Why Accountability Is OOH’s Next Growth Lever
By Jason Kunkel-de Cesero, Vice President, Demand & Analytics Partnerships
The Trump-era FTC is recalibrating, not retreating and that shift is putting new pressure on advertising to prove how it targets, measures, and drives outcomes, including OOH. Today’s risk for OOH isn’t a sudden wave of FTC cases: it’s being judged by the same standards that already govern today’s dominant channels and coming up short. What matters now is whether OOH can clearly explain how its numbers are constructed, what’s genuinely observed, modeled, where the limits are, when advertisers and regulators start asking those questions.
Location data
Years of FTC action against opaque location-data practices have made one thing clear: any channel leaning on black‑box mobility data without clear consent and governance is in the blast radius. OOH impressions, audiences, and attribution often sit on those pipes, with shaky viewsheds, uneven panels, and aggressive extrapolation creating more confidence than the inputs justify. When asked whether data is observed, inferred, or effectively guessed, most of our ecosystem still struggles to give a clean answer.
Legacy metrics
GRPs, reach curves, and older concepts like Daily Effective Circulation were built for a broadcast, pre‑mobile world and only later imported into OOH to help slot the medium into cross-channel planning frameworks. They’re useful as a translation layer, but bury uncertainty inside big, single numbers just as brands increasingly expect impression- and outcome-based views that can be reconciled across channels. The message isn’t “stop using GRPs”; it’s “don’t let GRPs be the ceiling on proving value.”
Some incumbents cling to legacy ratings and proprietary models to smooth over data quality gaps and protect pricing leverage. That comfort is costly: growth budgets and advanced analytics teams gravitate toward formats that can be interrogated at the same depth as search, social, and streaming, while also surviving MMM and procurement scrutiny.
AI can’t outrun bad inputs
Everyone will soon want to say they’re using AI to optimize OOH, but AI is just a force multiplier on the underlying data. If exposure is built on contested geolocation sources, incomplete inventory metadata, or fuzzy outcome linkages, machine learning will happily optimize against those flaws at scale. Before OOH can credibly sell “AI‑powered planning,” it must close its data blind spots: auditable placement data, transparent visibility assumptions, and clearly governed, consent‑aligned signals for KPI effects.
OOH’s evolution
The opportunity is to use regulatory pressure as a forcing function for modernization rather than a drag on growth:
- prioritizing privacy‑aligned measurement that documents consent and use limitations end‑to‑end,
- investing in independent verification–validated inventory metadata, exposure panels, calibrated modeling, and log-level transparency–so buyers can truly rely on OOH data,
- using legacy ratings for cross-channel compatibility alongside audiences and CFO-type results for credibility,
- retiring one‑number mystique in favor of ranges and scenarios that match how serious advertising investors evaluate every other channel.
If OOH shows that its numbers are verifiable, its models explainable, and its data lawful by design, this era of scrutiny becomes a platform for channel‑wide upgrading, not a reason for brands to continue reallocating budgets elsewhere.