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Programmatic DOOH After the SSP Shake-Up: How Screen Networks Regain Pricing Control

As SSPs consolidate and SPO reshapes buying paths, programmatic DOOH screen networks have a rare chance to reclaim pricing power and reset the rules.

Programmatic DOOH After the SSP Shake-Up: How Screen Networks Regain Pricing Control

Introduction: Programmatic DOOH Enters Its Awkward Teen Years

Digital out of home has always been a bit of a paradox. It is physical and location based, yet traded increasingly through digital pipes. It looks and feels like high impact brand media, but buyers want it to behave like any other programmatic channel, with bidstreams, frequency caps, and attribution. Over the last few years, programmatic DOOH (pDOOH) has grown out of its experimental phase. Large buyers now include it in omnichannel plans. Dedicated DOOH SSPs have matured. OpenRTB has standardized more of the basics for screens in the wild, particularly through updates like OpenRTB 2.5 and 2.6 that account for non standard environments such as digital out of home and audio. At the same time, the broader SSP ecosystem has been going through a violent rebalancing. Supply path optimization, fee compression, and consolidation have reshaped web, app, and CTV publishing. Several high profile SSP exits, restructurings, and pivots have reminded everyone that middlemen do not have a guaranteed right to exist just because they connect buyers and sellers. Programmatic DOOH sits right at the intersection of these two forces. Screen networks have leaned on SSPs to bring incremental demand and operational scale. Yet as SSPs are pressured by buyers to be more transparent and more efficient, there is a growing opportunity for media owners to rethink how they manage pricing, packaging, and the paths that buyers use to reach them. In other words, the SSP shake-up is not only a risk for DOOH networks. Handled properly, it is the best chance in a decade to regain real pricing control. This article explores how we got here, what the new economics of programmatic DOOH look like, and how screen networks can use this moment to build a smarter, more sustainable yield strategy. I will also connect the dots to what we see at Red Volcano across web, app, and CTV supply, and how those lessons apply directly to DOOH.

1. What Just Happened in SSP Land, And Why DOOH Should Care

To understand where programmatic DOOH is going, you have to look at the broader SSP story over the last five years.

1.1 The pressure cooker of SPO and fee transparency

On the buyer side, agencies and DSPs have aggressively pushed supply path optimization (SPO). They realized that having 10 or 15 SSPs all offering the same inventory, often via a daisy chain of resellers, created cost, complexity, and fraud risk. So buyers began to ask hard questions.

  • Who actually has a direct contract with the publisher or screen network: And therefore, who deserves a meaningful share of the fee?
  • How many hops are there from my DSP to the impression: Each hop was a potential margin stack, data leak, or quality risk.
  • What are the true effective fees: Not only the SSP’s stated take rate, but any hidden arbitrage or markups along the way.

As a result, large buyers started to prefer a smaller set of “clean” SSP paths. That meant fewer partners, stricter seller.json and ads.txt / app-ads.txt enforcement, and increased demands for log-level transparency. For SSPs, the impact has been brutal. Take rates fell. Margins compressed. Venture fueled growth strategies that assumed perpetual fee stability suddenly looked shaky. We saw consolidation, pivots into CTV specific products, exits from certain geographies, and more specialization.

1.2 Why this matters specifically for DOOH

DOOH has some unique characteristics that interact with this SSP shake-up in interesting ways:

  • Supply is more constrained: You cannot spin up infinite billboards or transit screens the way you can add more web pages or app placements.
  • Impressions are more variable: Weather, footfall, and seasonality all affect how many impressions a screen realistically delivers.
  • Measurement is still evolving: Compared with web or in-app environments, DOOH measurement and attribution rely on panels, mobility data, and modeled outcomes, rather than deterministic IDs.
  • Demand is more brand heavy: A large share of DOOH spend is upper funnel, where context and creative quality matter at least as much as pure reach.

Because of this, DOOH networks have historically leaned heavily on SSPs and aggregators to unlock programmatic budgets without re-architecting their entire business. SSPs offered quick access to a wide set of DSPs, standardized reporting, and in many cases, some consultative support on packaging and deals. The SSP shake-up now changes the calculus. If buyers want fewer, cleaner paths, and SSPs must justify their fees, then DOOH networks have more leverage to decide which partners they trust, how much data to share, and where pricing decisions are actually made. This is the moment where screen networks can choose to be passengers, or step into the driver’s seat.

