marketing strategist, shared a pivotal experience from early in my career that taught me invaluable lessons.
My first client was launching an ecommerce store featuring beauty products from well-known brands. On paper, it looked promising, but deep down, something felt off. The products were available at the same price elsewhere, giving consumers little reason to choose our store. Despite this, I ignored my instincts and accepted the project.
Despite our team’s best efforts with search campaigns, Meta ads, seasonal offers, and product bundles, we didn’t manage a single sale over three months. The issue wasn’t with our marketing strategies—it was the lack of a unique value proposition in the business model itself.
I’ve learned that great marketing won’t fix a weak business proposition. Engaging with a new client now involves ensuring they’ve done market validation before investments in advertising.
This experience also revealed the importance of not letting personal preferences cloud judgement in marketing. We focused heavily on creating visually appealing content without realizing that resonating with customer needs and desires is what truly drives sales.
The emotional turmoil from this misstep was profound, affecting my confidence to the point where I took a break from PPC clients. I realized I was unfairly shouldering the blame for a structural business issue beyond my control.
Setting clear expectations from the start with every client has become another cornerstone of my practice. I ensure advertising is positioned as a way to test assumptions instead of promising immediate growth. This approach helps in maintaining honest conversations and prevents misunderstandings.
I’ve also decided never to mix business with personal relationships. Working with friends and family often involves emotional challenges that can interfere with objective decision-making.
Protecting one’s reputation is crucial, especially when campaigns don’t meet expectations. Honest dialogue, even if it means discussing failures or refunding fees, is necessary to build trust, which is invaluable in our referral-driven industry.
Through auditing various PPC accounts, I often encounter the mistake of treating campaigns as “set and forget.” It’s vital to constantly refresh ad copy, scale winning creatives, and streamline lead-generation processes for better conversion rates.
AI has become a significant asset in automating routine tasks, allowing more time for strategic thinking and client interactions. However, I advise marketers to maintain human oversight to avoid the pitfalls of poor-quality AI outputs.
I’m excited to share that Google has introduced new methods for advertisers to expand their campaigns while keeping a close grasp on efficiency targets. This expansion in Smart Bidding Exploration is sure to be a game-changer.
Google is unveiling a new series of updates designed to help advertisers discover fresh demand, take advantage of seasonal opportunities, and achieve more consistent campaign performance. I’ve always valued predictable outcomes in advertising, and these updates seem to focus exactly on that.
What’s new. The enhancements include a larger scope for Smart Bidding Exploration, the introduction of a new Promotion Mode beta, and updates to bidding target optimization specifically for campaigns with limited budgets.
Driving discovery. This enhancement allows me, as an advertiser, to set a return on ad spend (ROAS) tolerance, so my campaigns can capture additional conversion opportunities from search queries that currently might be overlooked.
From what I’ve seen, campaigns utilizing this feature experience about an 18% boost in unique converting search query categories and a 19% increase in overall conversions.
This capability is now extended to Performance Max campaigns without product feeds and is being tested in beta for Shopping ads within both Performance Max and Standard Shopping campaigns.
Peak period bidding. The new Promotion Mode empowers advertisers to adjust ROAS targets temporarily and increase the daily budget during peak periods like seasonal events, new product launches, and flash sales. I think this is a fantastic tool for maximizing high-demand opportunities.
What else is changing. Starting August 17, Google will update bidding target optimization for budget-constrained campaigns with the aim of delivering more consistent performance. This aligns better with our CPA and ROAS targets, which is reassuring for me as a campaign manager.
Notifications will begin rolling out in Google Ads on July 6, alerting advertisers about recommended campaign adjustments. I appreciate such timely updates that help me stay ahead in planning.
Why we care. These advancements allow Google’s AI bidding systems to explore incremental conversions beyond our current keyword and audience settings. This potential unlock of new demand could be pivotal in redefining campaign success for me.
The Promotion Mode stands out for retailers and seasonal advertisers by enabling temporary adjustments to ROAS targets and budgets during peak periods without needing a complete campaign overhaul. Additionally, the changes in bidding optimization aim at making performances more predictable in campaigns limited by budget.
The bottom line. Google’s recent bidding updates are designed to help advertisers, like me, find new conversion opportunities, react more assertively during peak demand times, and maintain consistent performance as campaigns scale.
