호주 광고업계의 창의적 옥외광고 캠페인이 자국 최고 광고상을 넘어 세계 무대에서도 다시 한 번 경쟁력을 입증했다.

호주 옥외광고협회 (Outdoor Media Association, OMA)는 VML과 웨이브메이커 (Wavemaker)가 제작한 ‘더 히든 아이 테스트(The Hidden Eye Test)’ 캠페인이 2026 세계옥외광고협회 (World Out of Home Organization, WOO) 어워즈에서 크리에이티브 어워드 클래식(Creative Award – Classic) 부문을 수상했다고 밝혔다.

이 캠페인은 앞서 2025 OMA 크리에이티브 어워즈에서도 베스트 클래식 캠페인(Best Classic Campaign)을 수상한 바 있으며, 이번 WOO 어워즈 수상으로 글로벌 무대에서도 작품성을 인정받게 됐다.

‘더 히든 아이 테스트’는 시각 착시와 인공지능(AI), 검안 전문가의 전문성을 결합해 기존 빌보드를 대중을 위한 시력 검사 도구로 재해석한 것이 특징이다.

캠페인은 일상적인 옥외광고 매체를 공공 시력검사 공간으로 전환함으로써 시민들의 자연스러운 참여를 유도했다. 특히 광고를 보는 행위 자체가 시력 확인 경험으로 이어지도록 설계해 높은 주목도와 화제성을 이끌어냈다.

OMA는 “이 캠페인은 사람들의 관심을 끌고 대화를 촉발했으며, 단순하지만 정교하게 실행된 아이디어가 얼마나 강력한 영향력을 발휘할 수 있는지를 보여줬다”고 평가했다.

업계에서는 이번 수상이 첨단 기술보다 소비자의 실제 문제를 해결하는 창의적 아이디어와 공공적 가치가 옥외광고의 효과를 극대화할 수 있음을 보여준 대표 사례로 평가하고 있다.

An advertiser who buys a programmatic digital out-of-home campaign wants to know one thing at the end of the flight: did the screens drive people into the store. The impression count and the reach figure tell them how many people had an opportunity to see the creative. Attribution is the layer that tries to connect those impressions to a measurable visit, a sale, or a downstream action. It is the question that programmatic DOOH spent the last five years trying to answer properly, and the question that more advertisers in 2026 are willing to spend against once the answer is auditable.

This post walks through how attribution actually works for a programmatic DOOH buy in 2026: how the data flows through a clean room, what deterministic and probabilistic matching are doing inside that flow, what a lift study is measuring and how it is structured, what attribution windows make sense by retail category, and where in-venue counting fits into the chain. It is aimed at the planner, the measurement lead, or the operator's data team specifying or auditing attribution on a digital signage network, not at the person writing the creative.

The end-to-end attribution flow

An attribution report for a programmatic DOOH campaign rests on six pieces of data flowing through one secure environment. Each piece comes from a different party, so the work is mostly about getting them into the same place under terms that the lawyers will sign.

1. Playout log. The operator's record of which creative played on which face at which timestamp. This is the spine of any attribution model; everything else hangs off it.
2. Impression audience. The audience figure associated with each play, produced by the currency body (GeoPath in the US, the relevant JIC in Europe) or by an accredited measurement provider. This is what turns a play into an impression count.
3. Exposure geography. The location and coverage footprint of each face: where it sits and which audience it could plausibly have reached. This is what allows the model to define an exposed group and a control group later.
4. Mobility data. Aggregate device-movement panels, supplied by location-data providers, that show how groups of devices moved through the exposed and control areas during and after the campaign. This is the input that lets the model decide whether someone exposed to the face later visited the store.
5. Visit or sale signal. The downstream event being attributed: a store visit, a transaction, an online action. For physical-retail attribution, the visit signal usually comes either from the same mobility panel or from the advertiser's own first-party data on stores and POS.
6. Identity bridge. Whatever mechanism the clean room uses to match exposure to outcome without exchanging raw identifiers. This is the part that makes deterministic matching possible where the data permits it and falls back to probabilistic matching where it does not.

The data clean room: where attribution actually runs

A data clean room is a controlled environment where two or more parties can run joint analysis on each other's data without either party exposing their raw records. The clean room enforces a few hard properties. Inputs are encrypted in transit and at rest. Queries are pre-approved against an agreed set of operations. Outputs are aggregated to a minimum cohort size before they leave the environment, which prevents the result of any single query from re-identifying an individual. Audit logs record every query and every output. The clean-room operator is usually a neutral third party or one of the major cloud providers' clean-room products.

