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Crm dm

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Unformatted text preview: Building Profitable Customer Relationships With Data Mining Herb Edelstein, President Two Crows Corporation You’ve built your customer information and marketing data warehouse – now how do you make good use of the data it contains? Customer relationship management (CRM) helps companies improve the profitability of their interactions with customers while at the same time making the interactions appear friendlier through individualization. To succeed with CRM, companies need to match products and campaigns to prospects and customers — in other words, to intelligently manage the customer life cycle. Until recently most CRM software has focused on simplifying the organization and management of customer information. Such software, called operational CRM, has focused on creating a customer database that presents a consistent picture of the customer’s relationship with the company, and providing that information in specific applications such as sales force automation and customer service in which the company “touches” the customer. However, the sheer volume of customer information and increasingly complex interactions with customers have propelled data mining to the forefront of making your customer relationships profitable. Data mining is a process that uses a variety of data analysis and modeling techniques to discover patterns and relationships in data that may be used to make accurate predictions. It can help you select the right prospects on whom to focus, offer the right additional products to your existing customers, and identify good customers who may be about to leave you. The result is improved revenue because of a greatly improved ability to respond to each individual contact in the best way, and reduced costs due to properly allocating your resources. CRM applications that use data mining are called analytic CRM. This white paper will describe the various aspects of analytic CRM and show how it is used to manage the customer life cycle more cost-effectively. Note that the case histories of these fictional companies are composites of real-life data mining applications. Data mining The first and simplest analytical step in data mining is to describe the data — for example, summarize its statistical attributes (such as means and standard deviations), visually review it using charts and graphs, and look at the distribution of values of the fields in your data. But data description alone cannot provide an action plan. You must build a predictive model based on patterns determined from known results, then test that model on results outside the original sample. A good model should never be confused with reality (you know a road map isn’t a perfect representation of the actual road), but it can be a useful guide to understanding your business. Data mining can be used for both classification and regression problems. In classification problems you’re predicting what category something will fall into – for example, whether a person will be a good credit risk or not, or which of several offers som...
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