Brand strength translates into audience loyalty.

A recognizable publisher will find it easier to convince its readers to install their dedicated app, subscribe to their newsletters, watch their videos, and listen to their podcasts.

A strong brand presence on the web is also, ironically, a cornerstone of AI visibility optimization.

LLMs are, after all, regurgitators of the web’s content, so if your brand is mentioned frequently on the web (i.e., in LLMs’ training data), you are more likely to be cited as a source in LLM-generated answers.

Exactly how to build a strong online publishing brand is the real question. Without going into specifics, I’ll repeat what I’ve said many times before: You need to have something that people are willing to actively seek out.

If you’re just another publisher writing the same news that others are also writing, without anything that makes you unique and worthwhile, you’re going to have a very bad time. The worst thing you can be as a publisher is forgettable.

There is a risk here, too. In an effort to cater to a specific target segment, a publisher could fall victim to “audience capture“: Feeding your audience what they want to hear rather than what’s true. We already see many examples of this, to the detriment of factual journalism.

It’s a dangerous pitfall that even the biggest news brands find difficult to navigate.

Optimizing For AI

In my previous article, I wrote a bit about how to optimize for AI Overviews.

I’ll expand on this in future articles with more tips, both technical and editorial, for optimizing for AI visibility.

More Resources:  

This post was originally published on SEO For Google News.

Featured Image: BestForBest/Shutterstock

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Digiday covers the latest from marketing and media at the annual Cannes Lions International Festival of Creativity. More from the series →

As thousands of agency folk grab their passports, sunblock and antacids to head to Cannes next week for the annual Lions fest, Omnicom has quietly hammered out its strategy and message it’s taking to the Croisette. That message is, it’s time to capture the myriad opportunities for live — be it live-streaming, sponsorship of live sports, live shopping, etc. 

Digiday has learned of a number of partnerships Omnicom and its various units (Omnicom Media Group, influencer arm Creo, commerce arm Flywheel) will be announcing next week with major platforms and retail media networks, all with an eye toward better understanding, harnessing and exploiting live opportunities for clients. Several Omnicom executives who spoke with Digiday talked of “the power of live content, conversation and commerce to drive brand growth.”

It’s all based on research Omnicom Media Group conducted that shows live can deliver more for clients thanks to evolving consumer habits and likes. Joanna O’Connell, Omnicom Media Group North America’s chief intelligence officer, spearheaded the research after OMG’s chief product officer Megan Pagliuca ID’ed the topic as one on which to focus. Nearly 1,500 U.S. consumers matched against U.S. Census data were surveyed the last week of April to generate the Rethinking Live study. 

“There is truly this kind of rebound toward shared experiences,” said O’Connell. “Even as we’ve moved in a direction of so much personalization and so much … time spent alone on screens, people crave togetherness, they crave community, they crave connection. So, ‘live’ in that sense, is even more important than ever, because it’s kind of creating moments that bring people together, and they’re drawn to that.”

“At Philips, we recognize the opportunity that livestreaming and creator-led content unlock for deeper customer engagement,” said Faith Lim, ASEASN digital and media lead for Omnicom client Philips. 

Some stats support Pagliuca’s hunch and O’Connell’s research as expressed in the report — which takes the concept of live far beyond live events like the Oscars or the Super Bowl, and into live-streaming, live shopping on a number of platforms (such as Amazon), second-screen experiences (such as conversations on X) while watching something live. For one, the study revealed that 75% of Gen Z watch social media live-streams while only 57% watch live TV content. In total, the live-stream e-commerce marketplace is estimated to hit almost $20 billion this year. 

“When we asked people what they associate with live, more people (all U.S. consumers, not a specific age group) now say ‘live streaming on social platforms’ than they will live TV. And that’s bananas,” said O’Connell. 

“Those are just massive numbers,” added Kevin Blazaitis, U.S. president of Creo, Omnicom’s influencer arm.

Three general findings came out of the research, said O’Connell. First, live-streams are the new prime time for younger people. One-third of younger respondents said they watch live-streamed content weekly, and they said it creates shared memories. What Omnicom will announce next week are a series of partnerships with a variety of platforms to harness this for clients, primarily through the influencers and creators who are live-streaming. 

