Global advertising expenditure has surpassed the $1 trillion mark for the first time.
Digital advertising continues to dominate this growth, with digital channels encompassing search and social media forecast to account for 72.9% of total ad revenue by the end of the year.
From a platform perspective, Google, Meta, Amazon, and Alibaba are expected to capture more than half of global ad revenues this year.
In-house and agency-side paid media teams are working harder than ever to grow ecommerce businesses efficiently, and the amount of data being used day-to-day (even hour-to-hour) is enormous.
With this growth and investment, something is clearly working, and given that brands can map new/returning audiences to their advertising funnel and serve ads across billions of auctions, it’s a lever that millions of businesses pull.
However, with budgets being split across channels (search, social, out-of-home, etc) and brands using CRM data, analytics platforms, third-party attribution tools, and more to define their “source of truth,” fragmentation begins to appear with reporting. Only 32% of executives feel they fully capitalize on their performance marketing data for this reason.
With data being spread across several sources, ad platforms having different attribution models, and the C-suite likely asking, “Which source of truth is correct?”, reporting paid media performance for ecommerce isn’t the most straightforward task.
This post digs into key performance indicators, platform attribution & modeling, business goals, and how to bring it all together for a holistic view of your advertising efficacy.
To begin navigating paid media reporting, it starts with the KPIs that each account optimizes towards and how this feeds into channel performance.
Each of these has purpose, benefits, limitations, and practical use cases that should be viewed through a lens of attribution unique to each platform.
This metric measures the revenue generated for every dollar spent on advertising.
If your total ad cost was $1,000 and you drove $18,500 revenue, your ROAS would be 18.5.
This metric shows the average cost to generate a sale (or lead, depending on the goal, e.g., an ecommerce brand could be measuring using CPA to sign up new customers for an event).
For example, if your total ad cost was $5,000 and you drove 180 sales, your CPA would be $ 27.77.
This metric measures what % of revenue is spent on advertising.
Say a brand spends $20,000 on Meta Ads and generates £100,000 in revenue, their resulting CoS would be 20%.
This metric may reflect either marketing costs associated with driving new customer acquisition or a holistic view of all costs associated with acquiring new customers.
Let’s say a business has a CAC of $175 and an AOV of $58, they will need each new customer to repeat purchase ~3x to make acquisition profitable.
This metric shows how efficiently your total ad spend is converting into revenue, regardless of the channel.
Where MER is especially useful is when brands are active on multiple ad networks, all of which contribute in some way to the final sale, and where siloed platform attribution is inconsistent.
Used alongside CAC, this metric is essential for understanding the true value of both acquisition and retention, which is important for almost all ecommerce models, and especially important for brands looking to capitalize on repeat purchases and subscription-based models.
So, which one should you be reporting on for your ecommerce brand?
Speaking from experience, there isn’t a right or wrong answer, nor is there a blueprint for which KPIs you should be reporting on.
Having a multifaceted approach will enable more informed decision making, combining short-, medium-, and long-term KPIs to form a holistic model for measuring performance that feeds into your reports.
However, even after choosing your KPIs, different attribution models across advertising platforms add another layer of complexity, as does the ever-evolving customer journey involving multiple touchpoints across devices, channels, etc.
Each ad platform handles attribution and tracking differently.
Take Google Ads, for example, the default model is Data-Driven Attribution (DDA), and when using the Google Ads pixel, only paid channels receive credit.
Then, with a GA4 integration to Google Ads, both paid and organic are eligible to receive credit for sales.
Click-through windows, value, count, etc, can all be customised to provide a view of performance that feeds into your Google Ads campaigns.
Using the Google Ads pixel, say a user clicks a shopping ad, then a search ad, and then returns via organic to make the purchase, 40% of the credit could go to shopping, and 60% to the search ad.
With the GA4 integrated conversion, shopping could receive 30%, search 40%, and organic visit 30%, resulting in 70% of the value being attributed back to the campaigns in-platform.
Now, comparing this to Meta Ads, which uses a seven-day click and one-day view attribution window by default, when a user converts within this time frame, 100% of the credit will be attributed to Meta.
This is why the narrative for conversion tracking on Meta is one of overrepresentation, with brands seeing inflated revenue numbers vs. other channels, even more so with loose audience targeting, where campaign types such as ASC can serve assets to audiences who have already interacted with your brand.
