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Retail Media Examples that Illustrate a New Market Reality

Author: Ciente Editorial Team

Retail Media Examples
Blogs

Retail Media Examples that Illustrate a New Market Reality

Retail Media Examples that Illustrate a New Market Reality

Everyone cites Amazon detergent ads as a common retail media example. But for B2B, the ‘shelf’ means something completely different.

When buyers search for “retail media examples,” they’re shown screenshots of sponsored detergent or snack brands on a grocery app. But these examples are irrelevant for the B2B domain.

The examples are describing a high-volume, low-friction transaction that bears no resemblance to the complex, multi-stakeholder procurement process of the rigid B2B world.

Here, the intent of retail media is not to trigger an impulse purchase. The intent is to reduce friction in the buyer’s workflow.

If you are marketing industrial components, professional services, or specialized equipment, your retail media strategy must function as a utility. It should instill technical clarity, help ensure contract compliance, or automate replenishment.

But that’s easier said than done.

Below, we outline specific retail media examples in a B2B context, building on key retail media trends to help vamp your retail strategy in 2026.

Retail Media Example 1

The Technical Integration Example

Technical integration

In B2B sectors such as electronics, construction, and engineering, the purchase occurs long after the specification is made. An engineer or architect first evaluates a product’s technical compatibility.

If your product doesn’t entail design specifications, it’s not on your buyer’s consideration list.

A manufacturer does not merely buy a search banner on platforms such as Arrow Electronics, reflecting a shift beyond the traditional media buying process. But they sponsor a technical comparison tool or a CAD Library download.

When an engineer filters for specific voltage, tolerances, or dimensions, the sponsored result provides a “Validated Data Sheet” or a downloadable 3D model for their design software.

The Intent:

The functionality changes here- from awareness to utility. By offering the technical documentation the engineer needs to complete their design, the brand secures a place in the final Bill of Materials (BOM).

Success rate here is measured not by clicks, but by Spec-Sheet Download Rates.

Retail Media Example 2

The Inventory Logic Example

Inventory logic exmple

B2B buying is most often cyclical. And that is known as Maintenance, Repair, and Operations (MRO). A facility manager doesn’t merely discover air filters or lubricants out of thin air. They either replace them when they deplete or when a machine is due for service.

Using the purchase history data on a platform like Grainger or Amazon Business, a brand triggers a sponsored replenishment prompt. If the data illustrates that a customer purchases a specific industrial lubricant every six months, the ad appears in the buyer’s procurement dashboard 15 days ahead of the predicted need.

The Intent:

The intent is to capture the Share of Wallet before the buyer even starts a new search. By leveraging the retailer’s first-party purchase data to predict the next need, the brand becomes a permanent fixture in the customer’s supply chain.

That’s a necessary pivot from search advertising to inventory integration, aligning with broader cross-media ad strategies.

Retail Media Example 3

The Compliance Example

Compliance example

One of the largest barriers in B2B sales is the Approved Vendor List (AVL). A buyer may want your product, but if their procurement software flags you as a non-approved vendor? The transaction stops there.

On a B2B Retail Media Network (RMN), a brand uses Contract-Aware targeting, a model increasingly discussed in the future of retail media.

When a buyer from a specific corporation or government agency logs in, the RMN identifies the existing contracts or compliance mandates, such as “Buy American” or specific sustainability certifications.

The sponsored results shown to that buyer are limited to products cleared for purchase.

The Intent:

It solves the procurement friction problem.

The intent is to ensure that 100% of the ad spend is directed toward buyers who have the legal and corporate authority to complete the purchase immediately. That is a high-nuance application of retail media that’s impossible in a B2C environment.

Retail Media Example 4

The Data-as-a-Service Example

Data as a service example

B2B retailers hold high-intent data that is often more accurate than job titles on social media, unlike assumptions often made in social media marketing strategies. That has led to the rise of non-endemic ads- where companies that don’t sell products on the shelf buy access to the retailer’s audience.

A business insurance provider or a global logistics company buys ad space on The Home Depot Pro or Ferguson. They aren’t bidding on product keywords in 2026 but on the user’s behavioral identity.

If a customer is buying bulk electrical supplies and industrial breakers, the retailer’s data confirms they are an electrical contractor currently managing a large-scale project.

