Managing Retention in Service Relationships

Aflaki, Sam; Popescu, Ioana

doi:10.2139/ssrn.1603964

Cited by 22 publications

(22 citation statements)

References 11 publications

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“…Papers that focus on explaining or predicting customer retention and churn include Verhoef (2003), Braun and Schweidel (2011), and Ascarza and Hardie (2013). Studies that link service quality, customer satisfaction, retention, and other CLV components include Anderson and Sullivan (1993), Rust et al (1995), Zeithaml et al (1996), Bolton (1998), Ho et al (2006) and Aflaki and Popescu (2014).…”

Section: Crmmentioning

confidence: 99%

“…Compared to the CRM literature, the key distinction of our model is that we explicitly link customer acquisition and retention to the capacity-allocation-dependent service quality. Studies that optimize some notion of service quality (Ho et al 2006 andAflaki andPopescu 2014) ignore capacity constraints (and customer acquisition). The papers that consider a capacity constraint Ovchinnikov 2011 andOvchinnikov et al 2014) study a firm's optimal spending on customer acquisition and retention of two base customer types ("low" and "high") that do not switch.…”

Section: Crmmentioning

confidence: 99%

See 1 more Smart Citation

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

Afèche¹,

Araghi²,

Baron³

2017

M&SOM

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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 policydependent 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.

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Section: Crmmentioning

confidence: 99%

Section: Crmmentioning

confidence: 99%

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

Afèche¹,

Araghi²,

Baron³

2017

M&SOM

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“…In particular, Nasiry and Popescu (2011) study pricing and Dixon and Verma (2013), Dixon and Thompson (2013a,b) study event scheduling decisions, and Verhoef et al (2004) study call center satisfaction in the light of peak-end rule. Aflaki and Popescu (2013), Popescu and Wu (2007), and…”

Section: Service Operationsmentioning

confidence: 99%

The Design of Experiential Services with Acclimation and Memory Decay: Optimal Sequence and Duration

2016

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For many consumer-intensive (B2C) services, delivering memorable customer experiences is a source of competitive advantage. Yet, there are few guidelines available for designing service encounters with a focus on customer satisfaction. In this paper, we show how experiential services should be sequenced and timed to maximize satisfaction of customers who are subject to memory decay and acclimation. We find that memory decay favors positioning the highest service level near the end, whereas acclimation favors maximizing the gradient of service level. Together, they maximize the gradient of service level near the end. Although memory decay and acclimation lead to the same design individually, they can act as opposing forces when considered jointly. Overall, our analysis suggests that short experiences should have activities scheduled as a crescendo and duration allocated primarily to the activities with the highest service levels, whereas long experiences should have activities scheduled in a U-shaped fashion and duration allocated primarily to activities with the lowest service level so as to ensure a steep gradient at the end.

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“…Generally, the assumption that the customers' demand behavior depends on past service quality is also considered in the field of marketing and inventory control. Aflaki and Popescu [22] maximize the CLV by varying the level of service quality that influences customers' decisions to renew their contract. Gaur and Park [23] and Liu et al [24] make similar assumptions concerning the customers' reactions toward a company's service quality in a competitive environment.…”

Section: Related Literaturementioning

confidence: 99%