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Interactive’s Lizzy Jones Calls On Marketing Teams To Embrace ‘AI-Curious’ Culture

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By Melania Watson

Interactive’s head of data and AI, Lizzy Jones, has urged marketing and advertising teams to create workplaces where employees feel comfortable experimenting with AI, making mistakes and sharing unfinished ideas.

Speaking to B&T at the recent TechLeaders conference in Bowral, Jones said businesses needed to move away from cultures where employees feel they must have the “perfect answer” before speaking up, arguing that experimentation is essential as AI tools continue to evolve.

For Jones, creating an “AI-curious” culture is not about mandating the use of AI, but giving employees the time and psychological safety to experiment with it.

At Interactive, employees are given dedicated time to explore AI through fortnightly “AI Playground” sessions, where staff from across the business demonstrate how they are using the technology.

The sessions run for around 30 minutes on Friday lunchtimes, with employees typically spending 10 to 15 minutes showcasing an AI experiment, tool or process.

“Some people have slides. Some people do a bit of a demo. Some people walk through the process they’ve gone to get something up and running,” Jones said. “Some people come and show stuff that’s not quite working yet, so it’s really cool.”

Jones said the approach had helped employees think more laterally about how AI could be applied to their own work, rather than simply learning how to use individual tools.

“It’s how do you think, in a diverse way, laterally about how you could use it and how you could optimise for it?” she said.

The approach is also designed to tackle a cultural barrier Jones said could prevent employees from experimenting in the first place: a fear of being judged.

She said people at Interactive could sometimes be reluctant to speak up unless they were confident they had the right answer, particularly given the company’s mix of experienced technology professionals.

But AI has created an environment where “not everyone knows everything”, making it easier for employees to admit they do not know whether something will work and try it anyway.

“I think the AI thing is really cool because not everyone knows everything,” Jones said. “And so that’s been a really big shift for us, and it’s something we’re still promoting.”

Jones believes leadership also has an important role to play in creating that environment.

At Interactive, senior leaders were encouraged not only to learn about AI, but to demonstrate that they were using it themselves and openly discuss what worked and what did not.

“If you know people at the top are trying it, maybe it’s working, maybe it isn’t, that sort of gives you licence to try things as well,” she said.

Rather than creating additional AI-specific meetings and requirements, Interactive has also incorporated AI discussions into existing team meetings, making it a standing agenda item.

Jones said this helped keep AI “top of mind” without adding another layer of work to employees’ calendars.

Why teams shouldn’t be locked into one model

While Interactive has a company-endorsed AI tool, Jones said she did not believe creative and marketing teams should necessarily be restricted to one model.

The company endorses Microsoft Copilot for business use, particularly from a security and governance perspective, but employees are not prevented from accessing other models such as ChatGPT or Claude through their browsers.

Jones said organisations should continue testing different models because they are evolving at different rates and can produce different results.

She pointed to Microsoft’s efforts to bring multiple models into its Copilot environment, allowing users to compare outputs from different systems.

“I think it’s good to keep testing because they advance at different levels, and so different models are going to be better at doing different things,” Jones said.

However, once a team identifies a model that consistently delivers strong results for a repeatable task, Jones said there was little value in constantly switching between tools.

“You can just go ‘Great this model works really well, I’m not going to even think about it again for another sort of few months,.’”

The key, she argued, is avoiding a “model monopoly” without creating unnecessary noise for employees.

And as AI becomes more embedded in agency and marketing workflows, Jones also expects greater transparency around how the technology is being used.

She suggested agencies could increasingly spell out their AI practices to clients, while AI usage could also become part of contractual discussions.

Some clients may welcome AI if it creates efficiencies, while others may explicitly prohibit its use.

Jones said Interactive already works with some large government clients that have instructed the business not to use AI.

At the same time, she believes agencies could eventually build AI-driven productivity gains into their commercial arrangements, potentially reducing costs as certain processes become more efficient.

“You could start to build into some of your deals is like a productivity gain and you say, ‘Well, maybe we’ll use AI, and our services won’t be as expensive over time’,” she said.

Ultimately, however, Jones said organisations should focus less on forcing employees to use AI and more on giving them the opportunity to learn.

“The message we try to give people is this is your opportunity to learn and the company’s got your back, and we’ll actually create space for you to learn and try,” she said.

“This technology is not going anywhere, so it’s an opt-in thing. We don’t certainly mandate people do it or use it, but we’re trying to inspire them to get on the train, because the train has definitely left the station.”

Feature image credit: Lizzy Jones.

By Melania Watson

Melania is B&T’s senior reporter, covering all things martech and adtech across the industry. When she’s not chasing breaking news, she’s chatting with industry leaders to discuss the big changes in the marketing, advertising, and media landscape. She kicked off her journalism career in 2022 at TV3 in New Zealand as a digital reporter and producer, later moving into a technology reporter role that brought her to Sydney. Driven by a desire to push herself into a new niche, she joined B&T at the start of 2026.

Sourced from B&T

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Google Ads Display campaigns retire as Demand Gen takes over in 2026 Google’s Display Ads are dead – here is what replaces them in 2026

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By Luis Rijo

Google is phasing out standalone Display Ads campaigns and folding the Google Display Network into Demand Gen, with a migration tool launched in June 2026.

Google last month confirmed that standalone Display Ads campaigns are being retired, with the Google Display Network folded into Demand Gen as the new unified home for visual advertising across more than 2 million sites, videos and apps. A phased migration tool is now rolling out, and the full transition is expected to complete by 2027.

What is actually changing

The announcement, published on May 26, 2026 on the Google Ads and Commerce Blog, marks the end of Google Display Ads as a standalone campaign type. According to Google, advertisers can now manage their Google Display Network (GDN) presence directly through Demand Gen campaigns. The campaign creation workflow changes, but the underlying network remains unchanged – the same inventory of more than 2 million sites, videos and apps remains accessible.

The move is significant in scale and in what it signals. Display Ads have been part of Google’s advertising infrastructure for well over a decade. Folding them into Demand Gen is not a minor interface update. It is a structural consolidation that changes how advertisers set up, manage, and report on their display activity going forward.

What remains available? Advertisers who want to serve ads exclusively on GDN can still do so. According to Google’s Help Center documentation, customers looking for Display-only campaigns can continue purchasing them within Demand Gen using channel controls – a feature that lets advertisers isolate specific placements. The difference is that the campaign shell itself is now Demand Gen, not a standalone Display campaign.

The migration tool and how it works

Google is launching a phased rollout of its migration tool in June 2026. Eligible advertisers can begin moving existing Google Display Ads campaigns to Google Display Network on Demand Gen using the tool directly inside their Google Ads account.

The tool is the recommended path. According to Google’s Help Center, it allows advertisers to update live campaigns with performance history going back 42 days ported over to the new campaign. That historical data transfer reduces learning time to approximately 1 to 2 days and avoids a “cold start” – the period during which Google’s bidding models lack sufficient data and can underperform. The alternative is a manual budget transition, where advertisers gradually shift spend from existing Display campaigns to Demand Gen by decreasing budgets on the former while proportionally increasing them on the latter.

