//Gravy's "Criminalization of Location Data" seeks to identify and minimize fraud in programmed circuits
1545827613 gravys criminalization of location data seeks to identify and minimize fraud in programmed circuits 760x490 - Gravy's "Criminalization of Location Data" seeks to identify and minimize fraud in programmed circuits

Gravy's "Criminalization of Location Data" seeks to identify and minimize fraud in programmed circuits

 

 

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Location Intelligence Company Gravy Analytics introduces what he calls "location data for science". the goal is to filter unreliable, inaccurate or fraudulent location data from bidirectional flow.

Location data is often unreliable. Exchange mobile bid requests, including localization, often have a much higher value than others. This results in a lot of questionable location data in the system. An editor or application may go through a questionable or fake location only to satisfy the bid request. According to Gravy's CEO, Jeff White, between 40 and 80% of Bidstream's location data that he sees are "fraudulent or suspicious".

White added that the scale of the problem is largely unknown by advertisers and even by trade. . Location data is used for a growing number of marketing and analytics purposes: audience segmentation, offline attribution, proximity marketing, internal benchmarking and competitive intelligence, among others.

Location accuracy is more important in some scenarios. For example, the allocation of store visits requires more precision than proximity marketing. And if you buy an audience of "auto lenders", for example, you want people who have actually worked with car dealers in the last 30 days and so on. The reliability of the data is crucial: empty entry and exit.

Suppression of "abnormal" location signals. Many sources of location data are found in the bidirectional flow: tower-to-cell triangulation, GPS signals, Wi-Fi and other sources. Then there is fraud and falsified location. Gravy's White indicates that the company offers its partners total transparency around each signal and location data source, which can be audited if necessary. Its machine learning algorithms will separate valid signals from dubious signals.

White states that the system will help its partners, including DSPs and DMPs, to "suppress abnormal location signals". He adds that Gravy start putting blacklists on publishers, apps, locations and devices that are deemed bad or inaccurate. These will be filtered automatically.

Why You Should Care Location data is an increasingly critical (19459010) and controversial feature of mobile and programmatic advertising. Inaccuracy and fraud have been part of the program inventory since the beginning. Many companies sought to educate marketers about the problem. Thanks to its new legal expertise in location data, Gravy is trying to build confidence in its location data sets as well as those of its programmatic partners.

This story was first published on MarTech Today. For more information on marketing technology, click here.

About the Author

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Greg Sterling is a collaborative editor at Search Engine Land. He writes a personal blog, Screenwerk on the links between digital media and consumer behavior in the real world. He is also Vice President of Strategy and Knowledge for the Local Search Association. Follow him on Twitter or find him at the address Google+ .