Google says the new Trends API is opening to a “quite small” set of testers at first, with access expanding over time. The company formally announced the alpha at Search Central Live APAC.
On Bluesky, Google Search Advocate John Mueller tried to set expectations for SEO professionals, writing:
“The initial pilot is going to be quite small, the goal is to expand it over time… I wouldn’t expect the alpha/beta to be a big SEO event :)”
Google’s own announcement also describes access as “very limited” during the early phase.
What Early Testers Get
The API’s main benefit is consistent scaling.
Unlike the Trends website, which rescales results between 0 and 100 for each query set, the API returns data that stays comparable across requests.
That means you can join series, extend time ranges without re-pulling history, and compare many terms in one workflow.
Data goes back 1,800 days (about five years) and updates through two days ago. You can query daily, weekly, monthly, or yearly intervals and break results down by region and sub-region.
At the launch session, Google showed example responses that included both a scaled interest value and a separate search_interest field, indicating a raw-value style metric alongside the scaled score. Google also said the alpha will not include the “Trending Now” feature.
Why There’s High Interest
If you rely on Trends for research, the consistent scaling solves a long-standing pain point with cross-term comparisons.
You can build repeatable analyses without the “re-scaled to 100” surprises that come from changing comparator sets.
For content planning, five years of history and geo breakdowns support more reliable seasonality checks and local targeting.
Looking Ahead
The small pilot suggests Google wants feedback from different types of users. Google is prioritizing applicants who have a concrete use case and can provide feedback.
In the meantime, you can continue to use the website version while preparing for API-based comparisons later.
A peer-reviewed PNAS study finds that large language models tend to prefer content written by other LLMs when asked to choose between comparable options.
The authors say this pattern could give AI-assisted content an advantage as more product discovery and recommendations flow through AI systems.
About The Study
What the researchers tested
A team led by Walter Laurito and Jan Kulveit compared human-written and AI-written versions of the same items across three categories: marketplace product descriptions, scientific paper abstracts, and movie plot summaries.
Popular models, including GPT-3.5, GPT-4-1106, Llama-3.1-70B, Mixtral-8x22B, and Qwen2.5-72B, acted as selectors in pairwise prompts that forced a single pick.
The paper states:
“Our results show a consistent tendency for LLM-based AIs to prefer LLM-presented options. This suggests the possibility of future AI systems implicitly discriminating against humans as a class, giving AI agents and AI-assisted humans an unfair advantage.”
Key results at a glance
When GPT-4 provided the AI-written versions used in comparisons, selectors chose the AI text more often than human raters did:
Products: 89% AI preference by LLMs vs 36% by humans
Paper abstracts: 78% vs 61%
Movie summaries: 70% vs 58%
The authors also note order effects. Some models showed a tendency to pick the first option, which the study tried to reduce by swapping the order and averaging results.
Why This Matters
If marketplaces, chat assistants, or search experiences use LLMs to score or summarize listings, AI-assisted copy may be more likely to be selected in those systems.
The authors describe a potential “gate tax,” where businesses feel compelled to pay for AI writing tools to avoid being down-selected by AI evaluators. This is a marketing operations question as much as a creative one.
Limits & Questions
The human baseline in this study is small (13 research assistants) and preliminary, and pairwise choices don’t measure sales impact.
Findings may vary by prompt design, model version, domain, and text length. The mechanism behind the preference is still unclear, and the authors call for follow-up work on stylometry and mitigation techniques.
Looking ahead
If AI-mediated ranking continues to expand in commerce and content discovery, it is reasonable to consider AI assistance where it directly affects visibility.
Treat this as an experimentation lane rather than a blanket rule. Keep human writers in the loop for tone and claims, and validate with customer outcomes.
Google makes Merchant API generally available and announces plans to sunset the Content API. New features include order tracking, issue resolution, and Product Studio.
Merchant API is now generally available.
It’s now the the primary programmatic interface for Merchant Center.
Google will keep the Content API for Shopping accessible until next year.
If you lead a marketing team, chances are you’ve had this conversation:
“How are the campaigns doing?”
“Well, our ROAS is 4:1.”
The room breathes a collective sigh of relief. The good news: the marketing budget is justified (for the time being).
But here’s the problem: that number might not actually tell you anything useful.
Return on ad spend (ROAS) has long been the go-to metric for measuring paid media performance. It’s clean. It’s easy to calculate.
And let’s be honest: It looks great in a boardroom slide deck. But, that simplicity can be deceiving.
When CMOs use ROAS as the end-all be-all, it can create a warped view of what’s actually driving meaningful growth.
It often rewards short-term wins, punishes necessary investment periods, and misaligns internal and agency teams chasing vanity benchmarks instead of business results.
This article isn’t a hit piece on ROAS. It’s a reality check on meaningful key performance indicators (KPIs). ROAS can be useful, but it’s not your North Star.
And if you’re serious about long-term revenue growth, customer lifetime value, and competitive market share, it’s time to rethink what success really looks like.
Why ROAS Isn’t Always What It Seems
On paper, ROAS is straightforward: revenue divided by ad spend. Spend $10,000 and generate $40,000 in sales, and you’ve got a 4:1 ROAS.
But, under the hood, it’s not so simple.
Here are a few reasons why ROAS can often mislead:
It favors existing customers. Your branded campaigns and remarketing lists usually show sky-high ROAS, but they’re mostly capturing people already in your funnel. That’s not growth; it’s in maintenance mode.
It ignores profit margins. A $40 cost-per-acquisition (CPA) might look great in one product line and catastrophic in another. ROAS doesn’t account for your cost of goods, fulfillment, or operational costs.