For programmatic DOOH attribution, the clean room is what makes the rest of the flow legally and contractually possible. The operator brings the playout log and exposure geography. The currency body or measurement partner brings the audience and impression data. The mobility provider brings the device panel, joined against the exposure footprint. The advertiser brings their store list and visit signal. The clean room joins the data inside its secure environment, runs the agreed attribution queries, and returns aggregated results to each party. No raw device IDs cross between parties. No advertiser POS records leave the advertiser's side. The result is the attribution figure, not the underlying data.

Under the GDPR and equivalent regimes, this matters for two reasons. First, the clean-room architecture supports lawful processing on a joint-controller or processor basis where the contractual layer is properly drawn up. Second, the aggregation thresholds are the technical mechanism that keeps the output from being personal data: a result reported only at a cohort of N or larger, where N is set high enough that no individual can be re-identified from the figure. The 2026 norm for DOOH attribution is to run the join in a clean room rather than to ship raw data between vendors.

Deterministic vs probabilistic matching

Inside the clean room, the join between exposure and outcome happens one of two ways, and most real attribution models use both. The choice is a function of what identifiers are present in each data set and how reliably they tie back to a real visit.

Deterministic matching

Deterministic matching joins two records when they share an identifier that points to the same person or device. The classic example outside DOOH is a hashed email matched between an advertiser's CRM and a publisher's logged-in user file. For programmatic DOOH, deterministic matching is harder because the medium itself is not logged in. There is no DOOH equivalent of a cookie or a logged-in user at the screen. The deterministic identifiers that do exist sit in the panels feeding the attribution model: a hashed mobile advertising ID in the location panel, a loyalty ID in the advertiser's first-party data, a transaction ID joined to a visit at POS.

Where deterministic matching applies in DOOH attribution is on the outcome side, not the exposure side. The exposed cohort is defined by being in the screen's audience footprint during the play window, which is a probabilistic membership. The outcome cohort, the people who visited or transacted, can be defined deterministically when the advertiser brings first-party visit or sale data into the clean room and matches it against the panel through a hashed identifier. The benefit of going deterministic on the outcome is the report is harder to overstate: each attributed visit is tied to a known device that actually entered the store, not modelled from movement patterns alone.

Probabilistic matching

Probabilistic matching joins records based on shared patterns rather than a shared identifier. For DOOH attribution, the probabilistic part of the model is doing two things. It is deciding which devices in the mobility panel were exposed to a given face during the play window, using their location pattern relative to the face's coverage footprint and the audience model behind the impression. It is also extrapolating from the panel to the full population: the panel is a sample, not a census, and the attributed visit count has to be scaled by the panel's coverage in the market.

Probabilistic methods carry more methodological weight, but they are not weaker by definition. The currency bodies' impression figures are themselves probabilistic, computed from traffic models with visibility corrections. The attribution model is doing the same kind of work, applied to the question of whether a member of the exposed audience later visited a store. The discipline that matters is whether the panel size, the modelling assumptions, and the scaling factors are documented and auditable. A probabilistic attribution figure with a published methodology is auditable. One without is a black box.

How the two combine

In a typical 2026 DOOH attribution flow, exposure is defined probabilistically (a device was within the face's coverage footprint during a play window), and outcome is defined as deterministically as the advertiser's data allows (the device matched a first-party visit or sale record, or, where that is not possible, the visit is inferred from the panel itself). The clean-room query joins the two cohorts, applies the panel scaling, and reports lift against a control group. Reports that present a single attributed-visit number without separating the deterministic component from the probabilistic component are hiding the methodology behind a tidy figure. Read the breakdown, not just the headline.

Lift methodology: what the report is actually measuring

Attribution for programmatic DOOH does not just report an attributed-visit count. It reports a lift figure: the difference in visit rate between the audience that was exposed to the campaign and a comparable audience that was not. Lift is the figure that controls for everything that is not the campaign, which is most of why a store gets visits in a given week. If you do not measure lift, you cannot tell the difference between the campaign's effect and the underlying base rate of visits.

A lift study for DOOH is structured the same way as any other media-lift study, with channel-specific definitions for the cohorts.

• Exposed group. Devices in the panel whose location pattern places them within the audience footprint of at least one campaign face during a play window. The threshold for inclusion (was the device within 50 metres of the face, did the device dwell within the coverage zone for at least N seconds) is set in the methodology before the campaign runs.
• Control group. Devices in the panel that match the exposed group on the relevant covariates (geography, demographic where available, baseline visit propensity) but were not within reach of any campaign face during the flight. Picking the control well is the hardest part of the model: a poorly chosen control inflates lift, and an over-tight control deflates it.
• Visit window. The period after exposure during which a visit is counted as attributable. This is the attribution window, treated separately below.
• Visit rate. Visits per device in each group, normalised to a comparable base. Lift is the ratio (or absolute difference) between the exposed and control visit rates.

