Essay On Data Mining

Data mining software Data mining has the potential to give businesses a competitive edge in Customer Relationship Management. Organizations use methods such as complex algorithms, artificial intelligence, and statistics to mine meaningful patterns from large sets of data. These patterns can then be utilized to do a number of things including targeting customers by predicting future behavior and learning more about present behavior. One widely accepted model is the Cross-Industry Standard Process for Data Mining (CRISP-DM) which has six phases: business understanding, data understanding, data preparation, model building, testing and evaluation, and deployment. These six phases are shown in this diagram that was included in the Data Mining

As new data is discovered or changes happen over time companies may need to re-do their models to ensure that their original findings are still relevant (Olsen & Delen, 2008). Without data mining technology companies find little value in raw data gathered from customers despite investing in databases and processors to house it. Accumulated data can reflect transactions, customer contacts, descriptions, how they have reacted to past marketing, and more (Gupta & Aggarwal, 2012). Doug Alexander (n.d.), a Professor at the University of Texas, calls this being “data rich, information poor”. Data mining software can convert this otherwise useless data into valuable information that benefits the entire company. As stated by the researchers Gupta and Aggarwal (2012), “The more effectively you can use the information about your customers to meet their needs the more profitable...successful business requires a marketing manager that understands its customers and their requirements and implements data mining”. They explain how data mining makes CRM more potent when used correctly by opening the door for higher quality and more interactive relationships with a staggering amount of customers that wouldn 't be possible

According the writers of the book “Building Data Mining Applications for CRM” modern store owners have to deal with “more customers, more products, more competitors, and less time to react.” In a time when understanding customers is more critical to success it is becoming hard to do because of the above reasons (Berson, Alex, & Thearling, 2000). can help by profiling customers based on features like geographic location, culture and ethnicity, economic conditions, age, gender, values, beliefs, life cycle, customer knowledge of product, lifestyle, and recruitment method (Jansen, 2007). From this data mining can highlight which leads to follow, help retain profitable customers, decide which product to offer to who, reduce costs, and more. Data mining gives CRM the information that it needs to function effectively. This was the case with Best Buy who implemented a strategy to identify the 'angels ' and 'devils ' in their consumer base, the most profitable and least profitable. Gary McWilliams (2004), from The Wall Street Journal, discusses how the electronics company managed to increase profits by catering to angels and culling devils who couldn 't be