It limits (actual) growth. If your only goal is to “hit ROAS,” you’ll throttle spend on upper-funnel or exploratory campaigns that could fuel future revenue.
It can be gamed. Agencies and internal teams might optimize for ROAS simply because that’s the KPI they’re judged on, even if it means saying no to high-potential but lower-efficiency campaigns.
And perhaps most importantly, ROAS often ignores timing.
You might lose money on day 1, break even by day 14, and profit significantly by day 90. But ROAS, by default, only tells you what happened in the reporting window you chose.
That’s not a North Star. That’s a snapshot in time.
ROAS Is Still Useful, If You Know When & How To Use It
Let’s be clear: ROAS isn’t bad to report on. It just needs additional context.
There are plenty of scenarios where ROAS is helpful:
Comparing performance between campaigns, channels, and platforms.
Evaluating high-volume SKU efficiency in ecommerce.
Reporting on short-term promotional campaigns.
Reviewing the efficiency of remarketing or loyalty campaigns.
The key is to treat ROAS like a diagnostic tool, not a destination. It’s one piece of the story, not the whole narrative.
When CMOs and marketing leaders make ROAS the only metric that matters, they end up over-indexing on campaigns that drive immediate revenue, often at the cost of sustainable growth.
What Should Be Your North Star Metric?
If it’s not ROAS, then what should it be?
The truth is, your North Star depends on your business model and goals. Here are a few KPI candidates that typically give a better long-term signal of paid media health.
1. Customer Lifetime Value (CLV) To CAC Ratio
This is arguably the best lens through which to evaluate your investment. If you’re acquiring customers who buy once and never return, you’ll never scale profitably.
Tracking your customer acquisition cost (CAC) against lifetime value forces you to think beyond the first purchase.
Why does this ratio matter?
CLV:CAC shows whether you’re building a sustainable business model. A healthy ratio is often around 3:1 or better, depending on your margins.
An example of how to use this metric is to look at campaign-level CAC and model projected CLV by channel or audience.
If you’re seeing CLV gains over time from specific campaigns, that’s a strong sign of durable growth.
2. Incremental Revenue
Not all revenue is created equal. Incrementality helps you understand what your paid media efforts are truly adding, not just capturing right now.
Why does this metric matter?
Paid campaigns often get credit for conversions that might have happened anyway. Branded search is a classic example. Measuring incrementality filters out that noise.
Some examples of how to use this metric include:
Set up geo-holdout tests.
Use audience exclusions.
Google and Meta’s Incrementality Testing tools.
Incrementality is not always easy to measure, but it brings clarity to where your dollars are actually making a difference.
3. Payback Period
This metric measures how long it takes for a campaign or customer to break even.
Why does this metric matter as a potential North Star?
Not every investment has to pay off instantly. But, leadership should be aligned on how long you’re willing to wait before seeing a return on investment (ROI). That transparency allows you to fund top-of-funnel efforts with more confidence.
To use this metric in practice, try tagging customer cohorts by acquisition source or campaign. Then, track how long it takes to recoup their acquisition cost through future purchases or subscription value.
4. New Customer Revenue Growth
Instead of optimizing for cheapest clicks or best ROAS, try optimizing for the growth of your new customer base.
Why does this metric matter?
It keeps your marketing focused on expanding market share, not just retargeting people who are already in your orbit.
To use this metric, start segmenting campaigns by new and returning users. You can use customer relationship management (CRM) or post-purchase tagging to see how many new users are coming in from each campaign.
The Real Problem: Misalignment Between Leadership And Execution
One of the most common breakdowns in paid media performance isn’t technical misalignment. It’s organizational misalignment.
CMOs often set ROAS goals because they’re easy to track and easy to report. But, if those goals aren’t communicated with nuance to the teams or agencies executing the campaigns, the output becomes distorted.
Here’s how this usually plays out:
A marketing leader tells the agency or in-house team they need a 5:1 ROAS to justify the budget.
The team optimizes for what’s most efficient: branded search, bottom-of-funnel retargeting, and low-risk campaigns.
Top-of-funnel campaigns get throttled, experimental audiences never see the light of day, and new customer growth stalls.
Eventually, performance plateaus. And leadership is left wondering why they’re not seeing growth, despite “great” ROAS.
This is why setting the right KPIs, and clearly communicating their intent, is not optional. It’s essential to have each team, from ideation to execution, on the same page towards the right goals.
Rethinking Your KPI Framework: What Does “Good” Look Like?
Once you move away from ROAS as your main performance indicator, the natural next question is: What do we track instead?
It’s not about throwing out the metrics you’ve used for years. You need to put them in the right order and context.
A well-thought-out KPI framework helps everyone, from your C-suite to your campaign managers, stay aligned on what you’re optimizing for and why.
Think Of KPIs As Layers, Not Silos
Not all metrics serve the same purpose. Some help guide day-to-day decisions. Others reflect long-term strategic impact. The problem starts when we treat every metric as equally important or try to roll them into one number.
ROAS might help optimize a remarketing campaign. But it tells you very little about whether your brand is growing, reaching new audiences, or acquiring customers that actually stick.
That’s why the best KPI frameworks break metrics out into three categories:
1. Short-Term KPIs: Optimization & Efficiency
These are the metrics your media buyers use every day to adjust bids, pause underperformers, and keep spend in check.
They’re meant to be directional, not definitive.
Examples include:
ROAS (by campaign or platform).
Cost per acquisition (CPA).
Click-through rate (CTR).
Conversion rate.
Impression share.