The output a buyer sees is usually expressed two ways. Incremental lift is the percentage difference in visit rate between exposed and control (an exposed visit rate 18% above control means 18% incremental lift). Incremental visits is the count of additional visits the campaign is credited with, derived from the lift figure and the scaled exposed cohort. Both numbers should ship with the cohort sizes, the panel coverage, and the confidence interval; a lift figure without a confidence interval is not enough to plan against.

Attribution windows by category

An attribution window is the period after exposure during which a visit or sale is credited to the campaign. Pick the window too short and the campaign undercounts its real effect; pick it too long and the model is just counting visits the campaign did not drive. The right window depends on the category and the purchase cycle, not on what the operator's default report uses.

The figures below are typical industry ranges in 2026, not figures from a specific named study. They are intended to set the order-of-magnitude expectation for a planner specifying a window; the actual window for any campaign should be set against the advertiser's own purchase data where it exists.

• Quick-service restaurants, convenience, coffee. Same-day to 48 hours. The purchase decision is short and proximate; if exposure to a screen near the venue drove a visit, that visit happened soon after the play.
• Grocery and pharmacy. Two to seven days. Most shoppers have a weekly or twice-weekly cycle, and a screen seen at a transit stop on Monday might show up in the basket on Wednesday.
• Apparel and general retail. Seven to fourteen days. Browsing cycles are longer; visits cluster around the weekend after exposure.
• Consumer electronics, appliances, larger considered purchases. Two to four weeks. The visit follows research and comparison shopping; attribution windows shorter than two weeks miss most of the effect.
• Automotive and the longest considered categories. Four weeks and up, often with the brand-level effect measured separately from the dealership-visit effect.
• Entertainment, attractions, time-limited events. The window is the event window itself, set by the campaign rather than the category. Same-day exposure for a screening; a few weeks for a multi-day exhibition.

Two operational notes. First, the window applies per device, not per campaign: a device exposed on day one is counted against visits within the window from day one, regardless of when the campaign ends. Second, the window should align with the control group's reference period, so that the visit-rate comparison is on a like-for-like basis. A 14-day exposed window compared to a 7-day control window is a meaningless lift figure.

The role of in-venue counting

Outside-the-store attribution for DOOH leans on mobility panels and clean-room joins; that is the part of the chain that runs on devices and data. Inside the venue itself, where the screen actually plays to a real audience walking past it, the input the attribution model needs is something different. It needs to know how many people had an opportunity to see the screen, how long they stayed in its coverage zone, and how that audience changed across the day. That is the input people counting at the screen and across the venue provides, and it is the part of the chain that determines whether the impression count entering the attribution model is real or modelled.

There are three places in-venue counting feeds the attribution model directly.

• Impression weighting at the screen. The opportunity-to-see weighting that turns a play into an impression is more accurate when the actual audience walking past the face is counted at the face, not modelled from venue averages. For in-venue inventory, a count at the screen replaces a venue-level estimate, and the impression figure entering the clean room is one degree closer to the ground.
• Dwell as an attention proxy. Time spent in the screen's coverage zone is the closest in-venue analog to viewability. A creative seen by a fast-walking audience and one seen by an audience waiting in a queue produce very different attention levels, even at the same impression count. Dwell at the screen, measured without identifying anyone, feeds the attention layer of the report and lets a buyer compare faces on more than reach alone.
• Venue-level visit data. Where the venue runs people counting at its entries and across its zones, that data anchors the audience model. The attribution clean room can be told what the actual venue traffic was during the campaign, against which the device-panel exposure can be scaled and corrected. The panel is a sample of devices; the venue counter is a count of people. Pairing them tightens the attribution figure.

The 2026 expectation among more careful buyers is that in-venue inventory should carry venue-level audience data into the attribution flow, not just a roadside-style traffic model. The standards bodies have been pushing in this direction (MRC's revisions to its OOH guidelines, IAB's DOOH guidance on impression definitions, GeoPath's place-based methodology), and the practical effect is that an operator running unverified audience figures is increasingly at a disadvantage on the buy.

How Ariadne fits

Ariadne supplies the in-venue audience layer that feeds a programmatic DOOH attribution model. It does this without cameras, without face detection, and without capturing any personal data at the screen.

Ariadne measures this with Hybrid Fusion, its patented camera-free method. Time-of-Flight depth sensing counts every visitor at the entrances, capturing geometry rather than images, while patented phone signal sensing follows movement through the interior, detecting the signals a phone emits even in airplane mode. The sensor streams both feeds to Ariadne, where Hybrid Fusion combines them into one trajectory per visit and computes counts, dwell, and paths. The streams carry no identifier: no MAC address, no device ID, no biometric data, and no camera is involved. Identifiers are stored only when a visitor explicitly opts in, which keeps the method GDPR-friendly and outside biometric territory.

