Geo Audience Insights – Using Mobile Data to Inform OOH Location Marketing Planning

This is a four part series exploring the blurred lines between digital and OOH.

   Part One – Describes the 3 forces driving disruption in OOH
   Part Two – Explores Geo Audience Insights; using mobile data to inform OOH planning
   Part Three – Discusses how advertisers can utilize dynamic creative to stand out from the crowd
   Part Four – Examines the future of OOH buying and the shift toward real-time OOH

Data in media means different things to different people. In Search marketing, this data means keyword volumes, CPC fluctuations, and conversion rates. In TV, data includes reach, ratings, and demographics.

In OOH Location-Marketing, data is a combination of two things: audience insights overlaid with geo-location. An appropriate descriptor for this is Geo Audience Insights (GAI).

GAI are a holistic understanding of aggregated groups of audience behavior, and understanding the migration patterns of these groups. This is a significant step forward for the OOH industry as it evolves from demographic to audience targeting.

There is a huge convergence of mobile and OOH. While the broader industry still largely assumes that audiences consume media in silos (an assumption I don’t agree with), this convergence allows us to apply the same concepts of online marketing to the physical offline world of OOH.

We can gather GAI from various digital data sources such as mobile exchange data, social sentiment and web/app browsing behavior. In short, GAI can be derived from any data source that describes the affinity of an audience at a particular geographical location.

By using this data, GAI can help us answer questions such as:

• What neighborhoods in NYC are likely to be receptive to the launch of a new baby formula?
• What suburbs in Los Angeles contain the most number of soccer moms in market for a new SUV?
• On which exact billboards and street furniture should a sports apparel brand advertise to target passionate basketball fans?

Let’s see how we can bring this to life using mobile and online exchange data.

Say a baby formula advertiser targeting new moms in NYC wanted to know what OOH sites have the highest likelihood to be seen by new moms. The advertiser can use mobile exchange data to develop an understanding of the neighborhoods and streets new moms spend their time, and use this data to score OOH sites accordingly. There are four steps to this process.

1. Define: We define our segment by describing online behaviors of a new mom as someone that will likely visit parenting blogs, download baby apps. and spend time researching baby-related content online.
2. Match: We can develop this new-mom segment by finding mobile devices using ad-exchange data in NYC and match this to browser cookies online. Probabilistic cross-device graphs based on mobile and desktop behavior take into account frequency, recency, and IP address popularity. We can subsequently link a user’s behavior across devices to find our audience based on the above definition of a new mom’s behavior.
3. Score: We can score each OOH location (alternatively a ZIP code, DMA, or any other geographical area) based on behavioral attributes of our matched users vs the general population. An affinity score index is allocated to each location that is a proxy indication of our new-mom segment to view the OOH site. Advertisers can utilize either internal planning tools (e.g. Posterscope ECOS), or find data partners that can provide this capability to score OOH inventory and geographical locations.
4. Activate: These GAIs can be utilized to inform decision making in OOH planning. Further, GAI can be used to help determine which creatives are best suited to a particular geographical location based on the propensity of the target audience in a particular area.

Brands with existing defined audience segments built out for mobile media are already one step closer. There are many mobile-data partners taking a GAI approach including Dstillery, PlaceIQ, PlaceCast, Barometric, NinthDecimal, and xAD.

Using this mobile data to understand where audience groups spend their time and activating accordingly will help evolve our industry from focusing on demographics, to focusing on audiences.

Published: July 6, 2016