Post-Occupancy Evaluation (POE) is a systematic process that assesses building performance after occupancy, with a growing focus on lighting quality. Effective POE methods for lighting feedback integrate subjective occupant perceptions with objective physical measurements. First, standardized occupant surveys, such as the LSEQ (Lighting Satisfaction and Experience Questionnaire), capture qualitative data on glare, visual comfort, and daylight adequacy. These surveys are often complemented by real-time wearable sensors that track light exposure and circadian rhythms, providing continuous feedback. Second, physical audits using portable photometers or luminance cameras measure variables like illuminance (lux) and uniformity ratios (U0) on workplanes. For dynamic analysis, Building Automation Systems (BAS) log dimming levels, shade positions, and energy consumption. Third, simulation-based POE uses validated daylight models (e.g., Climate-Based Daylight Modeling) to compare actual vs. predicted performance, identifying discrepancies in design intent vs. real-world use. Advanced methods now include machine learning algorithms that correlate sensor data with survey responses to predict discomfort thresholds. By triangulating these quantitative and qualitative data streams, POE enables facility managers to fine-tune lighting control strategies, balance visual needs with energy savings (often achieving 20-30% reduction), and guide retrofit decisions. Ultimately, such feedback loops foster human-centric lighting that adapts to user behaviors while verifying long-term sustainability targets.