For an attribution model, that hardware and data design produce three useful properties. The impression weighting at the screen rests on a real visit count and a real dwell figure for the screen's coverage zone, both reported continuously through the campaign. The audience input crossing into the clean room carries no personal data, so the contractual and privacy work between the operator, the measurement partner, and the advertiser is simpler. And the venue-level counts that anchor the model are produced by the same sensor unit, so an operator running an Ariadne deployment is reporting from one source rather than reconciling two. The data handling and the no-personal-data design are described in the privacy policy, and the campaign-side reporting feeds the operator's visitor marketing reporting for the advertiser.

A buyer checklist for DOOH attribution

If you are auditing or specifying attribution on a programmatic DOOH buy in 2026, these are the questions worth putting to the operator, the measurement provider, and the clean-room partner in writing.

1. Where does the attribution join actually run? It should run inside a named clean-room environment with an agreed query set and aggregation thresholds, not on a vendor laptop. Get the clean-room operator and the threshold figures in writing.
2. What is the panel and what is its coverage? The mobility provider's panel size, geographic coverage, and known biases shape every figure in the report. Ask for the panel description and the scaling factors used to extrapolate to the market.
3. How is the control group constructed? A good control is matched on geography, baseline visit propensity, and the relevant covariates. Ask how the control was drawn and how it was validated.
4. What attribution window is being reported, and why? The window should reflect the category's purchase cycle, not the operator's default. Confirm the window and the rationale before the campaign launches, not in the report.
5. Is the lift figure reported with cohort sizes and a confidence interval? A lift figure without an interval is not a planning input. Reject reports that present a single percentage without supporting figures.
6. What is the in-venue audience input for indoor inventory? For digital faces inside a venue, ask what produces the visit and dwell figures at the screen. The answer should not be a camera and should not be a system that infers demographic categories from facial features.
7. What identifiers, if any, cross between parties? The clean-room design exists so that raw identifiers do not. Confirm that the attribution flow is consistent with that design, and that the output thresholds keep results from being re-identifiable.

FAQ

Does programmatic DOOH attribution require cameras at the screen?

No. Ariadne counts with Hybrid Fusion: Time-of-Flight depth sensing plus patented phone signal sensing, never cameras. Time-of-Flight captures geometry rather than images, and signal sensing captures no MAC address by default, so the measurement involves no video, no faces, and no biometric data.

The audience input the attribution model needs at the screen is a visit count and a dwell figure, both of which can be produced without images, faces, or biometric data. Camera-based audience measurement at the screen is hard to defend under the GDPR and the EU AI Act, and the standards work has been converging on camera-free methods for in-venue inventory.

What is the difference between deterministic and probabilistic matching in this context?

Deterministic matching joins two records that share an identifier, like a hashed mobile ad ID present in both the location panel and the advertiser's first-party visit data. Probabilistic matching infers the join from patterns, like deciding a device was exposed because its location pattern placed it within the face's coverage footprint during a play. In practice, DOOH attribution uses both: exposure is defined probabilistically against the audience footprint, and outcome is defined as deterministically as the advertiser's data permits.

What attribution window should I use?

The window should reflect the category's purchase cycle, set against the advertiser's own purchase data where it exists. Industry-typical ranges run from same-day for quick-service restaurants and convenience, through one to two weeks for general retail, to four weeks and up for considered categories like consumer electronics and automotive. Pick the window before the campaign launches, document the rationale, and align the control group's reference period to match.

Is the attribution figure GDPR-compliant?

It can be, and the design that gets it there is the clean room plus aggregation thresholds: each party brings their data into a secure environment, the join runs inside that environment, raw identifiers do not cross between parties, and the output is aggregated to a cohort size that prevents re-identification. On the in-venue audience side, a counting method that captures no images, no faces, and no device identifiers by default is not processing personal data, which keeps the input crossing into the clean room outside the heaviest GDPR obligations. Confirm the specifics with your data protection officer and the clean-room partner, but the architecture is well-established in 2026.

세계옥외광고협회 (World Out of Home Organization, WOO)가 프로그래매틱 디지털 옥외광고(pDOOH) 시장의 글로벌 규모를 처음으로 집계한 연구 결과를 발표했다.

WOO는 영국 런던에서 열린 'WOO 글로벌 콩그레스 2026(WOO Global Congress 2026)'에서 2025년 전 세계 pDOOH 광고비가 약 14억 달러를 기록했다고 밝혔다. 이는 전체 디지털 옥외광고(Digital Out of Home, DOOH) 광고비의 약 7%에 해당하는 규모다.

이번 조사는 글로벌 SSP(Supply-Side Platform) 사업자 11개사가 참여한 가운데 진행됐다. 참가 기업들은 데이터를 비공개 방식으로 제출했으며, 글로벌 회계법인 프라이스워터하우스쿠퍼스 (PricewaterhouseCoopers, PwC)가 독립적으로 데이터를 집계·분석했다.

