At LinkedIn, we’re committed to upholding quality standards for advertiser campaigns on LinkedIn and the LinkedIn Audience Network. We employ a wide range of measures to detect, remove, and measure invalid traffic (IVT) so that advertisers have confidence in traffic quality. We also partner with third-party industry experts such as DoubleVerify, HUMAN Security, and industry groups such as TAG (the Trustworthy Accountability Group) and the Media Rating Council (MRC), to detect invalid traffic across LinkedIn, LinkedIn Audience Network, and Connected TV (CTV) publishers. We offer MRC-accredited click and ad impression metrics for most ad formats on LinkedIn, with some exceptions. We know how important this is for the brands that choose to advertise on LinkedIn and we are continuously investing in new methods to address this complex challenge and to protect our advertisers and members.
What is invalid traffic?
IVT and ad fraud can include activities and ad engagements that originate from automation or bad actors. IVT has evolved and comes in many different forms. At LinkedIn, we employ a full-funnel approach to work to address both general invalid traffic (GIVT), such as known automation tools and crawlers, and sophisticated invalid traffic (SIVT), such as botnets and click farms.
How we prevent, detect, and remove IVT
First, we maintain high quality content and authentic accounts on the LinkedIn feed, and we curate and monitor quality publishers on the LinkedIn Audience Network and our CTV offering. Authenticity matters on LinkedIn. Our latest global transparency report indicates that we proactively stopped 99.6% of detected fake accounts before a member report. On the LinkedIn Audience Network and our CTV offering, our trust team reviews and curates trusted, engaging publishers for our network before we onboard them and monitor them.
Second, we apply pre-bid filtering to filter traffic from ad requests before bidding on them. Our automated detection systems use machine learning models, first-party signals, and third-party signals to make these real-time decisions and filter the majority of fraudulent and automated traffic on our platform.
Third, we apply post-bid filtering using real-time or near-time signals to filter additional invalid traffic and remove it from our metrics and billing information. We don’t charge advertisers for filtered invalid traffic. As we detect invalid traffic, it allows us and our third-party partners to enhance our systems and to improve our pre-bid filtration.
Lastly, together with our third-party partners, we review reported cases of invalid activity that may have evaded our automated systems. We investigate unusual traffic patterns and reports from advertisers and publishers, which helps us improve our automated detection.
Throughout the process, we have integrations with MRC-accredited partners like Integral Ad Science (IAS), DoubleVerify, Pixalate, and HUMAN Security for their expertise in IVT.
How we measure IVT on LinkedIn ads
We understand the importance of transparency and using data from impartial sources. That’s why we rely on partners like DoubleVerify and HUMAN Security to independently detect post-bid IVT on our platform.
If you suspect your campaigns are impacted by IVT, visit the Marketing Solutions Help page to create a support ticket or contact your LinkedIn Sales Representative with a brief description of the trends in your reporting that prompts you to suspect invalid activity.