Then, when you dig into third-party analytics, the comparisons between Google Ads, Meta Ads, Pinterest Ads, etc., are almost the complete opposite.
So, what should this data be used for, and how does it factor into the bigger picture?
In-platform metrics are best viewed as directional.
They help optimize within the walls of that specific platform to identify high-performing audiences, auctions, creatives, and placements, but they rarely reflect the true incremental value of paid media to your business.
The data in Google, Meta, Pinterest, etc. is a platform-specific lens on performance, and the goal shouldn’t be to pick one or ignore these metrics.
It should be to interpret these for what they are and how they play into the overarching strategy.
KPIs such as ROAS and CPA offer immediate insights but provide a fragmented view of paid media performance.
To gain a comprehensive understanding, brands must combine medium- to long-term KPIs with broader modeling and tests that account for the multifaceted nature of performance marketing, while considering how complex customer journeys are in this day and age.
Introduced in the 1950s, MMM is a statistical analysis that evaluates the effectiveness of marketing channels over time.
By analyzing historical data, MMM helps advertisers understand how different marketing activities contribute to sales and can guide budget allocation.
A 2024 Nielsen study found that 30% of global marketers cite MMM as their preferred method of measuring holistic ROI.
The very short version of how to get started with MMM includes:
Unlike platform attribution, this doesn’t rely on user-level tracking, which is especially useful with privacy restrictions now and in the future.
From a tactical standpoint, your chosen KPIs will still lead campaign optimizations for your day-to-day management, but at a macro level, MMM will determine where to invest your budget and why.
Instead of relying on attribution models, this uses controlled experiments to isolate the impact of your paid media campaigns on actual business outcomes.
This kind of testing aims to answer the question, “Would these sales have happened without the paid media investment?”.
This involves:
This isn’t one that’s run every week, but from a strategic point of view, these tests help to validate the actual performance of paid media and direct where and what spend should be allocated across ad platforms.
These are equally as important (if not more) for ecommerce reporting and absolutely need to be considered when setting KPIs and beginning to think about modeling, testing, etc.
Without considering these factors, brands will use inaccurate data from the get-go.
Think about the impact of buy now, pay later. Providers such as Klarna or Clearpay can lead to higher return rates, as bundle buying and impulsive purchases become more accessible.
Without considering operational factors, using this example and a basic in-platform ROAS, brands would be optimizing toward incorrect checkout data with higher AOV’s and no consideration of returns, restocking, etc.
Ultimately, building a true picture of paid media performance means stepping beyond the platform KPIs and metrics to consider all factors involved and how best to model the data to uncover not just “what” is happening, but “why” it is and how this impacts the wider business.
No single tool or model tells the full story.
You’ll need to compare platform data, internal analytics, and external modeling to build a more reliable view of performance.
The first step is getting watertight KPIs nailed down that consider every possible operational factor so you know the platforms are being fed the correct data, and if you need to modify these based on platform nuances due to differing attribution models, do it.
Once these are nailed down, find a model that you trust and that will show you the holistic impact of your paid media spend on overall business performance.
You could explore the use of third-party attribution tools that aim to blend data together, but even with these, you’ll still require clear and accurate KPIs and reliable tracking.
Then, when it comes to the visual side of reporting, the world is your oyster.
Looker Studio, Tableau, and Datorama are among the long list of well-known platforms, and with most brands using three to four business intelligence tools and 67% of analysts relying on multiple dashboards, don’t stress if you can’t get everything under one lens.
When all of this is executed and made into a priority over the short-term ebbs and flows of paid media performance, this is the point where connecting media spend to profit begins.
More Resources:
Featured Image: Surasak_Ch/Shutterstock
As FIFA Club World Cup observers fret about ticket sales, Warner Bros. Discovery shares no such concerns about the event’s ads.
TNT Sports has partnered with DAZN on a sublicense for English-language coverage of the event, broadcasting 24 matches on its TNT, TBS, and TruTV channels—with all 63 matches streamed on DAZN from June 14 through July 13.
According to Jon Diament, WBD’s evp of ad sales, ad inventory for the TNT Sports slate of matches is “virtually sold out” heading into its June 14 kickoff between Lionel Messi’s Major League Soccer (MLS) side Inter Miami and Egyptian Premier League team Al Alhy at 8 p.m. ET on TBS.
