Recently, many in the world of mobile advertising have been talking about the issue of location data inaccuracy. Inaccurate, fraudulent and incomplete location data, often supplied by publishers to increase impression value, is an important discussion point.

Verifying data quality should be standard practice in any business dealing with huge volumes of data and not some form of secret sauce.

Importantly, it needs to be acknowledged that removing data also removes audience scale, which is required to make the proposition work for advertisers.

Herein lies the question: How do you remove bad data while maintaining a scalable audience? If location advertising is done poorly, it doesn't bode well for advertisers’ as it is costly and impacts negatively on ad relevancy, as ads are sent to the wrong location.

The industry itself is trying to address this issue and, while location-targeting verification does filter out bad location data, it can only remove as much as 85%.

Location data provided by certain dating apps, for example, might have accurate latitude longitude GPS coordinates, but it may not be an appropriate ad placement for an advertiser and could lead to a negative brand association.

If, and this is a big 'if', your location provider has built the right proprietary tools to understand where the location data is coming from, it can remove the wrong impressions for your brand.

This kind of filtering should be standard across the industry. Latitude longitude does provide good proximity, but it doesn’t provide real scale. If a location-specific ad is to generate successful engagement, a brand needs a bigger audience.

As an example, Mobiclicks build audience scale into location-targeting by understanding the relationship between an IP address at a specific building and the devices connected (whether that be mobiles, tablets or laptops).

The platform constantly matches IP addresses to locations, globally, at a rate of 10 million per day. We refresh this data every 24 hours to ensure its accuracy and context.

Another way to build audience scale is through data partnerships. By overlaying second-party location data to and within campaigns, significant audience scale can be added for an advertiser that would not have been previously possible through only relying on latitude-longitude data.

Taking a measured approach, through adding scale, without compromising data, and working with different companies in data partnerships, will ensure that your organisation will be able to measure where its audience is accurate, at scale and in a way that is not a brand negative. 

For more information, visit www.mobiclicks.co.za. You can also follow Mobiclicks on Facebook or on Twitter.  

When machine learning and location data are combined, the applications are vast. Read more in our article, How machine learning is improving location-based services.