Summary
Service providers can show customers the chances of receiving certain service levels when booking, helping customers make better choices and providers manage demand. These chances change depending on how busy the system is, but customers can't easily check if the probabilities are accurate. The authors study when and how providers might alter these probabilities to influence customer decisions. They create a model and algorithms that approximate truthful probabilities and balance transparency with the provider’s goal to manage demand effectively. Their study focuses on parcel deliveries to lockers and finds trade-offs between being clear with customers and improving overall satisfaction.
What this means in practice
- •For logistics planners: Optimize delivery slot offerings by dynamically adjusting and communicating service probabilities to balance demand and improve customer satisfaction.
- •For e-commerce operations teams: Manage out-of-home parcel delivery demand by using probabilistic information to guide customer choices toward less congested delivery points.
Abstract
To enable well-informed customer choices and redistribute demand across service options, providers can display probabilities regarding predicted service levels at the time of booking, effectively offering probabilistic service options. In many service settings, the underlying truthful probabilities endogenously depend on the system state, rather than being static product attributes controlled by the provider. While customers rely on this information when making decisions, they cannot readily verify its accuracy. Consequently, providers may be tempted to manipulate the displayed probabilities to guide customers' choices in their favor, creating a novel lever for demand management. In this work, we model the resulting problem as a sequential decision process, define truthful probabilities recursively, and introduce transparency constraints that limit their distortion. Because the exact computation of truthful probabilities is intractable for realistic settings, we develop an adapted reinforcement learning algorithm to approximate them. Furthermore, we propose a sampling-based mixed-integer program to optimize the information communicated by the provider in each state. Our framework enables providers to gauge the benefits of dynamically disclosing probabilistic information at the time of booking and sheds light on incentives for manipulation. Our computational study is motivated by out-of-home delivery, i.e., the delivery to parcel lockers or shops, and reveals a trade-off between individual-level transparency and aggregate customer utilities.