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A Framework for Customer Relationship Management and Data Mining - Literature review Example

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As the paper "A Framework for Customer Relationship Management and Data Mining" outlines, in looking into the contribution of data mining to customer relationship management (CRM), the starting point of the evaluation centered upon understanding the two terms as a basis for making an assessment…
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Download file to see previous pages Customer Relationship Management is, in essence, a data capture tool that in and of itself has no value unless an organization has taken the time, effort and analysis to understand what it can do concerning increasing its completeness with respect to data collection areas (Berson and Smith, 2002). Company management, along with its marketing, sales, forecasting and other related divisions need to understand the data needed to be gathered. This includes the collection points for such information as a means to make the Customer Relationship Management database as complete and relevant as possible (Berson and Smith, 2002). Winer (2001) concurs with this view and states in order to reap benefits, all points of customer contact need to gather information and correlate it back to the individual database records to build on its value content as well as worth for future use. He adds that correlation methods can use different measures to tie data together as represented by telephone, credit cards, addresses, zip codes, and other data sets (Winer, 2001). Without the needed, relevant and encompassing database collection and correlation measures, no amount of data mining will be useful in yielding effective, timely or useful information as the foundation would not be there. Data mining is the means by which information, discoveries, and useful data are pulled from a Customer Relationship Management database (Cabena et al, 2007). Rygielski et al (2002) make the assertion that CRM and data mining, in terms of their use as predictive sources concerning customer behavior, is limited by its lack of widespread usage. There is difficulty in agreeing with this view as the usefulness of an application, technique, statistical tool or data method is not dependent on wide-scale usage. Instead, it relies on the comprehensiveness of its users in understanding the data needed to be collected and how to access (mine) it (Xu et al, 2002). This view is further supported by (Ngai et al, 2009) who state data mining is a set of processes as well as enabling systems geared to support the business strategy of an enterprise. They add it is constructed with the understanding it is long term and entails building a profitable set of relationships with customer as well as the business (Ngai et al, 2009). As the above reveals, the effectiveness and efficiency of data mining have to do with the thought, planning, and extent of the database it has access to (Rygielski et al, 2002). In addition, it is pointed out the skill set of the people doing the analysis, along with their familiarity with varied statistical tools, systems, and other disciplines are highly important in extracting data that has value and usefulness (Rygielski et al, 2002). The end of the prior section left off with the importance and significance of the skill set of the individuals conducting the data mining process as being key to obtaining valuable and useful information. An example of this is found in the four dimensions of CRM as represented by customer identification, attraction, retention, and development (Ngai et al, 2009). These are made more meaningful under data mining techniques that exploit the value of the database via association, classification, clustering, forecasting, regressions, sequence discovery and visualization (Ngai et al, 2009).  ...Download file to see next pagesRead More
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