The Intent:

The intent is to reach a professional in a “Work Mode” context. For the insurance provider, this is a more efficient spend than broad LinkedIn targeting because it is based on verified transactional behavior.

The retail media network acts as an identity provider here, not merely a storefront—an evolution also explained in retail media networks in 2026.

Retail Media Example 5

The Automated Procurement Example

Your modern B2B buyers act increasingly like AI-driven procurement agents. And these agents aren’t looking at banners. They scan through structured data to find the best match for a given set of requirements.

A brand on a platform like Staples Advantage or CDW optimizes its retail media spend by investing in attribute depth.

They pay for premium data positioning rather than traditional creatives. This ensures that when a procurement bot queries the retailer’s database for “laptops with 32GB RAM and 24-hour delivery,” the brand’s product attributes are prioritized in the bot’s recommendation list.

The Intent:

The intent is to remain visible in an automated commerce environment. As human search behavior declines in favor of bot-led procurement, “the media” becomes the quality and accessibility of your product data.

Why Traditional Metrics Fail These Retail Media Examples

Why Traditional Metrics Fail These Retail Media Examples, especially when compared to attribution models in retail media advertising. If you apply B2C metrics to these B2B retail media examples, your ROI will look poor. A 0.1% click-through rate on a spec sheet might seem low, but if that one click leads to a $200,000 industrial contract, the value is immense.

To evaluate these examples properly, marketers must shift to account-based metrics:

  1. Contract Retention: Did the media spend prevent the customer from switching to a competitor during a replenishment cycle?
  2. Lead Velocity: Did the technical utility of the ad shorten the time between the evaluation and purchase phases?
  3. Pipeline Value: What is the total contract value of the accounts interacting with the retail media placements?

Retail Media as the Strategic Utility Layer

Retail Media as the Strategic Utility Layer, closely tied to distinctions highlighted in commerce media vs retail media. The primary takeaway for any marketing professional searching for a retail media example is this: retail Media in B2B is a utility layer, and not an advertising channel.

In the consumer world, retail media is about winning the “moment of choice.” In the professional world, it is about being the most integrated, compliant, and technically accessible option in the buyer’s system.

When you stop trying to advertise and start trying to integrate, i.e., build an ecosystem, your retail media strategy will finally align with how your customers actually work, similar to approaches discussed in B2B media partnerships.

Whether you are providing a CAD file to an engineer or a compliance filter to a procurement officer, your success depends on how much friction you can remove from their day.

Paid Marketing vs Organic Marketing in SaaS: When Ads Feel Like a Growth Switch
Blogs

Paid Marketing vs Organic Marketing in SaaS: When Ads Feel Like a Growth Switch

Paid Marketing vs Organic Marketing in SaaS: When Ads Feel Like a Growth Switch

Most SaaS leaders treat organic and paid marketing as a budget choice. But the real gap isn’t between speed and cost in 2026; it’s how you bridge the paid vs organic debate.

SaaS founders and marketing leaders constantly search for the exact differences between organic marketing and paid marketing in SaaS. They usually want to solve a massive budget problem, especially when planning their SaaS marketing budget. They need to know exactly where to allocate their next $50,000 to acquire users efficiently.

For years, the internet provided the same lazy answer. Bloggers said organic is free but slow. They said paid is expensive but fast.

That advice is entirely outdated. Worse, it is financially dangerous for any software company operating in 2026.

The B2B SaaS landscape looks completely different today.

The B2B SaaS landscape looks completely different today, shaped by evolving SaaS marketing challenges. CACs skyrocket every quarter. Search engines run entirely on AI overviews. Buyers conduct 80% of their research in dark social channels. They check private Slack communities. They listen to niche podcasts. They text their peers. They do all of this research before they ever speak to your sales team.

You must stop looking at organic and paid marketing as competing line items, a mistake often seen in early-stage SaaS startup marketing strategies. They do not fight for the same dollars. Instead, you need to view them as two distinct halves of a single engine. Organic handles demand creation. Paid handles demand capture.

Let us look at how modern SaaS companies actually deploy both channels to grow.

The New Reality of SaaS Organic Marketing

Most people hold a massive misconception about organic marketing in SaaS, often confusing it with a limited SaaS content marketing strategy. They think it simply means writing SEO blog posts to rank on Google. Search visibility absolutely still matters. However, the traditional SEO playbook is dead. Keyword stuffing fails. Generic how-to articles lose entirely to AI-generated overviews.

