In 2012 a nineteen-year-old in Bromsgrove was screen-printing gym vests in his parents’ garage while driving Pizza Hut deliveries to pay for fabrics. Ben Francis could not afford marketing his gym vests and so he did what you would expect from a teenager with no money and a YouTube account. He sent free kit to fitness creators, the ones filming their workouts for niche audiences and asked them to wear it. Gymshark was carried into the category on the shoulders of people its customers already trusted. The brand is now valued north of a billion pounds and sells in more than 180 countries.

That was the ceiling a decade ago when only the upstarts thought this way. The incumbents have caught on. Coach, L’Oréal and Unilever for instance are brands with the budget to run nationwide TV campaigns but chose to grow through YouTube creators instead, and their own independent numbers say that it worked against their own business outcomes. Most established brands are still nowhere near it. They treat YouTube as a cost efficient place to run the thirty-second film they already made for TV, run it as skippable pre-roll, and count completed views. That is the commodity path that competes away the moment the rest of the category runs the same play.

Run your TV ad as pre-roll AND build with the creator into the living room, and you have a differentiated route into the category.

In a nutshell

• YouTube is the most-watched video-sharing platform on the British TV sets, and the TV set is now the main way British households watch it.

• Commercial broadcaster revenue fell last year while the total commercial TV and online video market grew. The money is certainly migrating.

• Most brands still use YouTube as a cost efficient - measured against view rates -pre-roll for a TV ad they already made. That is the commodity path and it competes away in the category if run in isolation.

• Creators are a unique non-commodified route into a category rather than a line on the media plan. Gymshark, Feastables and Prime all entered through them.

• Incumbents are shining a light for the rest. Coach, L’Oréal and Unilever grew brand metrics through YouTube creators, measured by their own independent models.

• The inventive brands engineer one creator asset to work across the phone, the living-room screen and the checkout.

• It’s an AND not an OR. Run video ads + creator partnerships.

YouTube took the TV set while broadcaster revenue fell

Ofcom counted how Brits watched for two decades, and its 2026 Media Nations reported average daily YouTube viewing on the TV doubled from nine minutes per person in 2022 to nineteen in 2025. Across every connected device in the home the figure reached forty-one minutes a day. The share of video-sharing platform viewing that happens through TV climbed from 32 percent to 37 percent, and YouTube drove it. Among 16 to 34 year-olds, YouTube now takes the largest share of in-home video viewing of anything, broadcasters included. The living room used to be the one room the internet could not reach.

The money has followed.

The UK commercial TV and online video market grew to £18.4 billion in 2025, up from £17.1 billion. Over the same year commercial broadcaster revenue fell from £4.98 billion to £4.83 billion. So the market got bigger and the broadcasters got smaller.

And broadcasters know it. In June 2026 ITV, Channel 4 and Sky launched Universal Ads, a self-service platform built to make buying TV feel like buying social and Global launched AdPower for audio and outdoor in the same month.

The buy-side battleground has already turned.

The share of UK media agencies planning to run YouTube on the TV screen went from 23 percent to 85 percent in a single year, and more than half say they are targeting high-value creators rather than pro-broadcast content. That number comes from Pixability’s annual agency survey, and Pixability sells into this market, so read it what you will.

Gymshark and Feastables entered their categories through creators, and Prime shows the limit

Gymshark’s garage story is not a fluke. It is a repeatable mechanic, and the brands that understand it treat a creator as a differentiated way into a category rather than a bolt-on to a campaign.

Feastables is the clearest current example. MrBeast, the most-subscribed individual on the platform with north of 450 million subscribers built his own chocolate brand and used the audience as the distribution. Then Feastables put its logo on the Charlotte Hornets’ jerseys, the first time a creator’s brand had taken a patch on a major American sports team, and MrBeast has since hired a former YouTube and Meta executive to run brand partnerships and signed a multi-video sponsorship with the jerky brand Jack Link’s. The chocolate is real. It sits in real supermarkets and it reached the shelf on the back of smart creator work.

