for helpful comments on this article. This manuscript was processed and accepted during the tenure of the previous editor, Russell S. Winer. VIKAS MITTAL and WAGNER A. KAMAKURA*Despite the claim that satisfaction ratings are linked to repurchase behavior, few attempts can be found that relate satisfaction ratings to actual repurchase behavior. This article fills this void by presenting a conceptual model for relating satisfaction ratings and repurchase behavior. The model is based on the premise that ratings observed in a typical customer satisfaction survey are error-prone measures of the customer's true satisfaction, and they may vary systematically on the basis of consumer characteristics. The authors apply the model to a large-scale study of 100,040 automotive customers. Results show that consumers with different characteristics have different thresholds such that, at the same level of rated satisfaction, repurchase rates are systematically different among different customer groups. The authors also find that the nature and extent of response bias in satisfaction ratings varies by customer characteristics. In one group, the response bias is so high that rated satisfaction is completely uncorrelated to repurchase behavior (r = 0). Furthermore, the authors find that, though nonlinear, the functional form relating rated satisfaction to repurchase intent is different from the one relating it to repurchase behavior. Although the functional form exhibits decreasing returns in the case of repurchase intent, it exhibits monotonically increasing returns in the case of repurchase behavior.

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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 and level. The 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 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 managem...

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