The step-by-step process inside Google Ads involves navigating to the Campaigns menu, filtering by campaign type for “Display,” selecting the campaigns to migrate, then choosing “Upgrade to Demand Gen” from the Edit dropdown. The migration can handle multiple campaigns simultaneously, though Google does not recommend batches of more than 100 campaigns at a time.

A few technical details matter here. Migrated campaigns will be renamed following the convention “[Original campaign name] #2.” The original Google Display Ads campaigns are not deleted – they are set to “Removed” status and remain in the account for reporting purposes for up to five years under Google Ads’ standard retention policy. Advertisers running lift measurement studies – such as Conversion Lift – are advised not to use the migration tool while a study is active, because the converted campaign will not be automatically associated with the ongoing study.

Budget handling on migration day carries a specific wrinkle. According to Google’s documentation, any budget spent earlier in the day on the Google Display Ads campaign will not be recognised by the new Demand Gen campaign. If a campaign has spent $10 of a $50 daily budget before migration, the new Demand Gen campaign resets and starts with the full $50 for the remainder of the day. Temporary underdelivery or overdelivery is possible within the first 24 hours.

Performance claims and real-world data

Google is citing two sets of numbers to support the migration. On average, according to the announcement, advertisers adding GDN in Demand Gen campaigns see a 9.5% increase in return on investment (ROI). The company also pointed to GoFood, a food delivery platform, as a case study. According to Google, GoFood saw a 24% decrease in cost per acquisition (CPA) and a 19% higher conversion volume after adding GDN to its Demand Gen campaigns.

These figures come from Google’s own data and should be read in that context. The 9.5% ROI improvement and GoFood case study reflect conditions that may not transfer uniformly across industries, account sizes, or competitive environments. Advertisers who have relied on standalone Display campaigns for years will need to test and monitor performance independently after migration, particularly during the first several days when fluctuations are expected.

Feature changes: what is gained and what is lost

The move from Google Display Ads to GDN in Demand Gen involves a meaningful shift in feature availability. Not everything carries over directly, and some capabilities available in Display Ads are not available in Demand Gen.

On the inventory side, Demand Gen expands reach beyond what Google Display Ads offered. Where Display Ads ran across GDN, YouTube, and Gmail, Demand Gen adds Discover and Maps (Maps currently in beta). The ad surfaces available expand from three to five, giving advertisers who opt into them a broader footprint.

Creative formats also expand. Google Display Ads supported Responsive Display Ads, uploaded Display Ads (image ads, HTML5, GIF), and product feeds from Google Merchant Center and Business Data Feeds. Demand Gen adds carousel ads, generative image tools, a wider range of video ad formats, and HTML5 (described in the documentation as “coming soon” for GDN in Demand Gen). GIF support, however, is not listed among the available formats in Demand Gen.

On the audience targeting side, the transition introduces Lookalike segments as a replacement for Similar Audiences. Lookalike segments allow advertisers to reach new users who share characteristics with existing customers. The core targeting options – optimised targeting, remarketing lists, custom segments, first-party data, interest, demographics, and contextual targeting – remain available in both formats.

Bidding changes in specific ways. Manual CPC, Viewable Impressions, and Pay for Conversions are not available in Demand Gen. What replaces them are Max conversions, Max conversion value, tCPA, tROAS, Max clicks, and a new option called tCPC (target cost per click). Demand Gen also introduces Flighted Campaign Total Budgets, which are not available in Google Display Ads.

Pay for Conversions campaigns specifically are handled by the migration tool automatically. According to the documentation, these campaigns will be switched to pay for clicks, while continuing to use target CPA to optimise for conversions at the advertiser’s stated target.

Reporting gains one notable capability in Demand Gen: format segmentation reporting. This breaks down performance data at the format level, including In-Feed, Skippable In-Stream, and Shorts, giving advertisers visibility they did not have within standalone Display campaigns.

Logos and business names are pre-populated from existing Google Display Ads campaigns during migration. If a Display Ads campaign lacks a logo, the migration tool creates a placeholder image to ensure continuity. Advertisers can edit this after migration completes.

Key dates and what comes next

The timeline for this migration has three stages, according to Google’s Help Center documentation.

June 2026: The phased rollout of the migration tool begins. Eligible advertisers can start moving existing Google Display Ads campaigns to GDN on Demand Gen using the tool in their Google Ads accounts.

Coming later (no specific date provided): New Google Display Ads campaigns can only be created within Demand Gen. Advertisers can still access and edit existing Display Ads campaigns until they are migrated.

Coming later (no specific date provided): Remaining eligible Google Display Ads campaigns will be automatically migrated to GDN on Demand Gen without any advertiser action required.

The full migration is expected to complete by 2027. Google has said it will provide account notifications in Google Ads as key dates approach.

Where the money goes – and does not go

There is a dimension to this migration that sits outside the migration checklist: the question of who ultimately benefits from ad spend flowing into Demand Gen rather than standalone Display, and how the balance between Google-owned and publisher inventory shifts in the process.

Google Display Ads – the format being retired – ran by default across the Google Display Network, a third-party publisher ecosystem spanning more than 2 million sites and apps. When an advertiser ran a Display campaign and ads served on an external publisher’s website, a portion of that revenue was shared with the publisher via AdSense or Google Ad Manager. That revenue-sharing model has underpinned publisher monetisation across the open web for over a decade.

Demand Gen has a more layered inventory structure, and understanding it requires separating the surfaces available within the campaign type. The primary surfaces in Demand Gen – YouTube, Discover, Gmail, and Maps – are all Google-owned properties. Discover is Google’s personalised content feed. Gmail is Google’s email service. Maps is Google’s navigation product. According to PPC Land’s analysis of Demand Gen placements, unlike traditional YouTube ads where Google must split revenue with video creators, Demand Gen placements on surfaces like Discover and Gmail allow Google to retain a larger portion of advertising revenue. Those surfaces carry no revenue share obligation to external publishers.

GDN is also available within Demand Gen – but the mechanics are different from how standalone Display Ads worked. In a standard Display campaign, GDN was the entire network, and every impression served on a third-party publisher site generated revenue for that publisher. In Demand Gen, GDN is one channel among several, accessed via explicit channel controls at the ad group level. The default campaign configuration points spend toward YouTube, Discover, Gmail, and Maps. Advertisers who want GDN inventory must actively opt into it. That shift from default to opt-in is consequential at scale.

In Display & Video 360, the distinction is encoded directly into the reporting infrastructure. According to PPC Land’s coverage of DV360’s granular inventory controls, the standard Inventory Source reporting dimension labels YouTube, Discover, and Gmail traffic as “Google Owned Properties,” while GDN traffic appears separately as “Google Display Network.” Google itself maintains that boundary in its own systems – a clear delineation between inventory where revenue stays with Google and inventory where a share flows to external publishers.

