We build on the theoretical foundations of consumer engagement from the marketing literature to propose a novel way to measure consumer brand engagement (CBE) using machine learning and natural language processing of consumer-generated online content. We conceptualize customer-written product reviews as more than just eWOM influencing purchase decisions, but as indicators of CBE. Our method is operationalized through a general-purpose artifact that allows continuous, time-variant, and flexible measurement of CBE. We demonstrate the feasibility of our approach through a large dataset of product reviews of multiple brands of a Fortune 500 garment retailer. Our contribution has implications for research, in that, it creates an opportunity to investigate the antecedent and consequent relationships between CBE and other critical marketing constructs such as intention to purchase and customer loyalty. Further, the ability to measure the time-variant nature of CBE allows for testing leading and lagging relationships between CBE and other key business indicators.