This study explores the feasibility of forming detailed inferences about museum visitor behavior based on analysis of data collected via "Dr. Discovery"--a mobile question-and-answer app. We analyzed 5656 questions asked by 795 visitor groups recorded by Dr. Discovery across two museums in the American Southwest. Analysis of this data supported the act of intuiting visitor movement through museum exhibit halls without the use of costly tracking or location technology by leveraging question keyword content, knowledge of exhibit hall layout, and question timestamp information. Additionally, data on question topic frequency enabled us to infer visitor engagement levels with specific exhibit hall content. We conclude that analysis of seemingly limited app-based data carries implications for the practice of museum evaluation since evaluators can gain evidence-based insight into visitor behaviors as well as illustrate helpful and promising technology-supported alternatives for conducting affordable, dependable, and scalable evaluations.