Secondly, co-viewing is more of a thing than ever before, particularly in the esports world. But other examples such as YouTube’s “watch with” feature adds a creator commentary layer to live sports streaming, while Thursday Night Football on Amazon also offers creator-led commentary.

Finally, private channels can’t be overlooked, since they also factor heavily into the shared experience — be it private chats, or membership in clubs. It’s a way for people to communicate with each other one-on-one as they digest shared experiences. Fans watching a Knicks game live on their TV at home might be texting each other with comments. 

“This is more about questioning traditional notions than it is saying don’t do it anymore,” said O’Connell. “Because a live strategy needs to be a lot more expansive and sophisticated. You’ve got to be much more prepared to make real-time decisions. Your budgeting has to be more flexible. You need to be paying attention to social listening so you can get involved in conversation if it’s happening. There’s so much more depth and breadth to it.”

https://digiday.com/?p=580790

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Who are these 20-somethings? What shapes their worlds, and how do they see themselves within it? Across the event, students explored the textures of contemporary life with sincerity, navigating themes of identity, transition, belonging and disquiet. The goal was not to present polished answers but to offer something truer: a multifaceted portrait of youth, made with honesty and complexity.

From the surreal to the intimate, the projects spanned emotional and technical registers. Fragments of Becoming by Riccardo Vacca (IED Roma) used a mix of AI, visual anthropology and analogue photography to explore the liminal space between adolescence and adulthood. In Bianco Latte, Valentina Doddato (IED Milano) created a quiet reverie of memory, where nature and domestic space melt into a landscape of suspended time. Meanwhile, In Between by Sofia Valabrega (IED Torino) captured the stasis and uncertainty that defines so much of young adult life today: the longing to move forward, tempered by the fear of getting it wrong.

Curators and course coordinators Giulia Ticozzi (IED Torino), Carlotta Cattaneo (IED Milano) and Daria Scolamacchia (IED Roma) described the event as “a participatory performance” in which the table served as a stage, inviting physical movement, informal critique and shared reflection. The process was designed to empower students not just to show their work but to actively shape the space around it.

In doing so, The Dreamers became more than an exhibition. It evolved into a live archive, constantly reshaped by those who engaged with it. By placing vulnerability and agency at the centre, IED offered a vision of arts education that feels refreshingly grounded: creative, yes, but also responsive, participatory and deeply human.

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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.

Key Performance Indicators (KPIs)

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.

Short-Term Performance

Return On Ad Spend (ROAS)

• Definition: revenue/cost.

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.

• Benefits: Direct measure of advertising efficiency and helps provide a snapshot of campaign profitability.
• Limitations: Does not account for customer acquisition costs (CACs), margin, LTV, returns, shipping, etc.

Cost Per Acquisition (CPA)

• Definition: cost/sales or leads.

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.

• Benefits: Easy to monitor over time and helps assess efficiency.
• Limitations: Neglects revenue, customer acquisition cost, margin, LTV, etc., and treats all sales equally regardless of value.

Cost Of Sale (CoS)

• Definition: total ad spend/revenue.

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%.

• Benefits: Useful for margin-sensitive businesses and marketplaces where prices and/or Average Order Value (AOV) are volatile.
• Limitations: Can mask unprofitable sales (in some scenarios) if margin, returns, shipping, etc., are not considered.

Mid-Term Efficiency

Customer Acquisition Cost (CAC)

• Definition: total marketing costs spent on acquiring new customers/total number of new customers.
• Detailed definition: total marketing costs spent on acquiring new customers + wages + software costs + agency/consultancy fees + overheads/total number of new customers.

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.

• Benefits: Holistic view of acquisition cost, ideal for longer-term profitability analysis for paid media investment.
• Limitations: Not always the most suitable for channel-specific reporting (think account structuring, audiences, etc.), and can be a lagging metric as it doesn’t reflect short-term changes in performance like ROAS or CPA would.

