Advertisers and marketers are always looking to remain competitive in the current digital landscape, especially as the challenge of signal loss continues to prompt them 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, you will need to find new ways to identify and reach your target audience.
Contextual advertising offers an innovative solution; a way to combine contextual signals with machine learning to engage consumers more deeply through highly targeted accuracy.
While contextual advertising can help you reach your desired audiences amid signal loss, what exactly is it, and how can it help optimize digital ad success?
Macro impacts affecting marketers
Being proactive now by predicting potential impacts of cookie deprecation is essential for maintaining business growth and marketing impact.
You must think differently about how to reach your 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. Impacts include:
- Digital identifiers continue to be restrictive
- The need to increase CPMs for alternative identifiers
- Marketers will need to learn to use these new solutions
- Cookie fidelity is extremely competitive
A solution for signal loss
Machine learning is bringing contextual advertising to the next level, and can help you find new ways to reach and engage with consumers. You can unlock new insights from data beyond what a single page can tell you about your 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 you to use.
As cookie syncing becomes outdated, you will have to look for alternative methods to reach your target audiences. There has been a renaissance in contextual advertising over the last couple of years, due to a few key drivers. For instance, the loss of identity signals. This has forced the industry to change, and you must look elsewhere and figure out how to reach your audiences differently.
There also have been considerable advances in marketers’ ability to store and operate across a set of contextual signals far more extensive than anything they’ve ever worked with and in far more granular ways. That’s a huge deal because when it comes to machine learning, the power and the impact of those models are entirely based on how extensive and granular the data set is that you can collect. Machine learning can pull together critical contextual signals and figure out which constellations, or which combinations of those signals, are most predictive and valuable to a given advertiser.
You can tailor machine learning models to individual advertisers using all those signals and find patterns across those in ways that were previously impractical or unfeasible. The transformation is occurring because of the industry’s ability to capture much more granular data, operate across it and then build models that work for advertisers.
Connect your campaigns to consumers
How does advanced contextual advertising help marketers reach non-addressable audiences? With advanced contextual advertising, you can take a set of known data (identity) and draw inferences from it with all the other signals seen across the bitstream.
Machine learning is bringing contextual advertising to the next level, and can help you find new ways to reach and engage with consumers.
For instance, consider contextual indexing. You may know the identity of a particular group of households, and with contextual indexing, you can look at how those households index against any of the rich data sets available in any data marketplace. You can look at how that data indexes to those known users to find patterns in that data and then extrapolate from that.
The exciting opportunity for many marketers is figuring out how to reach their known audience in a non-addressable space—based on environmental and non-identity based signals—that helps a campaign perform. Your known audience are people who are already converting—those who like your products and services and are engaged with your ads.
Addressable users, the new identity-based users, are critical to your performance initiatives. They’re essential to training the models being built with contextual advertising. Together, addressable users and contextual advertising are a powerful combination to meet your performance needs. It’s not just using advanced contextual, and it’s not just using the new identifiers.
Recommendations for marketers for 2023 and beyond
Be proactive and start testing and learning these new solutions. Addressability and being in the right place at the right time might be easier in today’s third-party cookie world. But as traditional identity is further constricted, you will have first-party solutions that will not be at scale, so you’re less likely to find your user at the scale you want.
It’s also critical to think about how to reach the user at the right place at the right time. They may not be seen from an identity basis. They might not be at the right place at the right time when you’re delivering an ad. But you increase your chance of reaching them by building these advanced contextual targeting audiences using this privacy-safe seed opted-in user set; this is a way to cast that wider net and achieve targeted scale.
At the end of the day, it’s making the unaddressable, well, addressable. Addressable needs to be part of your overall marketing strategy, and you need to complement it with other strategies like advanced contextual advertising. Now is the time to take advantage of what is possible, start testing and understanding how these solutions work.