Today, your organic marketing must build an uncopyable trust moat, which directly impacts your ability to achieve strong SaaS product-market fit.

Here is what organic marketing actually looks like for a modern software company:

  • Original Research and Proprietary Data: SaaS companies sit on mountains of behavioral data. You should publish a benchmark report detailing how enterprise teams actually deploy cloud infrastructure. AI bots cannot hallucinate this data. Your competitors cannot replicate it. This makes it incredibly valuable to your buyers.
  • Founder-Led Content: Buyers refuse to read a junior marketer’s summary of a highly technical topic, which is why founders increasingly drive strategies like SaaS influencer marketing. True organic marketing relies heavily on your subject matter experts. Your engineers, product managers, and founders must publish their distinct viewpoints. They must write daily on LinkedIn. They must speak on industry podcasts.
  • Community-Led Growth: Build a dedicated space for your target users, similar to how SaaS referral marketing thrives on user-driven engagement. Sponsor a Discord server. Host a private Slack group. Offer them a place to converse about their daily operational challenges.

Organic marketing serves as your demand creation engine. Its job is to educate the market on a completely new way of working. Buyers consume this content. They eventually realize they need a software solution to fix their underlying problem.

This process operates on a long timeline. Yet, it remains the only proven way to lower your blended acquisition costs over time.

The Evolution of SaaS Paid Marketing

Organic marketing builds the narrative. Paid marketing distributes that narrative.

The old playbook gave terrible advice. It suggested paid ads existed strictly for lead generation, often ignoring the balance discussed in content marketing vs sales for SaaS growth. Marketers ran a LinkedIn ad. They pushed users to a gated PDF. They captured an email address. Then a sales development rep cold-called that person aggressively.

Modern SaaS buyers despise this tactic. They input fake email addresses merely to avoid your sales team.

Consequently, the role of paid media has shifted completely. It moved from forcing a cheap conversion to guaranteeing content distribution.

Here is how smart teams use paid marketing today:

  • Zero-Click Content Distribution: Take your best piece of organic content. You could have a two-minute video of your founder explaining a complex industry problem. Put paid budget behind it on LinkedIn. Guarantee your target accounts actually see it in their feed. Do not ask them to click a link. Pay strictly to build brand memory.
  • Account-Based Targeting: Paid marketing allows you to draw a digital fence around 500 specific target companies, which is the foundation of account-based marketing for SaaS. Imagine you sell enterprise compliance software. You can ensure only the Chief Information Security Officers at those exact 500 companies see your case studies. You stop wasting money on unqualified clicks.
  • High-Intent Search Capture: A buyer will eventually search for a competitor alternative. They will search for enterprise software pricing. Paid search guarantees you appear at the very top of the page. You catch them at the exact moment their wallet is open.

Paid marketing serves as your demand capture engine and plays a critical role in improving overall SaaS marketing benchmarks. Its job is to catch a buyer who is finally ‘in-market.’ This group makes up only 5% of your total addressable market at any given time. Paid ads provide incredible speed and precision. However, the payback period becomes incredibly steep if you have not built brand trust first.

The Unit Economics Trap

You cannot treat organic marketing vs paid marketing in SaaS as an either/or scenario, especially when evaluating pricing models of SaaS marketing agencies. The unit economics of a SaaS business absolutely forbid it.

SaaS operates on a subscription model. You don’t make profits on day one. You make it back over months or years. We track this through the Customer Acquisition Cost Payback Period. This metric reveals exactly how many months of subscription revenue it takes to pay back the cost of acquiring the user.

If you rely totally on paid channels?

If you rely totally on paid channels, you risk repeating common mistakes in outsourcing SaaS marketing. Your costs will rise every single year. Ad platforms become more crowded constantly. Bidding wars escalate daily. Your payback period might stretch past 18 months. You will burn through your venture capital reserves before you ever reach profitability.

If you rely totally on organic channels?

You surrender all control to algorithmic distribution. You might publish the best whitepaper in the world. The distribution algorithms could change tomorrow.

Your target enterprise accounts might never stumble across your content. Your sales pipeline dries up overnight.