“The upfront had a lot of live sports increases, and the second quarter of this year has just been terrific,” Diament said. “I think if you’re an advertiser, you’re looking for reach, engagement, the live nature of sports, multiplatform—all those things keep reiterating the value of live sports in the second quarter, and that got us into a sellout position.”
Diament mentioned that TNT Sports already strong ad sales during the quarter from college basketball’s March Madness, both the NHL and NBA playoffs, and the company’s inaugural domestic broadcast of the French Open at Roland-Garros, which not only sold out its ad inventory but saw a 25% increase in viewership from a year earlier.
Some of that second-quarter success came from official partners of professional leagues gravitating toward playoff season, Diament noted, but also from strong scatter demand for live sports. For TNT Sports’ Club World Cup coverage, for example, ad inventory was picked up by FIFA partners including Visa, Bank of America, and Michelob Ultra—which sponsors post-match coverage and the Superior Player of the Match—but also by other brands including Vanda Pharmaceuticals (halftime and half highlights), DraftKings (pregame odds and promo), Verizon (studio club profiles), Lowe’s, Apple, Heinken, Adidas, Nike, Starbucks, Amica, JP Morgan Chase, and even Mas+ by Messi.
“Most of the FIFA sponsors took advantage of the new tournament,” Diament said. “We have a combination of official partners, but also, because we’re TNT Sports and we’re not DAZN or FIFA, we can go to the overall market.”
Through the partnership with DAZN, TNT Sports has been able to offer those advertisers more in-game options, including two-box ad presentation (or squeeze back) during player substitutions, water breaks or other pauses in the action, and a game-clock feature that lets a brand take over all on-screen graphics for 10 seconds before putting its logo next to the clock for two minutes.
But TNT Sports sees opportunity in its digital and social media platforms for Bleacher Report and House of Highlights, which enhanced its Roland-Garros coverage a few weeks ago by bringing sports figures like Odell Beckham and Derrick Rose in for cameos and commentary. Diament noted that TNT Sports’ coverage of U.S. Soccer and its work with veteran soccer talent like analysts Luke Wileman, Brian Dunseth, and Melissa Ortiz have made it familiar with soccer’s younger viewership in the U.S. and helped its broadcast and social strategy to a daylong event not dissimilar to March Madness or Roland-Garros.
That monopoly on fans’ attention hasn’t gone unnoticed by event advertisers.
“There’s a lot of push notifications, people signing up for what they’re interested in, they have a good feeling for how to create excitement before and after the matches themselves,” Diament said. “This is one of the great things about social media, it’s 24/7 and it’s just not when the live games are happening, so people are carrying around their phones and that younger demo stays connected.”
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Printers get a lot of the spotlight and rightly so. 2D printers have been around for so long that not much has moved on with the technology in a long time. Things are changing, though, and eufyMake is leading the charge with its eufyMake E1 UV printer. Billed as a business in a box, this printer allows you to print images on just about any material you care to think of, from wood to metal. Not only that, but it promises to print on multiple shapes, from the perfectly flat to completely round, like glasses and coffee cups. To make the E1 even more appealing, it can also print with depth, add a gloss finish and much more.
For makers, creators and artists, this opens up a whole world of possibilities, from selling more individual prints to merch, assuming the E1 delivers.
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Inks and cleaning kit are included (Image credit: Rob Redman)
Everything is ready to go out of the box (Image credit: Rob Redman)
Texture printing, with brush stroke generation is very good (Image credit: Rob Redman)
The E1 prints easily on rough or uneven surfaces (Image credit: Rob Redman)
The launch of ChatGPT blew apart the search industry, and the last few years have seen more and more AI integration into search engine results pages.
In an attempt to keep up with the LLMs, Google launched AI Overviews and just announced AI Mode tabs.
The expectation is that SERPs will become blended with a Large Language Model (LLM) interface, and the nature of how users search will adapt to conversations and journeys.
However, there is an issue surrounding AI hallucinations and misinformation within LLM and Google AI Overview generated results, and it seems to be largely ignored, not just by Google but also by the news publishers it affects.
More worrying is that users are either unaware or prepared to accept the cost of misinformation for the sake of convenience.
Barry Adams is the authority on editorial SEO and works with the leading news publisher titles worldwide via Polemic Digital. Barry also founded the News & Editorial SEO Summit along with John Shehata.
I read a LinkedIn post from Barry where he said:
“LLMs are incredibly dumb. There is nothing intelligent about LLMs. They’re advanced word predictors, and using them for any purpose that requires a basis in verifiable facts – like search queries – is fundamentally wrong.
