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Contextual offers the answer to the cookie question

machine learning

To marketers used to using the cookie for targeting, the next year or so is looking bleak. Apple ID is fading away in a matter of weeks, and many businesses are in a spin as to what to do. The behemoth players have all staked their claims, limiting data targeting to inside their walls only.  ID 2.0 is all but dead with no Google support and match rates in the 30% range. For marketers, it’s a nightmare as they are being forced to hand over more control over their brand. 

But there is a solution.

Innovation has always been the catalyst for digital advertising growth, and there is universal consensus contextual targeting will play a key role.

But, before we understand how to harness the modern era of contextual targeting solutions, let’s take a trip down memory lane to avoid the mistakes of yesteryear.

 Back to the future of marketing

Many will be familiar with the definition of contextual targeting – the practice of driving user engagement by serving ads to likely consumers based on the relevancy of the content. Simple, but effective, right?

Coming into prominence in the early 2000s, contextual took second place as marketers favoured the promise of one-to-one marketing from the cookie. In quick concession, contextual morphed into a keyword, brand safety avoidance solution. In fact, ‘generation one’ platforms are fundamentally the same today (albeit repackaged to do more) and core capabilities remain to be the ‘trash collectors’ of the web, aggressively removing off-brand content from the media buy.

However, as we enter a new era, a gaggle of new companies have made major advancements in contextual capabilities that go far beyond the limited scope of the ‘generation one’ platforms. 

Built on the latest advancements in machine learning, natural language processing, sentiment analysis and big data, modern-day contextual platforms identify in-the-moment marketing opportunities by analysing hundreds of data points; from first-party sources, (data management platforms, and/or customer data platforms), the programmatic bid stream, second-party publisher data, and a broad array of new third-party data created by other innovative companies. Unlike the original keyword platforms, modern day context engines analyse a complete view of every ad opportunity,  providing advertisers the ability to suitably target the moments consumers are receptive to.

 What does modern contextual look like?

The demise of the third-party cookie has put an accelerator on the resurgence of contextual. Some of the most critical components as we step into this new marketing age, include:

  • Driven by outcomes: Outcomes for businesses is ultimately what makes the wheels go round, and shifting dollars from behavioural to contextual targeting will improve ROI for businesses of all shapes and sizes
  • Unlocking virtually any data point: The speed of the market and the plethora of data being created means the definition of ‘contextually relevant’ is constantly in flux. To be a viable, long-term contextual platform, solutions must easily ingest outside data to meet the changing world
  • Video Compliancy: Connected TV and other online video channels are the coveted format for brands to message consumers in the moment. It is critical that modern era solutions incorporate the context of the video imagery, in addition to the audio and surrounding page content

 Planning ahead is vital

Within weeks, Apple will remove their ID, and within nine months cookies will be all but gone. Now is the most critical time for marketers to explore their options and develop a plan. 

In 2020, Silverbullet launched its Context Outcomes Engine – 4D – a next-generation contextual solution that identifies in-the-moment marketing opportunities by tying together first-party data to the broader ecosystem. Want to learn more?

Download our white paper now.

By Mark Pearlstein, CRO

Silverbullet and 4D

Silverbullet is the leading marketing transformation company that helps the world’s biggest brands to unlock the potential of first-party data and contextual intelligence, empowering better, faster and smarter business outcomes.

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Mark Pearlstein
Mark is responsible for driving global revenue at DoubleVerify through partnerships with brands and agencies. Mark has 20 years of experience selling enterprise software solutions to the world’s largest brands. Prior to DV, Mark served as SVP, Business Development & Sales at Ringleader Digital, the first 3rd-party mobile ad-serving platform. Over his career, he has held key roles at both early-stage and well-established enterprises, including Entriq, Motive, Broadjump, and Oracle. Mark earned his B.A. in Philosophy from Brandeis University.

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