Introducing Movement-Based Audiences

With our new scoring feature, we've built the ability to index OOH placements against 1st and 3rd party audiences helping you select billboard locations and/or markets where you have the highest likelihood of reaching your target audience.

Introducing Movement-Based Audiences

A few months ago, we launched our programmatic offering. Today, we're excited to share that we’ve launched the ability for advertisers and agencies to target specific demographic and behavioral audiences in both our programmatic DSP and our traditional OOH buying platform.

With our new scoring feature, we've built the ability to index out-of-home media placements against 1st and 3rd party audience movement data, helping you select billboard locations and markets where you have the highest likelihood of reaching your target audience.

Movement-based audiences improve the ROI of your out-of-home advertising campaigns by ensuring your ads are reach the segments that your target customers belong to.

Makes sense right?

Here's the best part: any campaigns executed on the AdQuick platform (and coming soon for agencies and media owners) will have access to these audiences at no additional cost.

Audience Types

We have four distinct audience groupings coming to the platform:

Purchasing audiences are built on purchasing habits of an individual.

Lifestyle audiences use consumer demographic information, brand affinities, or shopping, dining, and other habits.

In-Market audiences include consumers who are likely to now be considering a purchase in the category.

Enthusiast audiences are not necessarily looking to buy right now, but reach hobbyists, fans, and activists with shared interests.

Why Audiences are Important

In the online world, marketers will target audiences based on their online behaviors. This can include behaviors like visiting a website or clicking on certain items.

We’ve brought this same concept to the out-of-home advertising world by layering in behavioral audience data that’s built using the movement patterns of individuals.

People follow patterns in their day-to-day lives that can allow us to understand the behavioral attributes of these individuals.

For example, if we know an individual attends a gym on a daily basis, we’re able to classify them as a gym-goer.

Using the example above, if a newly-opened fitness center wanted to attract new members, they can leverage this behavioral data to find out which OOH locations would allow them to get exposure to avid gym goers that may be interested in trying out a new gym.

How We Built Audiences

Each of the audiences in our system is built by using billions of anonymized mobile location pings from 150M+ mobile devices to understand where individuals are going in the real world.

After capturing this movement data, these patterns are interpreted to understand consumer interests, in-market buying behavior, and various customer affinities.

To figure out how these audiences are distributed across the various markets and OOH locations, we applied an indexing methodology.

These indexes allows us to see how well each OOH location represents a specific audience segment versus the national average.

To do this, we first determined the number of unique mobile IDs that match each audience segment in a census block group.

This gives us a sense of how many individuals in that census block group represent that audience segment.

We then divided that value by the total population in that block group to understand how much that audience segment makes up of the census block group.

Once we have the value, we average the value for each audience segment across all of the block groups in the United States in order to determine the national average for each audience segment.

Lastly, to calculate the Audience Index Score in each block group, we compare the percentage of total devices in each census block group for an audience and divide it by the national average.

To summarize this methodology in layman's terms, we compared the representation of each audience segment across census block groups and compared them to a national average.

This ensures you're targeting locations that index highly (or lowly depending on the use case) for your target audience.

How It Works

Many OOH media planning software offerings rely on heatmaps for their audience targeting, we decided to take a difference approach: indexes.

An index is essentially at heatmap at the unit level.

Indexes are better than heatmaps – heatmaps don’t clarify you the system’s decision about which media placements specifically should be included in the campaign.

And because indexes score audiences against each individual unit in the AdQuick platform, you can use a slider to filter inventory in real-time, leaving you with only the relevant pieces of inventory that matched.

Choosing Your Audience

Figuring out which audience to target can be a daunting task especially given that your ideal customer can fit various behavioral profiles.

For example, if you’re looking to advertise the launch of a new car model, you may want to reach “Car & Motorcycle Enthusiasts” or conversely, individuals who are classified under the “Luxury Lifestyle” category.

Alternatively, you may want to target the audience that is classified under “Luxury Traveler”.

One great feature is that you don't have to choose only one audience, you can choose many.

Combining Audiences

Our new movement-based audiences are stackable. You can combine multiple audiences to get an unparalleled level of granularity given the large variety of audiences that are available at your fingertips.

Conclusion

We’re excited to introduce audiences so you can find the most optimal locations and markets to execute your out campaigns.

We hope these features help your business make more data-driven decisions that result in a higher return on investment for each dollar spent.

Book time with us instantly to get a demo and keep an eye out for demographic-based audiences & audience analytics (coming soon).