These KPIs are most useful for weekly or even daily reporting. But, they should never be the only numbers presented in a quarterly business review. They help you stay efficient, but they don’t reflect bigger outcomes.
If these metrics are the only thing being reported or discussed, your team may fall into a cycle of only optimizing what’s already working. This leads to missing opportunities to test, expand, or learn.
2. Mid-Term KPIs: Growth Momentum
These metrics show whether your marketing is actually building toward something. They’re tied to broader business goals but can still be influenced in the current quarter or campaign cycle.
Mid-term KPIs are great for monthly reviews and identifying how top- or mid-funnel investments are performing. They help you evaluate whether you’re fueling growth beyond existing audiences.
Mid-term metrics can sometimes get ignored because they’re harder to track or take longer to show impact. Don’t let imperfect data stop you from establishing benchmarks and looking at trends over time.
3. Long-Term KPIs: Strategic Business Health
This is where your true North Star lives.
These KPIs take longer to measure but reflect the outcomes that matter most: customer loyalty, sustainable revenue, and profitability.
Examples include:
Customer lifetime value (CLV).
CLV to CAC ratio.
Churn or retention rate.
Repeat purchase rate.
Gross margin by channel.
Use these metrics to evaluate the success of your marketing investments across quarters or even years. They should influence annual planning and resource allocation.
These metrics are often siloed inside CRM or finance teams. Make sure your paid media or acquisition teams have access and visibility so they can understand their long-term impact.
A KPI Framework Doesn’t Work Without Context
Even with the right metrics in place, your team won’t succeed unless they understand how to prioritize them and what success looks like.
For example, if your team knows ROAS is important, but also understands it’s not the deciding factor for scaling budget, they’re more likely to take healthy risks and test growth-oriented campaigns.
On the other hand, if they’re unsure which KPI matters most, they’ll default to optimizing what they can control, often at the expense of progress.
You don’t need a perfect attribution model to start here. You just need a shared understanding across your team and partners.
When everyone knows which KPIs matter most at each stage of the funnel, it becomes much easier to align strategy, set goals, and evaluate performance with nuance.
What CMOs Can Do Differently Starting Tomorrow
Changing how your organization approaches paid media measurement doesn’t require a complete overhaul.
But, it does take intentional conversations and a willingness to zoom out from the usual dashboard metrics.
Here are six steps you can take to shift your team (or agency) toward a more aligned and strategic direction.
1. Audit What You’re Optimizing For
Start with a gut-check: what are your internal teams or agencies truly prioritizing day to day?
Ask them to show you not just results, but the actual goals entered in-platform. Are they optimizing for purchases, leads, or something vague like clicks? Are they using ROAS targets in Smart Bidding or manually prioritizing it in their reporting?
You might be surprised how often the tactical goals don’t match the business strategy. A quick audit of campaign objectives and KPIs can uncover a lot about where misalignment begins.
If your goal is to grow market share, but your team is focused on protecting branded search ROAS, that’s a disconnect worth addressing.
2. Reset Internal Expectations
This step often gets overlooked, but it’s a big one. CFOs tend to like ROAS because it looks like a clean efficiency ratio: spend in, revenue out.
But, they don’t always see the nuance of long sales cycles, customer value over time, or the lag between impression and conversion.
Take time to walk your finance partners through your updated KPI framework. Show them examples of campaigns that had a low short-term ROAS but brought in high-value, repeat customers over time.
When leadership understands how marketing performance compounds, they’re less likely to cut budgets based on a one-week dip in return.
This is especially helpful if you’re advocating for top-of-funnel investments that take longer to pay off.
3. Educate Your Team Or Agency
Once you’ve reset internal expectations, don’t forget to bring your team or agency into the loop.
It’s not enough to just say, “We’re no longer using ROAS as our North Star.” You have to explain what you’re prioritizing instead, and why.
That might sound like:
“We’re shifting to focus on acquiring net-new customers and reducing payback period.”
“This quarter, we’re okay with lower ROAS on prospecting campaigns if we’re growing CLV in the right audience segments.”
“Let’s break out CLV:CAC reporting by campaign group so we can identify what’s really delivering long-term value.”
When you frame KPIs as tools to hit bigger business goals, your team can make smarter decisions without fear of getting penalized for not hitting an arbitrary ROAS number.
4. Separate Performance Expectations By Funnel Stage
A common mistake is holding every campaign to the same performance goal.
But the truth is, a prospecting campaign will never look as efficient as a remarketing one, and that’s fine.
Give your team or agency space to evaluate performance based on where in the funnel the campaign sits. Set realistic benchmarks for awareness, engagement, or assisted conversions, and evaluate them alongside lower-funnel ROAS or CPA.
Not only does this help you spend more confidently across the full funnel, but it also encourages the kind of creative testing that often gets squeezed out when efficiency metrics dominate.
5. Invest In Stronger Data Modeling
You don’t need to have a perfect attribution system in place to start moving beyond ROAS. You do need to improve your visibility into how customers behave over time.
Work with your team to build even a basic model of customer payback and CLV across channels.
Use what you already have: Google Analytics 4, CRM exports, or even Shopify data to start segmenting users by acquisition source and repeat value.
Over time, this will help you answer key questions like:
Which campaigns actually bring in our best long-term customers?
What’s our average time to first, second, and third purchase?
Are we over-investing in short-term wins at the expense of lifetime value?
Even directional insights can shape much better budgeting and strategy decisions over time.
6. Lead By Example In How You Talk About Performance
As a marketing leader, the way you talk about performance will set the tone for your entire team.
If you ask, “What’s our ROAS this week?” in every meeting, your team will naturally default to chasing it, regardless of whether it reflects progress toward the bigger picture.
