By 2023, the use of third-party cookies will be utterly phased out in all popular web browsers. The challenge this presents for brands is to adopt a new approach in retargeting tactics for advertising and marketing efforts. In particular, how would you measure and report on in-channel performance if you do not have access to all the data you’d normally use? What would this mean for cross-channel data attribution?
With third-party cookie retirement becoming imminent, advertisers and agencies will need to invest in resources to implement new targeting and measurement tools that are technologically sound, scalable, and in alignment with regulatory requirements. Having to move to new tactics begs the question: Is there a privacy-centric solution?
5 solutions to stop the crumbling
Although third-party cookies are going away, it doesn’t mean targeted marketing and advertising efforts, along with the value associated with the metrics they provide to inform campaigns and investment strategies, will fall away. There are other courses of action to take, which can replace and even improve upon the waning standard.
- Unique ID (UID) can be effective, as consumers must give consent for their use. fully restore the functions that 3rd party cookies offer today, in addition to being in alignment with privacy requirements.UIDs allow targeting of audiences through a variety of channels, including offline data and shopper data. However, this option may not cover your targeting needs at scale.
- First-Party Cookies are not impacted by the phase-out of third-party cookies. They allow continued identification of users and can be used in measurement functions, such as site visit frequency. While first-party cookies can’t actually be used to identify user behavior offline, thus restricted in usefulness to only in-session user activity. Also, some smaller brands may be challenged to adhere to legal and technical requirements for user consent.
- Predictive Targeting can be vital for the implementation of real-time prediction tactics. While in-session, Artificial Intelligence (AI) can predict which audience campaign best fits a user, enabling your online campaign targeting to do its job. Predictions are derived from in-session behavior by analyzing such data points as IP address, device, time, and other contextual information. Not only does predictive targeting operate at scale, but it provides an accurate balance for the most common audiences and at a granular level. Such information can be used to model data segments for greater accuracy in predictive targeting.
- Google Chrome Privacy Sandbox is the world’s largest browser, powering digital advertisement. Google’s approach ensures scalability and privacy compliance in targeting across multiple domains. On consideration for potential users: Individual user data will remain in the browser, thus isolated in the control of Google’s digital ecosystem.
- Contextual Targeting – whether based on simple keywords, advanced semantic analysis, or image analysis – does not rely on user IDs at all. It’s a direct and simple way to reach receptive users in the moment to deliver marketing messages. However, reliable contextual targeting is likely to be challenged by achieving accuracy, scale, and effectiveness. Accuracy can be achieved by keen, hands-on analysis and implementation of keywords for contextual interests and carefully selecting keyword groups. Although scale is more limited than audience targeting, an organized contextual catalog can help provide control in accuracy and scale as needed.
