IAS Expands Social Optimization for TikTok with Launch of Pre-Bid Video Exclusions

IAS Expands Social Optimization for TikTok with Launch of Pre-Bid Video Exclusions

New IAS Features Empower TikTok Advertisers with Enhanced Control Over Brand-Suitable Ad Placements

Integral Ad Science, a leading global media measurement and optimization platform, announced an expansion of Social Optimization for TikTok to include pre-bid Video Exclusion Lists. As advertisers continue to look for ways to preserve brand equity and drive performance across digital platforms, pre-bid Video Exclusion Lists across TikTok empower advertisers with greater control over their advertising investments on the platform.

Paired with TikTok’s Inventory Filter, advertisers applying pre-bid Video Exclusion Lists benefit from pre-bid granular exclusions, powered by IAS’s multimedia technology, and based on brand-specific needs. This expansion ensures advertisers can meet individual brand suitability and contextual requirements across their ad placements on TikTok.

Advertisers can now couple Social Optimization with IAS’s AI-driven Total Media Quality (TMQ) for post-bid measurement on TikTok, to leverage IAS’s best-in-class Brand Safety and Suitability, Viewability, and Invalid Traffic (IVT) detection across a total of 75+ markets with support in more than 30 languages. Together, IAS optimization and measurement solutions create a closed-loop on TikTok, enabling greater media efficiency and returns for advertisers spending on the platform.

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“We are excited to expand our TikTok partnership with the global availability of Video Exclusion Lists, which will further drive performance for advertisers,” said Lisa Utzschneider, CEO of IAS. “Utilizing our technology, advertisers can reach TikTok’s rapidly growing audience while reducing waste and driving higher returns on their ad spend.”

IAS’s expansion of Social Optimization for TikTok provides global advertisers with:

  • Enhanced Customization: Advertisers can drive further custom suitability on TikTok with IAS pre-bid Video Exclusion Lists by avoiding unsuitable content based on brand specific needs.
  • Inventory Filter Support: Advertisers can create a custom suitability profile to block content that is not suitable for their brand, as a layer of added protection to complement TikTok’s Inventory Filters.
  • Comprehensive Global Coverage: IAS’s post-bid reporting suite, TMQ for TikTok, can be paired with Social Optimization to ensure advertisers have a comprehensive and global solution for the platform.
  • Ease of Activation: IAS’s pre-bid Video Exclusion Lists are seamlessly activated within IAS Signal, and impressions from individual campaigns will automatically be measured and classified to exclude content that does not meet brand preferences. Advertisers can validate lists work as intended within IAS Signal and its Custom Report Builder tool, and seamlessly adjust their suitability strategy as needed.
  • Frame-by-Frame Analysis: IAS TikTok products are powered by IAS’s AI-driven Multimedia Technology, which enable advertisers to accurately classify content at scale through frame-by-frame analysis, combining image, audio, and text signals.

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“TikTok is committed to creating the most trusted platform for advertisers to build brands,” said Lorry Destainville, Global Head of Product Partnerships at TikTok. “Our partnership with IAS demonstrates how we’re innovating beyond traditional suitability standards and building solutions that empower advertisers with even greater control and confidence in their investments across our platform.”

This latest announcement further solidifies IAS’s deep partnership with TikTok since first launching a pre-bid optimization product in 2021. Most recently in October 2024, IAS announced the expansion of Total Media Quality for TikTok to 75+ New Markets, in addition to Viewability, IVT, and Brand Safety and Suitability Measurement for advertisers across TikTok’s newly available ad placements within the Profile, Search, Following Feeds and TikTok Lite (in supported markets).

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