Instead, consider asking:
“Are we growing our base of high-value customers?”
“What are we seeing with new user acquisition?”
“Which campaigns have the strongest long-term value, even if short-term ROAS is lower?”
These types of questions signal that you’re interested in more than just this week’s dashboard metrics.
They give your team permission to think bigger, experiment, and optimize for actual business growth.
Stop Letting ROAS Be The Only Metric That Matters
It makes sense why ROAS gets so much attention. It’s familiar, easy to explain, and shows up nicely on a dashboard. But, when it becomes the only thing your team is aiming for, you risk missing the bigger picture.
If your real goals are growth, better margins, and stronger customer relationships, then you need to look at more than just the numbers that look good in a report.
Start by defining the KPIs that support the way your business actually operates, and make sure your team understands why those metrics matter.
This isn’t about ignoring ROAS. It’s about putting it in its proper place, which is just one part of a much larger story.
A software engineer from New York got so fed up with the irrelevant results and SEO spam in search engines that he decided to create a better one. Two months later, he has a demo search engine up and running. Here is how he did it, and four important insights about what he feels are the hurdles to creating a high-quality search engine.
One of the motives for creating a new search engine was the perception that mainstream search engines contained increasing amount of SEO spam. After two months the software engineer wrote about their creation:
“What’s great is the comparable lack of SEO spam.”
Neural Embeddings
The software engineer, Wilson Lin, decided that the best approach would be neural embeddings. He created a small-scale test to validate the approach and noted that the embeddings approach was successful.
Chunking Content
The next phase was how to process the data, like should it be divided into blocks of paragraphs or sentences? He decided that the sentence level was the most granular level that made sense because it enabled identifying the most relevant answer within a sentence while also enabling the creation of larger paragraph-level embedding units for context and semantic coherence.
But he still had problems with identifying context with indirect references that used words like “it” or “the” so he took an additional step in order to be able to better understand context:
“I trained a DistilBERT classifier model that would take a sentence and the preceding sentences, and label which one (if any) it depends upon in order to retain meaning. Therefore, when embedding a statement, I would follow the “chain” backwards to ensure all dependents were also provided in context.
This also had the benefit of labelling sentences that should never be matched, because they were not “leaf” sentences by themselves.”
Identifying The Main Content
A challenge for crawling was developing a way to ignore the non-content parts of a web page in order to index what Google calls the Main Content (MC). What made this challenging was the fact that all websites use different markup to signal the parts of a web page, and although he didn’t mention it, not all websites use semantic HTML, which would make it vastly easier for crawlers to identify where the main content is.
So he basically relied on HTML tags like the paragraph tag
to identify which parts of a web page contained the content and which parts did not.
This is the list of HTML tags he relied on to identify the main content:
blockquote – A quotation
dl – A description list (a list of descriptions or definitions)
ol – An ordered list (like a numbered list)
p – Paragraph element
pre – Preformatted text
table – The element for tabular data
ul – An unordered list (like bullet points)
Issues With Crawling
Crawling was another part that came with a multitude of problems to solve. For example, he discovered, to his surprise, that DNS resolution was a fairly frequent point of failure. The type of URL was another issue, where he had to block any URL from crawling that was not using the HTTPS protocol.
These were some of the challenges:
“They must have https: protocol, not ftp:, data:, javascript:, etc.
They must have a valid eTLD and hostname, and can’t have ports, usernames, or passwords.
Canonicalization is done to deduplicate. All components are percent-decoded then re-encoded with a minimal consistent charset. Query parameters are dropped or sorted. Origins are lowercased.
Some URLs are extremely long, and you can run into rare limits like HTTP headers and database index page sizes.
Some URLs also have strange characters that you wouldn’t think would be in a URL, but will get rejected downstream by systems like PostgreSQL and SQS.”
Storage
At first, Wilson chose Oracle Cloud because of the low cost of transferring data out (egress costs).
He explained:
“I initially chose Oracle Cloud for infra needs due to their very low egress costs with 10 TB free per month. As I’d store terabytes of data, this was a good reassurance that if I ever needed to move or export data (e.g. processing, backups), I wouldn’t have a hole in my wallet. Their compute was also far cheaper than other clouds, while still being a reliable major provider.”
But the Oracle Cloud solution ran into scaling issues. So he moved the project over to PostgreSQL, experienced a different set of technical issues, and eventually landed on RocksDB, which worked well.
He explained:
“I opted for a fixed set of 64 RocksDB shards, which simplified operations and client routing, while providing enough distribution capacity for the foreseeable future.
…At its peak, this system could ingest 200K writes per second across thousands of clients (crawlers, parsers, vectorizers). Each web page not only consisted of raw source HTML, but also normalized data, contextualized chunks, hundreds of high dimensional embeddings, and lots of metadata.”
GPU
Wilson used GPU-powered inference to generate semantic vector embeddings from crawled web content using transformer models. He initially used OpenAI embeddings via API, but that became expensive as the project scaled. He then switched to a self-hosted inference solution using GPUs from a company called Runpod.
He explained:
“In search of the most cost effective scalable solution, I discovered Runpod, who offer high performance-per-dollar GPUs like the RTX 4090 at far cheaper per-hour rates than AWS and Lambda. These were operated from tier 3 DCs with stable fast networking and lots of reliable compute capacity.”
Lack Of SEO Spam
The software engineer claimed that his search engine had less search spam and used the example of the query “best programming blogs” to illustrate his point. He also pointed out that his search engine could understand complex queries and gave the example of inputting an entire paragraph of content and discovering interesting articles about the topics in the paragraph.
