The service-profit chain (SPC) is a framework for linking service operations, employee assessments, and customer assessments to a firm's profitability (Heskett et al. 1994). The SPC provides an integrative framework for understanding how a firm's operational investments into service operations are related to customer perceptions and behaviors, and how these translate into profits. For a firm, it provides much needed guidance about the complex interrelationships among operational investments, customer perceptions, and the bottom line. Implementing the SPC is a pervasive problem among most service firms, and several attempts have been made to model various aspects of the SPC. However, comprehensive approaches to model the SPC are lacking, as most studies have only focused on discrete aspects of the SPC. There is a need for approaches that combine data such as measures of operational inputs, customer perceptions and behaviors, and financial outcomes from multiple sources, providing the firm with not only comprehensive diagnosis and assessment but also with implementation guidelines. Importantly, an approach that is sensitive to and can accommodate the strengths and weaknesses of such data sets is required. We outline and illustrate such an approach in this paper. Our approach has the potential to both identify and quantify the benefits of implementing a service strategy, especially for firms having multiple units (e.g., banks with branches, retail outlets, and so forth). The implementation approach is illustrated using data from a national bank in Brazil. We used customer surveys from more than 500 branches of the bank. Each individual customer's marketing survey data was linked to a number of operational metrics. First, behavioral measures of retention, such as the length of the customer's relation with the bank, the deposit amount, and number of transactions with the bank, were obtained and merged with the survey data. Second, the main branch used by each customer was identified and operational inputs (e.g., number of employees, number of available automated teller machines (ATMs)) used at that branch were obtained and merged with the data set. This data set was used to model the SPC at a strategic and operational level. The strategic analysis consisted of a structural-equation model that identified the critical conceptual relationships that parsimoniously articulate the SPC for this bank. For instance, from among a variety of attribute-level perceptions, the bank was able to identify those perceptions that were critical determinants of behavioral intentions. Similarly, from a variety of available behavioral metrics, the bank was able to identify those behaviors most relevant to profitability. The operational analysis utilized Data Envelopment Analysis (DEA) and provides customized feedback to each branch in implementing the strategic model. It provides each branch with a metric of its relative efficiency in translating inputs such as employees and ATMs into relevant strategic outcomes such as customer intentions and behaviors. Our illustration shows how top management can use the strategic and operational analysis in tandem. Whereas the strategic model provides the key relationships and metrics that are needed to ensure that all subunits of the firm follow a consistent strategy, the operational analysis enables each branch to benchmark its unique position so that the branch can implement the strategic model in the most efficient way. Thus, simultaneously implementing the strategic and operational model enables a firm to have a centralized focus with decentralized implementation. For this bank, the operational analysis shows that for a branch to achieve superior profitability, it is important that the branch manager not only be efficient in achieving superior satisfaction (as indicated in positive behavioral intentions) but also be efficient in translating such attitudes and intentions into relevant behaviors. In other words, superior satisfaction alone is not an unconditional guarantee of profitability.