A heed of warning.

Prime - the drink from KSI and Logan Paul - entered the sports-drink category at rapid pace. In its first year it did around a quarter of a billion dollars in retail sales. Cans resold for absurd amounts and it outsold Gatorade at Walmart and held more than 40 percent of the sports-drink segment at its peak. Then the scarcity declined and the product turned out to be perceived as ordinary and UK turnover fell from £112.2 million to £32.8 million in a single year, with segment share down from 41.2 percent to 10.4. So Creators handed Prime category advantage at rapid pace and its decline is down to a disproportionate focus on creators getting a brand into a category and not enough focus on staying there.

Coach, L'Oréal and Unilever now run the same play with purpose

Heritage brands now win younger categories through creators.

Coach is the best in class case. Its ‘Explore Your Story’ campaign put creators at the heart and reported a 60 percent jump in brand awareness among Gen Z, a sixfold rise in consideration, and sustained acceleration in Gen Z acquisition. A leather-goods brand founded in 1941 leveraged creators to find its way back into a generation.

L’Oréal made it an operating model rather than a campaign. It produced more than a thousand creator-led videos in a single year, and its ‘Infallible foundation’ campaign reported six times incremental return on ad spend, with 61 percent of that contribution coming from new category buyers - creators were bringing net new people.

Unilever ran it at portfolio scale, using creators, fandoms and cultural moments to grow its YouTube return more than 50 percent year on year across several of its brands. Additionally, Duolingo and Ulta build creator-first rather than ad-first.

Read those numbers with one eye open, as most come from YouTube’s own upfronts and playbooks. The neutral read comes from Kantar whose study found creator partnerships lifted aided brand awareness by 9 percent, running 1.6 times ahead of standard brand ads.

One thing is for sure. Global brands treating creators as a build program are pulling away from the ones treating YouTube as cost efficient reach.

One creator video now has to work on the phone, the TV and at the checkout

A single creator video now travels across three different layers of attention. Someone finds it on a phone on the morning commute. They push it to the living-room screen that evening and watch it leaned back. They buy from a shopping prompt or a QR code the next morning. The commodity buyer makes three assets for three channels. The inventive buyer engineers one creator asset to carry across all three layers and lets YouTube seemlessly move it between the layers.

Creators can leave swappable slots inside their videos that a brand segment drops into, so one piece of content carries different sponsors by audience. Shoppable formats let a viewer browse and buy from the big screen. A brand can sponsor a creator’s series end-to-end, a takeover built for the living room rather than a bolted on pre-roll.

And the hardest part is finding the creator before they get too famous and too expensive. Pixability feed audience-velocity signals into targeting so a brand can back and ride a creator on the way up. One wellness brand used it to partner with a Maine lobsterman who films his catch for two and a half million people before the rest of the market had noticed him.

YouTube, the creator, the connected screen and the checkout is the most powerful lever not pulled by brands.

A launch stops being a media plan-powered reach announcement and becomes a creator putting the product in front of an audience that trusts them on the screen in the living room with a shopping prompt attached.

Retail media stops being a banner on a grocer’s website and becomes a creator demonstrating the thing watchable on the big screen and measurable to the checkout.

Brand-building stops being a wholly reach and frequency play and becomes a series people choose to watch rather than choose to skip.

Ben Francis took a chunk from the category creating inventive ways in through creators. The living room he reached is now the biggest screen on the platform and for anyone under thirty-five the platform is now the biggest thing on that screen. The brands still treating it as a cost efficient reach buy slot for TV ads are doing the exact thing Mind the Commodity gaps warns about.. They are pressing the default button while brands are winning by building. Building a mini ecosytem powered by creators of which a reach buy is a small but still important part.

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This is Erfan Djazmi, sharing notes on the commodity gap in marketing, from someone who has spent the past five years decommoditising agency and client investment.