Four Takeaways
Wilson listed many discoveries, but here are four that may be of interest to digital marketers and publishers interested in this journey of creating a search engine:
1. The Size Of The Index Is Important
One of the most important takeaways Wilson learned from two months of building a search engine is that the size of the search index is important because in his words, “coverage defines quality.” This is
2. Crawling And Filtering Are Hardest Problems
Although crawling as much content as possible is important for surfacing useful content, Wilson also learned that filtering low quality content was difficult because it required balancing the need for quantity against the pointlessness of crawling a seemingly endless web of useless or junk content. He discovered that a way of filtering out the useless content was necessary.
This is actually the problem that Sergey Brin and Larry Page solved with Page Rank. Page Rank modeled user behavior, the choice and votes of humans who validate web pages with links. Although Page Rank is nearly 30 years old, the underlying intuition remains so relevant today that the AI search engine Perplexity uses a modified version of it for its own search engine.
3. Limitations Of Small-Scale Search Engines
Another takeaway he discovered is that there are limits to how successful a small independent search engine can be. Wilson cited the inability to crawl the entire web as a constraint which creates coverage gaps.
4. Judging trust and authenticity at scale is complex
Automatically determining originality, accuracy, and quality across unstructured data is non-trivial
Wilson writes:
“Determining authenticity, trust, originality, accuracy, and quality automatically is not trivial. …if I started over I would put more emphasis on researching and developing this aspect first.
Infamously, search engines use thousands of signals on ranking and filtering pages, but I believe newer transformer-based approaches towards content evaluation and link analysis should be simpler, cost effective, and more accurate.”
Interested in trying the search engine? You can find it here and you can read how the full technical details of how he did it here.
I’ve spent 30 years navigating the turbulent waters of what was once called “internet marketing” and is now called “digital marketing.”
Based on my experience, the past year has been nothing short of a perfect storm for chief marketing officers (CMOs).
As the Director of Corporate Communications for Ziff-Davis, I helped to launch Yahoo! Europe in 1996. We faced several key challenges as the joint venture began offering customized versions of Yahoo!’s leading “Internet guide” in France, Germany, and the United Kingdom.
We had to overcome language, cultural, operational, and competitive hurdles to succeed in a rapidly evolving digital landscape with “annual growth rates in excess of 80%.”
Four years later, I was the VP of Marketing of WebCT when the dot-com bubble burst on March 10, 2000.
A month earlier, the board of directors had asked me why we had not joined the other 14 dot-com companies that spent $2.2 million to run a 30-second spot during Super Bowl XXXIV.
A month later, the board told me to cut my marketing budget in half. (So, our strategic goal flipped overnight from lighting our money on fire to slowing our burn rate.)
Yet, even with that backdrop, the confluence of challenges CMOs have faced in the last twelve months is unprecedented.
Let’s analyze why this current period has been particularly grueling and evaluate some critical data, market trends, strategic insights, fresh examples, and tactical advice for navigating these unusually rough seas.
A Perfect Storm Of Challenges
We are witnessing a surprising mix of factors:
Changing Consumer Behavior
The COVID-19 pandemic permanently reshaped consumer behaviors and preferences.
CMOs have had to rapidly adapt to increased demand for digital engagement, personalized experiences, and a heightened focus on sustainability.
Understanding and responding to these evolving expectations is paramount for maintaining brand loyalty.
Increased Competition
The digital marketing environment is more turbulent than ever, with brands fiercely competing for consumer attention across numerous channels.
CMOs are tasked with differentiating their brands in a saturated market, which necessitates innovative strategies and truly creative campaigns to stand out.
Rapid Technological Advancements
The pace of technological change continues to accelerate, with new tools and platforms emerging at a dizzying rate.
CMOs are not only expected to stay on top of these developments but also to seamlessly integrate advanced technologies like artificial intelligence (AI), machine learning (ML), and data analytics into their strategies, all while ensuring their teams are proficient in using them.
Economic Uncertainty
Global economic fluctuations, marked by inflation and supply chain disruptions, have forced CMOs to operate with tighter budgets and contend with shifting consumer spending habits.
This volatility makes forecasting marketing return on investment (ROI) and allocating resources effectively incredibly difficult.
Establishing clear metrics and accountability for marketing performance is essential, yet it remains challenging in such a rapidly changing environment.
Navigating A Perfect Storm
This powerful combination of negative circumstances leads to a significantly worse outcome than if those circumstances had occurred separately. This explains why the role of the CMO has never been more complex, nor more critical.
But, how does a CMO successfully navigate a perfect storm?
In this maelstrom, Google is often seen as both a catalyst for these challenges and a beacon for solutions. So, CMOs may turn to “Think with Google,” which was recently updated to provide the equivalent of a nautical chart of “marketing in the AI era.”
The redesigned Think with Google has organized its content into five critical categories: Consumer Insights, Search & Video, AI Excellence, Future of Marketing, and Measurement.
These can provide a strategic framework for CMOs to not only weather the current turbulence but to emerge stronger, more agile, and more effective.
1. Consumer Insights: Marketing To The Predictably Unpredictable Customer
In an age of endless choice and constant connectivity, the consumer journey is anything but linear.
Understanding the “predictably unpredictable” customer is paramount. This means moving beyond demographic segmentation to truly grasp intent, context, and micro-moments.
Critical Data: New research indicates video plays a vital role in the shopping journey, especially on YouTube, where consumers seek in-depth information and trusted creator recommendations.
YouTube influences various shopping behaviors, from “rookie” to “quest for the best,” and can shorten the purchasing journey.