WOO는 이번 연구가 프로그래매틱 DOOH 시장을 객관적으로 측정한 세계 최초의 글로벌 조사라는 점에서 의미가 크다고 설명했다. 그동안 업계는 국가별 또는 사업자별 통계에 의존해 왔으며, 글로벌 시장 전체를 아우르는 공신력 있는 지표는 부재한 상황이었다.

조사 결과 지역별 pDOOH 광고비는 미주 지역이 6억7,000만 달러로 가장 큰 규모를 기록했다. 전체 DOOH 시장 대비 pDOOH는 14.2%로 나타났다.

유럽·중동·아프리카(EMEA) 지역은 5억2,100만 달러로 9.4%를 기록했으며, 아시아·태평양(APAC) 지역은 1억4,900만 달러로 1.7%로 집계됐다.

특히 APAC은 세계 최대 규모의 DOOH 시장임에도 불구하고 프로그래매틱 거래 비중은 가장 낮은 것으로 조사됐다. WOO는 디지털 인프라 확대와 자동화 거래 환경이 성숙할 경우 APAC이 향후 가장 높은 성장 잠재력을 가진 시장으로 부상할 것으로 전망했다.

거래 방식별로는 옥외광고 전문 DSP(Demand-Side Platform)가 전체 pDOOH 광고비의 65.5%인 8억7,700만 달러를 차지했다. 이번 WOO 글로벌 pDOOH 광고비 조사에는 다음의 SSP(Supply-Side Platform) 사업자들이 참여했다

반면 더 트레이드 데스크 (The Trade Desk)와 구글 DV360 (Google DV360) 등 옴니채널 DSP를 통한 거래는 4억6,300만 달러로 전체의 34.5%를 차지했다. 이는 디지털 광고 구매자들이 pDOOH를 기존 디지털 미디어 전략에 적극 편입하고 있음을 보여주는 결과로 평가된다.

찰스 패리-오케든 (Charles Parry-Okeden) 세계옥외광고협회(World Out of Home Organization, WOO) 글로벌 오디언스·데이터 총괄 부회장은 다음과 같이 말했다.

"이번 조사는 옥외광고 산업에 있어 역사적인 이정표가 되는 순간이다. 우리는 처음으로 프로그래매틱 디지털 옥외광고(pDOOH) 광고비를 독립적으로 검증하고 글로벌 기준에 따라 일관되게 측정할 수 있게 됐다. 이번 결과는 시장의 현재 위치를 명확히 보여주는 동시에 매우 고무적인 의미를 갖는다. 2025년 pDOOH 광고비 13억3,900만 달러는 pDOOH가 이미 본격적인 광고 채널로 자리 잡았음을 입증한다. 하지만 동시에 앞으로 남아 있는 성장 기회가 훨씬 더 크다는 사실도 보여준다. WOO의 역할은 업계를 하나로 모으고, 표준을 만들며, 객관적인 데이터와 근거를 바탕으로 시장 발전을 지원하는 것이다. 이번 연구는 이러한 세 가지 역할을 모두 충실히 수행한 사례라고 생각한다."

기디언 아데이 (Gideon Adey) 구루(GUROOH Ltd) 대표이자 세계옥외광고협회(World Out of Home Organization, WOO) 측정 컨설턴트는 이번 연구의 가장 큰 의미로 조사 방식 자체를 꼽았다.

"이번 조사가 진정으로 특별한 이유는 단순히 시장 규모 수치를 발표했다는 데 있지 않다. 서로 다른 시장에서 각기 다른 방식으로 운영되는 11개 SSP가 하나의 공통된 프레임워크 아래 데이터를 제출했고, 이를 통해 처음으로 전 세계 pDOOH 시장을 통합적으로 조망할 수 있게 됐다. 조사 방법론은 엄격하게 설계됐으며, 데이터 비밀 유지 체계 또한 강력하게 구축됐다. 무엇보다 집계 결과는 독립적인 검증 과정을 거쳤다. 이번 연구는 앞으로 구축해 나갈 글로벌 pDOOH 시장 측정 체계의 출발점이다. 향후에는 국가별 세부 데이터와 매체 유형별 분석, 채널별 세분화 정보 등을 추가하고, 보다 정기적인 보고 체계를 마련해 시장의 투명성과 신뢰성을 더욱 높여 나갈 계획이다."

댄 번연 (Dan Bunyan) 스트래티지앤드(Strategy&, PwC 네트워크 소속) 파트너는 이번 연구가 글로벌 pDOOH 시장의 성장과 발전을 측정하는 중요한 기준점이 될 것이라고 평가했다.