The most efficient SaaS companies combine them aggressively. They use paid marketing to target specific, high-value accounts. They rely on their organic content to prove their ultimate authority once the buyer clicks through.

Organic lowers the floor of your blended costs. Paid raises the ceiling of your revenue velocity.

The 2026 Playbook for Organic vs. Paid Marketing: A Non-Linear Funnel

Modern SaaS marketing professionals map out the integration of both channels in four distinct phases. Let us look at how you can make this actionable today.

  1. Phase 1: Organic Data Sourcing. Analyze your paid search data first. Find exactly what long-tail, high-intent queries your buyers actively search for right now. Look for the technical questions your competitors ignore.
  2. Phase 2: Organic Asset Creation. Build a highly technical guide or an interactive tool. Make it comprehensive. Leave it completely ungated. Answer that specific query better than anyone else on the internet.
  3. Phase 3: Paid Distribution. Run targeted ads on LinkedIn. Push that organic asset strictly to the exact job titles in your CRM. Only pay to reach the specific people you actually want to sell to.
  4. Phase 4: Paid Retargeting. Wait for those specific users to consume the content. Then retarget them. Show them a highly specific product demo ad.

Organic builds trust in this model. Paid guarantees the right person sees the message, often reinforced through targeted SaaS email marketing examples. Together, they capture the final conversion.

The Paradigm Shift

We can summarize the massive shift in SaaS marketing through this table:

FeatureStandard “Vs” ApproachThe Nuanced 2026 Reality
Organic GoalDrive website traffic via SEOBuild a Trust Moat & Brand Authority
Paid GoalCapture emails via Gated PDFsDistribute narrative & capture in-market intent
RelationshipSiloed budgets and teamsPaid amplifies Organic assets
Primary MetricCost Per Lead (CPL)Pipeline Velocity & Blended CAC

Stop Choosing Between Organic and Paid, and Start Aligning

Successful marketing leaders know a crucial secret. They do not pick a winner between organic and paid marketing. They simply know which tool to use for each stage of the buyer’s journey.

Nobody knows who you are. Nobody knows why your software matters. It means you have a massive demand creation problem. You need organic marketing immediately. You need strong points of view. You need founder-led content. You need deep technical resources.

Everyone might know your category perfectly. But you lose deals to competitors at the final hour. That means you have a critical demand capture problem. You need paid marketing immediately.

You need sharp retargeting. You need competitor conquesting. You need a precise account-based deployment.

The modern B2B SaaS buyer is incredibly smart. Generic SEO posts will not trick them. They stay far too busy to fill out a lead generation form from an ad they do not trust.

You must earn the right to their attention organically. Then, you must pay for the privilege to appear in their feed at the exact moment they are ready to buy.

Sora
News

Has Sora Become Too Huge a Liability for OpenAI? Disney Exits the $1 Billion Deal

Has Sora Become Too Huge a Liability for OpenAI? Disney Exits the $1 Billion Deal

Was Sora’s computing demand that high for OpenAI to decide to shut it down? There’s more than what meets the eye.

It was merely a couple of months ago that OpenAI and Disney struck a three-year deal. The overall project centered on the use of Sora to create vertical video content, hinging on the AI startup’s access to over 250 Disney character licenses.

However, now that OpenAI is roping in Sora, just six months after it was made available to the public, Disney is also exiting the deal. And it wasn’t a small deal at that- while the AI organization was planning on maintaining access to hundreds of beloved characters, Disney was investing $1 billion to amplify this.

The truth is- Sora is a TikTok-like social feed. But it’s all AI.

So, there are two ways to keep users occupied on the platform: you create your own realistic deepfakes, or you use someone (or something) else’s. More users are, very rarely, willing to do the first.

Sora, with its impressive video generation qualities, witnessed an upheaval of deepfake videos that focused on real public figures or Disney characters. It was fun while it lasted. But public figures don’t hold the option to explicitly opt-in to being at the center of this- that’s where the problems begin. And entertainment ends up breaching personal boundaries.

Even with all the fantasy world characters, there wasn’t any explicit nod by Disney. Most thought OpenAI could end up in muddy waters with Disney, but obviously, that didn’t happen.

But Sora’s longevity was always in question.

AI slop has become ‘the’ reason for content fatigue- why would users specifically tap into an app that feeds them more of it?

