How Fraudsters Are Derailing User Acquisition Ad Campaigns – Part 1

This blog post is part one of a two-part series that details the fraud problems in user acquisition ad campaigns. The series highlights the impact of UA fraud problem, the tools and techniques fraudsters use and why UA fraud is getting harder to detect.

eMarketer forecasts that digital ad spending worldwide will reach approximately $357.31 Billion by 2020. And a recent IAB U.S. benchmarking study conducted by EY estimates that malvertising, infringed content, and fraudulent ad impressions, costs the digital marketing, advertising, and media industry $8.2 billion per year. 

Every mobile app company aims to onboard new app users and have their apps reach the top of the charts on app stores. Mobile app companies use several marketing strategies to achieve these goals. One of those strategies is creating and paying for app install campaigns on multiple ad networks. Unfortunately, fraudsters are increasingly setting their sights on mobile app install advertisers. Tune estimates in a recent report that 7.8% of global app installs from third-party networks are fraudulent, and app install fraud will cost marketers up to $2 billion a year.

Mobile app companies are finding that many of the app installs generated from their ad network campaigns are fraudulent- these companies are getting hit hard with user acquisition (UA) fraud. The stats below give an idea just how big the impact of fraud is in the mobile marketing industry.


UA Marketing Basics

Before covering what UA fraud is, we should go over some of the basics when it comes to digital advertising and UA marketing. 

Mobile app companies typically want to publish ads to acquire users. Companies often publish ads via an ad network or an affiliate network. The mobile app marketing team will set specific KPIs. Common KPIs include Cost-Per-Install (CPI), Cost-Per-Click (CPC), and Cost-Per-Action (CPA). Once the KPIs have been set, the ad networks choose the publishers from inventory that will best drive app install traffic for advertisers. 

Some ad networks have systems that include publishers and sub-publishers. The sub-publisher layers are often not fully visible or lack controls which complicate the ad space inventory. Fraudulent sub-publishers obfuscate their activity through multiple layers of publishers and ad networks. The sub-publisher layers make it easy for fraudsters to commit UA fraud through fraudulent clicks and app installs.

There are numerous components when it comes to digital advertising campaigns. Adjust has a comprehensive glossary that includes many of the terms related to digital advertising and UA including cost models.  

What is UA Fraud?

Now that we’ve provided some background on digital advertising and UA marketing, what is UA fraud?

UA fraud is a type of fraud where fraudsters trick advertisers into spending money on fake users and fraudulent traffic. This type of fraud often involves the generation of fake downloads and installs via automated tools or cheap human labor. 

In recent years mobile app marketers have shifted their strategies regarding user acquisition. Traditionally, the common UA strategy was digital advertising with a CPC model. While the CPC model is still widely used, many mobile app marketers are moving to models based on CPI and Cost-Per-Engagement (CPE). For fraudsters, CPI and CPE models can be far more profitable than the traditional CPC model. 

Ideally, every app install generated from advertising would be based on genuine interest from legitimate users. However, many fraudsters are taking advantage of the CPI model. Some are creating fraudulent publishers to earn revenue from the ad market. And then massive volumes of simulated installs are used to get a huge payout from ad networks. 

Fraudsters use a variety of tools and techniques to commit UA fraud. We cover some of these tools and techniques in part two. 

Sean McDermott

Sean McDermott

Sean leads the social commerce sales efforts at Datavisor, a cutting-edge fraud detection platform based on AI and machine learning. As a sales manager with over 12 years of experience in sales, Sean is adept at selling complex and sophisticated technologies. In the last 5 years, he has focused on selling Enterprise AI Fraud Products, quickly gaining expertise in different machine learning techniques including supervised, and unsupervised for the data science buyers.