Global losses from digital advertising fraud reached an estimated $6.5 billion in 2017, according to a recent study by the Association of National Advertisers. The problem is widespread: ad fraud losses will consume 9 percent of desktop display spending and a staggering 22 percent of video spend.
Advertising fraud is a constantly evolving crime, with perpetrators often deploying the latest technologies. This means organizations must take proactive measures to anticipate the fraudsters’ next move. Big data and analytics—which can help brands and marketers collect information on advertising patterns and trends—can also enable organizations to better determine which data is falsified and which is real.
There are multiple varieties of ad fraud, notes AdProfs founder Ratko Vidakovic, a leading authority in programmatic advertising technology. These include:
While many tools exist to help companies fight fraud, they’re often one step behind. According to CNBC, a recent CMO Council study found that ad fraud and ad placement in offensive contexts represent two major concerns among marketers. In fact, 27 percent of CMOs have pulled advertisements from digital channels because their ads ran next to inappropriate content, and another 10 percent of CMOs plan to do so in the future.
The problem of ad fraud may seem insurmountable, and marketers may often feel like they’re not getting an appropriate return on their ad spend. But a proactive, data-driven approach can help companies beat the bad guys and bots. Today’s successful digital ad strategy makes use of big data—the collection, integration, and analysis of a mix of internal and external data streams.
To manage the growing volume, variety, and velocity of their digital information, enterprises are turning to advanced analytics powered by Hadoop to detect questionable patterns and alert organizations to potential anomalies. “Accessibility to Hadoop, as a part of or as an extension to the corporate information framework, is necessary,” says Technavio analyst Amrita Choudhury, who adds that fraud detection is becoming one of the key uses for Hadoop in the enterprise setting.
Analytics can play a major role in helping advertisers fight fraud. As ClickZ notes, tools that enable organizations to take an in-depth look at key user metrics for ad campaign–driven traffic, including bounce rate and time-on-site, can help sniff out bots. “Analytics data can also help identify ad campaigns that are significantly underperforming, which is often the first sign of fraud,” writes ClickZ contributor Al Roberts.
In addition to Hadoop, companies have a variety of fraud-fighting tools at their disposal. A publisher, for instance, might use whitelisting and blacklisting to protect brand integrity by restricting the types of ads that appear on its site. Some advertisers enlist third-party audits to uncover fraud and waste. Emerging technologies can help as well. One such example is the IAB Technology Laboratory’s ads.txt project, which maintains a public record of authorized digital ad sellers. Its goal is to create greater transparency in the inventory supply chain and make it harder for fraudsters to sell counterfeit inventory.
To combat the increasingly clever forms of fraud, organizations need advanced big data analytics applications running on top of a Hadoop infrastructure. This setup will allow you to quickly analyze real-time data streams, generate massive volumes of data, identify problems, and apply predictive analytics to stop fraud before it impacts your bottom line.
Learn more about how big data analytics is transforming advertising.