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Data Mining

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Data Mining
Data Mining
DeMarcus Montgomery
Dr. Janet Durgin
CIS 500
June 9, 2013

Determine the benefits of data mining to the businesses when employing
1. Predictive analytics to understand the behavior of customers
Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model, which has, in turn been trained over your data, learning from the experience of your organization. Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer 's predictive score informs actions to be taken with that customer.
1. Associations discovery in products sold to customers
The way in which companies interact with their customers has changed dramatically over the past few years. A customer 's continuing business is no longer guaranteed. As a result, companies have found that they need to understand their customers better, and to quickly respond to their wants and needs. In addition, the time frame in which these responses need to be made has been shrinking. It is no longer possible to wait until the signs of customer dissatisfaction are obvious before action must be taken. To succeed, companies must be proactive and anticipate what a customer desires. For an example in the old days, the storekeepers would simply keep track of all of their customers in their heads, and would know what to do when a customer walked into the store. Today’ store associates face a much more complex situation, more customers, more products, more competitors, and less time to react means that understanding your customers is now much harder to do. A number of forces are working together to increase the complexity of customer relationships, such as compressed marketing cycles, increased marketing costs, and a stream of new product offers. There are many kinds of



References: Alexander, D. (2012). Data Mining. Retrieved from: http://www.laits.utexas.edu/~anorman/BUS.FOR/course.mat/Alex/#8 Josh, K. (2012). Analysis of Data Mining Algorithms. Retrieved from: http://www-users.cs.umn.edu/~desikan/research/dataminingoverview.html Exforsys. (2006). Execution for System: Connection between Data Mining and Customer Interaction. Retrieved from: http://www.exforsys.com/tutorials/data-mining/the-connection-between-data-mining-and-customer-interaction.html Frand, J. (1996). Data Mining: What is Data Mining? Retrieved from: http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/index.htm Pupo, E. (2010). HIMSS News: Privacy and Security Concerns in Data Mining. Retrieved from: http://www.himss.org/ASP/ContentRedirector.asp?type=HIMSSNewsItem&ContentId=73526 Stein, J. (2011). Data Mining: How Companies Now Know Everything About You. Retrieved from: http://www.time.com/time/magazine/article/0,9171,2058205,00.html#ixzz25MwYNhuh

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