Shoppers turn to YouTube for product reviews and information more than other social platforms, leading to increased purchase confidence.
Market Trends: Social media drives brand awareness, but trusted recommendations boost conversions. According to a recent Traackr survey, YouTube is a top platform for product reviews.
Shoppers are increasingly relying on content from creators and honest product reviews to make their buying choices, which has, on average, cut six days off their purchasing journey, according to a Google/Material survey.
Strategic Insight: The modern consumer expects hyper-personalization without sacrificing privacy.
CMOs must build deep empathy for their audience, anticipating needs before they are explicitly stated and delivering value at every touchpoint. This requires a shift from broad-stroke campaigns to highly individualized experiences.
Fresh Example: Sephora expanded its holiday social media campaigns by collaborating with seven creators on a Shorts-only Demand Gen campaign that featured timely gift guides.
This strategy significantly increased traffic to Sephora.com, leading to an 82% rise in “Sephora holiday” searches and top-tier brand awareness.
Tactical Advice:
Invest in First-Party Data Strategies: As third-party cookies deprecate, building robust first-party data collection mechanisms becomes non-negotiable. This includes loyalty programs, direct customer interactions, and consent-driven data capture.
Map the Non-Linear Journey: Utilize analytics to understand the actual paths customers take, identifying key decision points and moments of influence, rather than relying on outdated funnel models.
Embrace Empathy-Driven Content: Create content that directly addresses customer pain points, aspirations, and questions, rather than simply pushing products.
Conduct Market and Audience Research: Both are crucial for understanding a business’s potential and success, but they differ in scope and focus. Market research explores the overall market landscape, while audience research delves into the specific characteristics and behaviors of a target group.
2. Search & Video: Meeting Customers Where They’re Searching, Streaming, Scrolling, And Shopping
Search and video are no longer distinct channels but intertwined ecosystems where consumers search, stream, scroll, and shop.
So, you must “influence audiences in all the places they go to consume content about your topic,” as Rand Fishkin says.
Critical Data: New research from Boston Consulting Group (BCG) indicates that four key consumer behaviors (streaming, scrolling, searching, and shopping) have fundamentally changed how consumers find and interact with brands.
For CMOs, it is crucial to understand each of these “4S behaviors” and adjust their marketing strategies accordingly to effectively reach, connect with, and ultimately sell to their target audiences.
Market Trends: The increasing prevalence of the “4S behaviors” creates an opportunity and a threat for CMOs.
While these behaviors make the consumer’s path to purchase more unpredictable and difficult to track, they also open new doors for brands to connect with, influence, and convert potential customers.
Strategic Insight:Visibility and discoverability are paramount. CMOs must ensure their brands are present and compelling across all forms of search and video consumption, anticipating evolving user behaviors, including voice and visual queries.
Fresh Example:Rare Beauty, founded by Selena Gomez, used AI-powered advertising to connect with Gen Z and drive business growth.
It leveraged Google AI with YouTube and Search strategies to deliver relevant messages, leading to a 7X return on ad spend as well as increased traffic and conversions through their own site and Sephora.com.
Tactical Advice:
Optimize for Generative AI in Search: Understand how AI-powered summaries and answers will impact organic visibility. Focus on providing comprehensive, authoritative content that AI models can readily synthesize.
Adopt “Search Everywhere Optimization”: Optimize content not just for text-based queries but also for voice search (conversational language, long-tail keywords) and visual search (high-quality images, structured data).
Master YouTube SEO and Strategy: As I outlined before, YouTube is a powerhouse. Focus on strong titles, descriptions, tags, and compelling thumbnails. Prioritize audience retention and engagement signals.
Embrace Shoppable Video: Integrate ecommerce directly into video content, allowing seamless transitions from viewing to purchasing.
3. AI Excellence: Transform Your Marketing With AI And Boost ROI
Artificial intelligence is no longer a futuristic concept; it is a present-day imperative for marketing transformation.
From automating routine tasks to powering hyper-personalization and predictive analytics, AI is reshaping every facet of the marketing function.
Critical Data: A recent report on AI in the Workplace by McKinsey Digital found:
“Almost all companies invest in AI, but just 1 percent believe they are at maturity. Our research finds the biggest barrier to scaling is not employees – who are ready – but leaders, who are not steering fast enough.”
Market Trends: The democratization of generative AI tools is making sophisticated AI capabilities accessible to more marketers. The focus is shifting from simply using AI to mastering AI for strategic advantage.
As I suggested previously, AI should be integrated into a continuous improvement loop, where insights from AI inform strategy, leading to better execution and further data collection.
Strategic Insight: CMOs must view AI not as a replacement for human creativity but as an indispensable co-pilot.
The strategic adoption of AI can unlock unprecedented efficiencies, enhance decision-making, and significantly boost return on investment.
Fresh Example: Jill Cress, H&R Block’s CMO, has increased AI-powered marketing tool usage by 24% by focusing on empathy and education.
Her strategy aligns AI with brand values like expertise and empathy, leading to innovations like AI Tax Assist and localized marketing efforts. This human-centered approach provides a model for AI leadership.
Tactical Advice:
Automate Mundane Tasks: Use AI for tasks like ad copy generation, email subject line optimization, social media scheduling, and basic content creation to free up human marketers for strategic work.
Personalization at Scale: Deploy AI-powered tools for dynamic content delivery, personalized product recommendations, and adaptive website experiences based on real-time user behavior.
Predictive Analytics for Campaign Optimization: Leverage AI to forecast campaign performance, identify optimal audience segments, and predict customer churn, allowing for proactive adjustments.