I realized early on that merely reducing the cost per lead does not guarantee more signed cases for a law firm. Leads and signed cases differ in significant ways.
What stands between an ad click and a signed retainer is the intake process, speed of follow-up, and ultimately, conversion. Relying solely on cost per lead to gauge PPC success means making decisions with incomplete data.
Having managed over 1,000 ad accounts for plaintiff-side law firms, I’ve witnessed the same issues repeatedly. The ads fuel activity, but leakage occurs at various stages in turning leads to clients.
Law firms that successfully increase signed cases are those that integrate their ad data with intake performance and client retention. This requires a shift in approach to keywords, budget distribution, landing pages, and tracking.
I found most law firms approach campaigns backward, starting with generic keywords like injury attorney, yielding high-volume but low-quality traffic.
By reverse-engineering our keyword strategy from signed-case data, we can protect budgets and increase conversions. Instead of defaulting to Google’s suggestions, we analyze call transcripts and CRM records to find the actual language leading to retained clients.
Over time, I’ve become adept at identifying exact phrase-match terms potential clients use, like “truck accident lawyer near me” or “wrongful death law firm Tampa.”
It’s crucial to segment every keyword by funnel stage and intent. By allocating budget to high-intent terms and testing or excluding low-intent ones, we fine-tune our ad spend.
Integrating the search terms report into my workflow is the cornerstone of effective PPC management. This report reveals the precise phrases used before ad clicks, helping decide whether a lead is worth the cost. Continuous weekly reviews keep the campaign spend efficient.
Instead of treating Google Ads as a single entity, segmenting campaigns by funnel stage, intent, budget, and conversion objectives significantly improves ROI.
According to Pareto Legal’s report, Local Services Ads are the top-converting channel for personal injury firms. They’re pay-per-lead and don’t need a landing page setup. (I’m the CEO and co-founder of Pareto Legal.)
A simple yet effective adjustment we frequently make is refining LSA category selections to more precise case types like personal injury or motor vehicle accidents.
Mid-funnel incorporates non-brand searches and Dynamic Search Ads, evaluated on the rate of qualified leads rather than sheer volume. Too many unqualified leads can drain the budget, even if the cost seems reasonable.
Strategies involving Meta and YouTube retargeting work well post-website visitations. These should expand to cold audiences only when incremental lift is proven through accurate attribution.
Consider this simple framework to dramatically boost your PPC results. For instance, one injury firm achieved 273 signed cases from $765,000 without increasing the budget, just by restructuring Google Ads.
As I discovered, sending paid traffic to mismatched pages curbs conversion rates. While effective landing pages are crucial, they remain one of the most ignored aspects of PPC management, despite being well-known.
Your aim should be relevance: Landing pages need headlines matching search intent, transparency on settlement amounts, social proof via client reviews, and immediate contact options.
These pages should load quickly and adapt to mobile screens. Each practice area and intent deserves a unique landing page design for better results.
I improved one client’s generic page by creating intent-specific pages, adding recent reviews and results, and reducing form fields, doubling conversion rates with no extra ad spend.
A significant hurdle in law firm advertising is not the cost-per-click but the deteriorating intake process. Focus should be on post-contact processes rather than CPC.
Focus on key intake KPIs such as a 90%+ answer rate, sub-60-second response times, and a signed rate of 25%-40% of qualified leads.
Consider this: Spending $20,000 monthly at $250 per lead gets 80 leads. With optimal response and conversion, 30 cases can emerge from the same spend, vastly enhancing ROI.
Ensure marketing and intake teams share KPIs, ensuring media buyers don’t act on disparate targets.
Most reporting cuts off at ad platform metrics without tapping into where the action really happens—the CRM. An integrated attribution chain from ad click to signed retainer is indispensable.
Set up your attribution system: Track traffic sources through UTMs, capture call leads, monitor web behavior with Google Analytics, and track through CRMs like Lawmatics or Clio.
The keystone metric, Marketing Efficiency Ratio (MER), evaluates the marketing ecosystem rather than viewing channels separately, crucial for budget confidence and allocation.
I recommend a streamlined dashboard with key metrics—spend, leads, qualified leads, signed cases, CPL, CPA—segmented by both channel and practice area.
