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JANUARY-DECEMBER 2013 - Volume: 1 - Pages: [13 p.]
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ABSTRACT: Customer Relationship Management (CRM) is a key strategy for the survival of industrial firms, allowing them to face challenges as increasing competition and demand heterogeneity. But CRM does not always generate positive results, and could even deteriorate customer relationships and firm’s profitability. Reasons of CRM failure include the lack of an integral framework that allow measuring the benefits generated by multiple CRM dimensions, in order to take appropriate strategic decisions. This paper attempts to provide a multidimensional measurement model to analyse the level of development and success of CRM, based on five groups of benefit drivers: CRM organization (managerial level), CRM organization (human resources), key customer focus, technology-based CRM, and knowledge management. These five dimensions were validated on a sample of 110 data from an on-line survey of Spanish industrial firms. Exploratory factor analysis and cluster analysis were used to identify four groups with different CRM characteristics; in any case, low values for organisational values were obtained. Then, causal relationships between CRM dimensions and CRM benefits were analysed, including value-equity performance indicators (product’s relative quality, service and price), and financial performance indicators (earnings, profitability). Results confirmed a positive relationship between the development of CRM dimensions and performance. In particular, it suggested a key role of organizational factors on CRM financial success, with non-financial factors being a necessary but no sufficient condition for firm’s profitability. Besides, the proposed measurement scale could be suitable as a CRM diagnostic tool to easily compute CRM dimensions and benefits. Finally, best practices and clusters identification allows to estimate each firm’s particular competitive position. Keywords: customer relationship management (CRM), benefit drivers, value equity, profit, industrial firms, survey methodology, exploratory factor analysis, cluster analysis
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© Dyna Management journal 2013
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