Marketing Efficiency Ratio (MER)

• Definition: Sometimes referred to as blended ROAS, MER is calculated by dividing total revenue/total ad spend across all channels.

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.

• Benefits: Captures topline performance from a transactional perspective and simplifies multi-channel reporting.
• Limitations: Neglects exactly where the sales and revenue came from and obscures channel efficiency, especially important for search, social, etc.

Long-Term Strategic

Customer Lifetime Value (CLV Or CLTV)

• Definition: This metric estimates the total net revenue a customer brings over their relationship with a brand.

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.

• Benefits: Builds a foundation for tying performance marketing to long-term outcomes while helping give room to CAC targets across valuable customer segments.
• Limitations: Takes a fair amount of work to get set up and maintain, in addition to requiring a clean cohort and repeat purchase data. Additionally, when brands introduce new products/services, it can be hard to forecast accurate CLV numbers, and it will take time.

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.

The Ad Platforms

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.

The Bigger Picture

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.

Marketing Mix Modeling (MMM)

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:

1. Collecting aggregated data (roughly speaking, at least two years of weekly data across all channels, mapped out with every possible variable (e.g., pricing, promotions, weather, social trends, etc.)
2. Defining the dependent variable, which for ecommerce will be sales or revenue.
3. Run regression modeling to isolate the contribution of each variable to sales (adjusting for overlaps, lags, etc.)
4. Analyze, optimize, and report on the coefficients to understand the relative impact and ROI of your paid media activity as whole.

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.

Incrementality Testing

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:

1. Defining an objective or independent variable (e.g., sales, revenue, etc.)
2. Creating test and control groups. This could be by audience or geography – one will be exposed to the campaigns and the other will not.
3. Run the experiment while keeping all conditions equal across both groups.
4. Compare the outcomes, analyze performance, and calculate the impact.

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.

Operational Factors

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.

• Product margin.
• AOV variability.
• Shipping costs.
• Returns rates.
• Repeat rates.
• Discounting and promotions.
• Cancelled and/or failed payments.
• Stock availability.
• Attribute availability (e.g., size, color, model).
• Pixels and tracking.

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.

Bringing It All Together

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

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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.”

Setting the screens

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.

Having a lot of experience with photographic and 3D printers I was really excited to try this out for myself and spent a good couple of months trying things out, printing on everything from ceramic and wood, to canvas and acrylic.

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A side note, I was unable to test the rotary attachment as it got held in customs but I will update this review once it arrives with me.

Lastly, a thought on pricing. Currently, the E1 is £1257 for the base package. You can save with various bundles, so think about whether you want the rotary attachment or laminator, extra inks etc, as it looks to be jumping over 30% once the campaign ends.

What’s in the box?

Inks and cleaning kit are included (Image credit: Rob Redman)

E1 arrived in two large boxes. One housed the printer, along with the two print beds, a set of inks a cleaning head and a power supply. The other had a huge assortment of materials to print, from canvases to ceramic magnets and even a lightbox with some acrylic sheets to print. Everything was well packed and arrived safely.

The E1 is large but manageable. It has a similar footprint to a normal desktop printer but sits a little taller, to allow room for thicker materials and the rotary attachment. It’s worth noting that although the footprint of the E1 is a reasonable size, when in use, you will need extra space to the front and rear, especially when using the larger of the print beds, as it moves back and forward under the print head. Around an extra 40cm front and back is enough.

eufyMake E1: Setup and build

Everything is ready to go out of the box (Image credit: Rob Redman)

I was pretty excited to set the E1 up and get started but I did come up against a niggling issue. One of the inks wouldn’t register properly, and I kept getting an error message about the print bed. That said, a quick email to Efuy’s service team and they had me up and running in no time. I even got a video from them showing me what I needed to do, as well as a firmware update to fix the ink problem. This was a pre-production issue, so it won’t affect you if you buy one, but it was really refreshing to get such good service and very quickly, too.

The build quality is excellent. Everything feels robust, and the finish is nice. It definitely feels more like a home studio item than a workshop or more industrial unit.

