#adtrafficvalidation

Approximately, $1.14 billion in global CTV open programmatic ad spend was lost to invalid traffic in Q2 2024. This highlights the gravity of ad fraud in the CTV ecosystem. CTV ad fraud causes inflation in ad campaign metrics of CTV ads due to invalid traffic, bot-driven engagement, and app spoofing. Some of the most common CTV ad fraud include:  

Invalid or Bot Traffic: That does not come from a genuine user, including traffic generated by data centers (DCH), automated clicks, and other non-human sources. Bots generate fake ad views or clicks, making it appear as though there is legitimate engagement from real users. 

Device Spoofing via: Creating fake CTV devices or apps to mimic real ones. It often tricks advertisers into buying ads on non-existent or fraudulent platforms. 

VPN/DCH: Traffic coming from Virtual Private Networks (VPNs) or data centers, is often used to hide the true location of the user and generate fake engagement from non-targeted regions. 

CTV ad fraud also includes device spoofing, running ads through secondary devices while the TV is off, and invalid server-side ad insertion (SSAI). 

There is a need for a full-funnel ad fraud detection tool to provide omnichannel protection against general and sophisticated invalid traffic i.e. GIVT & SIVT. An additional brand safety layer of protection helps to boost campaign performance. To guarantee the success of digital marketing efforts, the traffic validation tools should provide coverage across app, web, OTT, and CTV ecosystems.  

Let’s understand how bot detection works, why it is so important in marketing campaigns, the technologies involved, and how we help organizations overcome the risks of ad fraud in the bot-driven world and enhance marketing analytics accuracy.

Device Fingerprinting 

Device fingerprinting helps track and identify unique devices across the internet. Each time a particular device accesses a website or clicks on an advertisement, a unique “fingerprint” can be created with various signals and device attributes. This technique is driven by AI & ML algorithms to analyze data and identify anomalies that pinpoint bot traffic in real time. Sophisticated bots are largely identified at down-the-funnel (i.e. clicks, visits, leads, or sales) metrics. The General IVTs are identified at the pre-bid impression stage. 

Behavioral Analysis 

Human users do not exhibit predictable patterns, but the behaviors are predictable in the case of bots. Behavioral analysis tracks user interactions to detect unusual activities. One of the most common types is mouse movement. Bots tend to have straight-line patterns vis-a-vis a random pattern in the case of real humans. For any traffic validation tool, behavioral checks should feed into the overall ad fraud checks.  

mFilterIt’s Ad Traffic Validation Suite is a holistic ad-fraud detection and prevention solution. It aims to validate the ad traffic sources using the capabilities of AI, ML, and data science. The Ad Fraud solution ensures to protect the digital advertising spends across platforms like web, app, programmatic, and walled gardens.