StreetMetrics goes beyond traditional methods by leveraging known conversion data from observed exposures to refine our understanding of ad viewability and audience engagement. Utilizing a unified single source of data as our foundation, we can directly connect impressions to a tangible ROI metric. We believe this approach underscores our dedication to a future where more precise, real-time data is essential for aligning ourselves with other advertising formats.
This post details our data-driven methodology for defining viewsheds and calculating impressions, ensuring transparency, comparability, and alignment with industry standards.
Viewability Method Definitions and Concepts
We align viewability thresholds with the Media Rating Council (MRC) framework for OOH measurement, utilizing three tiers of impression types:
Streetmetrics Impressions:
Gross Impressions: This metric indicates the total number of potential exposures within the Display Exposure Zone and does not account for visibility obstructions. This includes all impressions from the Gross, Opportunity-to-See (OTS), and Likelihood-to-See (LTS) viewability bins.
OTS Impressions (Now Viewable): OTS impressions require evidence of viewing based on conversions and factors such as distance, angle, and environmental conditions. These impressions have a lower probability threshold than LTS impressions. This includes all impressions from the OTS and LTS viewability bins.
LTS Impressions (Now Effective): LTS impressions have the highest probability of having viewed the ad, contingent on factors such as distance, angle, and environmental conditions where we see exposures yield higher conversions.
The update to our Viewable and Effective Impression labels is now live as of February 2026.
Terminology is officially standardized and consistent across the entire platform, so you’ll see uniform labeling reflected in all dashboards and reporting moving forward.
Method Definitions and Concepts:
Attribution/Conversion: A conversion is an exposed device that later took one of the 5 measurable actions we provide as added attribution or conversion services:
Footfall, store visit
Website visit
Mobile App visit/download
Retargeting click-through
Brand Survey (beta/pre-release offering)
Viewsheds: Every piece of media in our system gets a unique viewshed based on the size and geometry of the media, whether it’s stationary or transit. We then look at conversion across similar sets of inventory (same market, media-type, etc.) but normalize to the exact frame dimension so that viewability bins are always scaled relative to the max viewing distance and angles for each piece of inventory. The industry-approved maximum viewing distance and angle formulas define the Display Exposure Zone boundaries.
Match Parameter (Viewability Factor): A match parameter is any data point on the exposure and can factor into a “probability to convert”. We currently consider market, media/product-type, distance from ad, angle from ad, but as our conversion datasets grow and offer more statistical significance, we are actively pursuing many other dimensions of data such as time-of-day, weather, illumination factors, device/asset speed and direction, etc. into a multi-variable modeling approach.
Our goal is to understand the relationship between every exposure and each media panel across a vast set of data dimensions. Streetmetrics measurement is unique in that it is powered by a precise relative probability to convert that is unique for every ad-exposure combo (>300M a day) and is being continuously improved by conversion data. Viewability is dynamic and depends on numerous real-world factors, and the methods used to understand it should reflect that.
Evidence of Viewing Through Conversions and Key Advantages
Conversions strongly indicate that individuals saw the ad and engaged with its message, demonstrating a direct response prompted by their exposure. Relative conversions based on measurable factors further control for confounding factors and allow us to value each impression based on large data sets. Advantages of this approach include:
Scalability: As we conduct more attribution studies and gather more data, our viewshed definitions and impression calculations become increasingly refined and accurate.
Dynamism: We are incorporating various factors influencing real-world viewsheds, including distance, viewing angle, time of day, seasonality, and weather conditions. This allows us to create custom viewsheds for individual markets, assets, and specific timeframes, enhancing the precision of our measurement.
Alignment with Advertiser Goals: We provide a more accurate and relevant measure of OOH advertising effectiveness by focusing on conversions. This aligns with advertisers' primary goal of connecting with consumers and understanding how OOH drives tangible results.
Getting from Exposure to Viewability
Every exposed device is saved and measured across as many viewability factors as we can collect data for.
As we run attribution/conversion studies with our clients and partners, we can determine which devices convert most as a function of these viewability factors. This results in a “relative probability to convert” model based on all factors, where we look for statistically significant factors (typically a function of the amount of conversion data we have). Refer to the conversion map below as an example of a single frame across multiple studies.
To arrive at Gross, OTS, LTS bin sizes, we then look for the 3 “optimal clusters” (determined via machine learning clustering algorithms) per media and market based on distance for transit and distance & angle for stationary, all relative to the max viewing distance of each piece of inventory.
Below, we show this for a sample asset in stationary and transit. Still, these viewshed sizes allow us to allocate matched devices and, ultimately, upsampled impressions into the bins based on actual conversion data.
Gross and OTS are inclusive of lower-level viewability bins.
Gross impressions include all impressions from the Gross, OTS, and LTS viewability bins.
OTS impressions include all impressions from the OTS and LTS viewability bins.
Collect more data and repeat, iteratively improving our models and ensuring that we get more granular and precise on bin sizing, ultimately providing you with the most accurate picture of your media’s effectiveness along with a deep understanding of what kinds of exposures drive the most engagement at ever-more precise levels.
Expect reports and analysis from our team in the coming months showcasing why we believe strongly in this approach to drive the future of OOH measurement and performance data.