Setup is easy. It’s really just a case of slotting the inks into their section on the top of the machine, adding the cleaning tank on one side and the air filter to the front (which is hidden behind a cover). Then you follow a calibration process, which can be done from your phone using the eufyMake app. It takes around 15 minutes, but there’s little user input in that time. Mostly the machine is doing its thing, until it alerts you its ready for use.

A note on software. You can run the software on a computer or a phone, with little difference in the process. I tried both and in the end stuck with the phone app, as it was more convenient. All the features are there, including calibrations, ink levels, as well as the fun stuff like design, printing from your photo library or the community projects tab. There are some AI tools too, which I’ll talk about later, but be aware there is a small cost to access some of them, using Eufy Coins.

eufyMake E1: Performance

Texture printing, with brush stroke generation is very good (Image credit: Rob Redman)

During the setup process, you install one of the print bed sheets, which does a calibration test. You can peel off the paper now, revealing the tacky surface below (which holds your materials in place, or you can swap to the larger print bed. This is done with a quick-release lever on the front and takes seconds to do.

Then place your material on the bed and take a snapshot of the bed. This serves two purposes. Firstly, it uses lasers to scan your material. You can use thicknesses up to around 60mm, and they don’t have to be perfectly flat either. Up to 5mm of surface variation is fine. Secondly, this also takes a photo, which you can see in the app, allowing you to fit your design to the material very easily. If you have any overlap, the E1 will print on the bed too. To prolong its life, I’d suggest adding a bit of masking tape around the edge of your material to catch that, although it isn’t a big deal if you don’t. The tacky print bed sheets are available in packs, so it’s a consumable part.

I started small, printing a simple photo onto a ceramic fridge magnet, resized and placed in the app. Once happy, you hit ‘create print job’ and you’re taken to the next screen, where you choose resolution and material type. There are a bunch of presets here for the most common types, but you can customise your own too.

You also get to choose what layers get printed. For a simple white magnet, CMYK is fine, but if you are printing on something non white, you can print a white base layer first. There’s also the option to add a glossy top coat.

You can see a couple of the ceramic results here, as well as some prints on canvas and wood. Every result turned out very impressive, even at the standard 70dpi (high quality is 1440 dpi and takes a little longer to print).

The E1 prints easily on rough or uneven surfaces (Image credit: Rob Redman)

Next I wanted to try something I’ve not done before: printing on glass for clear acrylic, to create a light box. Normally with the E1, you would print a white base layer, but in this case, you print CMYK only, which gives a transparent, almost stained glass look. Again, results were excellent, but this can be taken further by printing separate layers, so that when the light is off, you see just the white layer with line art, but when you turn on the light, the colour shines through. This is very effective and easy to set up.

Another trick of the E1 is texture printing. The E1 uses UV inks, akin to the resin used in 3D printing, meaning you can print with depth, although limited to 5mm. The app has some AI tools to aid in creating height maps, including a handy tool for generating brush strokes from a photo. First try turned out perfectly, and the photo of my daughter I printed on canvas looks very close to impasto paint. This did take a while as it builds up layer by layer. All in all, the total run time for this A3 print with a maximum height of 3mm was around four hours. All things considered, I think that’s not bad and it really showcases the versatility of the machine.

Printing onto wood works a treat too. I tried printing on ply, with a slightly rough surface, and the results speak for themselves. There’s talk of a conveyor which would be good for signage, skateboards and larger batch printing.

AI is seen as a dirty word by some, especially amongst the art community and, while the E1 has some generative features, AI is implemented here in a way that aids creativity, rather than replacing it. As I mentioned earlier, the eufyMake app can create brush strokes from your photos or art, to add depth to an image, or it can add height, do face swaps or convert your images into a different style and, fun as these things are, I can’t see myself using them all the time. I think the core strength of the E1 is its print quality when using a massive range of materials. It’s really very impressive when you think about it. This is really the only option, other than expensive bureau services, for this kind of thing. But it’s nice to have these options, and for some, they could well be key factors in a buying decision.

