Recently, Integral Ad Science surveyed members of the online advertising industry – including agencies, brands, DSPs, networks, publishers, and trading desks – to better understand their concerns for the year ahead. Not surprisingly, ad fraud topped the list.
All agree that it’s a significant issue; 89% of the survey respondents believe it has a direct impact on media quality, and 39% think it trumps other ad-quality measurement factors: brand safety, viewability, transparency, and geo-compliance.
But, given the media buyers and suppliers responses about how ad fraud occurs and to combat it, it’s clear that more education on the issue is needed. Less than half of respondents say they understand what’s needed to detect and curtail fraud. That’s understandable, of course, as ad fraud is a complex and evolving issue. Anti-fraud technology changes quickly out of necessity, and can only be driven by teams of data scientists armed with knowledge and skills that the average buyer or supplier doesn’t possess.
While most of us are comfortable leaving the details of algorithms, temporal patterns, and big data parsing to the various data scientists and analysts who spend their days examining these issues, we should at the very least agree on the enemy. But, that’s harder than you may think. To start, the definition of fraud is often misconstrued, and there are precious few hard-and-fast rules for applying a nefarious label to a single impression or site. For instance, bots have a bad name in the industry, yet there are good bots and bad bots – only the latter of which perpetrate fraud. Additionally, while a domain or a Web page may be considered low quality, assessment alone does not make it legitimate ad fraud. Similarly, just because a website previously fell prey to fraudulent traffic doesn’t mean it should be avoided forever; after all, even the most premium publishers can be subject to hit-and-run bot attacks.
So, how should the industry define fraud? The generally accepted definition is any illegal activity that prevents ads from being served to human users. The culprit is nearly always illegal bot traffic, easily scaled and perpetrated by botnet operators and hackers who reap lucrative rewards for their tactics. Less prevalent offenses, such as ad stacking (i.e., placing multiple ads on top of one another in single placement so the only the top is in view), and pixel stuffing (i.e., stuffing an entire ad into a 1×1 pixel) also defraud advertisers of millions of dollars each year.
If we know the types of fraud favored by bad actors, why can’t the industry simply create rules to shut them down once and for all? Most fraud-detection efforts amount to a micro-level game of cat-and-mouse. As soon as one type of fraud is identified and stopped, the fraudsters develop new tactics to get around the current detection systems. That’s why the industry needs to target the fraudsters’ business model – not just their tactics. We need to create an environment where it’s no longer profitable for fraudsters to run manipulative operations. This requires a multi-pronged approach that includes both micro-level methodologies (catching the mice), as well harnessing big data on a macro-level (removing the cheese altogether).
At the 2014 IAB Annual Leadership meeting, the Bureau’s chairman, – from publishers and tech middlemen who are paid whenever they serve impressions, to agencies who buy fraudulent impressions that look real to their clients. While great advice, Shah presents a tough challenge to an industry where everyone is struggling to stay up-to-date on the latest technologies and trends.
Recent coverage of ad fraud has sparked awareness and the need for more education – showing that we’re making progress towards fighting it. The fact that we’re even discussing the complexities of anti-fraud methodologies is a clear indication that the entire online advertising industry is advancing in significant and meaningful ways – and tackling its toughest challenges head on.