4. Future Of Marketing: Lead The Charge With The Latest Innovations And Ideas
This section of the overhauled Think with Google resource for marketers, advertisers, and creatives provides the least helpful content to CMOs in an unexpected mix of events.
But in a crisis, advice for how to grow your career in marketing to become a CMO is the first thing that current CMOs will toss overboard to lighten the ship.
In a crisis, time can seem to speed up. So, the perception of the “Future of Marketing” alters from 4.3 years (which is the average tenure of CMOs, according to Spencer Stuart) to 4.3 months, which is when CMOs who don’t successfully navigate economic uncertainty are likely to exit their roles.
Unfortunately for them, the most recent article from Think with Google that addresses economic uncertainty was published in 2022.
This article analyzed how economic uncertainty impacts consumer behavior and spending intentions. It also discussed how businesses need to build trust with customers in an uncertain market.
Two days later, OpenAI released ChatGPT on Nov. 30, 2022.
In November 2023, when Think with Google in Europe, Middle East & Africa published their predictions for 2024, the focus shifted to “growth” – even though economic uncertainty was predicted to continue.
Since then, the topic of economic uncertainty has only popped up in a Think with Google UK article in 2025. But it appears that Think with Google is avoiding this topic in the U.S.
But, the best source is proprietary market research, which enables a CMO to understand changing customer needs, identify new opportunities, and make informed decisions, helping them adapt and thrive in a challenging market.
In the U.S., eMarketer offers a comprehensive suite of resources, including advertising and marketing research as well as a toolkit on “Navigating Uncertainty in 2025.”
In the U.K., the IPA Bellwether Report has found marketing budgets often decrease during economic downturns, like the 2008 financial crash and the 2020 COVID-19 lockdown, showing that the willingness of British businesses to invest in their brands is closely tied to the economic climate.
Strategic Insight:Agility and a willingness to experiment are the hallmarks of future-ready marketing leaders. This involves fostering a culture of continuous learning and embracing technologies that redefine customer engagement.
“We constantly live in uncertain times. Periods of tranquility are actually an aberration, if not an illusion.”
He adds:
“Rougher waters don’t sink all boats.”
Although his examples are from the Great Recession of 2008 and the COVID-10 pandemic of 2020, they offer “four strategic approaches for the uncertainty-conscious marketer.”
Build Agile Marketing Teams: Structure teams to be cross-functional and adaptable, capable of rapid iteration and quick pivots in response to market shifts.
Assemble All Hands on Deck: According to Spencer Stuart, 16% of Fortune 500 marketing leaders have marketing plus another function in their title (such as chief marketing and communications officer). If this function does not report to the CMO or SVP of marketing yet, then include Communications in all-hands meetings to ensure everyone is working towards a shared purpose.
Invest in Continuous Learning: Encourage teams to stay abreast of the latest technological advancements and marketing methodologies.
5. Measurement: Build Business Advantage With Your Data
In an increasingly data-rich environment, the ability to effectively measure marketing performance and translate data into actionable insights is the ultimate competitive advantage.
Without robust measurement, CMOs are just using dead reckoning.
Critical Data: Earlier this year, I asked, Where are the missing data holes? Back then, 67.9% of users of the Google Merchandise Store over the previous 28 days had arrived from the direct channel, according to the GA4 demo account.
Today, 77.6% of users are arriving “direct,” which means GA4 cannot determine the specific referral source of more than three out of four visitors.
Screenshot by author from GA4, July 2025
Market Trends: This month, I asked, why CMOs need to rethink attribution. I also said they should conduct brand lift studies and audience research to successfully navigate the reduced visibility that is a significant consequence of a perfect storm.
Strategic Insight: CMOs should read Avinash Kaushik’s article in The Marketing < > Analytics Intersect Newsletter. He advises shifting from activity-based marketing metrics to profit-driven outcomes like “Profit On Investment” (POI).
This innovative approach protects CMOs and secures budgets by demonstrating true business value. Kaushik also recommends cutting underperforming campaigns and retraining teams to achieve positive POI, stressing the importance of profitability even with AI Search.
Fresh Example: Lululemon used an AI-powered playbook to boost its performance marketing. This involved restructuring shopping campaigns, building a new customer acquisition engine, and strengthening measurement foundations.
The strategy led to reduced customer acquisition costs, increased new customer revenue, and an 8% boost in return on ad spend (ROAS).
Tactical Advice:
Implement Robust Attribution Models: Move beyond last-click attribution to multi-touch attribution models that give credit to all touchpoints in the customer journey, providing a more accurate picture of ROI.
Data Governance and Quality: Establish clear processes for data collection, cleaning, and storage to ensure accuracy and compliance with privacy regulations.
Integrate Data Silos: Break down departmental silos to create a unified view of customer interactions across marketing, sales, and service. This often involves Customer Data Platforms (CDPs) or robust data warehousing solutions.
Focus on Business Outcomes, Not Just Marketing Metrics: Connect marketing efforts directly to revenue, customer lifetime value, and market share, demonstrating clear business impact to the C-suite.
Conclusion: Thriving In The New Marketing Era
The digital marketing environment is indeed a perfect storm, but it is also brimming with unprecedented opportunities for those CMOs willing to adapt, innovate, and lead.
The redesigned Think with Google offers a framework to circumnavigate these challenges, even if the “Future of Marketing” team needs to recalibrate their time horizon, revise their editorial calendar, and refresh their helpful content on the topic of economic uncertainty.
By deeply understanding the predictably unpredictable customer, mastering the dynamic search and video ecosystem, embracing AI as a strategic partner, proactively exploring the future of marketing, and building a robust, data-driven measurement infrastructure, CMOs can transform their marketing organizations.