But people don’t seem to care. Google doesn’t seem to care. And the tech industry sure as hell doesn’t care, they’re wilfully blinded by dollar signs.
I don’t feel the wider media are sufficiently reporting on the inherent inaccuracies of LLMs. Publishers are keen to say that generative AI could be an existential threat to publishing on the web, yet they fail to consistently point out GenAI’s biggest weakness.”
The post prompted me to speak to him in more detail about LLM hallucinations, their impact on publishing, and what the industry needs to understand about AI’s limitations.
You can watch the full interview with Barry on IMHO below, or continue reading the article summary.
I asked Barry to explain why LLMs struggle with accurate source attribution and factual reliability.
Barry responded, “It’s because they don’t know anything. There’s no intelligence. I think calling them AIs is the wrong label. They’re not intelligent in any way. They’re probability machines. They don’t have any reasoning faculties as we understand it.”
He explained that LLMs operate by regurgitating answers based on training data, then attempting to rationalize their responses through grounding efforts and link citations.
Even with careful prompting to use only verified sources, these systems maintain a high probability of hallucinating references.
“They are just predictive text from your phone, on steroids, and they will just make stuff up and very confidently present it to you because that’s just what they do. That’s the entire nature of the technology,” Barry emphasized.
This confident presentation of potentially false information represents a fundamental problem with how these systems are being deployed in scenarios they’re not suited for.
I shared with Barry my concerns about an AI misinformation spiral where AI content increasingly references other AI content, potentially losing the source of facts and truth entirely.
Barry’s outlook was pessimistic, “I don’t think people care as much about truth as maybe we believe they should. I think people will accept information presented to them if it’s useful and if it conforms with their pre-existing beliefs.”
“People don’t really care about truth. They care about convenience.”
He argued that the last 15 years of social media have proven that people prioritize confirmation of their beliefs over factual accuracy.
LLMs facilitate this process even more than social media by providing convenient answers without requiring critical thinking or verification.
“The real threat is how AI is replacing truth with convenience,” Barry observed, noting that Google’s embrace of AI represents a clear step away from surfacing factual information toward providing what users want to hear.
Barry warned we’re entering a spiral where “entire societies will live in parallel realities and we’ll deride the other side as being fake news and just not real.”
I asked Barry why mainstream media isn’t more vocal about AI’s weaknesses, especially given that publishers could save themselves by influencing public perception of Gen AI limitations.
Barry identified several factors: “Google is such a powerful force in driving traffic and revenue to publishers that a lot of publishers are afraid to write too critically about Google because they feel there might be repercussions.”
He also noted that many journalists don’t genuinely understand how AI systems work. Technology journalists who understand the issues sometimes raise questions, but general reporters for major newspapers often lack the knowledge to scrutinize AI claims properly.
Barry pointed to Google’s promise that AI Overviews would send more traffic to publishers as an example: “It turns out, no, that’s the exact opposite of what’s happening, which everybody with two brain cells saw coming a mile away.”
I noted research that shows users do click on sources to verify AI outputs, and that Google doesn’t show AI Overviews on top news stories. Yet, traffic to news publishers continues to decline overall.
Barry explained this involves multiple factors:
“People do click on sources. People do double-check the citations, but not to the same extent as before. ChatGPT and Gemini will give you an answer. People will click two or three links to verify.
Previously, users conducting their own research would click 30 to 40 links and read them in detail. Now they might verify AI responses with just a few clicks.
Additionally, while news publishers are less affected by AI Overviews, they’ve lost traffic on explainer content, background stories, and analysis pieces that AI now handles directly with minimal click-through to sources.”
Barry emphasized that Google has been diminishing publisher traffic for years through algorithm updates and efforts to keep users within Google’s ecosystem longer.
“Google is the monopoly informational gateway on the web. So you can say, ‘Oh, don’t be dependent on Google,’ but you have to be where your users are and you cannot have a viable publishing business without heavily relying on Google traffic.”
I asked Barry for his recommendations on optimizing for LLM inclusion and how to survive the introduction of AI-generated search results.
Barry advised publishers to accept that search traffic will diminish while focusing on building a stronger brand identity.
“I think publishers need to be more confident about what they are and specifically what they’re not.”
He highlighted the Financial Times as an exemplary model because “nobody has any doubt about what the Financial Times is and what kind of reporting they’re signing up for.”