Instagram reels or YouTube Shorts, and even TikTok- the OG vertical video feeds remain addictive because of the inherent “human” element they still entail. Not just the creators, even the actors are unapologetically human (but that’s also changing steadily).

That’s what Sora lacked. Because Sora 2, the video and audio generation tool, remains, it’s the AI-first social feed that’s shutting down. One doesn’t have to think too hard to gauge the reason- it’s a liability for OpenAI, an institution that’s losing money faster than it can count.

Microsoft
News

Microsoft’s Data Center Rebound is a Lesson in High-Stakes Tech Real Estate

Microsoft’s Data Center Rebound is a Lesson in High-Stakes Tech Real Estate

Microsoft swoops in to lease a Texas data center dropped by Oracle and OpenAI. Here’s why this 700MW deal is a major power play in the AI infrastructure war.

The world of AI infrastructure often feels like a high-stakes game of musical chairs.

Recently, the music stopped for a massive data center project in Abilene, Texas, and while Oracle and OpenAI walked away, Microsoft was more than happy to take the seat.

That isn’t just a simple lease agreement.

It’s a 700-megawatt signal that the thirst for computing power is overriding the caution typically seen in such massive capital expenditures. The site sits right next to the famous “Stargate” campus, a project once heralded as the crown jewel of the Oracle-OpenAI partnership.

However, negotiations reportedly soured over financing hurdles and OpenAI’s shifting technical requirements.

For Microsoft, this is a pragmatic “trash to treasure” move.

Building these facilities from scratch takes years, but stepping into an existing developer agreement with Crusoe allows them to bypass the initial slog. It also highlights a growing rift in how the industry handles growth.

While some firms are tightening their belts due to high interest rates and the sheer cost of Nvidia’s latest chips, Microsoft seems content to double down, betting that there is no such thing as too much capacity.

Of course, this isn’t without risk.

Skeptics point out that the power grid in Texas is already under immense strain, and building the physical shells is only half the battle. Getting enough electricity to actually run 700 megawatts of AI hardware is a monumental task that could take until 2028 to fully realize.

This deal ultimately shows that scale is the only currency that matters in AI.

Microsoft is essentially betting that by the time this site is fully operational, the demand for generative AI will have caught up to the massive supply they are currently hoarding.

Google
News

The “North Star” Shift: Google’s Quiet Pivot to the Pentagon

The “North Star” Shift: Google’s Quiet Pivot to the Pentagon

Google DeepMind VP Tom Lue confirms the company is “leaning into” military contracts after scrubbing anti-weapons pledges from its 2025 AI principles.

For years, Google’s relationship with the military was a source of internal shame. The company effectively pinky-swore to avoid “weapons of war” after the 2018 Project Maven protests. But that era of Silicon Valley pacifism is officially over.

At a recent town hall, Google DeepMind VP Tom Lue dropped the pretense.

He reminded employees that the company’s AI principles were quietly updated in 2025, scrubbed of specific pledges against surveillance and weapons development. The new metric for taking a government contract is now remarkably flexible: whether the “benefits substantially exceed the risks.”

It isn’t just a change in wording; it is a change in the company’s soul.

While rivals like Anthropic are currently tied up in federal court for refusing to drop ethical “red lines” regarding autonomous weaponry, Google is leaning in. DeepMind CEO Demis Hassabis even noted he is “very comfortable”- working with democratic governments is a path to global safety.

The logic is simple.

The Pentagon is currently rolling out “Gemini for Government” to three million personnel, and Google wants a seat at that table. By framing the work as “administrative” or “clerical,” Google provides itself a layer of plausible deniability. Yet, the removal of the surveillance ban suggests the ceiling for this partnership is much higher than a glorified secretary.

Google’s “North Star” used to be its “Don’t Be Evil” manifesto.

Now, it mimics a calculated cost-benefit analysis. As the line between civilian tech and national security blurs, Google has decided that being a “supply chain risk” is a far greater danger to its bottom line than a few disgruntled employees.

Retail Media Trends
Blogs

5 Retail Media Trends that Could Make-or-Break the Industry

5 Retail Media Trends that Could Make-or-Break the Industry

Beyond the standard AI and measurement buzz, discover the hidden shifts in agentic commerce and data licensing that will redefine retail media winners in 2026.

