User Search Behavior involves the analysis of patterns in user interactions (type, click, dwell time, query reformulation, abandon queries) to understand engagement and interest levels. Search engines collect extensive data on user actions, such as Query Logs, Historical Data, and Mouse Hover Tracking, to refine search relevance and rankings. Behavioral data helps predict and modify rankings based on implicit feedback (e.g., long vs. short views of a document). For marketers, analyzing this behavior provides crucial data for optimizing the user flow, refining calls-to-action (CTAs), and informing UX improvements.