Leveraging High-Frequency Service Data: The Interplay of Objective Performance, Perceived Quality and Purchase

The advent of digitization has allowed firms to collect high-frequency data – subjective and objective – to monitor their service quality and performance. This paper proposes a comprehensive empirical framework to help firms understand the value of collecting these data. This framework is applied to novel high-frequency, individual-level, cross-sectional and time-series quality survey data (both subjective and objective) from the quick service restaurant industry. The results allow for the quantification of statistical and economic significance of subjective quality data, within- and across-customer selection in survey response and the tradeoff between service quality and service consistency. These results advance the literature on measurement and management of service quality and provide insights to managers for forecasting and resource allocation.

Keywords: Service Quality, Performance Inconsistency, Quick Service Restaurants Industry, Selection Bias, Machine Learning