Assignment : Data Mining Student : Mohamed Kamara Professor : Dr. Albert Chima Dominic Course : CIS 500- Information Systems for Decision Making Data : 06/11/2014
This report is an analysis of the benefits of data mining to business practices. It also assesses the reliability of data mining algorithms and with examples. “Data Mining is a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases, including data warehouses” (Turban,2011). The first benefit of data mining is that it employs predictive analysis to understand the behavior of consumers. Predictive analytics serves as a benefit of data mining because it is a process that uses machine learning to analyze data and make predictions.
This can be beneficial to a business because it can be helpful in understanding the behavior of customers. A good example of this would be a business using predictive analytics to decide what level of pricing should be used in correlation with sales information. A business could look at historical data for products, sales, and customers to determine the price for a given product and customer at the right time. Amazon is a heavy user of predictive pricing (Mehra, 2013). This technique is also used in Supply Chain Management because it helps you to understand consumer demand to manage the overall process. This includes delivery, returns, forecasting, sourcing, planning, and order fulfillment. The advantage is, if a retailer can predict revenue from a specific product in a reasonable amount of time, it will result in better inventory management, use of space, cash flow, and the elimination of out of stock items. A second benefit of data mining is association discovery in products sold to consumers. Association discovery in products sold to customers is used to determine if a pattern is discovered based on a relationship between items in the same transaction. It has also been defined as a market basket analysis to identify products that consumers are likely to purchase together. Retailers use historical data to research customers buying habits in the hope of finding correlation data. Examples of this would be if a customer buys mouth wash and tooth paste at the same time or beer and potato chips. The retailer would place these items together in order to save time for the customer and equally increase sales. A third benefit of data mining is its employment of web mining to discover business intelligence from Web customers. Web mining to discover business intelligence from Web customers is used in a variety of ways because this technique is designed to discover patterns from the web. One of the most popular ways is to determine the search patterns for a particular group of people from a particular region. Other means include visiting e-commerce websites to determine what the best and worst sellers are. Additionally popular sites can also be identified by determining the number of links that refer to the site. Advantages of using techniques like this for businesses are increased sales because you have the ability to track a web users browsing behavior down to the mouse clicks. The applications of web mining enable a business to personalize services for individual customers on a massive scale. This helps businesses by satisfying customer needs and increasing brand loyalty. By using a personalized and customer oriented approach, the content of a website can be updated and adapted to a customer’s preference. Efforts like this ensure the right offers can be made to the right customers. The fourth benefit of data...
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Harris, D. R. (2010, April 1). Why Predictive Analytics Is A Game-Changer. Retrieved August 30, 2013, from http://www.forbes.com/2010/04/01/analytics-best-buy-technology-data-companies-10-accenture.html.
Mehra, G. (2013, July 30). 6 Benefits of Predictive Analytics for online Retailers. Retrieved August 30, 2013, from http://www.practicalecommerce.com/articles/4122-6-Benefits-of-Predictive-Analytics-for-Online Retailers.
Shields. (n.d). Getting to Know Your Customers by Clustering on Product Purchase. Retrieved August 30, 2013, from http://www.nesug.org/proceedings/nesug06/po/po20.pdf.
Turban, E. &. (2011). Information Technology for Management. Hoboken, NJ:: John Wiley & Sons.
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