Mobile Ad Fraud By The Numbers
Ad fraud is a problem, but just how big of a problem?
Ad verification company Adloox found that in 2016 ad fraud cost marketers nearly 20 percent of their total digital ad spend — meaning one out of every 5 dollars marketers spent on advertising was wasted due to bots, spiders and other non-human actors viewing or clicking their ads.
And despite an increased focus on ad fraud prevention in the marketing industry, researchers project that the ad fraud problem will only grow in 2017. Agency group The&Partnership estimates that the cost of ad fraud will swell to nearly $16.4 billion globally this year, up from $12.5 billion last year.
By 2025, global ad fraud costs could reach $50 billion annually, the World Federation of Advertisers (WFA) said earlier this year — which is second only to the drug trade as a source of income for organized crime.
On mobile, gaming is by far the most susceptible to ad fraud, with 39% of all attempted fraudulent traffic aimed at gaming apps, following by Lifestyle apps (18%), Shopping apps (15%), Travel apps (15%) and Sports apps (14%). That’s according to a recent study by mobile ad network ClicksMob, which also found that fraud was more prevalent on iOS apps than Android apps. iOS apps account for 61% of attempted fraud, compared with 39% for Android apps.
As with most data-driven industries, organizations and fraudsters are in a race with each other. The organizations iterate on detection and prevention tactics, while fraudsters create more and more sophisticated and devious forms of fraud. As of now, the numbers seem to suggest that fraudsters in the digital advertising world are winning by a long shot.
New forms of fraud are constantly being uncovered, with the latest and most prevalent being Ad Stacking, Click Injection, Click Spamming and Mobile Location Data Spoofing. Frequently, by the time advertisers uncover new forms of fraud and implement prevention methodologies — which inevitably take time to catch on across advertisers and ad networks — the damage has already been done.
For instance, an investigation in 2015 found that thousands of mobile apps were secretly running ads that users never see. Ad fraud tracking firm Forensiq identified over 5,000 mobile apps that display unseen ads on both Apple and Android devices, costing mobile app advertisers roughly $850 million each year and churning through user data usage with malware, the report found.
Beyond the obvious damaging effects of ad fraud — namely, that it dramatically lowers app marketing ROI — one corollary effect of the growth in malware and ad fraud is that it encourages ad blocking adoption among users.
Over 40 percent of users said they use ad blockers to protect against malware and viruses, a recent survey by ad blocker software Optimal and Wells Fargo found. At the same time, ad blocking usage is on the rise, surging 30 percent in 2016. Over 60 percent of devices with ad blocking software installed are mobile devices, according to PageFair, which helps publishers recover revenue lost to digital ad blocking.
This trend alone should serve as a rallying cry for the industry and align the interests of both ad networks and advertisers to stamp out mobile ad fraud. After all, if the rate of ad fraud continues to rise, increasing users’ data costs and prompting more users to install ad blocking software that safeguards against such costs, the entire digital advertising ecosystem will suffer.
So what can marketers do to protect against mobile ad fraud? Below are 6 tips to identify and prevent ad fraud.
- Anti-Fraud Tools: Some attribution and analytics suites offer tools to help marketers identify and prevent fraud. Such tools may use signals like IP addresses, click and install pattern detection, and activity monitoring to pinpoint campaigns, partners and buying models that are driving suspicious app installs.
- Common sense: Gone are the days when savvy app marketers were taken in by promises from ad networks of massive install counts quickly or at extremely low cost. Today, marketers know that a deal that sounds too good to be true is likely to result in low-quality app installs.
- Focusing resources on larger, trusted partners. Large or niche vertical media companies are more likely to have the scale and resources to detect and prevent fraud. Further, properties like social networks can leverage user account information to help ensure that installs come from legitimate people. At Singular, we saw a 393% increase in the number of installs driven by the top ten media partners, and a 105% increase for the second ten, in 2016. The “losers” during that same period? Smaller players without a quality user story to tell. While the size of the media company is no guarantee of strong or weak app installs, this is an instance where big brands are gravitating toward big media to protect their investments.
- Leveraging retention and uninstall data: By comparing the set of user traffic attracted by different media companies, brands can learn a lot about user quality. Low user retention or high uninstall rates increasingly are seen as signals of possible fraudulent activity.
- Diversify key performance indicators (KPIs): Smart incentivized install campaigns drive users to install the app plus complete a post-install event, such as a registration process or tutorial completion. Develop KPIs & benchmarks that are harder to predict or unrealistic to incentivize — such as post-install events that are unique to your traffic, or long-term usage metrics that occur days or weeks after the install. Arguably, the best KPI to monitor and prevent fraud is ROI, or revenue divided by cost, as it is particularly difficult for fraudsters to simulate a sale, especially because most marketing analytics platforms — including Singular — verify in-app purchases with App Stores.
- Identify publisher-level anomalies: Sometimes ad networks may not be aware that their publishers are perpetrating fraud. By breaking out source- & campaign-level reporting to examine key metrics at the publisher level, it can be easy to spot publishers that drive abnormally high app install counts or abnormally low quality users.