School of Ad Tech: Tracing the Development of Ad Fraud

Welcome to yet another installment of our series “School of Ad Tech”, where we demystify the basics of ad tech for our readers. Today, we take a walk down in history and trace the development of mobile ad fraud.

Ad fraud is ubiquitous and continues to attract attention and discussions within the industry, with mixed estimates on the depth of financial losses due to ad fraud. One study puts the amount of global digital ad spend wasted on fraudulent traffic as high as $16.4 billion in 2017, twice than the previous projections. Another recent research projects $6.5 billion in financial losses due to ad fraud, down 10% from the estimated $7.2 billion in 2016.

Despite the numbers, one thing is certain: the scale and sophistication of ad fraud has grown since mobile advertising picked up in 2012. Ad fraud is pervasive at various levels, with fraudsters finding new ways to trick the system.

Let us take you through a short history of ad fraud to understand how it has developed through the years and put a perspective to the current challenges.

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The Early Years

Back in 2012, advertisers were focusing on growth and only looking for volumes. This is where it began: fraudsters found ways to deliver scale in a variety of ways, such as using incent traffic on non-incent campaigns, buying cheap traffic like adult or auto redirects, developing bots that emulated mobile devices and generating installs or investing into farms where people would be paid to install apps on mobile devices all day long.

Some of the techniques got rapidly identified by advertisers who started receiving complaints from their users who were being redirected automatically to the app store against their consent, or after they had seen an ad on an adult website. This was mid-2013 and the focus started to shift: advertisers started to care more about user experience and worked hand-in-hand with networks to block such practices efficiently. This meant stronger action against adult banner ads and redirects.

The Metamorphosis

After this period of rush for volumes, shareholders and C-level executives realized they were investing huge amounts in marketing, but they were not necessarily getting the returns expected from their newly acquired users. It can be said that this was the moment that started the trends we are currently seeing in the industry; advertisers focus more on post-install quality and ROI.

At that moment, advertisers started sharing post-install data with networks more systematically. Simple bots and farms that were not able to replicate post-install behavior quickly became less relevant for fraudsters — they were getting flagged easily by advertisers and networks.

Fraudsters, then, had to develop more sophisticated mechanisms to replicate real user behavior, by having their bots or farms engage with the app, even to a level where small in-app purchases were made. These bots and farms are identified most of the time now, since their user behavior replication is never perfect (e.g., through metrics such as no engagement after a certain period of time, unrealistically high engagement, etc.)

Current Challenges

With rising expectations in terms of quality from advertisers, often comparing quality from organics and paid installs, fraudsters started to ask themselves: how can we deliver installs at scale with a quality similar to organics? The answer came rather easily: what is as good as organics, if not organics themselves? This is when organic theft techniques started appearing in the industry. The current challenges that we know in the form of Click Spamming, Ad Stacking and Click Injection appeared on the surface. These three fraud mechanisms are now the big pie in the mobile fraud ecosystem, the techniques everyone is trying to detect and prevent efficiently. These sophisticated techniques are harder to detect without a sound understanding of pattern detection and heuristics, and requires the combined efforts of humans and technology to win the war against ad fraud.

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