2. How DOOH Pricing Drifted Out Of Media Owners’ Hands

Before we talk about regaining pricing control, it is worth being honest about how that control slipped away in the first place.

2.1 From loop based pricing to opaque packaging

Traditional DOOH sales revolved around loops, share-of-voice, and location tiers. A network would sell a “10 percent share-of-voice on premium CBD billboards” or a “full domination” of a certain mall for a given period. Pricing was negotiated on a mix of historical rates, demand in the market, and the perceived prestige of each asset. As programmatic pipes were introduced, many networks effectively bolted them on to the side of this model. They let SSPs expose some portion of impressions to RTB demand, often at lower effective prices than their direct sales. Over time, a few things happened:

  • Networks ceded some packaging decisions to intermediaries: SSPs or aggregators would create bundles, audiences, or deals that obscured exactly which screens and time slots were involved.
  • Price discovery moved into black boxes: Open auction dynamics meant that clearing prices were shaped by bid density and SSP yield algorithms, often with minimal control from the underlying media owner.
  • Rate cards became less relevant: When buyers could reach a network’s inventory through third parties at different price points, the anchor of the direct sold rate card began to erode.

To be clear, SSPs did not “steal” pricing control in any malicious sense. Networks willingly outsourced parts of the problem in exchange for convenience and incremental revenue. But the result was the same: it became harder for many DOOH owners to answer basic questions such as “what is our true average price per thousand impressions on this flagship screen, programmatic plus direct?”

2.2 The hidden impact of data and analytics gaps

On web, app, and CTV, publishers gradually built richer yield analytics. They invested in data warehouses, log-level analysis, and granular comparison of different SSP paths. Even medium sized publishers now often know which SSPs clear at higher prices, which resellers add value, and where floors are too low or too high. Many DOOH networks, however, are still earlier in that maturity curve. Their CMS and ad servers were designed for loop management and playout, not for auction analytics at the impression level. That meant:

  • Limited view of effective prices by screen, time of day, and buyer path
  • Weak feedback loops between direct and programmatic pricing
  • Over reliance on SSP dashboards that reveal only part of the story

In this environment, pricing “control” was often theoretical. Rate cards existed, floors were set, but the real economic engine lived in systems that the network did not fully own or understand.

3. The New Economics Of Programmatic DOOH After The SSP Shake-Up

With SSP consolidation in full swing, the programmatic DOOH market is being forced into more disciplined economics. This shift is not inherently good or bad. It simply means the rules of the game are changing, and those who understand the new incentives will win.

3.1 Fewer but deeper SSP relationships

From the buyer side, the preference is clear. They would rather see a DOOH network through a small number of partners that can prove:

  • Directness: A contractual relationship with the media owner, visible in seller.json like structures and deal IDs.
  • Transparency: Clear fee disclosure, as well as log files that allow path and price audits.
  • Quality controls: Brand safety, fraud prevention, and accurate screen metadata.

For networks, this is both a challenge and a gift. It is a challenge because relying on a long tail of resellers to “spray” inventory around is less viable. It is a gift because a smaller number of SSP relationships are easier to monitor, negotiate, and align with your pricing strategy. In practical terms, we are likely to see:

  • Primary SSPs that own the core connection to most DSPs, often in a cross channel capacity (web, app, CTV, and DOOH).
  • Specialist DOOH SSPs focused on handling physical screen complexity, measurement integrations, and DOOH specific deal packaging.
  • Limited but deliberate resellers that bring unique demand, such as retail media tie-ins, mobility data marketplaces, or vertical specific DSP access.

The key is that each of these partners should have a clear, measurable reason to exist in your stack. If they do not, you are effectively renting out your pricing power for marginal benefit.

3.2 Dynamic floors and context rich packaging

As the SSP market tightens, static floors and generic “run of network” deals become less defensible. Screen networks that treat all impressions as roughly equal will undersell their most valuable supply and oversell their weaker inventory. We should expect a shift in DOOH toward:

  • Dynamic floor pricing: Floors that change by hour, daypart, and contextual factors such as weather, events, or mobility data.
  • Context rich packages: Deals crafted around shopper journeys, commuter flows, or event attendance, rather than just “all screens in a city.”
  • Stronger PMPs and programmatic guaranteed: Private deal structures that preserve rate card integrity while still offering programmatic activation convenience.