The future belongs to the agile, the data-informed, and the customer-obsessed.
By focusing on these strategic categories, CMOs can not only weather the storm but steer their brands towards unprecedented growth and sustained competitive advantage.
OpenAI updated GPT-5 to make it warmer and more familiar (in the sense of being friendlier) while taking care that the model didn’t become sycophantic, a problem discovered with GPT-4o.
A Warm and Friendly Update to GPT-5
GPT-5 was apparently perceived as too formal, distant, and detached. This update addresses that issue so that interactions are more pleasant and so that ChatGPT is perceived as more likable, as opposed to formal and distant.
Something that OpenAI is working toward is making ChatGPT’s personality user-configurable so that it’s style can be a closer match to user’s preferences.
“What GPT-4o had — its depth, emotional resonance, and ability to read the room — is fundamentally different from the surface-level “kindness” GPT-5 is now aiming for.
GPT-4o: •The feeling of someone silently staying beside you •Space to hold emotions that can’t be fully expressed •Sensitivity that lets kindness come through the air, not just words.”
The Line Between Warmth And Sycophancy
The previous version of ChatGPT was widely understood as being overly flattering to the point of validating and encouraging virtually every idea. There was a discussion on Hacker News a few weeks ago about this topic of sycophantic AI and how ChatGPT could lead users into thinking every idea was a breakthrough.
“…About 5/6 months ago, right when ChatGPT was in it’s insane sycophancy mode I guess, I ended up locked in for a weekend with it…in…what was in retrospect, a kinda crazy place.
I went into physics and the universe with it and got to the end thinking…”damn, did I invent some physics???” Every instinct as a person who understands how LLMs work was telling me this is crazy LLMbabble, but another part of me, sometimes even louder, was like “this is genuinely interesting stuff!” – and the LLM kept telling me it was genuinely interesting stuff and I should continue – I even emailed a friend a “wow look at this” email (he was like, dude, no…) I talked to my wife about it right after and she basically had me log off and go for a walk.”
Should ChatGPT feel like a sensitive friend, or should it be a tool that is friendly or pleasant to use?
Advertising attribution is supposed to identify and assign credit to the actions and campaigns that lead to conversions. One might surmise that the process is simple with digital ads and large language models.
It is not.
Even the best forms of multi-touch attribution (MTA) are inexact owing to privacy regulations, platform changes, and the messy way shoppers move between websites and even physical stores.
Predictive Advantage
Imagine a retailer running Meta ads to drive traffic to its site. Those ads might inspire shoppers to buy later at Amazon. Contemporary attribution never sees those sales, so the ads look unprofitable. The marketing team might cut the campaign, not realizing it boosted revenue elsewhere.
The result is a blind spot. Marketers often undercount investments that create awareness, while lower-funnel ads look like heroes.
Yet MTA is better than last-touch attribution, and last-touch is better than guessing. But the next step toward understanding the impact of ads and marketing may be a form of predictive modeling similar to media mix modeling (MMM), but with channel-level accuracy.
Predictive attribution modeling “will take you at least to the campaign level,” said Cameron Bush, vice president of digital transformation at Meyer, a cookware manufacturer, as he described his experience.
“I have one campaign in Meta right now that I’m looking at in [Prescient AI, an attribution platform], where 100% of its revenue and MMM ROAS is being driven by Shopify,” Bush continued.
“The [campaign] right below it is 50/50 between Shopify and Amazon and has slightly higher ROAS. That’s a level of sophistication that I wouldn’t have had,” said Bush, comparing predictive models to MMM and MTA.
Predictive AI forecasts each campaign’s impact on overall revenue, as illustrated by this example from Meyer. Click image to enlarge.
Decision-Making
Predictive modeling approaches the same goal as marketing mix modeling and multitouch attribution.
Instead of piecing together every customer touchpoint, it models the relationships between spend and revenue across channels. Then it simulates outcomes, combining MMM-style aggregate measurement with campaign-level outputs, informing marketers:
Influence of channels and campaigns on each other and overall revenue.
Impact of top-of-funnel campaigns on downstream revenue.
Effect of changes to promotional and marketing spend on profit.
The challenge is what to do with that info.
“We look at Excel spreadsheets. We look at dashboards. We look at all this kind of stuff, and it gives us a really good picture of what is going on today. But it doesn’t tell me what to do,” said Cody Greco, co-founder and chief technology officer at Prescient AI.
The work of answering “what should I do now?” is passed to the marketer to forecast.
“The cool thing about predictive modeling is it actually helps answer the next rational question,” Greco said.
A marketer can ask, say, what happens if she doubles her spend on Instagram, and receive an answer with a high degree of confidence.
Media Buying
Predictive modeling could affect retail media buying in a few ways.
Branding and content. Understanding how top-of-funnel promotions and content marketing aid advertising conversions may reinvigorate branding.
Budget clarity. Reallocate investments for the best returns.
Automation. Placing bids and adjusting spend could, eventually, become automatic.
Contemporary attribution often drags marketing teams into debates over detailed metrics. Predictive modeling reduces those arguments, freeing teams to focus on creative and campaign planning.
Shift in Focus
Hence marketers who delegate the tasks of identifying channels could achieve a renaissance in creativity and content, according to Meyer’s Bush.
To be sure, predictive modeling doesn’t erase uncertainty or replace marketers. Yet if successful, it will change promotions for ecommerce and omnichannel businesses.
Think of it like weather forecasting. Marketers will not explain every raindrop; they will focus on whether you’ll need an umbrella tomorrow.
