Simple To Advanced Attribution Fraud

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Martin Zentrich

Cybersecurity Content Specialist

Visualization of attribution fraud by faking UTM parameter

Just because a URL contains a utm source, e.g. utm_source=msn or utm_source=cnn, it doesn’t necessarily mean the click came from an ad on msn.com or cnn.com. It could be legitimate, or it could be manipulated.

Since a utm source can be added to any URL, it’s possible for fraudsters to make a click appear as if it came from anywhere. This can mislead advertisers into thinking their campaigns are performing well when, in reality, the traffic may not be genuine. If advertisers believe their ads are driving strong results, especially from a recognizable site, they may increase their spending, unknowingly fueling more fraud.

This article will break down different ways attribution can be manipulated, from basic methods to more advanced techniques. The examples are based on real data, offering insights into how to spot and prevent these issues.

Simple Attribution Fraud

Here’s how it works: A fraudster manipulates a tracking URL to make it seem like traffic is coming from a legitimate source. For example, they could set utm_source=*TrustedNewsSite* in a URL, even though the click never actually came from that site. When this URL is loaded, whether by a bot or another fraudulent method, analytics tools like Google Analytics record it as traffic from that trusted news site.

Since these analytics platforms can’t verify whether a real user clicked, advertisers believe their ads are performing well and continue investing in that channel. Meanwhile, fraudsters profit from the artificially inflated engagement. This is just one example of how simple attribution tricks can lead to wasted ad spend.

With Fraud0 tracking site activity, it’s possible to determine whether a click came from a real person or an automated bot. Additionally, it can help verify if the click actually originated from an ad on the claimed source or if the data has been manipulated.

By analyzing referral information, we provide a clearer picture of where traffic is truly coming from, making it easier to detect misleading attribution and prevent wasted ad spend.

Below is an example illustrating this in more detail. The UTM_Source=facebook parameter indicates that a click originated from a campaign on Facebook, which is confirmed by the referrer data. Additionally, some clicks are coming from instagram.com or as a redirect (l.facebook.com). If you haven’t noticed yet, pages with UTM_Source=*something* but no referrer can be faked.

UTM Source & Referrer Screenshot

Fake Traffic Written Into Google Analytics

It is possible to inject fake data into Google Analytics, making it appear as though a website is receiving large amounts of traffic, even when no real visitors are present. This can be done using scripts that send false tracking information directly to Google Analytics, without any actual users interacting with the site. Fraudsters use this tactic to make fake websites seem legitimate, tricking advertisers and ad networks into believing their campaigns are driving real engagement.

Over the years, there have been multiple cases where false data was intentionally written into Google Analytics to mislead advertisers into thinking their campaigns were performing well.

CTV Ad Fraud

Another method involves CTV (Connected TV) ads, which play on smart TVs. Since hardly anyone clicks on ads from their TV screens, advertisers wouldn’t expect to see much direct traffic from CTV campaigns. However, some vendors have been caught injecting fake clicks into Google Analytics, making it look like their CTV ads drove significant traffic and sales, when in reality they had no impact.

Using Fraud0, advertisers can compare data sources to detect suspicious traffic patterns and uncover whether their campaign results are being manipulated. Without proper tracking, it’s easy to be misled by falsified attribution that inflates the success of ad campaigns.

Seeing Through Attribution Tricks

These examples clearly illustrate how attribution can be manipulated to make campaigns appear far more effective than they actually are. By distorting tracking data, they create the illusion of high returns, encouraging advertisers to increase their spending, when in reality, the results are misleading.

The good news? You don’t need complex studies to uncover this. By using Fraud0 in your ads and on your site, you can see for yourself whether any of your vendors are inflating performance metrics. If something looks too good to be true, it probably is.

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