SSPs will still play a role in executing these mechanics. But the intellectual property around what to price where, and how to package inventory, increasingly belongs back with the screen network.

3.3 The rising value of log-level data

Every conversation about pricing control eventually comes back to data. You cannot manage what you cannot measure. For DOOH networks, the most important shift is the move from aggregated reports to log-level data:

  • Per impression or per playout records that tie together screen ID, timestamp, deal, clearing price, and buyer.
  • Bidstream observation data, where available, to understand lost bids, bid density, and floor effects.
  • Cross channel mapping that links a screen network’s DOOH presence with its web, app, CTV, or retail media footprint at the publisher entity level.

At Red Volcano, we see cross channel publisher intelligence become a key differentiator for SSPs and intermediaries. SSPs want to know not just which screens exist, but how those screens tie back to a publisher that might also own an app, a site, or an AVOD service. That same cross channel graph becomes a pricing asset for DOOH owners who can prove their broader media value.

4. How Screen Networks Can Actively Regain Pricing Control

So what does it actually look like, on Monday morning, for a DOOH network to start regaining control over its programmatic pricing? Below is a pragmatic playbook grounded in what we see across supply side ecosystems.

4.1 Start with a brutally honest supply path map

Most networks underestimate the complexity of their own supply paths. A full map should trace how an impression on a specific screen can be purchased, across all intermediaries. This includes:

  • Direct deals run through a primary SSP.
  • Open auction access via specialist DOOH SSPs or cross channel SSPs.
  • Reseller paths where another entity represents your inventory into additional platforms.

From there, ask three hard questions for each path:

  • What incremental demand does this path truly bring that I could not get via my primary partners?
  • What is the effective take rate once all middlemen are accounted for?
  • Is this path consistent with my brand and price positioning or is it undercutting my flagship assets?

In many cases, you will find that 20 percent of the paths bring 80 percent of the value. That insight is your first lever for regaining control.

4.2 Redesign your floor strategy around value, not averages

Too many DOOH floor strategies are still built around averages. An “average” CPM for roadside billboards, an “average” for retail screens, perhaps a small premium for airports. Instead, treat each cluster of screens as its own micro market.

  • Footfall and audience quality: Screens with high, high intent traffic (for example near grocery store entrance) may deserve meaningfully higher floors.
  • Contextual relevance: Screens near sports venues, financial districts, or entertainment hubs can command higher rates during specific windows.
  • Uniqueness and scarcity: Truly iconic placements should be priced not on impressions alone, but on the brand impact they generate.

A better floor strategy might:

  • Define 6 to 10 “pricing cohorts” of screens based on data driven attributes.
  • Assign starting floor ranges for each cohort, both for open auction and for PMPs.
  • Adjust floors weekly based on sell through, win rates, and buyer feedback.

This is the point where analytics and code become very practical tools. Below is a simple example of how a network could analyze clearing prices and recommend new floors automatically.

import pandas as pd
# Example: log-level data extract from your SSP or ad server
# Columns: screen_id, timestamp, ssp, deal_id, floor_cpm, clearing_cpm, won, impressions
logs = pd.read_csv("pdooh_logs.csv")
# Focus on won impressions in the last 30 days
recent = logs[logs["won"] == 1].copy()
recent["date"] = pd.to_datetime(recent["timestamp"]).dt.date
# Aggregate by screen and hour to understand pricing dynamics
recent["hour"] = pd.to_datetime(recent["timestamp"]).dt.hour
agg = (
recent.groupby(["screen_id", "hour"])
.agg(
impressions=("impressions", "sum"),
avg_floor_cpm=("floor_cpm", "mean"),
avg_clear_cpm=("clearing_cpm", "mean"),
median_clear_cpm=("clearing_cpm", "median"),
)
.reset_index()
)
# Example rule:
# If median clearing CPM is 30 percent higher than the current avg floor,
# recommend increasing the floor by 15 percent.
def recommend_floor(row):
if row["avg_floor_cpm"] == 0:
return row["avg_floor_cpm"]
premium = row["median_clear_cpm"] / row["avg_floor_cpm"]
if premium > 1.3:
return row["avg_floor_cpm"] * 1.15
elif premium < 0.9:
return row["avg_floor_cpm"] * 0.9
else:
return row["avg_floor_cpm"]
agg["recommended_floor_cpm"] = agg.apply(recommend_floor, axis=1)
# Now agg contains a simple recommendation by screen and hour
agg.to_csv("recommended_floors_by_screen_hour.csv", index=False)

This is intentionally simplified, but it illustrates the principle. With basic log-level input, you can move from static, guess based floors to a feedback loop that keeps you much closer to true market value.