The financial trajectory of Google’s Network advertising segment – the one that pays out to publishers – makes the stakes concrete. Google’s advertising revenue distribution reached a point in mid-2025 where 90% of revenues were flowing to its own properties rather than through publisher partnerships, according to PPC Land’s analysis following Alphabet’s Q2 2025 earnings. Network advertising revenue – covering AdSense, AdMob, and Google Ad Manager – declined 1% year-on-year to $7.4 billion in Q2 2025. By Q1 2026, that figure had fallen further to $6.97 billion, a 4% year-on-year drop of approximately $285 million in a single quarter, according to PPC Land’s coverage of Alphabet’s earnings release.

The Display Ads migration adds structural momentum to that decline. In standalone Display campaigns, GDN publisher inventory was the default and the entire point of the campaign type. In Demand Gen, GDN is an optional channel that requires a deliberate activation step. Every advertiser who migrates without explicitly enabling GDN via channel controls will, by default, direct their visual advertising spend toward Google-owned surfaces where no publisher revenue share applies. Budget that previously flowed to external websites through AdSense now flows to YouTube, Discover, Gmail, and Maps instead.

Google’s Network ad revenue decline has also been attributed in part to AI Overviews reducing click-through rates from search results, which reduces traffic to publisher sites and therefore ad impressions served through AdSense and Ad Manager. The Display migration applies pressure from a different direction: it reduces the share of advertiser display budgets that flow to external publishers, independent of what happens to search traffic volumes.

GDN inventory remains technically available inside Demand Gen, and publishers on the network can still earn revenue from advertisers who opt in. That is the honest limit of the claim. But the structural change is real: the campaign type that made GDN the default has been retired, and the one replacing it treats GDN as an elective channel within a portfolio that tilts heavily toward Google’s own properties.

Why this matters for the marketing community

This consolidation is one of several structural moves Google has made to reduce the number of distinct campaign types in its advertising platform. The retirement of YouTube Video Action campaigns in favour of Demand Gen completed by April 2025, and Demand Gen was expanded to Display & Video 360 in October 2024. The pattern is consistent: Google is reducing campaign-type fragmentation and concentrating activity inside a smaller set of formats built around its AI-driven bidding and creative tools.

Google quietly removed Display and Video campaign support from Performance Planner on March 9, 2026, eliminating the ability to model those campaigns in its forecasting tool. Taken together with the Display migration announcement, the signals from Google have been clear for months. Standalone Display infrastructure is being wound down.

The Google Marketing Live 2026 announcements in May included Demand Gen’s expansion to Google Maps with Promoted Pins, and further AI-assisted campaign creation tools – all pointing toward Demand Gen as the primary canvas for visual advertising across Google’s owned-and-operated surfaces.

For advertisers, the operational consequences are real. Campaigns that have accumulated years of performance history inside Google Display Ads will need to be migrated. The migration tool attempts to transfer 42 days of historical data to ease the transition, but the learning reset is not zero – performance fluctuations within the first several days are explicitly flagged by Google’s own documentation. Advertisers running lift studies or time-sensitive campaigns will need to plan the migration window carefully.

The removal of Manual CPC and Pay for Conversions bidding in Demand Gen will also require workflow changes for teams that have built their optimisation processes around those options. February 2026 changes to how Lookalike segments function in Demand Gen – converting them from hard targeting constraints to audience suggestions – had already altered the audience control model. The Display migration adds another layer to the adjustment.

Advertisers who re-approve migrated ads should expect them to go through the standard approval process. According to Google, migrated ads are treated as newly created ads regardless of how they are transitioned, which means they must be submitted for approval before they can serve. This is particularly relevant for advertisers planning to migrate close to a campaign’s scheduled start date.

What best practices say

Google’s Help Center outlines recommended steps for advertisers using the migration tool. On channels, the guidance is to keep GDN-only selected in channel controls during migration to ensure a proper campaign setting transfer – additional channels like YouTube and Gmail can be added after the migration is complete.

On audiences, Google recommends replicating the audience approach from comparable Display campaigns and testing Lookalike segments. On bidding, the advice is to try similar bid levels to existing Display campaigns, set the conversion attribution window to more than 28 days, and limit bid changes to no more than plus or minus 15% – waiting at least a week between adjustments.

On creative, Google recommends expanding the number of assets, including a business logo and video assets. According to the documentation, this allows ads to scale across the widest possible range of inventory slots, which typically leads to stronger overall performance.

Campaign consolidation is also advised: combining similar audience themes across ad groups, and considering merging ad groups with fewer than approximately 30 conversions in 30 days. A consolidated campaign structure allows Google’s AI to learn more efficiently.

Timeline

Summary

Who: Google, affecting all advertisers currently running Google Display Ads campaigns globally, announced via the Google Ads and Commerce Blog.

What: Google Display Ads campaigns are being retired as a standalone campaign type. The Google Display Network is being folded into Demand Gen as the unified home for visual advertising, expanding the available ad surfaces from GDN plus YouTube and Gmail to also include Discover and Maps. A migration tool is launching in June 2026 to help advertisers transfer existing campaigns, with 42 days of performance history ported over. The full migration is expected to complete by 2027.

When: The announcement was published on May 26, 2026. The migration tool rollout begins in June 2026. A future date – not yet specified – will prevent the creation of new standalone Display Ads campaigns. A second future date will trigger automatic migration of all remaining Display campaigns.

Where: The change applies to Google Ads globally. Advertisers manage the migration from within their Google Ads accounts. Google Display Network inventory – more than 2 million sites, videos and apps – remains available via Demand Gen campaigns after migration.

Why: Google frames the migration as a response to shifting consumer behaviour and a push toward more unified campaign management. The consolidation aligns with a multi-year pattern of reducing standalone campaign types: Video Action Campaigns were absorbed into Demand Gen by April 2025, Display was removed from Performance Planner in March 2026, and Google Marketing Live 2026 confirmed Demand Gen as the primary vehicle for visual advertising across Google’s owned-and-operated surfaces. Advertisers adding GDN in Demand Gen campaigns see on average a 9.5% ROI increase, according to Google’s own data.

By Luis Rijo

Luís Rijo is a seasoned marketing professional with over 10 years of experience in Digital Marketing, Search, Social, Display, Video, and DOOH. Based in Europe. Reach out via [email protected]

Sourced from PPC Land

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Google Rewrote The Rules Of Search, And Many Marketers Haven’t Noticed

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By John Readman

Google announced a shift in how search works after more than 25 years of relative stability. It confirms what agencies like us have been telling clients for months: that Google is now an AI engine. From a familiar search box to AI Mode, the way online searches happen has changed.

​What Exactly Has Changed?

AI Mode and AI Overviews have been rolled further into Google Search, shifting the experience from a list of links to a synthesized response layer built on top of traditional search results.