Without granular reporting capability, your data might only be serving as an overview. Leveraging this tracking structure highlights effective campaigns that improve ROI sustainably.
The law firms thriving with PPC are those recognizing PPC as a comprehensive system. They apply precise keyword targeting, allocate budgets by intent, regularly scrutinize search terms, understand cost per case over cost per click, and connect ad clicks to results that matter.
As I delve deeper into the world of ad platforms, it’s fascinating to see how algorithms are transforming the landscape—putting a spotlight on marketers and their expertise.
Managing ad accounts today is a whole new ball game compared to just a few years back. With automation taking center stage through tools like Meta’s Advantage+ and Google’s Performance Max, the skills needed for effective account management have evolved significantly.
Recently, I’ve conducted numerous B2B account audits, for companies both in-house and those transitioning from agencies. It’s striking how rare it is to find operators who truly grasp these systems.
Even with advanced technology at our fingertips, many proficient B2B marketers continue to make costly errors. Here are the frequent mistakes I’ve identified recently on LinkedIn and Google.
Mistakes Advertisers Are Still Making on LinkedIn
LinkedIn is indispensable for B2B outreach, yet some advertisers persist with avoidable blunders like these.
1. Ignoring Audience Targeting
Shockingly, I still see this rookie error. Ads end up targeting entry-level individuals, students, and irrelevant businesses instead of the intended audience.
2. Neglecting to Adjust Targeting Over Time
LinkedIn’s professional targeting can be excellent, but not infallible. It often lets unrelated roles and titles slip through, wasting budget on irrelevant audiences.
3. Over-relying on Automated Settings
This is more common than a mere oversight. Keeping options like audience expansion active leads to ads appearing in less relevant, low-quality spots.
4. Stagnating Creative Content
Veteran LinkedIn marketers know that LinkedIn’s ad structure complicates creative testing, emphasizing the need for creative refreshes and theme variations.
Here are the top pitfalls I’ve discovered in Google Ads accounts recently.
5. Using Conversion Strategies Without Adequate Data
Ad accounts employing conversion-based strategies without any conversion data often mislead algorithms, targeting the cheapest and least effective actions.
6. Defaulting to Display and Search Partners
This blunder reallocates budget from high-intent searches to less effective, lower-quality placements, wasting resources.
7. Forgetting Sitelinks
Omitting sitelinks diminishes the potential to occupy more space in search results, reducing appeal to potential customers.
8. Ignoring Non-Converting Keywords
Failure to scrutinize and modify ads for non-converting keywords results in wasted investment.
9. Overusing Broad Match
Exclusively using broad match without employing phrase or exact match can lead to unnecessary expenditure.
10. Unrestricted PMax Utilization
While PMax for B2B has evolved, neglecting enhanced conversions and educational setup is a recipe for low-quality leads.
11. Discounting AI Max Testing
With Google constantly innovating ad serving and optimization through AI, early adoption can offer a competitive advantage.
Critical gaps often occur post-data collection, where follow-up sequences and accurate data linking are necessary to optimize Return on Ad Spend (ROAS).
Focusing solely on past practices like granular negative keywords without adapting to newer automation capabilities highlights inefficiencies.
Mistakes might appear minor, but they signal deeper mismanagement when left unattended, stressing the need for skilled supervision even in an automated environment.
Remember, effective ad management still necessitates quality inputs, accurate measurement, and vigilant oversight.
Hey there! I’ve got exciting news for advertisers like me who are constantly looking for better ways to fine-tune our Google Ads campaigns. Google has just introduced new Performance Max asset testing tools that make it easier to analyze creative performance and make data-driven decisions.
Google’s latest update is all about expanding our ability to experiment with Performance Max. Now, I can test creative assets and measure campaign performance more effectively before committing to large-scale changes.
What’s new? Google is enhancing how I run asset experiments in Performance Max campaigns. This update lets me test different creative assets to see which ones drive the best results.
The new feature allows me to compare entirely new asset groups, assess the impact of adding individual assets, or even measure how seasonal content stacks up against evergreen creatives.
I can also test assets generated through Google’s Asset Studio, opening up even more possibilities for creative experiments.
The bigger picture. While Performance Max has automated many aspects of campaign optimization across Google’s inventory, the real challenge has been understanding how creative changes impact results.