In 1928, Simon Marks registered a brand called St Michael, and Marks & Spencer began an experiment to create a shop that would eventually sell nothing it did not make to its own spec. The broader trade thought it was eccentric. The model was that manufacturers would make things and shopkeepers would sell them. The margin structure reflected that division of work. A century later, own-label goods take more than half of UK grocery spend, Aldi built the UK’s fastest-growing supermarket on a product range that is almost all own-label, and the manufacturers who once owned the shelf now rent the shelf and compete in a fierce battleground for shelf space.

Media agencies spent twenty-five years as distributors. They rented other people’s technology, DSP seats, verification licences, the platforms’ own buttons, marked it up where they could, and sold it as a service. Every layer of the stack belonged to someone else, which meant every layer’s margin belong to everyone else too. The interesting agencies have now started doing what Marks did - taking the supplier’s ingredients and building IP and products to their own spec, that solve very real client problems, and proving the impact brand and sales outcomes. They broker data under the deal, ingest it, that allows them to build their own inventory cluster models, and wire those models into custom bidding optimisers and curated inventory marketplaces that become agency IP that the agency can ligitimately charge as data and tech fees. So, own-label adtech, with the same commercial outcomes the grocers discovered where they have control of the recipe, and a margin that no longer belongs to the landlord.

Rent the tech and you rent the margin with it. Build the model and the fee finally stays in the building.

In a nutshell

• Media agencies spent twenty-five years operating as distributors, renting DSP seats and platform tools.

• The interesting agencies are now building their own contextual models on licensed vendor data, own-label adtech with the same economics grocers discovered decades ago.

• Contextual vendors like Peer39 and GumGum plug into the bid stream and let an agency define what ‘relevant’ and ‘premium’ mean at a client and campaign level, which the off-the-shelf bidding tools never allow.

• It is not only contextual. Attention data from Lumen, reliability data from NewsGuard, and creative scores from Human Made Machine feed the same model, so the agency prices quality on more than contextual page signals.

• The commercial model that survives scrutiny is a disclosed tech fee benchmarked against default platform as a holdout, not an undisclosed take hidden inside the supply chain.

• At a time when agencies are seeking revenue to rebalance a P&L built on headcount. A recurring, margin-rich fee boosts organic revenue and margin.

• Own-label only worked in retail because the goods stayed good. Incremental performance uplifts measured in MMM and brand surveys keep the agency’s model honest too.

In a world where agencies take positions on media and earn a margin, that model is becoming harder to sustain given more investment is moving into walled gardens, where those opportunities are finite.

This article sets out a defensive position against two growing pressure points. The risk of client fees falling as automation reduces the need for FTE support, while trading revenue is also shrinking as more media spend moves into walled gardens.

Contextual: the battleground sparked by privacy rules

The battleground is contextual for two main structural reasons. Privacy regulation and signal loss have raised the bid price of audience data while contextual data is essentially personal ads without personal data. And the data itself has become rich and dense. Contextual once meant a keyword and a category flag. It now means domain-level and page-level scoring with predictions. The emotional triggers a piece of content is likely to fire, the interest and intent signals in its semantic language, the programme-level context of a CTV slot rather than a broad genre label.

Peer39 and GumGum are neat examples. Both plug straight into the bid stream. Peer39 classifies each page in real time. It reads the page itself rather than spinning up to the domain, carries more than 2,000 pre-bid contextual categories, and evaluates hundreds of billions of URLs and videos a day, passing the result onto the DSP before the bid even fires. GumGum’s Verity engine goes wider. Its computer vision and language models read the images, audio and video on a page, not just the words, and return a contextual and brand-safety score in under ten milliseconds.

The off-the-shelf bidding tools cannot act on any of this. A DSP seat out of the box gives you a viewability floor and a blocklist, the same two levers every brand in the category pulls. It will not let you customise bids because articles related to the Women’s Super League are more valuable for the brand than articles about the Premier League. It will not let you pull that inventory into a private deal or proprietary agency marketplace.

Controlling bids and inventory at the level of context is not a setting that gets switched on. It is a capability you build, by licensing the vendor feed and integrating it into custom bidding logic and curated deal IDs the agency owns. This creates the ability for agencies to define what ‘relevant’ and ‘premium’ could mean at a client and campaign level.

