Why We Are Different

Tap Into a Uniquely Dynamic, Self-maintaining Category Basis

Unlike competing semantic approaches, which are based on a static set of rules or fixed taxonomies, Proximic works dynamically. Ads are matched against a living and self-maintaining net of hundreds of thousands of crowd sourced categories that are continuously following the evolution of the web's content and audience.

  • Not based on static set of rules, trained taxonomies or learning knowledge bases
  • Dynamic matching against a living and self-maintaining set of categories derived from synapses of crowd-sourced content with meta data
  • Resembles the topical and language evolution of the web's content and audience
  • Supports multiple languages such as English, Chinese and German

Increase Ad and Filter Precision With Ultra-fine Category Granularity

By exploiting crowd-sourced data from the web to generate the category net, Proximic finds useful category connections across a higher granularity than competing technologies (multiple 100.000 vs. 10.000 categories). Detaching this from manual training enables to scale this approach to your benefit and lets your targeting stay on top of the constantly changing content out there.

  • Targets with un-matched contextual depth of a living, crowd-sourced structure of 100's of thousands of categories
  • Not limited in granularity (scales in performance indexing and matching)

Access a Versatile Platform for Ad Targeting and Brand Protection

Thanks to the technology efficiency and the high level of granularity, Proximic can offer you a wider spectrum of ad related use cases and targeting challenges.

  • Access only ONE infrastructure for Page Contextualization, Audience Targeting and Brand Protection across your inventory and user base
  • Allows to target different types of ads either based on categories (display) or keywords (performance)
  • Ensures more precise, versatile and lateral analysis of various unstructured input variables
  • Can be seamlessly embedded into network ad targeting workflows and blend it with other behavioral and demographic targeting dimensions
  • Map customized category or keyword sets onto living category net
  • Access high performance contextual targeting on demand while increasing ad sales opportunities
  • Transparent performance-based model (revenue share)