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 A Primer on Data Mining  

In a world that is exploding with data, converting data into customer intelligence is key to survival. In such an environment, data mining has been proven to be a critical tool for marketing success.

Data mining is about streamlining the transformation of masses of information into meaningful business knowledge. It involves the process of selecting, exploring, and modeling large amounts of information to reveal previously unknown patterns for decision support and business advantage. It is a process that helps identify new opportunities by finding fundamental truths in apparently random data. The patterns revealed can shed light on business problems and assist in more profitable, proactive decision making. The insights gained can enable companies to perform "what if" scenarios and develop testing for new products, processes and services.

The goal of data mining is to produce new knowledge that decision-makers can act upon. Data mining of customer information is required in order to make decisions about which clients are the most profitable and desirable and what their characteristics are in order to find more customers just like them. Determining the causes and effects of brand switching is often a focus of data mining and, specifically, database marketing. The ability to predict which customers will be likely to switch products - and head them off at the pass with new offers - provides a definite edge. Data mining solutions come in many types, such as association, segmentation, clustering, classification (prediction), visualization, and optimization.

In practice, data mining has been around for quite a while. Today the majority of Fortune 500 companies view data mining as a critical factor for business success, and most of these companies are already collecting and refining massive quantities of information in data warehouses.

Data mining applications reach across industries and business functions. Telecommunications, stock exchanges, credit card and insurance companies use data mining to detect fraudulent use of their services; the medical industry uses data mining to predict the effectiveness of surgical procedures, medical tests, and medications; and retailers use data mining to assess the effectiveness of coupons and special events. For companies who use data mining effectively, the payoffs can be huge. By applying data mining techniques, companies can fully exploit data about customers' buying patterns and behavior, and gain a greater understanding of customer motivations to help reduce fraud, anticipate resource demand, increase acquisition, and curb customer attrition.

Of all the potential uses for data mining, perhaps the most popular are the areas of database marketing and customer relationship management. Marketers can identify candidates for targeted marketing campaigns and determine why customers leave for competitors. Both of these strategies help reduce costs by eliminating mass promotions and increase profits by helping retain customers.                                                                                                                                     
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