Contextual ad targeting paves the way for new opportunities

Advertisers and marketers are always looking for ways to remain competitive in the current digital landscape. The challenge of signal loss continues to prompt marketers to rethink their current and future strategies. With many major browsers phasing out support for third-party cookies due to privacy and data security concerns, marketers will need to find new ways to identify and reach their target audience. Contextual ad targeting offers an innovative solution; a way to combine contextual signals with machine learning to engage with your consumers more deeply through highly targeted accuracy. Contextual advertising can help you reach your desired audiences amidst signal loss—but what exactly is contextual advertising, and how can it help optimize digital ad success?

In a Q&A with our experts, Jason Andersen, Senior Director of Strategic Initiatives and Partner Solutions with Experian, and Alex Johnston, Principal Product Manager with Yieldmo, they explore: 

• The challenges causing marketers to rethink their current strategies 
• How contextual advertising addresses signal loss 
• Why addressability is more important than ever 
• Why good creative is still integral in digital marketing 
• Tips for digital ad success 

By understanding what contextual advertising can offer, you’ll be on the path toward creating powerful, effective campaigns that will engage your target audiences. Check out Jason and Alex’s full conversation from our webinar, “Making the Most of Your Digital Ad Budget With Contextual Advertising and Audience Insights” above.

Macro impacts affecting marketers

How important is it for digital marketers to stay informed about the changes coming to third-party cookies, and what challenges do you see signal loss creating?

Jason: Marketers must stay informed to succeed as the digital marketing landscape continuously evolves. Third-party cookies have already been eliminated from Firefox, Safari, and other browsers, while Chrome has held out. It’s just a matter of time before Chrome eliminates them too. Being proactive now by predicting potential impacts will be essential for maintaining growth when the third-party cookie finally disappears. 

Alex: Jason, you nailed it. Third-party cookie loss is already a reality. As regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) take effect, more than 50% of exchange traffic lacks associated identifiers. This means marketers must think differently about how they reach their audiences in an environment with fewer data points available for targeting purposes. It’s no longer something to consider at some point down the line – it’s here now! As third-party cookies become more limited, reaching users online is becoming increasingly complex and competitive. Without access to as much data, the cost per thousand impressions (CPMs) that advertisers must pay is skyrocketing because everyone is trying to bid on those same valuable consumers. It’s essential for businesses desiring success in digital advertising now more than ever before.

Contextual ad targeting: A solution for signal loss

How does contextual ad targeting help digital marketers find new ways to reach and engage with consumers? What can you share about some new strategies that have modernized marketing, such as machine learning and Artificial Intelligence (AI)?

Jason: We’re taking contextual marketing to the next level with advanced machine learning. We are unlocking new insights from data beyond what a single page can tell us about users. As third-party cookies go away, alternative identifiers are coming to market, like RampID and UID2. These are going to be particularly important for marketers to be able to utilize. As cookie syncing becomes outdated, marketers will have to look for alternative methods to reach their target audiences. It’s essential to look beyond cookie-reliant solutions and use other options available regarding advertising.  

Alex: I think, as Jason alluded to, there’s a renaissance in contextual advertising over the last couple of years. If I were to break this down, there are three core drivers: 

1. The loss of identity signals. It’s forcing us to change, and we must look elsewhere and figure out how to reach our audiences differently.  
2. Contextual signals have always been around. Still, there’s been considerable advances in our ability to store and operate across a set of contextual signals far more extensive than anything we’ve ever worked with in the past and far more granular. That’s a huge deal because when it comes to machine learning, the power and the impact of those machine learning models are entirely based on how extensive and granular the data set is that you can collect. And the neat thing about contextual is that it’s all privacy safe. When you’re collecting individual interaction data around how users engage with ads, the context matters. Site semantics, device information, user location, time of day, and the publisher’s inferred audience data, become critical contextual signals. Machine learning then pulls these contextual signals together to determine which combinations are most predictive and valuable for advertisers.
3. There have been massive advances in the power and predictiveness of machine learning models, and they’re becoming much more effective and specialized. We can tailor them to individual advertisers using all those signals and finding patterns in ways that we couldn’t before. This transformation is occurring because of our ability to capture much more granular data, operate across it, and then build models that work for advertisers.