Retail media has spent the last two years graduating from a nice-to-have budget line into something brands treat as a core performance channel. The money reflects it.

US retail media ad spend is projected to reach $69 billion in 2026- up from $60 billion in 2025. Europe grew 22% year over year, compared to an overall 6% for total ad spend. The market is not slowing down.

With growth like that comes a lot of trend pieces. Most of them are saying the same things: the measurement is broken, off-site is growing, AI is everywhere, and in-store is finally catching up, many of which are already shaping the future of retail media. All true. All is already on your radar.

This piece covers those, but it also decodes the trends those pieces are quietly skipping. The ones that will matter more by Q4 than anything currently getting the headline inches.

The Retail Media Trends Everyone Is Covering

A. Measurement is still the industry’s biggest unsolved problem

Every retail media trends piece leads with this. With good reason, because it’s still not fixed.

Each network runs its own attribution model, conversion definition, and reporting format, making standardization across retail media networks increasingly complex. A brand buying on Amazon, Walmart, and three regional grocery networks simultaneously is reconciling five different methodologies to answer one question: which of these is actually working?

36% of marketers say difficulty proving incrementality is the main reason they would pull back overall retail media spend.

Did the campaign drive new sales, or did it intercept purchases that would have occurred anyway? Most networks cannot answer that question cleanly.

The IAB has pushed for standardization.

Individual networks have their own incentives to keep their methodology proprietary. Progress is happening, but it is slow, and in the meantime, brands are making budget allocation decisions on incomplete information.

B. Off-Site Is Where the Growth Is

The ceiling on on-site sponsored listings is visible. Only so many slots on a search results page, and as more brands bid for them, CPCs climb and efficiency drops.

The growth move is off-site: leveraging the retailer’s first-party shopper data to reach those same shoppers on external publisher inventory, CTV, programmatic display, and social as part of broader cross-media ad strategies. The data travels without the real estate having to be owned.

Amazon DSP advertisers grew their spend 31% year over year in Q4 2025 as impressions climbed 32%. Over 60% of Walmart’s self-serve display spend now goes to off-site inventory.

The shift is already well underway.

C. In-store is finally getting serious infrastructure

80% of consumer spending happens in physical stores. Until recently, nearly all retail media advertising was digital-only. That gap made no business sense, and it is closing.

Digital endcap screens, checkout lane displays, and programmatic digital out-of-home tied to loyalty data are becoming foundational components of modern retail media advertising and adtech companies. In-store retail media is getting real infrastructure behind it. And what matters most is that in-store placements can now be tied to purchase data, as digital placements can.

Closed-loop attribution at the shelf is no longer a future roadmap item.

D. AI in campaign management

Dynamic creative optimization, predictive audience targeting, automated bidding, and real-time personalization are being accelerated by advances in artificial intelligence and adjacent technologies like deepfake technology. AI is layering into every part of retail media operations.

The more interesting AI story, though, is not in the ad tech stack. It’s what AI is about to do to the shopper side. More on that shortly.

The Trends Most Pieces Are Not Covering

1. Agentic Commerce Is the Threat Nobody Wants to Talk About Directly

AI shopping agents are already in early deployment. Tools that browse, compare, and purchase on a user’s behalf, surfacing a ranked shortlist of products rather than a full search results page.

Here’s why retail media has a problem with this.

Retail media is built on the premise that a shopper is browsing, searching, scrolling, and encountering a sponsored placement at the moment of consideration. An AI agent does not browse. It does not scroll. It processes inputs and returns a recommendation. The sponsored listing that sits at the top of a human’s search results page may not exist in an agent’s output at all.

Nobody has a clean answer to this yet.

The honest position is that if AI agents become a meaningful percentage of shoppers who discover and purchase products, the economics of on-site retail media change significantly. Check out retail media examples.

The networks that are building for that scenario now, rather than waiting to see if it scales, are the ones that will not be caught unprepared. especially as retail media networks in 2026 continue to evolve

Google’s Universal Commerce Protocol, developed with Walmart and other major retailers, is an early sign that the industry knows this is coming. It is designed to let AI agents handle discovery and checkout while keeping retailers as the merchant of record.

This framing matters: it is a defensive move dressed up as an innovation announcement.