"pDOOH는 기술과 미디어, 그리고 크리에이티브가 만나는 지점에서 빠르게 성장하고 있는 역동적인 분야다. 시장이 지속적으로 확대되고 진화하는 가운데 이번 연구는 글로벌 시장의 중요한 벤치마크 역할을 하게 될 것이다. 이를 통해 업계는 시장이 성장하고 성숙해 가는 과정을 보다 체계적으로 추적할 수 있게 된다. 또한 pDOOH 성장세에 대한 보다 명확한 데이터를 제공함으로써 업계 전반의 투자와 혁신, 그리고 시장에 대한 신뢰를 높이는 데 기여할 것으로 기대한다. 이번 조사는 글로벌 시장을 대상으로 한 최초의 연구로, 국가와 업계를 아우르는 폭넓은 협업을 바탕으로 추진됐다는 점에서 큰 의미를 갖는다."

조사 방법론 (About the Methodology)

이번 'WOO 글로벌 pDOOH 광고비 조사(WOO Global pDOOH Expenditure Study)'는 세계옥외광고협회 (World Out of Home Organization, WOO)가 2025년 전 세계 프로그래매틱 디지털 옥외광고(pDOOH) 매출 규모를 집계·분석하기 위해 수행했다. 데이터 집계는 프라이스워터하우스쿠퍼스 (PricewaterhouseCoopers LLP, PwC)가 독립적으로 진행했다.

주요 조사 원칙은 다음과 같다.

• 이번 조사에는 총 11개 SSP(Supply-Side Platform)가 참여했으며, 각 사업자는 매출 데이터를 PwC에 비공개 방식으로 제출했다. 제출된 데이터는 PwC가 독립적으로 집계했으며, 개별 사업자 또는 운영사의 매출 수치는 어떠한 경우에도 공개되지 않는다.
• PwC는 제출된 데이터의 일관성, 완전성, 합리성을 검토하기 위해 다양한 검증 절차(sense-check)를 수행했다.
• 다만 PwC는 참여 사업자로부터 제출받은 개별 데이터를 별도로 감사하거나 사실 여부를 독립적으로 검증하지 않았으며, 해당 정보에 대해 어떠한 형태의 보증이나 의견도 제공하지 않는다.
• 채널별 분석은 옥외광고 전문 DSP(Demand-Side Platform)와 옴니채널(범용) DSP를 시장 진입 경로(route to market) 기준으로 구분해 집계했다.
• 이번 조사는 글로벌 pDOOH 시장 측정을 위한 최초의 기준선(Baseline)을 구축하는 데 목적이 있다. 향후 발간될 보고서에는 지역별 및 국가별 시장 규모 분석, 매체 유형별 세부 데이터, 광고주 업종별 분석 등이 단계적으로 추가될 예정이다.
• 또한 향후에는 보다 정기적이고 빈도 높은 보고 체계를 구축해 글로벌 pDOOH 시장의 성장 추이를 지속적으로 추적할 계획이다.

WOO는 앞으로 더 많은 SSP 사업자들이 조사에 참여해 글로벌 pDOOH 시장 측정의 정확성과 대표성을 높일 수 있기를 기대한다고 밝혔다.

2026 세계옥외광고협회(WOO) 런던 총회에서는 AI와 자동화, 오디언스 측정, 데이터 활용 등 다양한 산업 이슈가 논의된 가운데, 옥외광고가 브랜드 신뢰를 구축하는 핵심 미디어라는 점이 다시 한 번 주목받았다.

라탐 네트워크 (Latam Network)의 사업개발 총괄 가브리엘 페로티 (Gabriel Perotti)는 총회 참관 후 공유한 글을 통해 “옥외광고는 단순히 브랜드의 가시성을 높이는 매체가 아니라 신뢰와 선호도, 브랜드 신인도를 구축하는 강력한 미디어”라고 평가했다.

그는 최근 조사 결과를 인용하며 옥외광고가 주요 브랜드 지표에서 다른 매체를 앞서고 있다고 설명했다. 브랜드 선호도는 옥외광고가 78%로 전통 매체(68%)와 디지털 매체(62%)를 상회했으며, 신뢰도 역시 72%로 전통 매체(64%)와 디지털 매체(54%)보다 높게 나타났다. 브랜드 신인도에서도 옥외광고는 75%를 기록해 전통 매체(68%)와 디지털 매체(58%)를 앞질렀다.

페로티는 이러한 결과의 배경으로 옥외광고가 실제 생활 공간에서 소비자와 만나는 미디어라는 점을 꼽았다. 거리와 공항, 쇼핑몰, 교통시설, 도심 공간 등 일상 속 다양한 접점에서 노출되는 옥외광고는 온라인 광고와 달리 차단하거나 건너뛸 수 없는 현실 기반 미디어라는 것이다.

