Designing Promises with Reference-Dependent Customers : The Case of Online Grocery Delivery Time

Firms often need to promise a certain level of service quality to attract customers, and a central question is how to design promises to balance the trade-off between customer acquisition and customer retention. For example, most E-commerce platforms need to promise a certain delivery time. Over-promising may attract more customers now, but its impact on future retention depends on consumer inertia, learning, and loss aversion. Empirical analysis of this topic is challenging because the realized and promised service qualities are often unobserved or lack exogenous variation. We leverage a novel dataset from Instacart that directly observes variation in promised and actual delivery time to study this problem. We apply a generalized propensity score method to nonparametrically estimate the impact of delivery time on customer retention. Consistent with reference dependence and loss aversion, we document that customers are around 92% more responsive once the delivery becomes late. Our results inform a structural model of learning and reference dependence that illustrates the importance of estimating loss aversion and distinguishing promise-based reference points from expectation-based reference points: the company would forgo millions of dollars in revenue if it underestimates loss aversion or assumes expectation-based reference points