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Purpose -Customer relationship management (CRM) projects have a low success rate, which can be solved by better measurement of CRM process. The purpose of this paper is to define the CRM processes within a company and propose a tool for CRM measurement. Design/methodology/approach -An empirical study is conducted in industrial organizations in Turkey. The research is designed according to scale development literature. The responses from various industries (manufacturing, information technologies, tourism, service, retail, finance, logistics) were collected. Using exploratory factor analysis, 167 valid responses are analyzed. Findings -The paper provides a general understanding of CRM processes in customer-oriented perspective and proposes a measurement tool that addresses seven main processes which are: targeting management, customer information management, production/service customization, expansion management, referrals management, termination management and win back. Practical implications -The results of the analysis provides useful information for managers to define, measure and improve CRM process. Originality/value -The paper provides an enhanced review about CRM measurement literature. Based on this review, the paper defines seven CRM processes and maintains a tool for evaluation of these processes within a company.

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The parametric tests often require that the population distributions be normal or approximately so. Statistical methods that do not require the knowledge of the population distribution or its parameters are called nonparametric tests. In this article, first we review some industrial applications of fuzzy parametric tests. Then we present some new algorithms for fuzzy nonparametric tests, namely a fuzzy sign test and a fuzzy Wilcoxon signed-ranks test. Later, we further give fuzzy parametric tests, fuzzy nonparametric tests, and their numerical applications, and also provide a comparison study on crisp and fuzzy nonparametric tests. When the data are vague, the result of the fuzzy nonparametric tests may be different from that of the crisp nonparametric tests.