In the service industry, customers often assess quality of service based on the behavior, perceived personality, and other attributes of the front line service employees they interact with. Interpersonal communication during these interactions is key to determine customer satisfaction and perceived service quality. We present a computational framework to automatically infer perceived performance and skill variables of employees interacting with customers in a hotel reception desk setting using nonverbal behavior, studying a dataset of 169 dyadic interactions involving students from a hospitality management school. We also study the connections between impressions of Big-5 personality traits, attractiveness, and
performance of receptionists. In regression tasks, our automatic framework achieves R2 = 0:30 for performance impressions using audio-visual nonverbal cues, compared to 0:35 using personality impressions, while attractiveness impressions had low predictive power. We also study the integration of nonverbal behavior and Big-5 personality impressions towards increasing regression performance (R2 = 0:37).