Introduction: Data mining is present in many aspects of our daily lives, whether we realize it or not. It affects how we shop, work, 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 Web search engines, customer-adaptive Web services (e.g., using recommender algorithms), “intelligent” database systems, e-mail 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 on-line stores that recommend additional items based on customer interests, data mining has innovatively influenced what we buy, the way we shop, as well as our experience while shopping. One example is Wal-Mart, which has approximately 100 million customers visiting its more than 3,600 stores in the United States every week. Wal-Mart has 460 terabytes of point-of-sale data stored on Teradata mainframes, made by NCR. To put this into perspective, experts estimate that the Internet has less than half this amount of data. Wal-Mart allows suppliers to access data on their products and performs analyses using data mining software. This allows suppliers to identify customer buying patterns, 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 on-line shopping experience. Many shoppers routinely turn to on-line stores to purchase books, music, movies, and toys. Section 11.3.4 discussed the use of collaborative recommender systems, which 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. CEO and founder Jeff Bezos had observed that in traditional brick-and-mortar stores, the hardest part is getting the customer into the store. Once the customer is there, 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 on-line stores, where customers can “walk out” and enter another on-line 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’re on the topic of shopping, suppose you’ve 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 (and long-distance telephone service providers, for that matter) 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 marketing. By studying browsing and purchasing patterns on Web stores, companies can tailor advertisements and promotions to customer profiles, so that customers are less likely to be annoyed with unwanted mass mailings or junk mail. These actions can result in substantial cost savings for companies. The customers further benefit in that they are more likely to be notified of offers that are actually of interest, resulting in less waste of personal time and greater satisfaction. This recurring theme can make its way several times into our day, as we shall see later.
Data mining has greatly influenced the ways in which people use computers, search for information, and work. Suppose that you are sitting at your computer and have just logged onto the Internet. Chances are, you have a personalized portal, that is, the initial Web page displayed by your Internet service provider is designed to have a look and feel that reflects your personal interests. Yahoo (www.yahoo.com) was the first to introduce this concept. Usage logs from My Yahoo are mined to provide Yahoo with valuable information regarding an individual’s Web usage habits, enabling Yahoo to provide personalized content. This, in turn, has contributed to Yahoo’s consistent ranking as one of the top Web search providers for years, according to Advertising Age’s B to B magazine’s Media Power 50, which recognizes the 50 most powerful and targeted business-to-business advertising outlets each year.
After logging onto the Internet, you decide to check your e-mail. Unbeknownst to you, several annoying e-mails have already been deleted, thanks to a spam filter that uses classification algorithms to recognize spam. After processing your e-mail, you go to Google (www.google.com), which provides access to information from over 2 billion Web pages indexed on its server. Google is one of the most popular and widely used Internet search engines. Using Google to search for information has become a way of life for many people. Google is so popular that it has even become a new verb in the English language, meaning “to search for (something) on the Internet using the Google search engine or, by extension, any comprehensive search engine.”1 You decide to type in some keywords for a topic of interest. Google returns a list of websites on your topic of interest, mined and organized by Page Rank. Unlike earlier search engines, which concentrated solely on Web content when returning the pages relevant to a query, PageRank measures the importance of a page using structural link information from the Web graph. It is the core of Google’s Web mining technology.