The most visible change is, of course, the search box. Relatively unchanged for decades, Google now prompts users directly into new AI-led features. Instead of just autocomplete suggestions, users are now offered routes into Gemini models, image and document uploads, and conversational prompts in a way that mirrors ChatGPT. Not only that, but Google now actively encourages long-form conversational queries rather than short keyword-based searches. The search engine is adapting to user behaviour in a way it never has before.

Other Shifts You May Have Missed

Alongside the update to search, Google also shared insights into how people in the U.S. are using AI Mode. Voice and image searches now account for more than one in six searches overall, with image searches growing over 40% month over month. The shift to more conversational search also has become evident, with the average AI Mode search being three times as long as a traditional search query. Perhaps most interesting is the reported 80% increase in “queries related to planning” over the last six months.

​In other words, many people aren’t using AI Mode to answer a question; they’re using it to achieve an outcome. They’re asking real questions that they might have asked a person before. These are queries that may have multiple stages and multiple constraints requiring consideration. Instead of asking questions about a holiday destination (e.g., “best time to visit Paris,” “best hotels in Paris,” “best restaurants near Pigalle”), people are using AI Mode to plan entire trips, with queries that look something like this: “I have a $2,000 budget, I’m free in July or August, I love art, plan me a week visiting Paris.”

​What Do These Changes Mean For Brands?

The entire user journey can now take place within a search engine. A 2024 study by SparkToro found that zero-click searches accounted for 58.5% of all U.S. Google searches. Google’s search revolution means that number is probably going to get much, much higher.

The shift is designed to make users’ lives more convenient, but it could make yours a lot harder. As AI agents bring more interactions away from your site and centralize them within search engines, the typical ways we track and analyse user data have to change. We no longer can rely on website sessions, UTM codes or clean user click journeys, making your current attribution model largely obsolete.

​What Should You Do About It?

While Google’ overhaul represents a new phase in how we view user journeys, build our brand presence online and measure success, the fundamentals of search are still the same. Google still values good quality brands that understand their customers, and are trusted by others—it has simply developed more sophisticated signals that can meet the needs of evolving user expectations and behaviours.

Here are some ways to start adapting:​

Unify Organic, Paid And AI Search Strategies

Stop treating organic, paid and AI search as separate channel conversations. Visibility is shifting from channel silos to a unified system. AI synthesizes information from multiple sources, so public relations, SEO, paid media and content all feed the same underlying entity signal.

Stop Focusing On Rankings, And Optimize Your Brand Entity

Visibility is shifting from pages to entities, so your brand, products, leaders and locations need consistent signals across the web. AI doesn’t just pick one ranking URL; it pulls and synthesizes information from across sources to fully understand your brand.

Stop Separating AI And GEO; Embed Them

There is no separate strategy for AI search. Answer engine optimization (AEO) and generative engine optimization (GEO) are essentially just SEO applied to an AI surface. Google still rewards helpful, structured and authoritative content, but now puts more emphasis on clarity and extractability.

Start Measuring Causal Outcomes, Not Clicks

AI search and zero-click environments mean users can be influenced without ever visiting your site. Click-through rates and sessions alone don’t show the full picture anymore. Instead, focus on things like brand lift, incremental conversions, geo experiments and assisted conversions.

Ensure You’re Present Throughout The Conversation

Search is becoming a planning and decision-making interface. Users are no longer just asking questions; they are trying to solve entire problems inside search. So you can’t just answer isolated queries with blog content. You need to support decisions by making sure your brand appears in comparison, constraint-based and recommendation-driven queries.

​As Google has adapted to keep users within its platform, you can no longer rely on a single page or ranking. You need to optimize your online footprint so your brand is present throughout complex planning journeys—not just answering one question, but being consistently surfaced across follow-up prompts, comparisons and refinement stages.

This update goes way beyond technical changes; it’s behavioural. Search is moving from a system that helps users find information to one that helps them make decisions. The question is no longer just “How do I rank?” It’s “How do I stay present while decisions are being made?” It’s time we all started taking a lot more notice.​

Feature image credit: Getty

By John Readman

Find John Readman on LinkedIn. Visit John’s website.

COUNCIL POST | Membership (fee-based)

John Readman is the CEO of ASK BOSCO, an AI-powered marketing platform that brings all your marketing and e-commerce data into one place. Read John Readman’s full executive profile here.

Sourced from Forbes

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The E-Commerce Profitability Crisis: Why Revenue Growth Has Become A Dangerous Metric

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By John Readman

​Over the past 25 years, I’ve seen e-commerce change in almost every way. With benefits such as improved accessibility and insight for customers, and innovation and efficiency for vendors, this evolution has been largely positive for buyers and sellers. In recent years, however, I’ve seen profitable businesses become unprofitable as they grow, and that should be a concern.

As acquisition costs rise and privacy restrictions challenge the established strategies businesses rely on, revenue growth as the sole measure of success has become a trap.

The Growing Gap Between Revenue And Profitability

Customer acquisition has always cost businesses money. In 2013, industry benchmarks were around $9 per customer. By 2022, that cost had risen 222% to $29. A 2025 Deloitte report estimated that, depending on the channel, acquisition costs across the board increased by 25% to 40%.

For every first purchase by a new customer, you could potentially be losing over $30 before considering the wider economics of that customer relationship.

While your revenue graph might show growth and customer acquisition numbers might be increasing, the hidden costs behind that growth could be masking a decline in profitability.

Rising Advertising Costs

One of the biggest drivers of increasing acquisition costs is advertising. According to a Triple Whale analysis of data from over 30,000 brands, cost per acquisition (CPA) for paid advertising increased by 8.64% in 2025. For businesses relying on a single channel to drive sales, increases like this year after year can quickly erode profitability.

Customer Loyalty

The cost of acquisition is only part of the problem. The SAP Customer Loyalty Index showed a 5% drop in customer true loyalty from 2024 to 2025. And many customers won’t return for repeat purchases, where businesses generate their strongest margins.

Privacy Changes

Changes to data privacy have perhaps had the biggest impact on the e-commerce growth model. As more users opt out of third-party tracking, the ability to optimize campaigns and target customers has been significantly reduced.

This lack of visibility has also impacted reporting and attribution, making it harder for businesses to accurately understand which activities are driving profitable growth rather than simply generating revenue.

ROAS Is No Longer Enough

The unfortunate truth is that Return on Ad Spend (ROAS) is increasingly limited as a measure of e-commerce performance. It’s a metric built around an e-commerce market that has changed dramatically. Looking at the revenue generated from acquisition spend without factoring in margin at a more granular level does not provide a complete picture. ROAS rewards revenue and revenue alone.

For example, imagine you are running two campaigns, Campaign A and Campaign B. On the surface, both appear successful, delivering five times ROAS and generating $50,000 in revenue. However, the product in Campaign A has a margin of 20%, compared with 50% for the product in Campaign B. While both campaigns generate the same revenue, Campaign B creates $25,000 in profit compared with just $10,000 from Campaign A. ROAS does not make that distinction. Profit on Ad Spend (POAS) does.

