Customer Acquisition, Retention, and Service Access Quality: Optimal Advertising, Capacity Level, and Capacity Allocation

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 Vμ index, where V is her one-time service value and μ her service rate; and her policy-dependent value, which reflects the Vμ 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 Vμ 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. The online appendix is available at https://doi.org/10.1287/msom.2017.0635 .

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