4.3 Use curated deals as a rate card ally, not a back door discount

Curated marketplaces and third party “network of networks” deals are a double edged sword. They can expose your screens to new budgets that want one stop buying across many owners. They can also quietly introduce discounted, blended CPMs that undercut your own direct and PMP deals. The key is to treat curated deals as an extension of your rate card, not an exception to it.

  • Define which cohorts of inventory are eligible for curated deals and ensure their pricing aligns with your desired floor ranges.
  • Negotiate clear business rules with curators about minimum prices, volume commitments, and branding of your network inside those packages.
  • Request data feedback on how your screens perform relative to others in the curated bundle.

If a curated marketplace cannot operate under those conditions, you have to ask whether it is adding value, or simply renting your screens for arbitrage.

5. The Changing Role Of SSPs In Programmatic DOOH

As screen networks become more sophisticated about their own pricing and paths, the role of SSPs will evolve rather than disappear.

5.1 From “monetization partner” to infrastructure utility

In the early days of programmatic, many SSPs positioned themselves as strategic monetization partners. They promised yield uplift through proprietary algorithms, demand relationships, and consulting services. In a world of SPO and DOOH specific complexity, a more sustainable model is to think of SSPs as infrastructure utilities:

  • They provide compliant, scalable connections to a diverse set of DSPs and buying tools.
  • They implement auction and deal mechanics in line with industry standards like OpenRTB 2.6, including pDOOH specific extensions.
  • They expose transparent data about bids, wins, and fees so that networks can make independent pricing decisions.

This shift does not diminish the SSP’s value. If anything, it makes their contribution clearer and more defensible. But it does move the “brains” of pricing back up the stack to where it arguably belongs: with the media owner.

5.2 Thin SSP stacks and owner operated gateways

In web and app environments, we already see some advanced publishers building their own “supply gateways” that sit in front of SSPs. These gateways handle:

  • Traffic shaping: Deciding which SSPs get called for which impressions.
  • Flooring and deal routing: Applying network specific rules before the bid request is sent.
  • Analytics: Creating a unified log of everything that happens across all partners.

For DOOH, there is an emerging opportunity to borrow that pattern. Screen networks could:

  • Use their CMS or ad server as a control point to manage when and how impressions are exposed programmatically.
  • Standardize naming, screen IDs, and contextual metadata before it hits any SSP.
  • Implement simple routing rules, such as “this curated retail deal only runs through SSP X at floors no lower than Y.”

SSPs then compete on execution quality, demand access, and service, not on proprietary opacity. The result is a thinner SSP layer, but a stronger and more informed media owner.

6. Cross Channel Intelligence: What Web, App, And CTV Can Teach DOOH

Red Volcano lives in the world of web, app, and CTV publisher intelligence, particularly for SSPs and intermediaries that need to understand their supply base at scale. Many of the themes we see in those channels are directly relevant to DOOH.

6.1 Entity level thinking beats placement level thinking

One of the most powerful shifts in supply side thinking in recent years has been the move from thinking in terms of individual placements to thinking in terms of publisher or media owner entities. For example, a “publisher” might comprise:

  • A flagship website with multiple subdomains.
  • A mobile app with its own ad stack.
  • An AVOD or CTV streaming service.
  • Digital out of home screens in a chain of retail locations.

When SSPs and advertisers can see that they are buying across all of these properties from the same owner, they can:

  • Consolidate spend intentionally into strategic partners.
  • Negotiate multi channel packages that tie together DOOH, CTV, and digital for full funnel coverage.
  • Align creative strategy so that messaging is consistent across environments.

