Like any sphere in which large sums of money are moved, the world of online advertising is an attractive one to fraudsters. Indeed, the impersonal nature of the transactions, a complex and often-opaque supply chain, and a reliance on easily-fiddled metrics mean that ad fraud is one of the biggest challenges facing advertisers, publishers, and ad-tech enterprises alike today.
One of the most difficult challenges around digital ad fraud is that no one really knows exactly how big a problem it is, with estimates ranging from high…to higher…to stratospheric.
Ignore it at your peril. Or at least the cost of a significant chunk of your advertising budget.
What is ad fraud? In what contexts does it primarily occur? and what is the cost of digital ad fraud to businesses estimated to be? Read on to find out the answers to these questions, and see many more ad fraud statistics.
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What is ad fraud?
According to ad fraud detection agency Interceptd, types of mobile ad fraud include:
- Click spamming – simulating a high number of clicks from real devices in order to get the credit for organic installs then made legitimately. This sees ad budget squandered on organic users who are already highly-engaged. It generally creates long CTIT (click-to-install-times).
- Click injection and CTIT anomaly – a fraudulent app creates fake clicks when app installs are taking place, claiming the attribution for the install. CTIT tends to be short.
- SDK (software development kit) spoofing – fraudsters create a bot within an app which then sends clicks, installs, and engagement to the MMP (mobile measurement partner) which registers them as if they were genuine. The average app has around 18 integrated SDKs which can be spoofed.
- Device farms – the use of real devices to click on ads and install apps, after which they are reset and the process begun again. This creates a suspicious pattern, which can result in ad fraud detection.
- Incent abuse – incentivising users to click and install apps. To help hide the high click-to-install conversion rate that results from this, fraudsters might send a high level of fake clicks to help mitigate the suspicion.
- Bots/emulators – using the same principle as device farms, except using devices which are not real, making the resetting process easier, though leaving behind a similar suspicious pattern.
Interceptd measured how great a proportion of mobile ad fraud was accounted for by each type of fraud in its 2018 Mobile Ad Fraud Report, analysing levels by quarter over the course of 2018.
Devices farms remain the most-commonly used form of digital ad fraud, though the proportion of fraud ascribable to this form declined throughout the year. On the other hand, bot farms/emulators seem to be accounting for a greater and greater proportion. If this continues, then we might expect a crossover, with bots/emulators becoming the most common form of ad fraud. Online ad fraud, however, comes in waves – so this may not be definitive.
Incent fraud in second-place remains relatively consistent throughout the year, as did click injection, while SDK spoofing – an increasingly hot topic – crept gradually upwards through the year.
Proportion of mobile ad fraud types 2018: Interceptd
MarTech Advisor’s Mobile Benchmark’s Tool shows an increase in SDK spoofing levels over the middle two quarters of 2018. Click injection remains the most prominent form of ad fraud over the year, seemingly on a gradual upward trajectory. Click spamming on the other hand, seems to be at its most problematic level at the start of the year, before dropping and remaining consistent, accounting for around a quarter of online ad fraud.
Proportion of fraud types 2018: MarTech Advisor
Data source: MarTech Advisor
If we compare fraud types with level of rejected fraudulent installs over the year, we might note that there is a spike in the quarters in which SDK spoofing is at its most prominent. It seems then, the rise of this newly prominent form of ad fraud comes in addition to rather than in place of other forms of fraud.
Proportion of rejected fraudulent installs 2018
Data source: MarTech Advisor