The new experiments provide a more controlled environment for evaluating creative decisions before rolling them out across all my campaigns.
Cutting through the noise. With an additional success metric, I can balance multiple objectives—like maximizing conversions and maintaining efficiency targets—by evaluating broader campaign performance rather than relying on a single KPI.
What to look out for:
All experiments, including conversion lift studies, are centralized under one Experiments page.
More experiment and measurement capabilities are on the way.
Support for manager accounts (MCCs) and the Google Ads API will start rolling out soon.
Why it matters. Creative assets are crucial in Performance Max campaigns, but testing new assets always carries some risk. With these new tools, I can validate my creative decisions using data before fully committing any budget.
Stay ahead of the curve. As Google continues to invest in automation and AI-generated creative, asset testing becomes even more vital. Being able to compare human-crafted, seasonal, and AI-generated assets provides deeper insights into what excels in Performance Max campaigns.
The takeaway. Google is empowering Performance Max advertisers like myself with sophisticated testing capabilities. I find it easier than ever to evaluate creative changes, measure results across multiple KPIs, and manage experiments from one place.
First sighted by. This update was first spotted by PPC News Feed.
I’ve realized that chasing the perfect PPC budget split can be a never-ending task. Fixed budget ratios often struggle to withstand real-world scenarios, which is why I’ve learned to assess funnel health and adjust spending as market dynamics evolve.
Most PPC budget discussions revolve around balancing brand awareness with conversion-driven campaigns, but I’ve found that this is often not the ultimate goal.
In my experience, the ideal balance is subject to constant change, influenced by our business stage, market saturation, seasonality, competitive pressures, and revenue goals.
Yet, I’ve noticed that many teams treat funnel splits as fixed decisions—set it and forget it. While it might work today, it could be completely inappropriate in six months.
Budget conversations often lead to debates: should we reduce brand awareness spend since it doesn’t convert directly, or are we risking future pipeline issues if we only focus on conversions?
Both viewpoints have merit, which makes these decisions challenging for us.
The Lower Funnel Case is Simple
When I think about the lower funnel, Shopping, Performance Max, and high-intent Search come to mind.
A term like “buy running shoes new york” signifies a ready-to-purchase mindset. Shopping categorically showcases the right product, while PMax exploits the conversion signals across all Google surfaces. The attributions are clear, ROAS is apparent, and this delights the CFO.
But I understand that these campaigns only capitalize on existing demand—they don’t generate new demand. Each conversion is fed by awareness sparked elsewhere:
A YouTube pre-roll.
A friend’s endorsement.
A social media post.
Years of brand presence.
I feel like I’m just picking fruit from a tree I didn’t plant.
Search is unique as it serves both ends of the funnel. For instance, a query like “best running shoes for marathon training” is more informational.
The individual is investigating rather than purchasing. With AI Max and broad match expansion, Google Ads pushes Search campaigns deeper into this space, enabling Search to straddle both ends of the funnel based on its configuration and captured queries.
It’s something I regularly review: Is our Search spend closing existing demand, or are we engaging with prospects earlier in their journey?
This strategy holds until it falters, often with slow warnings of decline.
Branded search volumes may stagnate, CPCs soar for core terms, and new customer acquisition rates may plateau as retention remains stable—symptoms of a brand living off existing demand without revitalizing it.
Lower-funnel efficiency is real, yet it counters future growth.
I’ve encountered issues quite specific to resellers and multi-brand ecommerce that don’t get enough attention.
If I sell branded products not owned by my organization, our lower funnel might perform well short-term.
Shopping and Search campaigns do wonders for established brands since brand owners have taken care of awareness. I’m simply reaping the demand built by major brands like Nike or Adidas.
Yet, I lack control over that demand. If a brand cuts back on marketing, exits the market, or loses relevance, our Shopping and Search performance suffers.
The ability to counter such shifts is hampered by the absent demand to harvest.
This predicament requires us to prioritize two strategic imperatives, something often overlooked.
Own-brand expansion: Allowing us to retain control and invest in independent awareness.
Enhancing reseller brand: By upping upper-funnel visibility, customers will recognize our name as a destination for all brands we offer.