그는 “잘 기획된 옥외광고는 단순한 광고 메시지를 넘어 브랜드의 존재감과 안정성, 신뢰성을 보여주는 공적 신호 역할을 한다”고 강조했다.

이어 옥외광고 산업이 지속적으로 성장하기 위해서는 측정 체계와 데이터 활용, 기술 혁신, 효과 검증 역량을 강화해야 한다고 밝혔다. 동시에 업계가 이미 보유하고 있는 강점인 규모, 존재감, 맥락성, 신뢰성을 보다 적극적으로 시장에 전달할 필요가 있다고 지적했다.

페로티는 “옥외광고의 미래는 단순히 더 디지털화되고 더 자동화되는 데 있는 것이 아니다”라며 “현실 공간에서 브랜드와 소비자를 연결하고 신뢰를 구축하는 미디어로서의 가치를 더욱 강화하는 데 있다”고 말했다.

이번 WOO 런던 총회는 AI와 데이터 기술이 옥외광고 산업의 성장을 이끄는 핵심 동력으로 부상하는 동시에, 업계가 궁극적으로 제공해야 할 가치는 여전히 ‘신뢰’라는 점을 다시 확인한 자리로 평가된다.

Havas has acquired a majority stake in Archrival, a U.S.-based youth-culture and experiential activation agency, further strengthening Havas Play’s ability to help brands earn relevance through culture, fandom, and participation. The acquisition expands Havas Play’s offering across sports marketing, live experiences, branded partnerships, creator engagement, and experiential activation, giving clients new ways to cultivate brand desire while deepening audience connection and delivering measurable growth.

Founded in 1997, Archrival has built a reputation for creating culturally relevant programs that connect brands with younger audiences through sports, collegiate activations, ambassador networks, and immersive experiences. The agency is known for helping brands show up authentically within the communities, passions, and moments that shape culture today.

Archrival has worked with some of the world’s most iconic brands including adidas, Red Bull, Netflix, Snapchat, Spotify, Tinder, and EA.

The acquisition marks another significant step in the expansion of Havas Play, strengthening its ability to connect brands with audiences through the passion points that drive participation, advocacy, and growth.

Together, Havas Play and Archrival will offer clients an expanded suite of capabilities spanning sports marketing, creator engagement, experiential activation, branded partnerships, ambassador programs, gaming, and community-led brand building.

Combined with Havas’s Converged.AI operating system, Archrival’s expertise in collegiate activations, ambassador networks, and grassroots engagement will help clients identify emerging cultural signals faster, activate communities more effectively, and translate engagement into measurable business outcomes.

We are very pleased to welcome Archrival to the Havas family. Their expertise enhances Havas Play’s offering in a high-growth area and represents an important step forward in expanding our capabilities and delivering long-term value for our clients. As sport and fandom play an ever more central role in shaping culture, they’ve become increasingly powerful drivers of growth for brands and businesses, helping build the kind of brand desire that creates a lasting competitive advantage.

Yannick Bolloré, Chairman and CEO, Havas

North America represents a significant opportunity for brands as sports, entertainment, creators, and fandom increasingly shape how audiences discover, engage, and build loyalty. Clients are looking for partners that can help them navigate this changing landscape and connect more authentically with consumers. Archrival expands our ability to scale those solutions and deliver them in a way that is connected, differentiated, and built for where the market is heading.”

Greg James, CEO, Havas Media Network North America

세계옥외광고협회(WOO)가 글로벌 이사회 역량 강화를 위해 호주와 브라질을 대표하는 업계 리더 2명을 신규 이사로 선임했다.

WOO는 최근 엘리자베스 매킨타이어(Elizabeth McIntyre) 호주옥외미디어협회(OMA) 최고경영자와 펠리페 비안테(Felipe Viante) 비드롭스(b.drops) 공동창업자 겸 브라질옥외광고협회(ABOOH) 회장을 이사회 신규 멤버로 선임했다고 밝혔다.

매킨타이어는 디즈니 출신 경영인으로, 호주 옥외광고 산업을 대표하는 OMA를 이끌며 업계 혁신을 주도해 왔다. 특히 OMA가 추진한 일부 측정·표준화 프로젝트와 산업 발전 프로그램은 세계 각국의 옥외광고 협회들이 벤치마킹할 정도로 높은 평가를 받고 있다.

비안테는 브라질의 대표 실내 미디어 기업인 비드롭스 공동창업자로, 미용실, 피트니스센터, 바버숍 등 생활 밀착형 공간을 기반으로 한 인도어 미디어 네트워크를 구축하며 시장을 선도해 왔다. 또한 브라질옥외광고협회를 이끌며 브라질 옥외광고 산업의 성장과 디지털 전환을 지원하는 데 중요한 역할을 수행하고 있다.