This clarity enables strong subscription conversion because readers understand the specific value they’re receiving.
Barry emphasized the importance of developing brand power that makes users specifically seek out particular publications, “I think too many publishers try to be everything to everybody and therefore are nothing to nobody. You need to have a strong brand voice.”
He used the example of the Daily Mail that succeeds through consistent brand identity, with users specifically searching for the brand name with topical searches such as “Meghan Markle Daily Mail” or “Prince Harry Daily Mail.”
The goal is to build direct relationships that bypass intermediaries through apps, newsletters, and direct website visits.
Barry stressed that publishers covering similar topics with interchangeable content face existential threats.
He works with publishers where “they’re all reporting the same stuff with the same screenshots and the same set photos and pretty much the same content.”
Such publications become vulnerable because readers lose nothing by substituting one source for another. Success requires developing unique value propositions that make audiences specifically seek out particular publications.
“You need to have a very strong brand identity as a publisher. And if you don’t have it, you probably won’t exist in the next five to ten years,” Barry concluded.
Barry advised news publishers to focus on brand development, subscription models, and building content ecosystems that don’t rely entirely on Google. That may mean fewer clicks, but more meaningful, higher-quality engagement.
Barry’s opinion and the reality of the changes AI is forcing are hard truths.
The industry requires honest acknowledgment of AI limitations, strategic brand building, and acceptance that easy search traffic won’t return.
Publishers have two options: To continue chasing diminishing search traffic with the same content that everyone else is producing, or they invest in direct audience relationships that provide sustainable foundations for quality journalism.
Thank you to Barry Adams for offering his insights and being my guest on IMHO.
More Resources:
Featured Image: Shelley Walsh/Search Engine Journal
Most ABM programs run on static data and generic assumptions. Marketers spend countless hours crafting personalized campaigns based on firmographics, technographic, and third-party intent signals—the same data their competitors have access to.
It doesn’t have to be that way. What if the most valuable intelligence for your ABM campaigns is already sitting in your CRM and buried in sales call transcripts?
The new integration of GTM enrichment product Clay and the Gong Revenue AI Platform is the beginning of a shift in how we think about account intelligence and ABM personalization. For the first time, marketers can systematically extract, analyze and operationalize the rich conversation insights that sales teams gather daily. Then, they can use that intelligence to fuel hyper-targeted campaigns at scale.
Traditional ABM has a fundamental flaw. Despite all the advances in intent data and predictive analytics, most programs use educated guesses about prospects’ needs. Up to 97% of marketers say ABM delivers a higher ROI than other marketing strategies, recent research shows, but only if done right.
The problem is data quality and relevance. Nearly half of organizations (47%) cite siloed data as their biggest barrier to gaining buyer insight. They target accounts based on industry classifications and company size, then personalize content around generic pain points that may or may not be relevant.
Meanwhile, your sales team is having actual conversations with prospects every day. They’re hearing specific challenges, budget constraints, technical requirements and competitive concerns directly from the buyer’s mouth.
That is the richest possible source of account intelligence. But historically, it’s stayed locked in Gong and been impossible to operationalize at scale—the integration with Clay changes that.
It lets you extract transcripts, identify mentions with Claygent and trigger automations with various data providers. The real power lies in what you can do with this conversation intelligence once it’s in Clay’s enrichment engine.
Clay can:
More importantly, Clay can use those insights to identify lookalike accounts with similar characteristics and apply the same intelligence to your broader target account list.
The integration includes three core capabilities:
Dig deeper: Could AI be what finally aligns marketing and sales teams?
Most marketers are missing the biggest opportunity here. The integration isn’t just about organizing call notes — it’s about using conversation intelligence to improve your ABM targeting strategy.
Let’s say you’re targeting financial services companies with 1,000+ employees. Your sales team has a discovery call with one of these and learns they’re struggling with regulatory compliance automation. They have a Q2 budget allocated for new tech solutions, and they’re currently evaluating three specific competitors.
Clay maps those conversation insights to that account’s profile, and then you can run a lookalike analysis against your entire target account list. Suddenly, you’ve identified 50 other financial services companies with similar characteristics, likely dealing with the same regulatory compliance challenges. You can now target those lookalike accounts with messaging directly addressing the pain points you got from customer conversations.
This is what account-based marketing was supposed to be: Personalized campaigns based on real customer needs rather than demographic stereotypes.