The Hidden Costs That ROAS Misses

So why is ROAS a less effective metric now? Advertising costs have increased significantly, meaning businesses are spending more to acquire customers, without those costs reflected in traditional ROAS calculations.

Product margins are also ignored when evaluating campaign effectiveness. Platform fees, shipping costs and operational complexity all impact profitability.

A shift in returns culture since the pandemic has also had a massive impact. In 2019, the average retail return rate was 8.1%. A 2025 National Retail Federation report estimated an overall return rate of 15.8%, with a 19.3% rate for online sales. Looking at revenue without accounting for almost one in five products being returned no longer provides an accurate picture of profitability.

Profitability Is Moving To Product Level

The problem is not that e-commerce businesses lack data. Most retailers have access to more reporting than ever before. The problem is that the data can lead many of them to the wrong decisions.

By focusing on the profitability of individual products, businesses can avoid the revenue trap and identify where real value is being created. For years, e-commerce teams have optimized around channels, but the channel is only part of the story. The real driver of profitability is the product itself.

Some, often less glamorous, products have strong margins, attract valuable customers and encourage repeat purchases. Others generate impressive revenue but hemorrhage profit once return rates, advertising costs and other fees are taken into account.

The next shift in e-commerce performance will be moving from channel-level reporting to product-level profitability. Retailers need to understand not just what is selling, but what is creating value. This requires connecting data that has traditionally been siloed: advertising performance, product margins, customer behavior and commercial outcomes.

A new generation of e-commerce analytics platforms is helping businesses make this shift, moving beyond reporting on past performance toward predictive analytics.

AI Will Make This Shift Unavoidable

AI shopping agents will accelerate this change. For years, e-commerce success has been about clicks and conversions. But that model is changing. As AI increasingly helps consumers compare products, evaluate options and make purchasing decisions, retailers will compete in an environment where product value is an essential metric.

AI systems are designed to understand context, compare alternatives and recommend what best fits a customer’s needs. To benefit from the rise of agentic commerce, businesses will need a much deeper understanding of their own products, margins and customer value.

Successful retailers understand which products deserve attention and where profit actually comes from.

E-commerce has spent the past decade optimizing for growth. But as acquisition costs increase, privacy changes reduce visibility and AI reshapes how consumers discover products, the smartest businesses will understand exactly where their profits come from and build strategies to maximize them.​

Feature image credit: Getty

By John Readman

Find John Readman on LinkedIn. Visit John’s website.

COUNCIL POST | Membership (fee-based)

John Readman is the CEO of ASK BOSCO, an AI-powered marketing platform that brings all your marketing and e-commerce data into one place. Read John Readman’s full executive profile here.

Sourced from Forbes

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McDonald’s new billboards are painfully relatable

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McDonald’s has dropped an ingenious new campaign that celebrates the joys of the humble McFlurry – no matter the occasion. From breakups to bad jokes, the simple billboard campaign is a perfect homage to life’s little moments, imperfectly perfect as they are.

In line with the best billboard ads, McDonald’s new campaign is a perfect blend of clever copy and bold, eye-catching design that shines in its simplicity. Quirky, charming and painfully relatable, McDonald’s perfectly nails the no-frills ad.

TBWA\Paris McDonald's France billboard ads

(Image credit: TBWA\Paris/ McDonald’s France)

Created by TBWA\Paris and McDonald’s France, this quirky campaign is all about life’s contrasts. In one example, the billboard compares being the one who “tells the joke” vs “be the joke”, while another compares “pure beauty” to the awkwardness of “puberty”. There are even a couple of World Cup tie-ins like “5-0” vs “0-5” and “game winner” vs “game whiner”, proving the flexibility of this clever ad formula.

TBWA\Paris McDonald's France billboard ads

(Image credit: TBWA\Paris/ McDonald’s France)

But while each scenario might be different, one thing remains constant – the iconic McFlurry. Positioned as the perfect companion for life’s highs and lows, the ice-cold treat becomes not just a dessert, but a universal symbol of celebration and consolation. With simple visuals and a distinctly human personality, the billboard campaign is a prime example of how billboard advertising doesn’t need to be flashy to catch attention.

TBWA\Paris McDonald's France billboard ads

(Image credit: TBWA\Paris/ McDonald’s France)

For more branding inspiration, check out Polaroid’s new anti-AI billboard or take a look at Ikea’s new ad that’s pure satisfaction in a billboard.

Feature image credit: TBWA\Paris/ McDonald’s France

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Natalie Fear is Creative Bloq’s staff writer. With an eye for trending topics and a passion for internet culture, she brings you the latest in art and design news. Natalie also runs Creative Bloq’s 5 Questions series, spotlighting diverse talent across the creative industries. Outside of work, she loves all things literature and music (although she’s partial to a spot of TikTok brain rot).

Sourced from CREATIVE BLOQ

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Starbucks is paying baristas to make TikToks. Expect more brands to follow.

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By Crystal Bell

Employee-generated content is quickly becoming one of the creator economy’s fastest-growing marketing strategies

For years, brands have spent billions chasing influencers with massive followings. Increasingly, they’re realizing some of their most persuasive creators are already on the payroll.

In June, Starbucks announced a TikTok update to its ongoing Green Apron Creators program, which will now pay select baristas to make TikTok videos through what the company says is the platform’s first custom Creator Network for a brand. In the pilot program, employees will receive creative briefs, create content for Starbucks’ TikTok presence, and share in advertising revenue — a notable shift from simply encouraging workers to post organically.

On its face, the program looks like another influencer marketing campaign. But it’s really a sign that brands are beginning to formalize employee-generated content as a core marketing strategy.

The timing isn’t accidental

For years, Starbucks has had a complicated relationship with Gen Z. The company became the target of widespread boycott campaigns amidst the Israel-Hamas war after what Starbucks called misinformation spread online about its political positions, while separate criticism surrounding its response to unionization efforts fueled additional backlash. Whether justified or not, the result was the same: Starbucks suddenly found itself fighting for credibility with a generation that increasingly values authenticity over polished advertising.

Now, rather than relying solely on outside creators, it’s betting that the people behind the counter can help rebuild that connection.

It’s a strategy that feels tailor-made for TikTok

Long before Starbucks announced Green Apron Creators in 2025, baristas were already making viral videos about complicated drink orders, secret menu hacks, and what really happens behind the espresso machines (especially when those limited-edition Bearista cups hit the shelves). Those posts often performed because they felt less like advertisements and more like behind-the-scenes access.

It’s the same phenomenon that turned everyday workers into unlikely internet personalities. A Staples Baddie explaining office supplies. Costco workers revealing hidden perks. Trader Joe’s crew members showing off their favorite seasonal finds. Audiences tend to trust people who actually do the job.

That authenticity has become increasingly valuable

Recent data from Sprout Social found that 61 percent of Gen Z discover products through employee-generated content, reflecting a growing preference for real people over highly produced influencer campaigns.