브라질 옥외광고 업계는 ABOOH와 센트럴 데 아웃도어(Central de Outdoor) 등 두 개의 주요 산업 단체를 중심으로 운영되고 있으며, 비안테는 이 가운데 ABOOH의 발전을 이끈 핵심 인물로 평가받고 있다.

톰 고다드(Tom Goddard) WOO 회장은 "WOO는 정기적으로 이사진을 교체하며 새로운 시각과 전문성을 조직에 반영하고 있다"며 "엘리자베스와 펠리페처럼 뛰어난 역량을 갖춘 업계 리더들이 합류하게 돼 매우 기쁘다"고 말했다.

이어 "이번 선임을 통해 WOO 이사회는 더욱 폭넓은 글로벌 대표성을 확보하게 됐다"며 "이는 세계 옥외광고 산업의 지속적인 성장과 협력 확대를 이끄는 중요한 자산이 될 것"이라고 밝혔다.

이번 인선으로 WOO는 아시아·유럽·북미에 이어 오세아니아와 남미 지역의 목소리를 한층 강화하며, 글로벌 옥외광고 산업을 대표하는 국제기구로서의 위상을 더욱 공고히 하게 됐다.

As competition intensifies among integrated resort destinations across Asia, airport advertising has become a strategic tool for reaching high-value international travelers at the very moment they are preparing to travel.

Two notable examples are Inspire Entertainment Resort in Korea and Solaire Resort in the Philippines, both of which have invested heavily in premium digital media assets at Seoul Incheon International Airport.

Inspire has focused its campaign on the airport’s iconic Twin Towers media. The synchronized digital installation features two massive screens measuring 37 meters by 5 meters, creating a striking visual experience that captures the attention of departing passengers. The location reinforces Inspire’s positioning as Korea’s leading entertainment and leisure destination located near the airport.

Solaire, however, has taken an even broader approach, deploying one of the most extensive airport media campaigns currently running at Incheon.

At Terminal 1, Solaire occupies the Antler Center, utilizing two synchronized digital panels measuring 15.4 meters by 4.5 meters to cover both the east and west sections of the terminal. At Terminal 2, the brand extends its presence through four Premium Square displays measuring 3.84 meters by 7.02 meters each, while also dominating the duty-free zone with the Big Square digital screen measuring 11.52 meters by 6.48 meters.

The campaign reflects Solaire’s growing commitment to the Korean market. After initially building awareness through entertainer PSY, the resort recently appointed actor Hyun Bin as its global brand ambassador, strengthening its premium positioning among affluent travelers.

For both brands, airport advertising is more than a media buy. It is a strategic investment designed to engage international travelers, business executives, and high-spending consumers in an environment where future travel decisions are often influenced. As airports continue to evolve into lifestyle and retail destinations, premium OOH media is becoming an increasingly important platform for luxury resorts seeking global visibility.

The award recognizes IRIS Optronics for redefining the role of digital signage through technological innovation, helping shift the industry from conventional attention-grabbing, advertising-focused displays toward information-centric signage designed for effective communication and enhanced user engagement. The recognition also underscores the growing market acceptance and commercial potential of full-color ChLCD e-Paper technology in Japan's digital signage sector.

"IRIS Optronics has been cultivating the Japanese market for many years. Following the opening of our Tokyo office and the launch of our latest B3-size product at the end of May, receiving the DSJ Award in early June is a strong endorsement of both our technology and our long-term commitment to the Japanese market," said Dr. Albert Liao, Chairman and CEO of IRIS Optronics. "The Digital Signage Award is one of the industry's most prestigious annual recognitions in Japan. Beyond technological excellence, the evaluation criteria emphasize user experience and social value creation, reflecting a broader industry transition from focusing solely on hardware specifications to delivering meaningful real-world applications. With its full 16-million-color display capability, ultra-low power consumption, and energy-efficient design, IRIS Optronics' full-color ChLCD e-Paper aligns closely with the evolving needs of Japan's commercial display market. This recognition is particularly meaningful to us."

Organized annually by the Digital Signage Consortium, the Digital Signage Award honors outstanding digital signage projects selected from publicly submitted entries, with the objective of promoting innovation and accelerating the development of the digital signage industry. This year, approximately one hundred submissions from DSC members were evaluated, resulting in the selection of one Grand Prize winner and ten Excellence Award recipients.

IRIS Optronics' proprietary ChLCD technology employs a display mechanism fundamentally different from conventional Electrophoretic Display (EPD) technology. It delivers exceptional durability, vibrant color reproduction, and excellent readability across diverse lighting environments while maintaining ultra-low power consumption. The technology can also be integrated with solar power systems to enable self-powered operation, further enhancing its suitability for sustainable information display applications. These capabilities were highly regarded by the award judging panel and contributed significantly to the company's recognition.