And it is not only contextual.

The same inventory pipe of media quality signals carries more than page meaning.

• Attention data from Lumen (who were first to market for this model) measures whether real people actually look at an ad rather than whether it technically rendered, so the model can price attentive inventory instead of served impressions.

• Responsible data from TheGoodNet - who rate news sites for trustworthiness, lets the marketplace fund real journalism at scale and keep clients off misinformation, without the blunt keyword blocklist that either blocks too much or not enough - that cant get to a sweet spot of misinformation.

• Creative scores from Human Made Machine, whose control-versus-exposed brand-lift experiments create structured creative signals that better inform media allocation decisions.

• Other partners with mature open-source data brokerage models include Peer39 (CTV + web context), RAAS LAB (context), Teads (curation) and Adelaide (attention).

How it works.

Integrate all of that with the agency’s own bid-stream pricing history and you can build curated deal IDs. Your own marketplaces, where inventory qualifies by the agency’s definition of quality and provides brands with value beyond the commoditised category all pressing the same off-the-shelf buttons. The holdcos are building the ingredients. Publicis is rumored to aquire Lotame to thicken its data spine. GroupM has built curated marketplaces with carbon and quality partners. Every group now has a version of this living in pitch decks and real IP.

That is the whole distinction. Slides create the idea. The product is the built model, the IP and the supply path that drive client value and outcomes. The only thing that turns one into the other is actually building the thing.

The build, and the fee: what agencies actually create

The workflow moves something like this. Agencies broker data from adtech either at an annual level of campaign level. Data feeds land in the agency’s instance. Data engineers join them at domain, page and programme level with clearing prices and outcome data. Models score and cluster the inventory. The clusters become custom bidding logic in the DSPs and deal IDs in the SSPs. Performance flows back and retrains the model. Multi-agentic agentskeeps this running daily without hoards of engineers. The output is a supply path the agency controls end to end, customised for each client that self learns and improves over time.

How the fee works.

This work justifies a technology and IP fee that is disclosed on a per-thousand CPM, % of media or a flat fee, in exchange for a capability the client can benchmark and earns category advantage vs. the rest that press defaul commoditised optimisation buttons. That test is what separates own-label adtech from the practice The Trade Desk has spent two years buiding, where curation means an undisclosed slice taken inside the supply chain and the client cannot see the fee or test the value.

What the client gets: augmented trasparent bidding and not a black box

Own-label survived in grocery by being good. The same logic applies to media buying. When a client invests in programmatic through an exchange, they’re basically buying from a big generic pool of inventory with average inventory, average quality. But if an agency builds its own proprietary contextual data with - real insight into what placements actually work - the client isn’t buying generic commoditised inventory anymore. They’re buying inventory that’s been clustered, filtered and scored specifically for their brand’s tactics, not just the market average.

It gets an escape from the sea of sameness of running the identical verification filters and identical platform default bidding settings as every brand in the category. And it gets a measurable outcome, which fifty years of media buying rarely offered. The curated path either beats the open default path in a holdout or it does not, and the fee is justified by that comparison or it does not.

Two caveats though.

An agency that owns the marketplace it recommends carries a conflict issue and the cure is the same. Disclosure and a clients right and ability to validate. And the models decay because publishers optimise toward whatever buyers score. A curation product built once and left running becomes a blocklist with a fee attached. The own-label grocers never stopped reformulating, which is why the range still sells.

St Michael took sixty years to go from a tad eccentric to the default economics of British retail, and the manufacturers never really got the shelf back. The agencies building their own contextual models are running the same game but much faster, and the vendors - like the best own-label suppliers - will profit from open sourcing their data and arming them. The platforms and exchanges, who have effectively owned the shelf for twenty years, might reflect on how that story ended for the folks who made the assumption that distribution was theirs by right. The shopkeepers learned to make things. Clients will pay a fair price for the recipe, and only for the recipe, as long as it looks and tastes good.

