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...
An important aspect of the new orientation on Customer Relationship Marketing is the use of customer transaction databases for the cross-selling of new services and products. In this study, we propose a mixed data factor analyzer that combines information from a survey with data from the customer database on service usage and transaction volume, to make probabilistic predictions of ownership of services with the service provider and with competitors. This data-augmentation tool is more flexible in dealing with the type of data that are usually present in transaction databases. We test the proposed model using survey and transaction data from a large commercial bank. We assume four different types of distributions for the data: Bernoulli for binary service usage items, rank-order binomial for satisfaction rankings, Poisson for service usage frequency, and normal for transaction volumes. We estimate the model using simulated likelihood.The graphical representation of the weights produced by the model provides managers with the opportunity to quickly identify cross-selling opportunities. We exemplify this and show the predictive validity of the model on a hold-out sample of customers, where survey data on service usage with competitors is lacking. We use Gini concentration coefficients to summarize power curves of prediction, which reveals that our model outperforms a competing latent trait model on the majority of service predictions.
Purpose – The purpose of this study is to test the effects of satisfaction, satisfaction with service recovery (SSR) and switching costs (SC) on loyalty and positive word-of-mouth (PWOM) of bank customers in a service recovery context, taking into account the interaction among latent variables and the effects of contextual factors. Design/methodology/approach – A theoretical model is proposed based on previous studies and tested using structural equation modeling technique and bootstrapping estimates. A survey was conducted with 1,878 bank customers of a large Brazilian bank. Findings – Results supported the positive effects of satisfaction and SC on loyalty, while PWOM was influenced mainly by SAT. In addition, SC significantly interacted with satisfaction, reducing the effects of satisfaction on loyalty. Finally, relationship time, gender and age were the most relevant contextual factors. Practical implications – This study highlights the importance of switching costs in the banking industry. Although satisfaction is a relevant predictor of loyalty, this influence is contingent on the customer's SC. Hence, investment on marketing strategies and campaigns should be oriented to better convert switching perceptions into effective loyalty. Originality/value – Despite recent investigations on the roles of SC in customer loyalty, results have indicated mixed findings and most of the studies do not consider interactions between latent constructs. This study addresses this issue using the orthogonalization procedure.
PurposeThe purpose of this paper is to develop and empirically test the antecedent, mediating and moderating role of switching costs on the relationship between satisfaction and loyalty.Design/methodology/approachCompeting models are proposed based on previous studies investigating the influence of switching costs on satisfaction and loyalty. A survey was conducted with 7,461 customers of a large Brazilian bank. The four competing models were tested using structural equation modeling technique.FindingsThe analysis revealed that: switching cost is a significant antecedent of both attitudinal and behavioral loyalty; the mediating effect of switching cost is stronger in the relationship between satisfaction and attitudinal loyalty; and the moderating effect of switching cost is stronger in the relationship between satisfaction and behavioral loyalty.Practical implicationsThis study emphasizes the relevance of the switching cost construct in the banking industry. Customers with different switching costs levels will manifest distinct relationship between satisfaction and behavioral loyalty. Thus, investment on marketing strategies and campaigns should be oriented to better convert switching perceptions into effective loyalty considering its mediating or moderating effects.Originality/valueEven though there are several different approaches (i.e. direct, mediator and moderator) concerning the effects of switching costs on the satisfaction‐loyalty relationship, there is a lack of integration between these approaches. The paper tests and compares the different roles of switching costs. Another contribution is the inclusion of both attitudinal and behavioral aspects of loyalty, given that the current literature is incipient concerning the role of switching cost when considering the distinct loyalty components.
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