The most successful implementations follow a systematic approach that puts conversation intelligence at the center of ABM strategy:
Instead of running static ABM campaigns, use conversation triggers to launch dynamic sequences:
The typical buying group for a complex B2B solution involves six to 10 decision-makers. Use conversation intelligence to map the complete buying committee:
Dig deeper: Is your ABM strategy keeping up with the times?
This integration represents something bigger than a new data connector. It creates a foundation for AI-driven ABM.
This approach recognizes that the most valuable account intelligence doesn’t come from third-party data providers or intent tracking platforms. It comes from actual conversations between your sales team and prospects.
Traditional ABM platforms excel at organizing static data and automating generic outreach. They’re fundamentally limited by the quality of inputs (garbage in, garbage out). When your ABM campaigns are based on assumptions about what accounts care about, you’re competing on the same playing field as everyone else.
AI-driven ABM built around conversations changes this. You’re not guessing what accounts need, you know. You’re not personalizing based on job titles and company size; you’re personalizing based on actual pain points and buying criteria expressed in the customer’s own words.
This is what puts AI to work for marketing in a meaningful way: not generating more content or automating more emails, but surfacing unique insights that can’t be found anywhere else and operationalizing them at scale.
Organizations that embrace AI-driven ABM will have a major advantage. They’ll speak directly to what prospects care about, while their competitors are still guessing based on demographic data.
The future of ABM isn’t about better targeting or more personalization. It’s about building campaigns on the foundation of what customers actually say they need. And for the first time, the integration of Clay and Gong makes that systematically possible.
Dig deeper: How ABM systems are evolving to meet changing B2B buying behaviors
Contributing authors are invited to create content for MarTech and are chosen for their expertise and contribution to the martech community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. The opinions they express are their own.
Famously, Anthony’s piece Work Hard and Be Nice to People was based on an open brief the designer set for himself to make his own manifesto (another one to consider for yourself, perhaps?), but in terms of favourites, he thinks of his work with Dutch creative Eric Kessels for Hans Brinker Budget Hotel decades earlier. “He’d seen some work that I’d done that he liked, and he didn’t even write a brief, really,” he recalls.
A brief chat and some suggested lines of copy later, Anthony had designed a set of 20 posters for the hotel. From there, his career took off – and with it, his confidence skyrocketed.
“Looking back, it was a real affirmation of my approach to making work,” he says. “So I learned to trust myself, and stick with what I wanted to do. It gave me more self belief and more confidence.”
If you’re a young creative making your way in the industry, Anthony recommends finding open briefs – and creatives – that align with your personal values.
“If you find an art director or a creative director or even somebody who wants to commission you, who can help you with that confidence, and feel like, ‘yeah, you can actually do this and you can make something interesting’ – those first steps set the tone for the rest of the work you want to do.”
Meanwhile, Los Angeles-based animator and illustrator Ramin Nazer finds that open briefs can go against natural instincts when it comes to the creative process. “Creatives like to be free. They don’t want to be told what to do,” he explains. “But with open briefs, you have less clarity. It’s counter-intuitive, but limitation is actually very helpful in creativity.”
One of Ramin’s favourite briefs involved creating a mind map. “No restrictions – just to put the overall theme in the middle and then branch out several nodes. Then branch those out further,” he explains.
Google’s Search Relations team has explained why their SEO advice often sounds vague or comes with conditions, such as “it depends.”
In a recent Search Off the Record podcast, team members Martin Splitt and Gary Illyes shared the challenges that prevent them from providing clear-cut answers.
The discussion was part of what the team referred to as a “more human episode.”
The Googlers acknowledged they sometimes come across as robotic and used this episode to show a more human side.
Splitt works as Google’s bridge between developers and SEO professionals. He provided an example of how good advice can be distorted when people overlook the broader context.
At a Tech SEO Summit, he presented a slide with a bold statement about JavaScript performance. To prevent confusion, he added a note stating that the slide lacked context and provided a full explanation during the talk.
But even with that, he said the statement still got pulled out and repeated on its own.
“I had a remark on that slide saying there’s context missing here, and then I gave all that context… The problem with me saying that in general is that people will just take that one sentence and ignore everything else I said before or after.”
He clarified that JavaScript plays an important role in many web experiences, like enabling offline support. But that nuance often gets lost when single lines are quoted in isolation.
This loss of context is one reason why Google teams don’t typically share their presentation slides.
