"Data mining enables marketers to better extract valuable business information from the “mountains of data” in a firm’s systems. It is a potential solution to a big problem facing many companies: an overabundance of data and a relative dearth of staff, technology, and time to transform numbers and notes into meaningful information about existing and prospective customers" (Thelen, S. Mottner, S. Berman, B., 2004).
The article was mainly about the importance of data mining for large businesses with huge amounts of in effectively used data. Businesses are sitting on very valuable information that could help increase their bottom line. Data mining streamlines the process of taking data and turning it into results. She compared traditional analysis methods vs data mining. "Two data mining techniques, neural networks and decision trees, can each handle up to 200 predictor variables. In contrast, a traditional technique such as multiple regression cannot cope with this level of complexity" (Thelen, S. Mottner, S. Berman, B., 2004). Data mining allows for the business to test multiple predictors and make the analysis very customized for the industry. The current methods of testing are rigid and generic. She concedes that data mining programs are expensive, but notes the costs are being reduced. "The costs of preparation and data transfer for a data mining system have also been drastically reduced" (Thelen, S. Mottner, S. Berman, B., 2004) Data mining successfully tracks cross channel factors and analyzes the correlations. Data mining can enhance the CRM of companies. “Linoff (1999) calls it “the intelligence behind CRM,” which addresses the downstream activities of marketing, sales, and customer service along the value chain in addition to operational technologies and analytical techniques” (Thelen, S. Mottner, S. Berman, B., 2004). She noted five points of establishing and maintain a data mining system:
1. Identify