This paper studies the problem of purchasing and allocating copies of movies to multiple stores of a movie rental chain. A unique characteristic of this problem is the return process of rented movies. We formulate this problem for new movies as a newsvendor-like problem with multiple rental opportunities for each copy. We provide demand and return forecasts at the store-day level based on comparable movies. We estimate the parameters of various demand and return models using an iterative maximum-likelihood estimation and Bayesian estimation via Markov chain Monte Carlo simulation. Test results on data from a large movie rental firm reveal systematic underbuying of movies purchased through revenue-sharing contracts and overbuying of movies purchased through standard ( nonrevenue-sharing) ones. For the movies considered, our model estimates an increase in the average profit per title for new movies by 15.5% and 2.5% for revenue sharing and standard titles, respectively. We discuss the implications of revenue sharing on the profitability of the rental firm.

Services such as FedEx charge up-front fees but reimburse customers for delays. However, lead-time pricing studies ignore such delay refunds. This paper contributes to filling this gap. It studies revenue-maximizing tariffs that depend on realized lead times for a provider serving multiple time-sensitive customer types. We relax two key assumptions of the standard model in the lead-time pricing literature. First, customers may be risk averse (RA) with respect to payoff uncertainty, where payoff equals valuation, minus delay cost, minus payment. Second, tariffs may be arbitrary functions of realized lead times. The standard model assumes risk-neutral (RN) customers and restricts attention to flat rates. We report three main findings: (1) With RN customers, flat-rate pricing maximizes revenues but leaves customers exposed to payoff variability. (2) With RA customers, flat-rate pricing is suboptimal. If types are distinguishable, the optimal lead-time-dependent tariffs fully insure delay cost risk and yield the same revenue as under optimal flat rates for RN customers. With indistinguishable RA types, the differentiated first-best tariffs may be incentive-compatible even for uniform service, yielding higher revenues than with RN customers. (3) Under price and capacity optimization, lead-time-dependent pricing yields higher profits with less capacity compared to flat-rate pricing.

Problem definition: We provide guidelines on three fundamental decisions of customer relationship management (CRM) and capacity management for profit-maximizing service firms that serve heterogeneous repeat customers, whose acquisition, retention, and behavior depend on their service access quality to bottleneck capacity: how much to spend on customer acquisition, how much capacity to deploy, and how to allocate capacity and tailor service access quality levels to different customer types. Academic/practical relevance: These decisions require a clear understanding of the connections between customers' behavior and value, their service access quality, and the capacity allocation. However, existing models ignore these connections. Methodology: We develop and analyze a novel fluid model that accounts for these connections. Simulation results suggest that the fluid-optimal policy also yields nearly optimal performance for large stochastic queueing systems with abandonment. Results: First, we derive new customer value metrics that extend the standard ones by accounting for the effects of the capacity allocation, the resulting service access qualities, and customer behavior: a customer's lifetime value; her V mu index, where V is her one-time service value and mu her service rate; and her policydependent value, which reflects the V mu indices of other served types. Second, we link these metrics to the profit-maximizing policy and to new capacity management prescriptions, notably, optimality conditions for rationing capacity and for identifying which customers to deny service. Further, unlike standard index policies, the optimal policy prioritizes customers based not on their V mu indices, but on policy-and type-dependent functions of these indices. Managerial implications: First, our study highlights the importance of basing decisions on more complete metrics that link customer value to the service access quality; marketing-focused policies that ignore these links may reduce profits significantly. Second, the proposed metrics provide guidelines for valuing customers in practice. Third, our decision guidelines help managers design more profitable policies that effectively integrate CRM and capacity management considerations.