Starbucks isn’t alone in recognizing that switch.

Dell has spent years building an internal ambassador program through its Social Media University, training roughly 1,200 employee creators across more than 80 countries. Sprout Social’s employee advocacy program has grown from just six participants to roughly 100 employees, who accounted for nearly 30 percent of the company’s video impressions in 2025 while producing less than 8 percent of its content.

 

The economics make sense

Brands spent an estimated $32.55 billion on influencer marketing in 2025, according to Later’s State of Influencer Marketing report, but the industry is steadily moving toward smaller creators. eMarketer projects that micro- and nano-creators will account for nearly half of influencer marketing spending in 2026, and Mashable’s own survey data found that among those who follow creators, 62 percent are attracted to creators who feel like “real people.”

Employees often fit that profile perfectly. They already understand the brand, already have firsthand expertise, and don’t require expensive talent contracts. More importantly, they feel authentic and relatable — qualities audiences increasingly value over polished, highly produced marketing.

In many cases, they’re also creating the kind of content audiences actually want to watch.

In 2026, the creator economy is moving closer to everyday people. First, brands shifted budgets away from celebrities toward influencers. Then they embraced micro-creators over mega-influencers. Now they’re discovering their own employees may be among the most credible creators they have.

Starbucks’ Green Apron Creators program may be the clearest sign yet that the next frontier of creator marketing isn’t finding new influencers. It’s recognizing the ones already wearing the uniform.

 

Feature image credit: Jeffrey Greenberg/Universal Images Group via Getty Images

 By Crystal Bell

Crystal Bell is the Digital Culture Editor at Mashable, where she leads coverage of the creator economy, internet culture, and digital life. Her work focuses on the people, platforms, and communities shaping modern entertainment, from YouTube creators and livestreamers to fandoms, social media trends, and the evolving relationship between technology and culture. She also oversees the Mashable 101, the publication’s annual list recognizing the internet’s most influential creators.

Previously, she was the entertainment director at MTV News, where she helped expand the brand’s coverage of fan culture, K-pop, and the internet’s most passionate communities. Her work has appeared in Teen Vogue, Rolling Stone, PAPER, NYLON, ELLE, Glamour, NME, W, The FADER, and elsewhere on the internet.

She’s exceptionally fluent in fandom and will gladly make you a K-pop playlist and/or provide anime recommendations upon request. Crystal lives in New York City with her two black cats, Howl and Sophie.

Sourced from Mashable

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Marketing Procurement Is Changing: What Agencies Need To Know

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By Michelle Douglas

​It wasn’t that long ago when agencies encountered procurement executives during contract negotiations, and rarely beyond that. But procurement executives’ roles are expanding, and they’re more visible than they’ve ever been. You might be seeing them at more industry events, but you’re also seeing them in more meetings every step of the way. And that’s because the needs of marketers have evolved. AI is upending the industry, performance metrics are critical to success, and technology stacks are ever-changing. As marketing departments respond to these market changes, procurement executives’ scopes are evolving in parallel. ​

On the agency side of the equation, the business is under pressure to prove value, and the expectations for the quality and effectiveness of the work are only going up. Shops these days have an opportunity to work with procurement as a strategic partner, one that seeks to help marketing drive impact and one that can help agencies show that value and effectiveness.​

Here are a few ways I’m seeing the agency-procurement relationship evolving, and how agencies can better work with an increasingly crucial client-side role.​

Embrace value-based pricing.​

Procurement leaders are increasingly in favor of moving away from billable hours and are keen to structure fees based on outcomes. The time-and-materials fee model is not going to be viable much longer, and despite widespread industry conversation, agencies have not adapted. It’s time. ​

Value-based pricing can mean different things depending on the client and the work, but agencies should prioritize these new models, whether that’s a fixed fee plus performance bonuses or pricing based on the value of each deliverable. When it comes to pricing by deliverable, it’s important to remember that each deliverable has a different level of impact. And in a world where AI is commoditizing some deliverables, there are still plenty that require human ingenuity and complex thinking. Those should be priced higher because they create more value in the end, while assets created with a heavy helping of AI can be a flat fee ​(oftentimes these are downstream production deliverables).

Charging hourly rates is outmoded; procurement is ready for next-gen pricing models that align with impact, creativity and technology-powered value, and value-based pricing is how we get there.​

Be transparent about the details.

In the past, agencies sometimes didn’t talk a lot about the tools they used or go deep on their proprietary processes, mainly because clients cared more about what was produced in the end. But with the next wave of innovation coming from AI, and the proliferation of tools that use it, there’s a greater need for transparency with procurement executives. They want to know how you plan to use AI to propel shared ambition while managing risk. ​

This will ultimately play into your value-based pricing strategy. So when you’re discussing your use of AI, whether that’s for concepting or versioning hundreds or thousands of assets or some other kind of automation, procurement can see exactly what goes into your workflow. Transparency around your process and the tools you’re using will strengthen the relationship and enable you to charge a premium for your higher-value and more strategic deliverables, even if you’re making less on the lower-value ones.​

Enable procurement to redefine its partnership with marketing.​

Many procurement executives are looking to build stronger relationships with their marketing departments and agencies because it gives everyone a more comprehensive sense of the work, where agencies stand and where there is an opportunity to work more cohesively across scopes. In the past, procurement was included mainly in downstream executional work related to contracts and budgets. But in the future, they have the opportunity to be more involved in higher-level annual marketing planning and road map development. ​

In an effort to get a more strategic seat at the marketing table, some procurement executives are working toward taking on stakeholder-management roles, where they coordinate across all budgets, scopes, agencies and internal stakeholders. This can lead to their inclusion in more upstream strategic initiatives managed by the marketing organization. ​

And agencies can help them succeed in that. Agencies can help procurement executives strengthen relationships with colleagues by bringing procurement into the agency’s work early on, partnering with procurement and marketing to define growth, metrics and value at an engagement level. ​

At a more executional level, agencies can help procurement get in the weeds to define the needs of reporting, familiarize them with existing dashboards and provide client-side analysts access to data. They can also proactively keep procurement up to speed on project milestones and share case studies so procurement can then illustrate the value of the investment to their organization. ​

Ultimately, procurement executives are trusted partners, and building a stronger partnership with them is the key to unlocking more budget for bigger projects. As their role evolves further, agencies have the opportunity to better understand what procurement needs to be successful and the ability to help them achieve that success.

Feature image credit: getty

By Michelle Douglas

Find Michelle Douglas on LinkedIn. Visit Michelle’s website.

COUNCIL POST | Membership (fee-based)

Michelle Douglas is a founder at Shophouse, an independent creative agency transforming brands, digital ecosystems, and retail experiences. Read Michelle Douglas’ full executive profile here.