Both strategies entail upper-funnel spending. Creating our brand necessitates campaigns to elevate product recognition. Building a reseller brand requires enduring efforts in Demand Gen, YouTube, and Display to ensure our brand is integral to the category, beyond individual brands. This applies beyond Google’s ecosystem.
Ultimately, these investments will not manifest in the short-term ROAS report but will signify next year’s resilience in business.
Upper Funnel as Inventory Management
I often see brand awareness spend as the uncertain, tough-to-quantify budget segment, earmarked for leftover funds. This perspective, however, is misplaced.
Investing in the upper funnel is about creating a pool of future converters. Every Demand Gen ad impression on YouTube or Google Display isn’t a wasted effort—it’s a potential high-intent search opportunity in coming weeks, nurturing the top of the funnel for Shopping and Search endeavors to reap later.
Google’s Demand Gen campaigns effectively highlight this throughout a single platform. I use Demand Gen to engage with audiences unfamiliar with our brand, then track Search impression shares and query volumes that surge in subsequent weeks. This lag is both tangible and trackable.
Upper-funnel spending impacts lower-funnel effectiveness the next month, not immediately. This delay prompts cuts when budgets shrink, causing impacts six to eight weeks later rather than instantly.
For effective demand management, I consider upper-funnel campaigns as pipeline investments. The central question isn’t “What is the ROAS on this campaign?” but rather “How much qualified demand is being generated for my Shopping and Search strategies to convert?”
Fixed rules like the 70/30 or 60/40 I often see are merely broad averages seen across different businesses and contexts. They’re decent starting points but poor long-term strategies.
I must account for what affects the optimal split.
Introducing a new product entails a robust upper-funnel effort given the minimal brand awareness.
Even mature products in competitive fields require the same, due to shared high-intent search pools with rivals—expanding the pool is the only growth method.
Seasonal ventures make it essential to complete upper-funnel efforts before peaks, as urgent awareness builds are ineffective in-season.
Conversely, when we face financial constraints or urgent revenue goals, patience for an eight-week upper-funnel maturation isn’t possible. In such cases, focusing on the lower funnel becomes necessary, accepting inevitable drawbacks while planning future awareness investments as pressures ease.
In essence, both choices are appropriate given context. A set split disregards context entirely.
Formulating a Dynamic Budget Split
Rather than adhering to fixed ratios, I advocate establishing criteria that trigger budget adjustments where needed.
Increase upper-funnel focus when:
Branded search remains static or declines over quarters.
New customer acquisition costs increase, while retention holds.
We’re entering new markets or launching new products.
Competitors significantly amplify brand presence.
We’re nearing peak season with ample preparation time.
Reselling top brands with dwindling search interest or decreased active marketing.
Emphasize the lower funnel when:
Immediate revenue targets cannot wait.
The upper-funnel campaigns begin showing measurable awareness, indicating readiness for conversion.
Shopping or Search costs per acquisition fall below target, justifying scaling.
Demand Gen audience reach saturates, indicating repetitive reach instead of expansion.
Within Google Ads, the necessary data for monitoring this is accessible without additional tools. Trends in branded query and impression share on non-branded terms, along with Demand Gen metrics and customer segmentation data, provide a comprehensive view of funnel health.
Consistent review is as critical as the metrics themselves. I aim for at least monthly funnel split reviews—quarterly rounds are often too infrequent. By the time quarterly evaluations reveal declining branded queries, vital pipeline time has already been lost.
The conversation on funnel balance isn’t typically a matter of analytics—it’s political.
In meetings, lower-funnel spending is easy to defend thanks to visible ROAS and conversion statistics. Conversely, arguing for upper-funnel spending involves creating narratives about future campaign efficacy—a trickier sell under pressure.
Rather than avoiding this justification, I focus on changing the evidence basis.
Tracking branded search volumes as predictive indicators.
Ploy a view integrating Demand Gen and Search conversions over time.
Making lag times distinct, showing evident relationships.
Ultimately, budget allocation isn’t static but a reflection of growth strategies.
Choosing to optimize solely for current ROAS is one decision; investing in future demand drivers another.
For resellers, it also entails whether the business base is self-controlled or rented from brand owners with independent priorities.
I believe the best PPC ventures strike a balance, knowing strategically when to shift focus.
