Wait Time Information Design

When customers arrive, service providers often collect information to generate delay forecasts. We study how delay data-collection and forecasting systems can be designed to improve customer satisfaction. We assume that customers may be loss-averse in the sense that an increase in the expected wait causes more distress than the positive response caused by an equivalent decrease and that they may be risk conscious in that an increase in the variance of expected delay reduces utility. Our goal is to find the structure of delay information that optimizes the customers’ experience while waiting. Delay forecasts follow Bayes’ rule, given a prior distribution, the additional information collected for a particular customer, and the passage of time.We find that when loss aversion dominates, the optimal delay information focuses on the tails of the delay distribution. When risk consciousness is dominant more traditional information about the duration of delay–along a continuum from ‘short’ to ‘long’–is optimal, and this information should be most precise about the longest delays. The optimal information design also affects the timing of delay revelation. When customers are loss averse, it is optimal to avoid changes in expected delay over time, so that waiting times are revealed as customers go into service. When customers are risk conscious, it is optimal to provide information so that they learn the good (or bad) news immediately, when they arrive