Sourced from Forbes

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Brand Marketers Are Catching On To What Affiliate Marketers Have Known For Decades

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By Lauren Newman

​For those of us who’ve spent our careers in affiliate and performance commerce, it raises a simple question: What took everyone so long?​

Earlier this year, Publicis Groupe announced it would acquire LiveRamp, a global data collaboration platform, for $2.2 billion. The stated rationale was to accelerate what Publicis calls “data co-creation” for the agentic AI era. The goal is to connect identity infrastructure, activate data across the entire customer journey and tie every dollar of media spend to measurable outcomes. To anyone who has worked in affiliate marketing for the last decade, it reads more like confirmation that what we’ve been doing for years is indeed the right way to operate. What other industry can boast proprietary, transaction-level purchase data at the scale of performance creators and affiliates? None.

The holding company world is spending billions of dollars to build systems in which real identities, connected journeys and verifiable outcomes form the foundation of advertising. Affiliate marketing built exactly that model, not by vision, but by necessity, and it’s been running at scale for years.

What Publicis Is Really Buying

The LiveRamp deal didn’t happen in isolation. Publicis acquired Epsilon in 2019 to build a proprietary identity layer. In early 2025, it acquired Lotame to expand its identity graph capabilities. Now, with LiveRamp, which connects over 25,000 publisher domains and 500+ technology and data partners across 14 markets, they are connecting all the dots in their largest and most consequential move yet.

The logic is clear: Epsilon provides the identity foundation, LiveRamp adds data collaboration and clean-room capability across publisher and partner ecosystems, and Publicis’s Marcel agentic platform activates it all across enterprise marketing functions. This solidifies the use case Publicis keeps returning to: connected retail journeys powered by CRM data, loyalty programs, in-store signals, retail media inventory and partner data, unified in one place and activated to drive measurable outcomes.

Framed that way, this is an “identity-to-outcome” stack—a system designed to know who a consumer is, follow their journey across touchpoints and attribute results to spend, accurately, at scale, across a fragmented ecosystem. Doesn’t that sound familiar?

Affiliate Marketing Has Always Been The Data Layer

Affiliate marketing was born performance-first, built around a simple and unforgiving principle: you don’t get paid unless something measurable happens. No conversion, no commission for publisher or creator, no user points or shopper cash back. That structural reality created discipline around identity resolution and journey tracking that brand advertising spent decades avoiding.

Before data clean rooms entered the marketing lexicon, affiliate networks were stitching together click-to-conversion paths across devices and sessions to assign attribution. Before identity graphs became a boardroom priority, performance marketers were resolving user identity across fragmented digital touchpoints because their economics demanded it. And, if they didn’t do this, their creators and publishers wouldn’t get paid properly. Before the cookieless era became an industry crisis, affiliate networks were building server-side tracking infrastructure to maintain signal fidelity, because their publishers needed it to survive.

As privacy regulations tightened and third-party signals degraded, performance-based partnerships gained ground: 74% of brands are increasingly shifting to affiliate programs and 59% of marketers are dedicating over a quarter of their budgets to Creators. That shift is a recognition that the affiliate model—outcome-first, identity-resolving, journey-connected—is better suited to the marketing environment we now live in.

You can build the most sophisticated identity graph in the industry, resolve users across every device and channel, and still lose the sale at the last step, because the journey itself breaks. When a consumer clicks a link from a creator, publisher or affiliate partner and lands on a degraded mobile web experience instead of the app they have installed, the conversion is lost. That loss never shows up in the analytics. It will only track completed journeys. The broken ones are invisible.

As commerce media spend approaches $100 billion and 45% of advertisers cite performance as their primary objective, the gap between data sophistication and journey execution is widening, not narrowing. Knowing your customer precisely doesn’t help if the path you send them down is broken. Plus, many brand advertisers still don’t understand foundational linking, despite now being focused on outcomes.

Affiliates have scale. What it has historically lacked is the strategic standing to match, and the positioning to walk into a CMO conversation and say, “The model you’re paying billions to build? We’ve been running it.”

This is the moment to make that case. Not defensively, but confidently. The principles that define modern marketing’s ambitions, real identity, connected journeys, measurable outcomes and performance accountability, are the principles that affiliate and commerce performance marketing has operated on since its founding and has now revolutionized the creator economy. The vocabulary has changed. The infrastructure has gotten more sophisticated and the content creators have evolved dramatically, but the model has been right all along.

The question for CMOs and revenue leaders isn’t whether this model matters. That question has been answered, loudly and expensively. The question is: Is your organization giving its affiliate and commerce partnerships the investment and accolades they deserve? Now that the rest of the industry has confirmed their model was right, are you ignoring your most valuable marketing team?

Affiliate marketing didn’t need a $2.2 billion acquisition to become outcome-oriented and data-driven. It started there. The rest of advertising is catching up, and the gap is closing faster than most people realize.​

Feature image credit: getty

By Lauren Newman

Find Lauren Newman on LinkedIn. Visit Lauren’s website.

COUNCIL POST | Membership (fee-based)

Lauren Newman is CRO at Button, an AI-powered commerce platform optimizing creator and affiliate marketing performance. Read Lauren Newman’s full executive profile here.

Sourced from Forbes

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Ai Marketing Pioneers: From moving beyond legacy metrics to unlock real-time relevance

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By Justin Pearse

As AI continues to reshape the digital marketing landscape, companies are emerging that harness AI and promise not just incremental improvement but genuine structural change. NDA’s interview series, AI Marketing Pioneers, talks to leaders in this space to discover what they are doing differently.

In the latest installment, New Digital Age sat down with Thomas Ives, Founder of RAAS LAB, and Christian McAlinden, Programmatic Director at dentsu. Their conversation tackled the industry’s deep-seated reliance on legacy metrics, why traditional DSPs fall short on context, and how an innovative programmatic campaign for home improvement giant B&Q delivered 813,000 unique creative variants tailored to real-time consumer intent.

Moving past 2015

For nearly two decades, digital advertising has leaned heavily on easily quantifiable metrics like click-through rates (CTR), reach, CPMs, and viewability. Yet, as both Ives and McAlinden argue, those surface-level indicators belong to an earlier technical era.

“Clicks were a metric for a system which had limitations,” says Thomas Ives, Founder of RAAS LAB. “We should be past it now. We should be thinking more about the quality of the placement, the relevance of the placement, and being a lot more intelligent at that impression level.”

Historically, assessing contextual relevance was treated as a creative judgment rather than a measurable media variable because analysing every impression was humanly impossible.

“If you had a campaign which served nine million impressions, it would take 13 and a half years of one person sat at a desk analysing how well that web page matched each impression,” Ives points out.

Christian McAlinden, Programmatic Director at dentsu, agrees that standard industry habits have held buyers back. “Historically, relevance has been  overlooked. Our industry measured what was easiest to measure, reach, CPMs, viewability, frequency, the basic metrics that we’ve always been able to report on,” McAlinden said. “The result was that campaigns could look efficient on paper, but still miss the nuance of whether the message, context, and consumer mindset were actually aligned.”

