In a time of dramatic changes, advertisers are course-correcting targeting efforts to adhere to the ever-evolving digital media landscape. One solution that addresses numerous areas of transformation — from consumer habits to privacy regulations and the demise of third-party cookies — is contextual targeting.

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningHowever, to succeed with this method, brands must seek partners with advanced capabilities to ensure the most substantial campaign outcomes and guarantee a leg up on the competition.  

A key ingredient to the best use of contextual targeting for advertisers is machine learning. Proprietary machine learning technology precisely categorizes content at scale, combined with media performance-optimization, brand-safe and quality environments — all critical components for combatting advertisers’ pain-points. 

The value proposition of machine learning for advertisers

To appreciate the synergy of contextual targeting and machine learning, it’s helpful to put a lens on recent history. Advertisers moved away from contextual once audience tracking entered the scene, but the present-day resurgence can be attributed in part to consumers and a growing concern for how personal data is being used.  

“Consumers are more conscious of how their data is being stored, however, 74 percent still want to see ads that match page content,” says Bichoï Bastha, Chief Business Officer at Dailymotion. “Contextual targeting is the answer to this. By nature, the solution is the most consumer-friendly targeting method as it uses context as the proxy for the audience in which to display an ad.” 

Contextual targeting — or better yet, contextual intelligence — is not the same as 10 years ago. Contextual intelligence solutions are rooted in providing the best possible user experience, leading to the best possible advertising experience. 

Critical criteria for contextually relevant campaigns

As new privacy regulations come into effect and browsers limit tracking capabilities, contextual advertising powered by machine learning is table stakes for standardizing targeting efforts and ensuring campaign success. But not all vendors have the advanced capabilities to deploy an effective contextually relevant campaign for partners, one that delivers — or increases — message receptiveness, ensures brand suitability and is privacy compliant.

Vendors must offer a robust list of criteria to help partners achieve campaign goals from start to finish, including relevant categories to increase media performance, best-in-class ad formats to ensure a positive user experience, granular and niche topic targeting, a depth of relevant inventory, proprietary machine learning models for precise content categorization, at scale and optimized performance metrics to ensure brands hit baseline KPIs such as viewability, VTR and completion rate. […]

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