Data mining is a concept that companies use to gain new customers or clients in an effort to make their business and profits grow. The ability to use data mining can result in the accrual of new customers by taking the new information and advertising to customers who are either not currently utilizing the business's product or also in winning additional customers that may be purchasing from the competitor. Generally, data are any “facts, numbers, or text that can be processed by a computer.” Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes operational or transactional data such as, sales, cost, inventory, payroll, and accounting. Data mining also known as “knowledge discovery”, is the process of analyzing data from different perspectives and summarizing it into useful information- information that can then be used to increase revenue, cuts costs, and continue the goals outlined for the company. Data mining consists of five major elements: “Extract, transform, and load transaction data onto the data warehouse system, store and manage the data in a multidimensional database system, provide data access to business analysts and information technology professionals, analyze the data by application software, present the data in a useful format, such as a graph or table.”2 Extracting this information for future use will keep the company growing and adapting as the customer preference changes. Tools and Benefits of data mining
Before examining the benefits of data mining, it is important to understand what data mining is exactly. Data mining is defined as “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 & Volonino, 2011). The information identified using data mining includes patterns indicating trends, correlations, rules, similarities, and used as predictive analytics. By employing predictive analytics, companies are actually able to understand the behavior of customers. Predictive analytics examines and sorts data to find patterns that highlight customer behavior. The important behavioral patterns are those that indicate what customers have responded to and will respond to in the future. Also, patterns can indicate a customer base that is in jeopardy with the company, customers that are not company-loyal and are easily lost. Predictive analytics of customer behavior can be of great benefit to the business (Turban & Volonino, 2011). Companies are able to build specific marking campaigns and models such as direct mail, online marking, or media marking based on customer preference and are better able to sell their products to a more targeted customer base. Knowing what the customer wants, what they will respond to, and which customer base to focus on takes the guesswork out of marking and product development. Taking the information retrieved and using it correctly will only increase profits (Advantages, 2012). Association discovery using data mining provides a huge benefit to companies. Association discovery is finding correlations or relationships between variables in a large data base. For example, in terms of a supermarket, it is finding out that customers who buy onions and potatoes together are also highly likely to buy hamburger meat. These correlations where one set of products predict the buying of another is referred to as associations. Data mining can employ association discovery allowing business to predict buying patterns and allow for more effective operations management and can better pinpoint marketing strategy of coupons and incentives (Association Rule 2012). Web mining is another aspect of data mining. Web mining uses the data collected on the internet to analyze customer data and gather information beneficial to the company. Any time someone visits a...
References: Advantages and disadvantages of data mining (2012). Retrieved May 30, 2012, from
13:46, May 30, 2012, from http://en.wikipedia.org/wiki/Association_rule_learning
Data mining: issues
Turban, E., & Volonino, L. (2011). Information technology for management improving strategic
and operational performance (8th ed.)
Please join StudyMode to read the full document