Speed vs. genuine innovation

While AI is often treated as a fast forward button for existing tasks, true innovation comes from unlocking completely new capabilities. Rather than relying on static site lists or broad keyword rules, RAAS LAB integrates directly into SSPs, scanning hundreds of thousands of incoming URLs per second to evaluate alignment before the ad is served.

“With AI, it’s the first time that we can possibly measure relevance,” Ives said. “There’s two ways that you can use AI. You can do things quicker, which everyone seems to want to use AI for, or you can do new things with it.

What we’re doing here is using AI to do something that wasn’t possible before.”

For agency leaders, evaluating context within the supply path provides a missing layer that standard buying tools can overlook.

“At the moment in the DSPs, we don’t have a relevancy score,” McAlinden said. “I can build out a site list, I can layer targeting in the open exchange, I know where the ads are going to run, but what are we missing? We’re missing a metric for relevancy.”

B&Q and 813,000 creative variants

The real-world value of AI-driven relevance is best illustrated by a recent campaign executed for retailer B&Q in partnership with dentsu.

Retailers with massive product ranges, B&Q carries over 500,000 products, face a major challenge matching creative messaging to diverse customer needs. A consumer researching shed assembly requires a completely different message than someone browsing blue bedroom paints.

“Every user’s needs are very different,” Ives said. “What we’ve deployed for B&Q across multiple campaigns essentially took in 813,000 unique creative variants that were deployed as part of that campaign. It really solves that problem which advertising’s always had: How do you bring together the creative and the targeting thinking?”

The results demonstrated the impact of context over simple reach. The campaign achieved click-through rates 4x higher than standard display benchmarks, maintained a content relevance score over 70%, and drove a statistically significant lift in brand consideration around ‘ease of shopping.’

Previous iterations of this relevance-first approach even generated higher physical store footfall than paid social efforts, said Ives.

The path to adoption

Despite clear performance gains, shifting an entire industry away from legacy habits takes time. Agencies play a critical role in bridging the gap and educating brand marketers who may be hesitant to stray from familiar metrics.

“It’s like everything in advertising, it’s reluctancy. You stick to what you know,” McAlinden said. “The education piece needs to come from the agencies. We need to be taking charge here, encouraging new strategies to move forward.”

With shortening attention spans in a fragmented media environment, Ives believes brands need a new approach to consumer communication.

“To get these attention-poor people to connect with you again is through being relevant,” Ives said. “It’s not just the message itself, it’s a message that matches the moment that the user is currently in.”

By Justin Pearse

Sourced from NewDigitalAge

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Agentic AI will give DOOH a central place at the planning table

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By Laure Malergue

In this article, Laure Malergue of Displayce, explains how agentic AI can bring DOOH into planning earlier, make recommendations easier to defend and connect data, activation and learnings. She explores what makes a strong DOOH agent, why specialist data and workflows matter and how this approach could help the media secure a more influential role in media strategies from the very first planning conversation.

Walk along the Croisette during Cannes Lions, and advertising is everywhere in its most physical form: posters, digital screens, branded spaces and large outdoor moments. This year, another presence was just as visible: AI agents.

For the past two years, most of the AI conversation in advertising has focused on production. This year’s adtech announcements mark a shift from AI-assisted tools towards agentic systems able to make, coordinate and execute advertising decisions.

That shift raises a more important question for the industry: Will AI agents make media decisions better?

Media planning has always been a discipline of choices: Which audience matters most? Which context will make the message stronger? Which channel should enter the plan earlier? Which budget split is easiest to defend? These choices shape the campaign long before it goes live.

For digital out-of-home (DOOH), this could be a turning point.

DOOH is invited too late to the party

DOOH has many of the qualities brands are looking for today: real-world and brand safe visibility, public attention, contextual relevance with the ability to adapt to location, weather, time of day, audience flows and live events. Yet, it still lacks discoverability within the media mix.

Too often, it enters the media plan late, once the brief, strategy and main budget choices have already been shaped. DOOH is then considered at the execution stage, although its strongest value could have influenced the plan earlier.

The challenge is practical. DOOH requires strong knowledge of audience data, mobility patterns, local specificities, screen environments, formats, availability, pricing, context, timing and measurement. The value is clear, but turning these parameters into a confident recommendation takes time.

Programmatic DOOH has solved a large part of the activation challenge by automating the buying process. The next challenge sits earlier, when a media planner decides whether DOOH deserves a place at the table, how much budget it should receive and why one scenario is stronger than another.

Making DOOH easier to recommend

This is where agentic AI can change the role of DOOH in the media mix. The strongest agents will be judged by the quality of the decisions they support: can they explain why one scenario is stronger than another, help a planner defend DOOH in a wider media strategy and turn campaign results into learnings for the next brief?

This is the concept Displayce is bringing to the market with Agentic DOOH: a way to connect the brief, recommendation, activation and learning loop, so DOOH can influence media thinking before the plan is locked.

This conviction comes from more than a decade building programmatic DOOH technology. Our goal is now to make DOOH easier to recommend, with specialist intelligence available inside the environments where agencies and brands are already building media plans.

This is why Displayce’s agents are available via Model Context Protocol (MCP). The MCP allows AI applications such as ChatGPT or Claude to connect with external data sources and tools. For agencies building their own agentic workspaces, this means DOOH intelligence can be accessed where planners already work. For Displayce, MCP is a way to bring specialist DOOH expertise into the existing media ecosystem, with more openness and interoperability.

What makes a good agent for DOOH?

A generic AI model can understand language, but it does not understand media by itself. For DOOH, a good agent needs to be built on a strong data ecosystem, expert workflows and simulation algorithms. This is the foundation behind Displayce’s agents.

The data ecosystem gives the agent access to the signals that matter: audience data, mobility patterns, inventory context, screen environments, local context and historical campaign performance.

The expert workflow helps the agent understand how DOOH planning actually works: the objective, the constraints, context, timing and budget, and the level of explanation a planner needs to defend a recommendation.

The simulation algorithm allows the agent to compare scenarios, test assumptions and understand why a city, venue type, screen, audience group, daypart or context is relevant.

Without these foundations, an agent can create a recommendation that looks convincing but has limited media value. With them, it can support stronger, clearer and more transparent DOOH decisions.

Make DOOH impossible to ignore from the very first media conversation

By bringing DOOH expertise into planning workflows earlier, Agentic DOOH can change how the medium is considered by agencies, brands and media owners.

For agencies, this means less time assembling data and more time shaping the argument. For brands, it means clearer recommendations and greater confidence in DOOH investment. For media owners, it means making local knowledge and premium inventory visible while the plan is still being shaped.

Human judgment remains central. Planners still decide what is right for the brand, the role of agents is to give teams better scenarios, stronger explanations and more time to think.

Agentic DOOH should be measured by one question: Does it help the medium earn a stronger place in the media plan? If the answer is yes, AI agents will help bring DOOH further upstream in the media decision-making process, at the moment when budgets are shaped, strategies are built, and channel choices are made.

 

By Laure Malergue

Sourced from The Drum

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