The E1 should ideally be left on, as it does run through some background processes, keeping the ink nozzles moist, cleaning and so on. It’s quiet enough when doing this, and you can turn off audio alerts. I did notice some odour, but nothing too intrusive, although I would suggest an open window might be wise. I doubt any fumes are harmful; otherwise you’d expect to see some kind of extraction port. That said, the open nature of the machine means that it would have limited capability anyway.

Note: Come back soon to see my thoughts on the DTF laminator and rotary accessories for this.

eufyMake E1: Price of printing

The E1 is From £1257 early-bird price, dependent on bundle, but that’s only part of the equation. I’ve seen a lot of questions online about the cost of ownership of the E1, and those are valid. All printers come with a cost associated with consumables, but this is the first of its kind in the home market, so there is really no benchmark. That said, the software does tell you how much ink is being used for a job, so you can work it out. The A3 print with depth came out at just under £1.82, and the ceramic magnets were about £0.08. I think these are very reasonable prices. Yes, a set of inks may be costly, but depending on what you are printing, they are fair. As it stands, you do have to use the Eufy-branded inks, but who knows, maybe third-party options will be released. I for one will probably stick with eufyMake, as they are consistently high quality and I would say cost-effective too.

Buy it if

• You like to customise things for clients and/or gifts
• You need the ability to print on a large selection of materials

Don’t buy it if

• A good paper inkjet already covers enough ground for you

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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.

Why Are LLMs So Bad At Citing Sources?

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.

Are We Creating An AI Spiral Of Misinformation?

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.”

Why Isn’t Mainstream Media Calling Out AI’s Limitations?

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.”

How Do We Explain The Traffic Reduction To News Publishers?

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.”

What Should Publishers Do To Survive?

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.

The Brand Identity Imperative

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.

Moving Forward

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 

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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.

The intelligence gap in traditional ABM programs

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.

How the integration works

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:

• Pull call transcripts from Gong.
• Analyze them using AI research agents to identify key insights, pain points and buying signals.
• Then automatically map those insights to accounts in your CRM.

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:

• Transcript analysis: It retrieves complete call transcripts and uses an AI agent to identify mentions of competitors, pain points, budget discussions, technical requirements and decision-making criteria.
• Signal-based automation: It can update account records, trigger follow-up sequences or alert sales teams when specific conversation triggers are detected, like a competitor mention or budget approval.
• Lookalike intelligence: It can also identify accounts in your target list matching the firmographic and behavioral profile of accounts where you’ve captured valuable conversation insights.

Dig deeper: Could AI be what finally aligns marketing and sales teams?

Conversation-driven lookalike targeting

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.

Strategic frameworks for implementation

The most successful implementations follow a systematic approach that puts conversation intelligence at the center of ABM strategy:

Framework 1: The insight capture loop

• Stage 1: Configure Clay to automatically pull Gong transcripts for target accounts. 
• Stage 2: Use an AI agent to analyze calls and extract key buying signals, pain points and competitive mentions. 
• Stage 3: Map insights to account records and score based on buying intent. 
• Stage 4: Identify lookalike accounts based on conversation intelligence. 
• Stage 5: Launch personalized campaigns to lookalikes using proven messaging.

Framework 2: Signal-based campaign triggers

Instead of running static ABM campaigns, use conversation triggers to launch dynamic sequences:

• Competitor mention trigger: When a prospect mentions evaluating competitors, automatically send battle card content and schedule competitive demos.
• Budget discussion trigger: When budget allocation is mentioned, trigger ROI-focused content and connect with sales for pricing discussions.
• Pain point identification: When specific challenges are identified, launch educational content series addressing those exact issues.

Framework 3: Multi-thread account penetration

The typical buying group for a complex B2B solution involves six to 10 decision-makers. Use conversation intelligence to map the complete buying committee:

• Analyze calls to identify mentioned stakeholders and their roles.
• Use Clay to find contact information for other buying group members.
• Create role-specific nurture sequences based on insights from similar accounts.
• Launch coordinated campaigns that engage multiple stakeholders with relevant messaging.

Dig deeper: Is your ABM strategy keeping up with the times?

The future of ABM is conversation-driven

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.

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