نظارت و بهبود خدمات بانکی یونانی با استفاده از شبکه های بیزی: تجزیه و تحلیل داده ها خرید رمز و راز
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|29179||2012||9 صفحه PDF||سفارش دهید||4968 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 11, 1 September 2012, Pages 10103–10111
Mystery shopping is a well known marketing technique used by companies and marketing analysts to measure quality of service, and gather information about products and services. In this article, we analyse data from mystery shopping surveys via Bayesian Networks in order to examine and evaluate the quality of service offered by the loan departments of Greek Banks. We use mystery shopping visits to collect information about loan products and services and, by this way, evaluate the customer satisfaction and plan improvement strategies that will assist banks to reach their internal standards. Bayesian Networks not only provide a pictorial representation of the dependence structure between the characteristics of interest but also allow to evaluate, interpret and understand the effects of possible improvement strategies. Highlights ► We use Bayesian Networks (BNs) to analyse data gathered from mystery shoppers’ report. ► We evaluate the quality of service offered by the loan departments of Greek Banks. ► The key factors that influence global satisfaction are identified via BNs. ► Using BNs real time results regarding the effect improvement action are obtained.
The banking industry is a highly competitive and customer oriented organisation. Customer retention and attraction is a core element of its managing strategy; customer service is one of the factors allowing to differentiate a bank from its competitors. Roughly speaking, customer satisfaction refers to the extent to which products and services supplied by a company meet or exceed customer expectation. Customer satisfaction levels can be measured using survey techniques and evaluation questionnaires. High levels of customer satisfaction indicate a good performance of the business since satisfied customers are most likely to be loyal to the specific company and use a wide range of services. Understanding which elements influence customer satisfaction is important not only to describe the actual situation but also to plan and implement possible improvement actions. In this paper we use Bayesian Networks (BN hereafter) to analyse data gathered from mystery shoppers’ report. To our knowledge, this is the first time that these techniques are used in combination. We present a real data analysis concerning customer evaluation of service provided by the loan unit of Greek Banks. For some recent works regarding customer satisfaction analysis of Greek Banks see e.g. Mihelis et al., 2001, Grigoroudis et al., 2002, Mylonakis, 2009 and Kagara and Voyiatzis, 2010. Mystery shopping is a well known marketing technique used by companies and marketing analysts to measure quality of service, and gather specific information about products and services. Nowadays, it is one of the most used techniques for performance evaluation of banks; see e.g. Schrader, 2006, Sherman and Zhu, 2006 and Roberts and Campbell, 2007 and references therein. A BN is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a Directed Acyclic Graph (DAG). The use of a graph, as a pictorial representation of the problem at hand, simplifies model interpretation, and facilitates communication and interaction among experts with different backgrounds. For these reasons, BNs are widely applied in different fields for the analysis of multivariate data, see Neapolitan (2004). Recently BNs have been successfully applied to the analysis of customer satisfaction data, see for example Salini and Kenett, 2007 and Renzi et al., 2009. Providing a DAG representation for the problem under investigation, BNs allow to easily identify the key elements influencing customer satisfaction. Furthermore they can be used to simulate improvement strategies, getting reliable results in a straightforward manner. The paper is organised as follows. In Section 2.1 we present the mystery shopping methodology. BNs together with the procedure to construct them are illustrated in Sections 2.2 and 2.3. Section 3 is devoted to the application of BNs to service quality improvement in Greek Banks. Finally, in Section 4 we end up with some comments and final remarks.
نتیجه گیری انگلیسی
Service performance and its impact on the customer experience are key factors for bank management. Customers are free to choose between competitive alternatives, therefore companies should pay attention not only to the quality of service provided but also to its effectiveness. One method for service evaluation, that has increased in popularity in recent years, is the use of mystery shoppers. Mystery shoppers are “fake customers” used to survey and monitor the quality of the service and to identify areas requiring enhancement. After each visit they complete a report prepared in advance on their service experience, obtaining in this way a clear picture of strength and weakness of the service provided. In this paper we have proposed BNs as a novel methodology for the analysis of mystery shopping data. The use of a BN allows to combine subject-matter knowledge and data derived information. BNs provide a structure that can be used for measuring and explaining overall customer satisfaction, and statistical methods to calculate the impact of different components on the overall satisfaction. Computationally efficient algorithms for evidence propagation in BNs are available; hence various possible improvement scenarios can be easily simulated and evaluated. We have presented the results of an application of BN to mystery shopping data set regarding customer satisfaction of clients of Greek banks. In the application we have identified the key factors that influence global satisfaction of the clients, suggesting potential improvement areas for service production processes. The results of this analysis have showed that BNs are an efficient tool for service improvement analysis, considering customer perceptions. Using the information enclosed in BNs and the know-how concerning the bank organization, the managers can take decisions supported by a scientific and objective tool. To sum up, BNs not only describe the actual situation but allows to simulate, in real time, the effects of any improvement strategies.