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This is Erfan Djazmi, sharing notes on the commodity gap in marketing, from someone who has spent the past five years decommoditising agency and client investment.

Michael Fish wrote himself into UK folklore and became an overnight cultural icon all for the wrong reasons. In October 1987, Michael Fish stood in front of the BBC weather map and responded to a woman who had rung in worried about a hurricane, and viewers were not to worry, there wasn’t one coming. That night the worst storm since 1703 took down fifteen million trees. The Met Office’s failure was cadence. The models were respectable but the observations feeding them arrived too sparse and too slow to see a storm assembling over the Bay of Biscay. The fix, over the years that followed, involved better and faster moving data.

Marketing mix modelling is the ad industry’s Met Office, and most brands still run it in legacy ways. The discipline works. Sixty years of econometrics can decompose your sales into channel contributions, saturation curves and carryover effects, and the post-cookie signal collapse has driven a full MMM renaissance.

Meta open-sourced Robyn and Google gave us Meridian, two free, open-source tools for building these models, bringing down the entry price to build models. Google’s 2025 measurement report recorded a 212% rise in MMM adoption since 2023, eMarketer finds nearly half of US marketers planning fresh MMM investment this year, and a 2025 MediaPost survey found 74% of marketers reporting that privacy rules have created blind spots into their attribution, accepting that click-based alternatives just don’t work. Then the model reports in March on what happened last summer. A model that describes last year is an almanac.

The category advantage goes to the brand that turns it into fast-moving date for this week’s bids, and that conversion, from a dense rigorous model and document into a live optimiser, is the subject of this issue.

In a nutshell

• Marketing mix modelling tends to come once a year as a report and at best one a quarter. An almanac that describes last year.

• The category edge goes to whoever turns it into a live optimiser, refreshed weekly and wired into this week’s bids.

• An MMM Optimiser comes across two builds. Build one puts the sales MMM on a fast clock. Build two models brand equity into the same bid using the brand’s own survey data.

• The two effectively compound paying for tomorrow’s prospects as well as today’s lifts the organic base, and the loop keeps running.

• For agencies it sells as an always-on optimizer on recurring margin-rich fees and agentic automation lets one senior head run both together.

Build one: the sales MMM on a fast clock

Start with what you already have, which is three years of sales, spend and channel performance, plus the category data (e.g competitor spend, pricing, seasonality and distribution). A Bayesian model refreshes weekly rather than annually, and validation of the model comes from geo holdouts and incrementality lift tests. Meridian accepts experiment results as assumptions which means the almanac and validation experimentation finally correct each other.

The model output translated into auction optimisers without a human in the loop for each decision. Channel elasticities become budget reallocation rules, saturation curves become spend caps that trigger before reaching diminishing returns, and the model’s read on true channel contribution inform bidding optimisations.

D2C brands Gousto and HelloFresh have talked publicly about rebuilding MMM in-house on exactly this logic. The model stops being a report and becomes a signal that powers bidding tactics.

Your competitor’s machine chases the platform’s number and default bidding levers. Yours chases a number the platform has no way to compute.

Build two: Fuse brand health metrics with channel performance

The second build essentially achieves brand equity at the speed of performance marketing. Binet and Field’s IPA work established that brand-building drives the base, the sales that arrive without a last click, and the base compounds while activation decays in weeks. The problem was that brand effects lived in a separate report, on a separate timescale, owned by a separate team, and no bidding system can act on a tracker deck.

Pricing brand equity into the bid

Resonance modelling closes that gap. It basically combines three years of channel spend data with brand survey insights - e.g awareness and perception - to show how different marketing activities truly influence audience response beyond just platform brand lift studies.

A brand can commission additional studies in its local markets and feed those in too, which gives all markets a weapon to optimise against, making it truly global. The model estimates two relationships. Firstly, which channels move which brand metrics, and secondly, how movements in those metrics convert into future base sales.

Think about what this does.

