Retail data mining can help identify customer-buying behaviors, discover customer-shopping patterns and trends, improve the quality of customer services, achieve better customer retention and satisfaction, enhance goods consumption, design more effective goods transportation and distribution policies, and, in general, reduce the cost of business and increase profitability. In the forefront of applications that have been adopted by the retail industry are direct-marketing applications. The direct-mailing industry is an area where data mining is widely used. Almost every type of retailer uses direct marketing, including catalogers, consumer retail chains, grocers, publishers, B2B marketers, and packaged goods manufacturers. The claim could be made that every Fortune 500 company has used some level of data mining in their direct-marketing campaigns. Large retail chains and groceries stores use vast amounts of sale data that are “information-rich.” Direct marketers are mainly concerned about customer segmentation, which is a clustering or classification problem.
Retailers are interested in creating data-mining models to answer questions such as:
What are the best types of advertisements to reach certain segments of customers?
What is the optimal timing at which to send mailers?
What is the latest product trend?
What types of products can be sold together?
How does one retain profitable customers?
What are the significant customer segments that buy products?
Data mining helps to model and identify the traits of profitable customers, and it also helps to reveal the “hidden relationship” in data that standard-query processes have not found. IBM has used data mining for several retailers to analyze shopping patterns within stores based on point-of-sale (POS) information. For example, one retail company with $2 billion in revenue, 300,000 UPC codes, and 129 stores in 15 states found some interesting results: “… we found that people who were coming into the shop gravitated to the left-hand side of the store for promotional items, and they were not necessarily shopping the whole store.” Such information is used to change promotional activities and provide a better understanding of how to lay out a store in order to optimize sales. Additional real-world examples of data-mining systems in retail industry follow.
Safeway, UK
Grocery chains have been another big user of data-mining technology. Safeway is one such grocery chain with more than $10 billion in sales. It uses Intelligent Miner from IBM to continually extract business knowledge from its product-transaction data. For example, the data-mining system found that the top-spending 25% customers very often purchased a particular cheese product ranked below 200 in sales. Normally, without the data-mining results, the product would have been discontinued. But the extracted rule showed that discontinuation would disappoint the best customers, and Safeway continues to order this cheese, although it is ranked low in sales. Thanks to data mining, Safeway is also able to generate customized mailing to its customers by applying the sequence-discovery function of Intelligent Miner, allowing the company to maintain its competitive edge.
RS Components, UK
RS Components, a UK-based distributor of technical products such as electronic and electrical components and instrumentation, has used the IBM Intelligent Miner to develop a system to do cross selling (suggested related products on the phone when customers ask for one set of products), and in warehouse product allocation. The company had one warehouse in Corby before 1995 and decided to open another in the Midlands to expand its business. The problem was how to split the products into these two warehouses so that the number of partial orders and split shipments could be minimized. Remarkably, the percentage of split orders is just about 6% after using the patterns found by the system, much better than expected.
Kroger Co. (USA)
The Kroger is the largest grocery store chain in the United States. Forty percent of all U.S households have one of Kroger’s loyalty cards. The Kroger is trying to drive loyalty for life with their customers. In particular, their customers are rewarded with offers on what they buy instead of trying to be sold something else. In other words, each of them could receive coupons different from each other, not the same coupons. In order to match the best customers with the right coupons, the Kroger analyses customers’ behavior using the data-mining techniques. For instance, one recent mailing was customized to 95% of the intended recipients. Such business strategy for looking at customers to win customers for life makes the Kroger beat their largest competitor, Walmart, for the last 6 years largely. [http://www.kypost.com/dpp/news/region_central_cincinnati/downtown/data-mining-is-big-business-for-kroger-%26-getting-bigger-all-the-time]
Korea Customs Service (South Korea)
The Korea Customs Service (KCS) is a government agency established to secure national revenues by controlling imports and exports for the economic development of South Korea and to protect domestic industry through contraband control. It is responsible for the customs clearance of imported goods as well as tax collection at the customs border. For detecting illegal cargo, they implemented a system using SAS for fraud detection, based on its widespread use and trustworthy reputation in the data-mining field. This system enabled more specific and accurate sorting of illegal cargo. For instance, the number of potentially illegal factors increased from 77 to 163. As a result, the detection rate for important items, as well as the total rate, increased by more than 20% [http://www.sas.com/success/kcs.html].
B.4 DATA MINING IN HEALTH CARE AND BIOMEDICAL RESEARCH
With the amount of information and issues in the health-care industry, not to mention the pharmaceutical industry and biomedical research, opportunities for data-mining applications are extremely widespread, and benefits from the results are enormous. Storing patients’ records in electronic format and the development in medical-information systems cause a large amount of clinical data to be available online. Regularities, trends, and surprising events extracted from these data by data-mining methods are important in assisting clinicians to make informed decisions, thereby improving health services.
Clinicians evaluate a patient’s condition over time. The analysis of large
