For most of its history, Out-of-Home advertising lived in a strange space. Everyone agreed it worked. Almost nobody agreed on how to count it.
That has finally started to change — and not just in theory.
The Media Rating Council (MRC) — the same independent body that audits how television, digital, and radio impressions are counted — has published comprehensive standards for measuring Out-of-Home and Digital Out-of-Home advertising. Those standards are now the reference point that publishers, agencies, and global brands align around. But standards on paper are only half the story. The harder question is: who actually delivers measurement that meets those standards in a real market?
In Turkey, that is the question Black C Media is answering through its partnership with Azira — a global location intelligence platform that has measured hundreds of millions of DOOH, mobile, and CTV campaign exposures against real-world visits.
Here is how the framework actually works, why Azira's methodology matters, and what it means for the Turkish market.
From "Spots Delivered" to "Incremental Visits"
The old OOH measurement model was simple but limited. Publishers reported how many times an ad played on a screen — the spot count. Buyers received that report and trusted it.
The modern approach is different in two ways.
First, the unit of counting changed. Under MRC standards, the unit is no longer the spot. The unit is the impression — a single human opportunity to see (OTS) a single ad, adjusted for visibility, dwell, and audience. OTS (Opportunity to See) counts everyone in the vicinity of the screen. VAC (Visibility Adjusted Contacts) narrows that down to people whose orientation and visual angle made them likely viewers. The MRC standards expect publishers to report on the visibility-adjusted audience, not the raw crowd.
Second, and more importantly, the question of success changed. Buyers no longer just ask "how many impressions did I deliver?" They ask "how many people actually walked into the store?" That second question — the outcome question — is where Azira's attribution methodology becomes the engine of modern DOOH.
What a Valid Impression Looks Like — and Who Proves It
The MRC framework sets specific criteria for when an impression can be counted. The screen must be on. The creative must play in full and at the correct duration. The viewer must be within a defined viewability zone. And there must be proof — verifiable, auditable proof — that all of this actually happened.
That proof is what the industry calls proof-of-play: every ad delivery generates a log of which screen, which creative, what time, what duration, and which audience model applied. Without that log, the impression cannot be claimed. Programmatic DOOH transactions take this further, requiring impression-by-impression reporting that matches the moment of delivery.
Proof-of-play is the supply-side half of measurement. The demand-side half — the half that agencies and brands actually buy against — is attribution: did the campaign produce real-world business outcomes? This is where MRC-aligned counting needs a second layer, and this is what Azira was built to deliver.
How Azira Actually Measures DOOH
Azira is a location intelligence platform that processes billions of privacy-compliant mobile location signals daily and uses them to reconstruct whether a person exposed to an ad subsequently visited a physical location. The methodology has four pillars that matter to anyone taking measurement seriously.
Data Confidence. Location data is only as good as the signals behind it. Azira aggregates opted-in location data from multiple sources — SDK partnerships and programmatic bidstream — then applies machine-learning-based quality scoring to filter out noise, spurious pings, and low-confidence signals before any of it enters an attribution study. Quality over quantity, validated against ground-truth datasets.
Observational Confidence. A panel that undercounts real-world visits will systematically understate campaign lift. Azira assigns every device a location-activity score based on the density, frequency, and hours-of-day of its signal coverage — and only devices that clear strict activity thresholds enter the measurement panel. This is the difference between "we saw a visit" and "we can see the visits that actually happened."
Visit Confidence. Azira uses a manually drawn library of location boundary polygons — created from high-resolution satellite imagery and on-the-ground POI data — to determine whether an observed ping occurred inside a measured venue or at a neighbor, a street, or a parking lot. This polygon-based approach is materially more reliable than point-in-radius or check-in methods, which introduce false positives into visitation data.