For DOOH networks that also operate other media channels, this is a golden opportunity. It is no longer “just” about setting the right floor for a single screen. It is about articulating the value of your entire media graph.

6.2 Data standards and transparency as long term assets

On web and app, publishers eventually embraced standards like ads.txt, app-ads.txt, and seller.json not just because they were pushed by Google or IAB Tech Lab, but because they reduced channel conflict and clarified who was allowed to sell what. DOOH is still early on that journey, but the direction of travel is clear:

  • Buyers will increasingly demand DOOH equivalents of ads.txt and seller.json, to know which SSPs and resellers are legitimately representing a given network.
  • Networks that document their supply paths clearly will be preferred partners in SPO filtered buying strategies.
  • Standardized metadata schemas for screens (location, environment, size, orientation, venue type) will become table stakes.

At Red Volcano we already see SSPs using publisher level intelligence to audit and rationalize their web and app supply. As DOOH matures, a similar intelligence layer for screen networks is inevitable. Those who invest early in clean, consistent data will find it much easier to negotiate favorable paths and pricing.

7. Practical Analytics: From Logs To Pricing Decisions

To make all of this concrete, let us look at a simple analytical workflow a DOOH network could use to tie SSP data back to pricing action. Imagine you have a central data lake where you ingest:

  • Log-level delivery data from each SSP.
  • Screen metadata from your CMS (location, venue type, size, etc.).
  • External signals such as weather, events, or footfall indices.

A basic SQL query to understand pricing by cohort might look like this:

WITH enriched_impressions AS (
SELECT
i.timestamp,
i.screen_id,
i.ssp,
i.deal_id,
i.floor_cpm,
i.clearing_cpm,
i.impressions,
s.venue_type,
s.city,
s.country,
s.screen_group
FROM
pdooh_impressions i
JOIN
screens s
ON
i.screen_id = s.screen_id
WHERE
i.timestamp >= CURRENT_DATE - INTERVAL '30 days'
),
cohorts AS (
SELECT
screen_group,
venue_type,
city,
CASE
WHEN venue_type IN ('Airport', 'Rail') THEN 'Transit'
WHEN venue_type IN ('Grocery', 'Retail') THEN 'Retail'
ELSE 'Other'
END AS vertical_segment
FROM
screens
GROUP BY
screen_group, venue_type, city
)
SELECT
c.vertical_segment,
e.screen_group,
e.venue_type,
e.city,
COUNT(*) AS impressions,
AVG(e.floor_cpm) AS avg_floor_cpm,
AVG(e.clearing_cpm) AS avg_clearing_cpm,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY e.clearing_cpm) AS median_clearing_cpm,
SUM(CASE WHEN e.clearing_cpm >= e.floor_cpm THEN e.impressions ELSE 0 END) * 1.0
/ NULLIF(SUM(e.impressions), 0) AS pct_above_floor
FROM
enriched_impressions e
JOIN
cohorts c
ON
e.screen_group = c.screen_group
GROUP BY
c.vertical_segment,
e.screen_group,
e.venue_type,
e.city
ORDER BY
median_clearing_cpm DESC;

This gives you, for each cohort of screens:

  • How often you are clearing above your floor.
  • Which groups have the strongest pricing power.
  • Where floors might be either too low (always clearing far above) or too high (low fill, many missed opportunities).

Armed with that insight, you can create a structured decision framework. For example:

  • If pct_above_floor > 0.8 and median_clearing_cpm > avg_floor_cpm * 1.25, increase floors by 10 to 20 percent for that cohort.
  • If pct_above_floor < 0.4 and fill is below target, consider reducing floors for less premium dayparts only.
  • If certain cohorts always clear at a premium in private deals but not in open auction, reduce their exposure to open auction and push buyers into PMPs.

This is not fancy AI. It is disciplined, repeatable analysis that converts data into pricing decisions. That discipline is what separates networks that merely participate in programmatic from those that actively shape their economics.

8. Risks To Watch: Privacy, Measurement, And Over Contraction

With all the talk of regaining control, it is important to recognize the risks of over correcting.

