As data privacy regulations evolve and smart bidding algorithms take center stage, traditional methods like manual bidding and per-user data analysis are rapidly becoming outdated. Instead, marketers are shifting toward portfolio-level targets and Marketing Mix Models (MMMs), allowing for holistic goal-setting while relying on ad networks to optimize based on signals such as device types, search queries, and demographics.
AI plays a pivotal role in auction bidding, yet leveraging first-party data—like Google's OCT and Facebook's CAPI—reintroduces the need to understand variables influencing audience value. This insight enables better AI training to adjust bidding strategies based on post-action revenue factors. For example, knowing that mobile leads might be less valuable than desktop ones for a SaaS company prompts adjustments in bid settings or signals to networks.
Previously, achieving such granularity required separating audiences into distinct campaigns, diluting learnings across multiple keywords. With OCT, however, it's feasible to maintain keyword groupings within the same campaign, providing precise click values and consolidating insights while accurately valuing leads.
Transitioning to an OCT-based approach necessitates revisiting processes to ensure a thorough grasp of lead value variables. Regular checks and adjustments are crucial, given that "change is the only constant" in digital marketing. Ready to delve deeper into AI bidding strategies? Dive into our next article linked here!
Comments