At Lucidity, we get a lot of questions about ad fraud.
It’s a major pain point for advertisers, publishers, and really anyone working in digital advertising. For good reason too, as ad fraud creates a whole slew of problems that go far beyond just the money lost.
To solve a problem, it helps to understand the problem, the causes of the problem, and who is most affected. So we’ve put together a guide to the most common types of ad fraud you might encounter in your campaigns.
Not only will you find the most up-to-date explanations of the kinds of fraud you’ve heard about (bots, domain spoofing, etc), but also breakdowns of the kinds of fraud that maybe you haven’t. Ever heard of a “man-in-the-browser” attack?
Now’s your chance to discover how it works.
The Ad Fraud Nightmare
If you aren’t convinced ad fraud is a problem worth solving, we’ve got you covered. Here are just a few ways in which fraud makes digital advertising a living nightmare:
The money loss is huge. Juniper Research estimated that ad fraud cost marketers $19 billion in 2018. If you do the math, that’s equivalent to $51 million per day.
The cost is more than monetary. Because the source of fraud is often unknown, advertisers’ ability to effectively optimize their campaigns is handicapped. Instead of knowing what works and what doesn’t, trial and error must be used, which can lead to a lot of guesswork.
Ad fraud sullies the reputation of publishers who rely on advertising to monetize their content. If a publisher is perceived (rightly or not) to be home to large amounts of bots, it can hamper its ability to fund its own content. That means more paywalls, and more publishers that go out of business.
Fraud erodes trust. The lack of visibility into where fraud comes from can result in a lot of pointed fingers which makes the business of advertising all the more difficult.
It’s a never-ending arms race. The digital advertising industry has developed a number of key solutions in the fight against fraud, including pre-bid filters, viewability solutions, and predictive analytics. But as the solutions we develop mature, so does fraud, meaning more time and money keeping up with a problem and less time actually eliminating it.