8.1 Privacy and data ethics in DOOH

As DOOH becomes more data driven, there is a temptation to push as close as possible to one-to-one targeting using mobility data, device graphs, or other inferred identifiers. Regulators and consumers are increasingly sensitive to this. Even if DOOH does not rely on cookies in the same way as web, using overly granular movement or behavioral data can trigger backlash. Networks should:

  • Favor aggregated, model based planning over deterministic individual level tracking.
  • Ensure all data providers adhere to regional privacy laws such as GDPR, CCPA, and equivalents.
  • Communicate transparently with landlords and venues about how audience data is used.

Pricing control that relies on shady data practices is a short lived victory.

8.2 Measurement inflation and the temptation to over promise

As measured outcomes become a key selling point, there is a risk of over claiming what DOOH can deliver in terms of direct sales impact. If screen networks anchor their pricing entirely on aggressive attribution models, they might set floors that the market cannot realistically sustain once scrutiny increases. A more durable approach is to:

  • Anchor DOOH pricing in a mix of measured outcomes (footfall, brand lift, sales correlations) and brand value (context, creative impact).
  • Collaborate with neutral measurement partners to avoid conflicts of interest.
  • Educate buyers on DOOH’s strengths and limitations, rather than chasing omni channel attribution hype.

8.3 Over contraction of paths

Finally, there is the risk of over applying SPO logic. If networks cut too many SSPs or reseller relationships, they may inadvertently reduce demand diversity and negotiating leverage. The aim is not to have one path only. The aim is to have a thoughtful, limited set of high quality paths, each with a clear role and transparent economics.

9. What This Means For Red Volcano And Supply Side Intelligence

Even though Red Volcano’s core products focus today on web, app, and CTV publisher research, the same underlying mission applies neatly to DOOH: help supply side companies understand, curate, and optimize their supply base. For SSPs and intermediaries increasingly active in DOOH, there are a few clear needs:

  • Cross channel publisher graphs: Knowing when a DOOH network is part of a broader publisher group that also owns digital or CTV properties.
  • Technology stack visibility: Understanding which CMS, ad server, and measurement tools a DOOH network uses, for smoother integrations and more accurate forecasting.
  • Sellers.json and similar transparency signals: Mapping the chain of authorized sellers for DOOH inventory as rigorously as for web and app.
  • Competitive intelligence: Benchmarking which SSPs have deep integrations with which DOOH networks, and where white space opportunities exist.

On the media owner side, DOOH networks can benefit from adopting the same mindset that leading web and CTV publishers now use:

  • Audit your presence across intermediaries: Know exactly how your brand appears in SSPs, curated marketplaces, and data platforms.
  • Align your external representation with your pricing strategy: Ensure that naming, packaging, and floor policies are consistently expressed across partners.
  • Use independent data platforms to validate what SSPs tell you about your own inventory and performance.

As programmatic DOOH grows, it is not hard to imagine a future where tools similar to ads.txt monitoring, sellers.json mapping, and publisher technology tracking become standard for screen networks. Red Volcano’s expertise in these areas across web, app, and CTV is directly portable into that future, once the right DOOH specific signals mature.

Conclusion: A Rare Window For DOOH To Reset The Rules

The SSP shake-up is often framed as a story about consolidation and risk. Some partners will exit. Fees will compress. Certain tactics that relied on opaque reselling will fade. For programmatic DOOH, though, this is also a rare opportunity. Screen networks that move quickly can:

  • Map and simplify their supply paths in line with buyer SPO expectations.
  • Redesign their floor and packaging strategies around real value and data.
  • Reposition SSPs as transparent infrastructure partners, rather than black box monetization engines.
  • Leverage cross channel intelligence to present themselves as strategic media owners, not just a set of disconnected screens.

Pricing control is not about winning a tug of war with SSPs or buyers. It is about building a clear, data backed understanding of your own value, then expressing that consistently across all the ways your inventory can be bought. In web, app, and CTV, the publishers who embraced that philosophy early are now the ones shaping the terms of trade, even in a tough market. Programmatic DOOH has the chance to learn from those channels and skip some of the more painful mistakes. If you operate a screen network, the question is simple. In three years, do you want to be a commodity line item inside someone else’s unified ID and curated marketplace strategy, or a clearly defined, premium partner with a confident pricing narrative backed by data? The SSP shake-up has made that choice more urgent, but also more achievable. Now is the time for DOOH media owners to step forward, take control of their paths and prices, and help write the next chapter of programmatic out of home.