DoubleVerify (DV) authenticates the quality of digital media for the world’s largest brands. DoubleVerify delivers a fully integrated solution set to drive pre-bid performance and increase transparency in the programmatic supply chain.
DV Authentic Brand Safety offers content avoidance settings, including Brand Suitability tiers to choose from, to enable more nuance and coverage to build effective and comprehensive brand safety. This maximizes buying effectiveness by aligning the client’s brand safety preferences throughout the bidding process (pre-bid to post-bid), enabling brands to allocate spend towards qualified impressions and expand audience reach.
With over a decade of experience, the DV Fraud Lab consists of dedicated data scientists, mathematicians, and analysts who analyze digital ad fraud methodology. Powered by this level of analysis and expertise, DV claims to have been found to have about 8 times fewer false positives than leading competitors based on a recent head-to-head test.
DV’s pre-bid viewability targeting is based on proprietary resource-based methods that ensure the most accurate measurement with the widest coverage in the industry, with support for all major devices and platforms.
Contextual classification is a core component of brand safety and establishes DV as the trusted leader in brand safety and suitability solutions. Leveraging machine learning and linguistic expertise, DV classifies millions of apps and websites on an ongoing basis. DV's semantic science engine is purpose-built to analyze and create a detailed content profile of any piece of content using a comprehensive ontology, covering over 200,000 concepts using over 5.3 million rules.
Each concept is associated with one or more patterns (words, phrases and regular expressions) that identify that concept, as well as disambiguation rules to ensure that the relevant context is accurately identified. Multilingual ontologies are constructed by translating these concepts and adding language and culture-specific patterns with support for over 40 languages. DV’s ontological approach is able to correctly determine contextual meaning between similar words and accurately classify content to power more precise contextual targeting.
- Mobile Web
- Mobile App
- Connected TV
- North America
As a Basis customer, you have access to DoubleVerify's syndicated taxonomy within the platform as well as the ability to add create custom segments specific to your requirements.
Standard Brand Safety Targeting: DV offers standalone content avoidance categories for advertisers to activate within Centro, including tiered risk categories that align Brand Safety and Suitability targeting with APB and GARM frameworks.
Fraud/SIVT Targeting: DV's MRC accredited fraud protection uses deterministic methodology. DV's omni-channel fraud detection and blocking methodology protects advertisers across media buys on all platforms and devices,including CTV.
Display and Video Viewability Targeting: DV recalculates viewability predictions several times per day to ensure that all recent measurement data is incorporated to provide the most accurate prediction. DV pre-bid viewability segments are based on historical ad viewability performance relative to all measured ad impressions on a platform.
Standard Contextual Targeting: DV's Brand Safety and Contextual Targeting solutions utilize DV's proprietary semantic analysis system to review site, page, and app content to determine which of the 400+ IAB categories to assign.
Authentic Brand Safety Targeting: DV’s Authentic Brand Safety targeting allows advertisers to create a centralized set of brand safety and fraud controls and automatically deploy these controls across multiple programmatic buying platforms and campaigns. Authentic Brand Safety targeting allows advertisers to build a targeting profile combining 89+ brand safety and suitability categories, inclusion/exclusion lists, keyword avoidance, global language avoidance, site/app IVT threshold avoidance, and custom-built brand categories. This is available only for pre-existing DV customers using their measurement or blocking product. Below are some important points to consider:
- Must not be activated against another Authentic Brand Safety segment on the same targeting unit (one at a time).
- When a client uses Authentic Brand Safety, they do not need to use standard Brand Safety Avoidance targeting.
- When using Authentic Brand Safety, the client does not need to use standard Site/App Fraud avoidance targeting.
- Use of Authentic Brand Safety segments will greatly minimize, but not completely eliminate, blocking, as in some cases URL information in a bid request might be incorrect, intentionally blinded, or semi-blinded (such as domain only)
This is available only to the pre-existing DV customers using their measurement or blocking product with access to DV's Pinnacle platform. Customers should reach out to their DV representative for instructions.
Segment addition is an automated process. Once the segment is created from DV's platform, it will appear in Basis within minutes.
Syndicated: $0.05 - $0.15
Custom Authentic Brand Safety: $0.20
Standard Contextual and Brand Safety: DV pre-bid Brand Safety and Contextual Targeting solutions utilize DV's proprietary semantic science to review site, page, and app content to determine which of the 400+ categories to assign to the content. Once analyzed for an app, page, or site, this classification information is distributed to both pre-bid and post-bid solutions for brand safety and contextual targeting. DV pushes brand safety and contextual categorizations for both the domain and page level to Centro every 4 hours.
Authentic Brand Safety: DV’s Authentic Brand Safety targeting allows advertisers to create and manage a centralized set of brand safety controls such as inclusion/exclusion lists, inappropriate content categories, and keyword avoidance within DV’s Pinnacle® platform, which is automatically pushed via API to Basis every 15 minutes. Advertisers receive a single segment ID that is the same across all DSPs, making activation simple and reducing the chance for manual errors.