Illyes confirmed that slides on their own can be misleading:
He stated:
“Our slides without context, they are useless.”
The team sees what happens when advice meant for one specific situation gets used everywhere. This can hurt websites that have different needs.
For example, advice that works for a small local business might be wrong for a global company with websites in multiple languages.
Both Google reps know the SEO community gets frustrated with “it depends” answers.
Splitt even called it his “pet peeve.” But they explained why they can’t give simple yes-or-no answers.
Splitt noted:
“Someone who is serving a very specific niche with highly regulated content in a single country in a single language might have very different requirements than a multilanguage multinational brand that sells everything to everyone.”
They try to give more complete answers by explaining what factors matter. But this makes their advice longer and more complex.
The Google team also worries about how people use their quotes. Splitt said people often pick one statement while ignoring other important information.
Splitt explained:
“It often makes things tricky because people might cherry pick and might pick one thing you said, take that out of context and use it as an example why people should follow their agenda rather than ours.”
While they know public statements can be quoted freely, both reps feel bad when selective quoting gets out of control.
The Google team’s openness about their struggles affirms the experience of many SEO professionals.
Google’s guidance often feels cautious because it needs to account for a wide range of use cases.
Instead of seeking simple answers, focus on the factors that influence Google’s recommendations.
Understanding the “why” behind Google’s advice is more useful than chasing one-size-fits-all solutions.
Listen to the full podcast episode below:
Featured Image: Roman Samborskyi/Shutterstock
Dynamics in ad tech, particularly within the demand-side platform sector, are in flux, causing media agency practitioners considerable head-scratching. That’s why it’s good to talk with peers.
Media buyers attending Digiday’s Programmatic Marketing Summit last month expressed a desire to evaluate their DSP partnerships and explore opportunities to shift more spend to more favorable, i.e., transparent, platforms that better align with client needs.
Many wanted to explore the impact of trends, such as the rise of Amazon’s DSP on the ad tech market (particularly its potential to undercut market leaders, including The Trade Desk and Google’s DV 360), so fervently that a virtual follow-up session was called for.
Below are direct quotes from media agency participants of a virtual town hall conducted under Chatham House Rule on June 6, where discussions included the risk-reward analysis of market leaders, as well as the potential (much-needed) consolidation in the DSP market.
Microsoft Advertising’s pending closure of its Xandr DSP has arguably been one of the top ad tech stories of 2025, offering expansion opportunities for smaller players as the software giant recalibrates its broader media offering. Townhall participants shared insights on how Microsoft Advertising’s flagship relationship with Netflix may have hindered its ad tech ambitions more than it helped.
“I spoke with somebody at Xandr recently, and they told me the whole initiative was a massive fail for a variety of reasons,” noted one participant, observing how many advertisers felt Netflix inventory was “nascent and expensive,” resulting in low take-up at launch.
“There were also a lot of issues for those that did activate, and that ultimately reflected most negatively on Xandr, not on Netflix,” added the source, noting how many holdouts were waiting on the streaming service to “get cheaper” and offer more measurement capabilities. “Xandr took the brunt of the frustration, so ultimately, it was a big loss for them.”
A more subtle narrative in recent years has been the steady rise of Amazon’s DSP, with the e-commerce giant now ranked as the third-largest DSP behind Google and The Trade Desk.
According to participants, Amazon’s zero-fee supply-side platform services for publishers is a big competitive advantage, particularly as it hones in on The Trade Desk’s number two slot in the marketplace, even if it is “still a bit stuck together with gum and glue.”
“They haven’t really marketed their open market capabilities, and they’re still trying to work it out, but they give you the Amazon Marketing Cloud data to play with,” said the source, who compared this offering to Google’s DV 360. “That data is always going to be more accurate as it’s declared first-party data compared to these third-party additions you have to sync with things like UID2.”
Separate participants noted how Amazon Ads “is still a work in progress.” Still, it does have the resources to scale quickly once it organizes its operational issues. However, many are wary of creating “another Google,” i.e., an inflexible behemoth that knows advertisers really can’t afford to omit it from ad campaigns. “Amazon is not on our side,” observed one source. “They’re only in it for themselves, and they absolutely will squeeze everyone out as much as they possibly can eventually, when the time is right.”
The extent of customer dissatisfaction with Google was underlined in evidence aired in last year’s antitrust trial. While much testimony came from the industry’s sell-side, participants in the virtual town hall demonstrated it was mirrored on the buy-side.
