Pixalate Releases Ad Industry’s First MALformed And Fraudulent Bundle IDs (MALAFIDE) Risk Report for Connected TV Advertising: 46% of Bundle IDs Are Malformed and/or Fraudulent

Pixalate’s data science team analyzed over 1.8 billion open programmatic advertising impressions across more than 16,000 CTV Bundle IDs mapped to over 6,000 unique CTV apps in March 2024 on Amazon Fire TV, Roku, Samsung Smart TV, and Apple TV to compile the research in this series 

LONDON, UK, 7 May 2024 – Pixalate, the global market-leading ad fraud protection, privacy, and compliance analytics platform, today released the Q1 2024 Connected TV (CTV) Malformed and Fraudulent Bundle IDs Risk Reports for Amazon Fire TV, Roku, Apple TV, and Samsung Smart TV CTV apps.

The reports include a detailed analysis of the global state of non-standard and malformed Bundle IDs in the open programmatic advertising supply chain as of Q1 2024. “Malformed” Bundle ID represents an app identifier used in the ad bid uncorrelated or mapped to any known app, according to Pixalate’s Bundle ID mapping technology. These “Malformed” Bundle IDs disrupt ad targeting and campaign measurement, and open the door to ad fraud.

Pixalate’s data science team analyzed over 1.8 billion open programmatic advertising impressions across more than 16,000 CTV Bundle IDs mapped to over 6,000 unique CTV apps in March 2024 on Amazon Fire TV, Roku, Samsung Smart TV, and Apple TV to compile the research in this series. 

Key Findings:

• Roku: 26% of Bundle IDs were malformed and/or fraudulent (2k out of 7.8k)
  • Only 31% of Bundle IDs utilized across Roku traffic used App Store IDs, which is the recommended method for app identification according to the IAB Tech Lab
• Amazon Fire TV: 62% of Bundle IDs were malformed and/or fraudulent (3.7k out of 6k)
  • Only 23% of Bundle IDs utilized across Amazon Fire TV traffic used App Store IDs, which is the recommended method for app identification according to the IAB Tech Lab
• Apple TV: 56% of Bundle IDs were malformed and/or fraudulent (1.3k out of 2.4k)
  • Only 27% of Bundle IDs utilized across Apple TV traffic used App Store IDs, which is the recommended method for app identification according to the IAB Tech Lab
• Samsung Smart TV: 67% of Bundle IDs were malformed and/or fraudulent (2k out of 2.4k)
  • Only 15% of Bundle IDs utilized across Samsung Smart TV traffic used App Store IDs, which is the recommended method for app identification according to the IAB Tech Lab

Top Sellers at Risk of Malformed CTV Bundle IDs

Sellers are ranked by their malformed Bundle ID exposure rate: The percentage of a malformed Bundle IDs on which the seller is found to have sold traffic, according to Pixalate’s data.

Top Malformed and/or Fraudulent Bundle IDs (by impression volume)

Download the full report to receive a list of the top 50 malformed Bundle IDs - ranked by impression volume and by CTV platform - as measured by Pixalate in March 2024.

Download the Malformed and Fraudulent CTV Bundle IDs Reports

About Pixalate

Pixalate is a global market-leading ad fraud protection, privacy, and compliance analytics platform. Pixalate works 24/7 to guard your reputation and grow your media value by offering the only system of coordinated solutions across display, app, video, and CTV for the detection and elimination of ad fraud. Pixalate is an MRC-accredited service for the detection and filtration of sophisticated invalid traffic (SIVT) across desktop and mobile web, mobile in-app, and CTV advertising. www.pixalate.com

Disclaimer

The content of this press release, and the Malformed Bundle IDs Reports (the “Report”), reflect Pixalate's opinions with respect to factors that Pixalate believes can be useful to the digital media industry. Pixalate's opinions are just that, opinions, which means that they are neither facts nor guarantees. Pixalate is sharing this data not to impugn the standing or reputation of any entity, person or app, but, instead, to report findings and trends pertaining to programmatic advertising activity across CTV in the time period studied.