How to Reach Consumers While Protecting Their Privacy
The debate surrounding probabilistic versus deterministic cross-device tracking is nothing new. But with the rapidly evolving online landscape and technological capabilities, and with customers increasingly engaging across multiple devices, brands and agencies should be having a different conversation: They need to look beyond which targeting method to use and determine how they can best identify customers for more personal and relevant engagement while continuing to maintain consumers’ privacy. This is especially true as the issues of trust and scale have recently been raised concerning first-party data companies, such as Facebook.
Utilizing deterministic methods, based on some form of specific identifying data on a consumer (commonly logins, registration data, physical addresses and sometimes offline customer data or IDs, etc.), a company can determine who a specific user is. But this method has caused major issues for first-party data companies. Facebook faces this dilemma, since not only is its data limited to the information from just its platform, that data also contain very specific user information, creating a privacy issue when used with third parties.
Probabilistic methods, on the other hand, use a data science approach to take a variety of signals across multiple channels to build user profiles with anonymous data, and can increase scale by predicting behaviors of users based on similar known users. With the multitude of devices and touch points for companies to compile data from, probabilistic methods have evolved past the point of just tracking cookies. With privacy being a huge concern in today’s data-driven world, probabilistic methods allow companies to create holistic customer profiles and target their desired customer segments without requiring the use of identifying information.
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