“You don’t get lots of support unless you’re spending millions and millions and millions of dollars a month,” said one source, “even if you spend $100 million on Google, they still send you help center articles when you raise a support issue.”
Several participants voiced their hope that Amazon’s professed customer obsession (in its consumer-focused business) will be mirrored in its pursuit of the number one spot on Madison Avenue, as a means of differentiating itself from Google.
One participant noted the perceived lack of customer support for advertisers, claiming, “Google expired that whole [support] team a couple of years ago, now, you have to go through the resellers for support or email tickets to some place – God knows where – and it never works. When you do reach someone, they just read the same articles in the help center and repeat them back to you.”
However, in many cases, media buyers will put up with the pain because Google has the inventory that performs. Still, some are monitoring the developments in its ad tech antitrust proceedings, as a potential divestiture could alter this tolerance. “If they have to severe DV 360, that could really shake up the DSP market, because if it gets cut off from Google data, then what’s its real value?” asked one exec.
Town hall participants at last month’s summit Town Hall labeled The Trade Desk as “the Spirit Airlines of the DSP world,” balking at its spiraling list of campaign charges.
“I think The Trade Desk is still the most sophisticated DSP out there as far as the toolset, but the thing is they nickel and dime you on using any piece of the tools they have,” said one source, comparing it with Amzon’s offering, calling it “the most bare-bones DSP out there.”
Separately, an additional participant described the ongoing Kokai rollout, which was recently burnished with the commencement of its Deal Desk experimentation, as “a nightmare,” adding that documentation for its API is often hard to find. “At least with Google’s API, you know what you’re getting, you know where it’s going to break, and what the issues are,” added the source. “But with The Trade Desk, it’s something different every day.”
Another participant went on to vent their frustration with the fee structure. “It’s the lack of transparency, or investigative math you need to do in order to understand the fees,” they noted. “They go out of their way to make it challenging to understand the total cost.“
We love a good logo design secret, but it’s not often that we discover one from a game show. At first glance, the Milwaukee Brewers logo looks like a ball in baseball mitt. At second and third glance too. In fact, it turns out that even players on the team took years to notice anything else.
But once it’s been pointed out on the quiz show Jeopardy, it suddenly becomes obvious that the design has a clever secret: it also references the team’s name. We might need to reassess our pick of the best MLB logos because we missed this too!
https://t.co/grUCHPgadT pic.twitter.com/Hmoit4kX0ZJune 9, 2025
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How old were you when you realized the glove is also an “m” and a “b”? #glovestoryNovember 19, 2019
The original ball-in-glove Milwaukee Brewers logo (left) and the current design (right) (Image credit: Milwaukee Brewers logo)
Google has confirmed an ongoing disruption that is preventing some results from appearing in Google Lens, Discover, and Voice Search.
According to the company’s Search Status Dashboard, the incident began on June 12 at 1:00 p.m. Pacific Time. A follow-up entry posted at 1:16 p.m. states:
“There’s an ongoing issue with serving Google Lens, Discover, and Voice Search results that’s affecting some users. We’re working on identifying the root cause. The next update will be within 12 hours.”
At press time, the disruption is still marked as “Incident affecting Serving,” meaning the underlying services remain online but are not consistently delivering results.
Google Lens, the Discover feed, and Voice Search collectively drive significant traffic to publishers, ecommerce catalogs, and local businesses.
When any of these surfaces go dark or return incomplete results, sites that rely on them can experience abrupt drops in impressions and clicks.
Check for sudden drops in Discover, image, or voice traffic starting around 1:00 p.m. PT. If you see a temporary decline that matches the time on Google’s dashboard, this is likely due to the outage, not a ranking change.
Share Google’s official dashboard notice with website stakeholders. Mention that there will be another update from Google in 12 hours and explain that performance should return to normal once the service is back up.
Google hasn’t offered an estimated time of full resolution, committing only to provide another status update within 12 hours of the 1:16 p.m. post.
Historically, incidents affecting a limited number of users have been fixed within hours, although larger issues can take longer to resolve.
Until Google publishes its next update, the safest assumption is that Lens, Discover, and Voice Search services will remain unpredictable.
The core web search experience is currently listed as “Available,” so blue-link ranking checks and traditional query troubleshooting can proceed as usual.
Featured Image: Roman Samborskyi/Shutterstock
