When the Cookie Crumbles – A Combined Solution for the Cookieless Future

cookieless future

By Łukasz Włodarczyk, VP of Programmatic Ecosystem Growth & Innovation at RTB House

The changes taking place in the digital marketing industry cannot be overstated — we are entering an entirely new era of advertising that will demand unique approaches from brands and marketers. This industry transformation is driven by the impending cookieless future, with Google removing third-party cookies from its Chrome browser by the year 2023.

Due to a lack of interoperability between the major browsers and yet-to-be agreed industry standards, the next couple of years will inevitably be complex for digital advertisers.

Thankfully, there are several key methods available now that both replace third-party cookies and significantly increase the efficiency and accuracy of advertising campaigns. And instead of waiting for the cookie to finally crumble, these methods should be implemented now.

It’s All About Contextual

Contextual advertising is already 20 years old, however, with cutting-edge Deep Learning technology increasing its efficiency and scale, it’s now one of the three most tangible ways to reach a target audience in the cookieless world.

Contextual targeting doesn’t rely on third-party data to follow a user across the web. Instead, the technology can understand which context suits the advertising campaign best by using keywords and other specified information related to the publisher.

By using Deep Learning algorithms, contextual targeting technology reads specific signals — such as a website’s URL, content category, text, images, and video — to understand the contextual relevance of each page. And as such, Deep Learning significantly improves the ability to match the correct ads with users.

Focus on the Consumer Needs

Individual-based targeting is driven by first-party data, an arguably underutilized data source that will become increasingly integral moving forward. The data is taken from a brand or publisher’s direct interaction with its customers, providing a high level of user insight using data that is voluntarily given.

This approach allows marketers and brands to venture beyond basic demographic segmentation and focus on content personalization, reducing the risk of generic campaigns. First-party data maintains a higher level of user privacy because it is both consented to and less freely available.

However, it also provides increased detail relevant to the individual user which can significantly improve targeting. This level of insight can be used to hone in on the individuals most likely to purchase certain products or services and avoid those that won’t, leading to potentially higher conversion rates.

Effective Group Targeting

Targeting groups, or in other words, a ‘cohort’, involves collating groups of individual profiles based on consumer interests. These interest groups can also be built by tapping into first-party data.

Currently, a targeted audience is identified as an individual by a third-party cookie, which allows for cross-site tracking between websites, but interest groups allow brands to anonymise their audience, significantly improving privacy guarantees.

Group targeting also meets the standards set by browser vendors. The initiative is designed to create the standards for websites to access user information, all without compromising their privacy, and will become increasingly important as third-party cookies are phased out of use.

Additionally, when using Product-Level TURTLEDOVE and Outcome-Based TURTLEDOVE, for example, ads will still deliver specific product recommendations, maintaining users’ privacy — they have become an integral part of Google’s privacy sandbox for vendors.

It can achieve this while maintaining the group’s original privacy guarantee. While the ad tech vendors cannot recognize the user as an individual, the ads rendered on their browser will match their interests.

Multi-Faceted Approach

Each of these methods listed has its pros and cons, but ultimately, a carefully strategized approach that utilizes all methods in different capacities will have the greatest effect. After all, there is no one size fits all approach.

For example, it’s possible to nurture both group-based and individual-based approaches in advertising using Deep Learning to more efficiently create accurate cohorts to target.

And with contextual, advertisers can serve personalised ads in the correct context first. For example, its level of analysis means an article about a car accident won’t advertise a BMW.

Harnessing modern technology is key to finding the most effective solution in the cookieless future and Deep Learning can truly unlock the full potential of each of these approaches.

When the cookie crumbles, instead of an industry bereft of accurate data, we’ll see an improved industry full of innovation, opportunity, and a privacy-first approach to marketing.

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