Customer data: how to recognize the most frequent fraudulent data points

Data has become a major priority for many businesses, where customer data is a focus area. Firms regularly capture, store and analyze large amounts of consumer data every day.

Mobile devices helps marketers to understand the aggregated behavior of consumers and as data collection becomes more sophisticated, so do the ways bad actors can generate faulty data and commit mobile location AdFraud.

According to Gravy Analytics, both bid stream and SDK-sourced location data contain fraudulent and low accuracy info (I am not surprised).

If we compare location data ad inventory with others, it’s clear that it’s more expensive – meaning CPMs are higher (criminals are very responsive to these incentives and pricing signals), and the size and complexity of the ad ecosystem, makes fraud challenging to detect.

According to the source, the most frequent fraudulent data points are:

– Apps engaged in fraud that send large numbers of signals

– Mobile websites disguised as apps that use calculated lat/long data

– Spoofed locations and devices and manually adjusted time stamps

– SDKs embedded in apps that generate false user activity

The challenge for location data processors is to know and use these points to improve detection in the future.

More insights in the post.
AdTech

Other sources: see in the LinkedIn comments.

Previously posted by Luca Brighenti via LinkedIn.