Inside a category where rivals run the default optimisation button, they bid on this week’s platform-measured conversions. Your are bidding on this week’s conversions plus a prediction of next year’s base, drawn from surveys of your actual consumers that no platform or competitor can see. What this means is figuring out the marginal cost per point of brand lift at cluster and channel level - which lets us model what it would take to close the gap in the category and building optimisation bidding strategies to get there.

The flywheel

The resonance layer directs money towards equity-building that a sales MMM alone would under-credit. Equity basically lifts the base of brand metrics, direct traffic, branded search, repeat purchase, and revenue. A rising base reduces dependence on paid harvesting tactics, which frees budget, which the model reallocates into whatever it has learned builds the base fastest, and self learning turn weekly optimisations into something incrementally better. Brands running the annual almanac experience this loop one to four times a year. On a weekly clock it turns roughly fifty times in the same period, and small compounding advantages taken fifty times a year are how category positions change hands.

The agency economics: selling an optimiser, not a one off build

Econometrics work is priced based on hours. Each new client meant new heads, each refresh meant a fee, and the P&L scaled at service margins. The two builds above break that relationship. The pipeline is a machine-learning product built once, refreshed by software, validated by incrementality experiments, monitored by a small central team.

An agency operating it for ten clients does not need ten times the analysts, and taking on the eleventh client adds a form of licence revenue with a marginal cost near zero. So revenue grows, headcount stays flat and contribution margin is boosted instead of eroding at service levels.

Agentic automation is what drops the marginal cost toward zero.

An MMM sales optimiser needs econometricians and data analysts to clean the panel, define assumptions, fit the Bayesian model, then manually code the elasticities into bidding rules.

Agents can cover it.

They ingest the sales and spend data, retrain the MMM on each weekly refreshes, and build the optimisation models that convert channel elasticities and saturation curves into bidding rules. The econometrician goes from constructing the model to interrogating it, setting the assumptions and catching the agent where things dont look right.

That changes the client conversation.

The agency starts selling an always-on optimiser, powered by the client’s own brand surveys, which makes the relationship harder to unwind. Switching agencies now means switching off the machine that steers business decisioning in real time. One honest caveat though. Weekly MMM is hard to wholly maintain through automation - e.g data pipelines break, Bayesian models need assumptions chosen by someone who knows the category, and a model refreshed can be wrong, which is why the incrementality lift-test validation in build one is so important.

The Met Office evolved with more observations and faster modelling into action and British weather forecasting is among the very best in the world. The tools exist for weather and for media. The brands still running annual MMM have chosen the legacy model, and somewhere in their category, low pressure is forming.

An MMM sales optimiser needs econometricians and data analysts to clean the panel, define assumptions, fit the Bayesian model, then manually code the elasticities into bidding rules.

Agents can cover it.

They ingest the sales and spend data, retrain the MMM on each weekly refreshes, and build the optimisation models that convert channel elasticities and saturation curves into bidding rules. The econometrician goes from constructing the model to interrogating it, setting the assumptions and catching the agent where things dont look right.

That changes the client conversation.

The agency starts selling an always-on optimiser, powered by the client’s own brand surveys, which makes the relationship harder to unwind. Switching agencies now means switching off the machine that steers business decisioning in real time. One honest caveat though. Weekly MMM is hard to wholly maintain through automation - e.g data pipelines break, Bayesian models need assumptions chosen by someone who knows the category, and a model refreshed can be wrong, which is why the incrementality lift-test validation in build one is so important.

The Met Office evolved with more observations and faster modelling into action and British weather forecasting is among the very best in the world. The tools exist for weather and for media. The brands still running annual MMM have chosen the legacy model, and somewhere in their category, low pressure is forming.

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This is Erfan Djazmi, sharing notes on the commodity gap in marketing, from someone who spent five years at IPG decommoditising agency and client investment.

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In 2005, Staples began selling the Easy Button. An advertising conceit. Bascially you press it and office supplies appear and it became a product. A red plastic thing that said ‘that was easy’ when pressed and millions of people bought one.

