Inbox filters are extraordinarily complex systems that evaluate email deliverability and placement based on a multitude of factors. These factors span technical aspects like sender reputation (determined by IP address history, domain age, and authentication records), authentication protocols (SPF, DKIM, DMARC), and infrastructure setup to behavioral aspects such as content quality, user engagement (opens, clicks, complaints), and adherence to best practices. Modern filters employ sophisticated methods like machine learning, artificial intelligence, Bayesian filtering, and real-time data analysis to adapt to evolving spam tactics and user behavior. Both email marketers and official documentation sources underscore that a holistic approach, considering both technical and behavioral elements, is crucial for maintaining consistent deliverability. Several hidden metrics and adaptive algorithms utilized by inbox providers further contribute to this complexity.