Statistical Significance. All lift metrics are reported with 99% confidence intervals, using established statistical methods. Breakouts (by publisher, format, geography, frequency) are only reported when the sample meets minimum thresholds — typically 100,000 exposed devices and 1,000 observed visits. When the numbers aren't stable, Azira doesn't report them. That discipline is what makes the results defensible in an agency boardroom.
Measurement Panels, Control Groups, and Real Lift
Counting impressions is the floor of measurement. The ceiling is isolating what the campaign actually caused — separating visits that would have happened anyway from visits the media drove.
Azira does this by constructing two matched groups for every campaign: an exposed group of devices that saw the ad, and a control group of devices that did not. The control group can be built in two ways. The default is a randomized PSA-based control, where a portion of impressions are served non-branded public service content; this produces a naturally matched control by virtue of randomization. The alternative is a synthetic control, programmatically generated by matching exposed devices on geography, behavioral affinities (retail/dining/recreation visit patterns), and individual location-activity level.
From there, the core metrics fall out:
- Visitation Rate = Total Visits / Total Devices
- Lift = (Exposed Rate − Control Rate) / Control Rate
- Incremental Visits = Lift × Exposed Devices
Because opted-in location data is inherently sparse — most people do not keep location sharing on at all times — Azira also applies a proprietary extrapolation model, grounded in a Poisson process framework, to estimate total visits from directly observed ones. Across millions of validated campaigns, this model has a median absolute percent error of 10% against ground-truth foot-traffic counters and a correlation of 0.85 with actualized visits.
The output is a number an agency can actually act on: "this campaign drove N incremental store visits at a cost per incremental visit of X." That is the language global buyers are speaking in 2026.
Cross-Media Attribution: Where DOOH Stops Being a Silo
One of the hardest problems in DOOH measurement has been bridging the gap between cookie-less OOH exposures and device-level location data. Azira solves this with a probabilistic attribution layer that uses the IP address of the OOH impression and the network connection data of nearby devices to estimate exposure probability.
The logic is intuitive. Over any short time interval, Azira knows how many OOH impressions were delivered to a given network and how many distinct devices were connecting to that network. If an IP served five impressions and ten devices were observed connecting through it, each device gets a 50% probability of exposure for that interval. Summing these probabilities across time intervals and networks produces a per-device exposure probability — and from there, the same lift math applies.
Validated against deterministic device-ID campaigns, this probabilistic approach has a median absolute percent error of 15%. More importantly, it lets advertisers compare DOOH against in-app, web, and CTV on the same attribution framework. DOOH stops being the channel that couldn't be measured and starts being a fully comparable line on the plan.
Why This Matters for Turkey
Turkey's DOOH market is at an inflection point. Inventory is growing fast. Programmatic capability is starting to enter the market. But the measurement layer that makes inventory tradable at international standards — the combination of MRC-grade counting and outcome-layer attribution — has been missing.
This creates a strategic opening, and Black C Media's partnership with Azira is designed specifically to close it. The publisher network that adopts MRC-aligned counting and Azira-powered attribution first does not just earn an audit badge. It earns the trust of agencies that buy globally — GroupM, Publicis, OMD, dentsu — and the right to be compared on equal terms with London, Dubai, or Singapore inventory. It becomes the reference point.
For buyers, this combination is the foundation of confidence. It is the difference between buying DOOH because the screens look impressive and buying DOOH because the incremental visits are measurable, comparable, and provable.
The Full Stack of Measurable DOOH
MRC standards do not stand alone. They sit alongside the WFA Global Guidelines on OOH Audience Measurement, which speak to the buy-side, and outcome-layer attribution providers like Azira that prove a campaign drove footfall, store visits, or conversions.
Together, these three layers form the full measurement stack of modern DOOH:
- MRC — "are we counting correctly?"
- WFA — "are we counting the right thing?"
- Azira — "did it actually happen?"
A market that adopts all three is no longer selling screens. It is selling measurable media.
That is the future Black C Media is building toward in Turkey. MRC defines the standard. Azira is how we deliver it.
