The advertising industry has seen a major shift towards hyper-targeted audiences, and a crucial component of it all is location data. This type of data, sourced primarily from mobile apps, provides advertisers with the ability to target users based on their physical movements in the real world. Whether it's measuring visits to a particular store or identifying patterns of behavior over time, device-level data has become essential in building highly targeted audiences.
What is Device-Level Location Data?
Device-level data refers to the granular data collected from mobile devices, typically through apps that users have downloaded and given permission to track their location. These apps, like weather or fitness apps, collect data on a user’s location, and in many cases, that data is shared with third-party providers or sold directly to companies like Unacast.
This data is made anonymous by assigning Mobile Ad IDs (MAIDs), which allow advertisers to track a device without directly identifying the user. MAIDs can tell advertisers that a certain device visited a Starbucks on multiple occasions over a set time period, making it easier to build targeted audiences based on real-world behaviors.
The Challenges of Device-Level Data
While device-level data offers immense potential, it also comes with challenges. A major issue is data quality. Not all data is created equal—third-party aggregators may resell outdated or low-quality data, a practice known as "replay." Replay occurs when old data is given a new timestamp, making it appear as though it's fresh. Unacast has found that up to 50% of some U.S. data and 80% of some international data suffers from replay issues.
Another challenge comes from the increasing complexity of managing multiple data sources. Some aggregators mix data from different sources, reducing its accuracy and making it harder for advertisers to get a true picture of consumer movements. This is why companies like Unacast implement rigorous filtering processes to eliminate duplicates and clean the data before it’s passed on to advertisers.
Best Practices for Using Device-Level Data in Advertising
To make the most of device-level data, advertisers need to focus on both quantity and quality. It’s tempting to prioritize scale—getting as much data as possible—but that can lead to wasted ad spend if the data is flawed or out of date. A better approach is to use trusted sources and implement filtering systems that ensure data accuracy.
For example, Unacast processes over 8 quadrillion data points each month to eliminate duplicates and reduce misconfigured records. This ensures that advertisers are working with clean, reliable data that drives real results.
If you’re looking to improve audience targeting and measurement using device-level data, make sure you’re relying on high-quality sources. To learn more about how Unacast ensures data quality, download our full report, The State of Location Data and Its Impact on Advertising.
