Data Mining_ Concepts and Techniques - Jiawei Han [372]
13.4. Data Mining and Society
For most of us, data mining is part of our daily lives, although we may often be unaware of its presence. Section 13.4.1 looks at several examples of “ubiquitous and invisible” data mining, affecting everyday things from the products stocked at our local supermarket, to the ads we see while surfing the Internet, to crime prevention. Data mining can offer the individual many benefits by improving customer service and satisfaction as well as lifestyle, in general. However, it also has serious implications regarding one's right to privacy and data security. These issues are the topic of Section 13.4.2.
13.4.1. Ubiquitous and Invisible Data Mining
Data mining is present in many aspects of our daily lives, whether we realize it or not. It affects how we shop, work, and search for information, and can even influence our leisure time, health, and well-being. In this section, we look at examples of such ubiquitous (or ever-present) data mining. Several of these examples also represent invisible data mining, in which “smart” software, such as search engines, customer-adaptive web services (e.g., using recommender algorithms), “intelligent” database systems, email managers, ticket masters, and so on, incorporates data mining into its functional components, often unbeknownst to the user.
From grocery stores that print personalized coupons on customer receipts to online stores that recommend additional items based on customer interests, data mining has innovatively influenced what we buy, the way we shop, and our experience while shopping. One example is Wal-Mart, which has hundreds of millions of customers visiting its tens of thousands of stores every week. Wal-Mart allows suppliers to access data on their products and perform analyses using data mining software. This allows suppliers to identify customer buying patterns at different stores, control inventory and product placement, and identify new merchandizing opportunities. All of these affect which items (and how many) end up on the stores' shelves—something to think about the next time you wander through the aisles at Wal-Mart.
Data mining has shaped the online shopping experience. Many shoppers routinely turn to online stores to purchase books, music, movies, and toys. Recommender systems, discussed in Section 13.3.5, offer personalized product recommendations based on the opinions of other customers. Amazon.com was at the forefront of using such a personalized, data mining–based approach as a marketing strategy. It has observed that in traditional brick-and-mortar stores, the hardest part is getting the customer into the store. Once the customer is there, he or she is likely to buy something, since the cost of going to another store is high. Therefore, the marketing for brick-and-mortar stores tends to emphasize drawing customers in, rather than the actual in-store customer experience. This is in contrast to online stores, where customers can “walk out” and enter another online store with just a click of the mouse. Amazon.com capitalized on this difference, offering a “personalized store for every customer.” They use several data mining techniques to identify customer's likes and make reliable recommendations.
While we are on the topic of shopping, suppose you have been doing a lot of buying with your credit cards. Nowadays, it is not unusual to receive a phone call from one's credit card company regarding suspicious or unusual patterns of spending. Credit card companies use data mining to detect fraudulent usage, saving billions of dollars a year.
Many companies increasingly use data mining for customer relationship management (CRM), which helps provide more customized, personal service addressing individual customer's needs, in lieu of mass