Twenty years on, the advertising industry has built a smiliar thing. Meta calls it Advantage+, Google calls it Performance Max and by 2024 Meta was reporting a $20 billion annual run-rate through its version. Essentially, you press ‘the button’, relax the levers, and the machine buys the media.

The button works. Here’s why.

The models train on purchase behaviour and they improve each quarter - morso than any brand ever gets access to. Give it a few weeks of learning and it becomes frighteningly effective. The trouble starts when the whole category presses it at once. The machines learn from the same signals, so they find the same shoppers, the ones already close to buying, and multiple brands’ bidding algorithms end up bidding against each other for the same people and auction prices rise for everyone.

Meanwhile the dashboards keep showing sales taking credit for shoppers who were coming anyway. The category sells no more than before and the same buyers get reallocated at a higher media cost. No single brand can stop pressing, since sitting out hands share to the neighbours, and so the category pays more each year to hold the same positions.

The button has become electricity. You cannot run the shop without it, and it differentiates very little.

In a nutshell

• When everyone presses Advantage+ or Performance Max , the efficiency gains get competed away and auction prices rise.

• The commodity gap is the margin between default bidding automation and a better-taught machine.

• Brands close the gap with better inputs. Agencies close it by augmenting platforms and turning those inputs into data and tech IP and recurring fees.

• Platforms and adtech monetise better data, better proof and better infrastructure.

• Agentic automation frees agency people from the drudgery so they can build those better inputs, feeding the augmented system.

The distance between that commodity baseline and what a better-taught machine earns is the commodity gap. And it’s zero-sum becuase platforms capture the value, and the rest of the ecosystem plays a kind of supporting act. This piece argues the reverse. The gap is a place where all boats rise - brands, agencies, platforms and adtech vendors can all make money at once, and mostly by helping and feeding each other.

The same button applies in the open web. The Trade Desk sells Kokai as autopilot and DV360 pushes its automated tiers for instance. The money leaks differently there through platform fees and curation take rates rather than one auction the seller also banks (that side is covered in depth in a future issue).

Here are the layers in turn.

Brands: feed the machine what your rivals don’t, and win the same auction

For a brand the commodity gap is a category-level opening, because your competitors’ default bidding settings are the baseline you get measured against. While the category presses ‘the button’ on default settings, you can augment the same machine and earn an advantage inside the auction. First-party data flows back through server-side signals instead of a pixel. Conversion values get rebuilt as margin-weighted lifetime value instead of revenue, so the algo stops treating a discount-hunter and a decade-long customer as the same value. Clean-room curation, the Amazon Marketing Cloud and Ads Data Hub produces audiences and incrementality reads.

Google and Meta have already built a path for brands to augment beyond the commodity gap. Advertisers don’t need to wait for permission or new tech - the tools already exist inside the platforms themselves. What they may lack, however, are the inputs and thats where agencies earn their value.

Agencies: sell the data and the model, not the hours

For agencies, the gap addresses the most talked-about commercial problem in adland. We have complicated the commercial model by pinning to time and there is a way out. Fee income is tied to headcount, easily benchmarked by procurement, and compressed by the same AI that is commoditising the buying. The gap is new revenue models.

Here’s how

Custom cluster models, audience and contextual segmentation and media quality signals built on the client’s own data instead of off-the-shelf taxonomies become agency IP with a tech fee attached. Marketing mix modelling has become the growth discipline of the decade, helped by the platforms themselves with Meta open-sourcing Robyn and Google’s Meridian, which are free toolkits for building a model of which channels drive sales.

Agencies can still build MMM from scratch, but Robyn and Meridian cut the engineering lift, standardise the core methodology, and return outputs faster, so the entry point has never been easier. It still needs an operating layer, and agencies can own it, refreshing MMM reads in real time and wiring them into bidding and budget allocation so the model steers investment, moving from slow, backward-looking data to fast moving forward data feeding bidding logic and custom optimisers.

