Geopath Composition

The media landscape has become incredibly advanced in the past decade. Audience profiling is used by marketers to efficiently and effectively deliver the correct message at the correct time to the correct people. While the total number of persons exposed and the likelihood of them consuming the media is critical in measuring audience to provide a currency for the marketing ecosystem, quantifying the makeup of those audiences is now core to the planning, transacting, and analysis of media.

Regarding advanced audience targeting, out-of-home is unique. Out-of-home is considered a mass-media by many, but because of the accuracy and precision of location data marketers are able to use audience composition to strategically utilize the one-to-many delivery mechanism.

In order to understand the makeup of the audience consuming each and every piece of audited out-of-home, Geopath must quantify three components of the target and exposed population:

1. Home location

2. Target population

3. Trip generation rates

Home Location

Every piece of out-of-home media has a unique delivery pattern of audience throughout the day, week, and year. Some inventory delivers more audience in the morning as people are commuting from their homes, while others may deliver audience on weekends while people are socializing and taking leisure trips.

Whatever the trip motivation, trip origin, and trip destination, it is more important to know where the exposed individuals live so that the demographics, household characteristics, and behaviors can be utilized for calculating audience composition. To do this, Geopath leverages aggregated location data from hundreds of millions of mobile devices. The patterns observed from mobile data informs the home locations and the activity helps quantify the representation of the observed panel of the full population. Combined with traffic data from transportation sources, connected cars, land use, and points of interest, a clear picture of all trips for all persons emerges for every roadway and point of interest in the U.S.

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Trip origins (blue) and trips destinations (red) visualized for all trips traveling northbound through a section of I-274.

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Home location density for persons traveling northbound on the same section of I-274.

Target Population

An integral part of understanding the movement of the full population is to understand the scope and details of the full population itself. To do this, Geopath leverages Claritas demographic, behavioral segmentations, attitudinal, and consumer profiles. With these detailed household and population profiles, Geopath is able to calculate audiences for all of the audited and measured out-of-home media locations. 

To build its detailed population profiling resource, Claritas starts off by creating a basic demographic profile of every individual and every address in the US. This is known as the Master Address File (MAF).

Individuals are grouped into households and all known household attributes are added to group the data nationwide. The result are the 68 PRIZM households whose individual populations have demonstrated that they behave in very similar ways. Every household in the country receives a unique PRIZM segment.

Using the PRIZM segment household assignments, new survey data or customer lists can be linked directly through the PRIZM household classification to all households nationwide. The PRIZM segment becomes the connection point between a household and the 1000’s of audiences/responses on a survey. This is accurate at an individual household level but even more accurate at aggregations of households spatially (zip code or county) or when aggregated by other attributes, like behaviors.

Combining Target Segments

Most demographics are reported at predefined aggregations at a local level to protect privacy.

For example, at the block group level the U.S. Census reports:

• Gender by age

• Household income by age

• Household income by race and ethnicity

• Race and ethnicity by age and household income

However, marketers often seek specific combinations of these demographic targets that may not be pre-aggregated and reported. Geopath creates these “cross-tab” combinations using a process known as “iterative proportional fitting” or IPF. IPF leverages respondent level panel data and locally reported pre-aggregated data to weight a unique and specific combination of variables at the local geography level (e.g. working hispanic males 25-39 with a household income $100K+).

Trip Generation Rates

Knowing that a certain percentage of the population from a neighborhood is traveling along a roadway at a particular time is very powerful, but assuming that the audience is an even distribution of the neighborhood’s population is inaccurate. During the commuting hours of 7 AM to 9 AM, it is inaccurate to assume that just because 20% of the population is younger than 18 year of age, that people younger than 18 years of age make up 20% of the audience in the cars commuting. Segments of the population have different travel patterns, trip purposes, and trip frequency. For example, persons 30-59 take more trips than the young and old.