Targeting audience personas with programmatic geographic segments using unsupervised methods

The digital advertising industry, where clients advertise to existing and potential customers through digital channels, is going through a rapid transformation. This is necessitated by evolving privacy laws such as General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), heading towards a cookieless future by decreasing reliance on personally identifiable information (PII). While approaches such as data clean rooms, federated learning have started gaining prominence, the current AdTech space is highly fragmented regarding channels, standards, platforms, inventory, cookieless targeting approaches, and compatibility. For marketers, it is crucial to have generalised and interoperable but privacy-compliant approaches to reach their audience. While it is safer not to limit them to a specific advertising stack, this must not come at a substantial performance cost. When a brand starts an advertising campaign, they usually have objectives and a target audience in mind. While their objectives are aligned with business goals and performance metrics, the desired audience personas are defined by either market research or new/historically well-performing consumer profiles suited to their products and services. This paper presents ways to convert multi-characteristic personas into geographic targeting without relying on cookie-based data. These geographic segments have broad compatibility across marketing platforms.