Clean-room brokerage adds a new intermediary role. The agency structures data collaborations between a brand, a retailer and a platform, and charges for the deal architecture, and a snip on incremental outcomes driven by the clean room, instead of the hours.

For example.

A clean room allows Retailer A to sell more incremental skews for Brand B due to having better-structured data from the clean room data. As a result, Brand B generates incremental revenue through increased sales. Brand B then shares an agreed percentage of that incremental revenue with Retailer A, and the agency receives an agreed percentage of Retailer A’s share as compensation.

The awkward question is who builds all this and agentic automation give us the answer.

The people capable of designing a clean room or an identity spine are the agency’s best, and today they spend excessive time on reports, reconciling numbers and feeding decks, because that is what the retainer pays for.

Agent workflows take away the drudgery, and the freed talent focus towards outcome-driven value for clients and the agency. Sitting with clients, diagnosing the commercial problem, and building the clean rooms, identity architecture and MMM optimisers that answer it.

The agency fee economics back this up. Client rarely pay FTE fees for a clean-room architects, and there’s rarely spare utilisation to build one, which is why most agencies never deliver these outputs at scale despite pitch decks claiming so.

The workflow looks something like this.

Agents can free talent up from the mundane, that spare capacity builds products and IP, and the IP is charged incremental fees through inventive commercial models. The same capacity finally lets agencies deliver the combined martech and adtech services clients desperately want and need but cannot currently buy from agencies where the CDP, the media activation and the measurement, get wired together by people who understand all three.

Each of these is tech and data-fee revenue, which is recurring and margin-rich.An agency earning a quarter of its income this way has rebalanced its P&L away from the thing AI deflates and toward the thing AI inflates.

Platforms: they want you to teach the machine, and prove it

The platforms profit from the commodity gap too, which is why they are incentivised to close the gap. A model that is informed by margin-accurate structured data and rich server-side signals performs better, and a better-performing model earns larger budgets. Meta’s own CAPI numbers speak for themselves. Advertisers who invest in inputs churn less and complain less.

They also hand the platforms valuable success stories and case studies, where the story reads ‘this brand taught the system its economics and beat its category,’ instead of the perennially suspect ‘this brand spent more and sales went up.’

Platform-graded homework is a trust problem with regulatory tension, and the platforms know it. Meta and Google both released open-source measurement frameworks that let advertisers mark them independently, that we cover extensively in issue two.

So if everything becomes a black-box ‘press button, get results’ world with no explainable cause and effect, that eventually hurts the platform itself because buyers lose trust and pull spend.

Adtech: feed the inputs and agency IP

For the valuable corner of the independent adtech layer the commodity gap is the whole business model. Contextual players like Seedtag build campaign-specific models trained on an advertiser’s own brief and attention players like Lumen augment platforms with far better predictors for media quality.

Video intelligence firms like Pixability sell the placement-level YouTube detail that goes deeper than standard Google reporting. The clean-room and collaboration layer (LiveRamp, InfoSum and their kin) is the plumbing every brand and agency use. These companies exist because the walled gardens accept your inputs but will not build them for you.

A vendor whose client deployed pre-tested creative into the contexts it scores best in through a custom model - and validated the lift in a clean room - sells itself.

Closing the gap

Each layer’s product is another layer’s input and each layer earns more when the layer beside it invests. In an industry built on adversarial economics, that alignment is rare. Put simply. If you’re not adding a layer, you’re someone else’s input and in this market, inputs get commoditised fastest.

Staples wound the original Easy Button down, then brought it back last year for the campaign’s twentieth anniversary knees-up. The reboot carried a different proposition however. The 2005 button promised office supplies on demand. The 2025 one dubbed “E.B.”, sells print, tech support, shipping and the rest of the company’s services. Staples aimed it at higher-margin work. The advertising industry’s button offers no such reinvention, and the brands funding it can ask themselves each quarter which side of it they sit on.

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This is Erfan Djazmi, fortnightly notes on the commodity gap in marketing, from someone who spent five years at IPG decommoditising agency and client investment.