2. The Revenue Versus Volume Disconnect Brands Are Not Talking About

Something interesting is happening across CPG brands running retail media at scale. Revenue numbers look healthy. Volume numbers are not keeping up.

The interpretation that is skipped in most trend pieces: retail media is increasingly effective at capturing existing demand and intercepting high-intent buyers who were already purchasing. What it is less good at is building the kind of upper-funnel awareness that creates new demand and grows category volume.

Brands are spending on retail media, seeing revenue, and quietly watching their total volume base not grow predictably. The media mix model says one thing.

The quarterly volume report says another. And because legacy MMMs were built for quarterly analysis in a market that shifts week to week, by the time the insight surfaces, the budget window has already closed.

The The brands that crack this in 2026 will be the ones treating retail media as a full-funnel channel, not a lower-funnel capture tool, aligning it with a more structured media buying process, and building the measurement infrastructure to tell the difference in real-time rather than in the next planning cycle.

3. Store Mode in Retailer Apps Is the Most Underrated Inventory in Retail Media

The in-store trend gets covered as endcap screens and digital signage. That framing misses the more interesting development.

Retailer apps running in store mode are turning the shopper’s phone into the most responsive inventory in the building, similar to how social media marketing transformed real-time engagement. Real-time offers triggered by aisle location. Scan-and-go integrations that know what’s in the basket as it’s being built.

Navigation that surfaces a sponsored alternative one shelf over from where the shopper is standing.

That is not a future capability.

Walmart, Kroger, and Target have active store mode features in their apps. The brands running sponsored placements inside those experiences are getting a context that no other format can replicate: the shopper is physically in the store, the product is within arm’s reach, and the ad serves at the exact moment the purchase decision is made.

Most retail media budget allocation conversations do not include this inventory. They will.

4. The Creative Problem Nobody Is Counting as a Trend

Measurement gets every hot take. Creative gets almost none. That is backwards.

Most retail media creative is still built to a format spec sheet: fit the dimensions, meet the file size, include the logo. The strategy ends there. And then brands spend significant money running ads that function as digital wallpaper because the creative was never built for the context in which it runs.

Sponsored product on Amazon does not require the same creative thinking as a CTV spot running against a shopper who bought from the brand twice in the last ninety days. An off-site programmatic placement targeting lapsed buyers demands different messaging than an on-site placement competing for a new customer mid-search. These are different conversations.

Most brands have one conversation across all of them, missing opportunities to tailor messaging the way effective social media branding demands.

Retail media networks running purpose-built creative designed for specific placements and shopper contexts consistently outperform generic assets. The performance gap is not marginal.

Brands treating creative as an afterthought in their retail media strategy are underperforming on their own media spend.

The networks that start functioning as creative partners, helping brands build assets that belong in each format and context, will pull advertiser spend from those handing over an ad server and label it a service, similar to how strong b2b media partnerships drive long-term value.

5. Non-Advertising Revenue Is Becoming Bigger Than the Ad Business

Most retail media coverage focuses on ad spend. The more significant long-term development is what sits next to it.

Retailers are learning that their data is worth more as a licensed asset than it is as a targeting tool for their own ad inventory. Data licensing to brands, strategic research partnerships, and insights feeds that help manufacturers understand their own category dynamics at the retail level. These are not advertising products. They are intelligence products.

IAB Europe projects that by 2026, over 60% of retail media network revenue growth will come from non-advertising services, including data licensing and strategic brand collaborations. The ad business funded the data infrastructure.

The data infrastructure is now its own business.

The retailers building toward this are thinking about their networks differently than the ones still treating retail media purely as an ad monetization play, reflecting a shift across the broader retail media ecosystem, as explained. One group is building a media business. The other is building a data and intelligence business that has media on top.

The Through-Line Across All of the Retail Media Trends

Retail media in 2026 is not one channel maturing. It entails several distinct capabilities, some well understood, some still being figured out, all moving at different speeds within organizations that were not originally built to run them.

The brands getting strong returns share one characteristic: they are treating retail media as a strategic function that requires its own thinking, its own creative, its own measurement framework, and its own seat at the planning table.

The ones struggling are the ones who handed the login to a junior team member and asked them to manage the sponsored products budget.

Both groups are spending. Only one of them knows what they are buying.