Data Mining - Mehmed Kantardzic [280]
Cablevision Systems, Inc.
Cablevision Systems Inc., a cable TV provider from New York, was concerned about its competitiveness after deregulation allowed telecom companies into the cable industry. As a consequence, it decided that it needed a central data repository so that its marketing people could have faster and more accurate access to data. Using data mining, the marketing people at Cablevision were able to identify nine primary customer segments among the company’s 2.8 million customers. This included customers in the segment that are likely to “switch” to another provider. Cablevision also focused on those segments most likely to buy its offerings for new services. The company has used data mining to compare the profiles of two sets of targeted customers—those who bought new services and those who did not. This has led the company to make some changes in its messages to customers, which, in turn, has led to a 30% increase in targeted customers signing up for new services
Worldcom
Worldcom is another company that has found great value in data mining. By mining databases of its customer-service and telemarketing data, Worldcom has discovered new ways to sell voice and data services. For example, it has found that people who buy two or more services were likely to be relatively loyal customers. It also found that people were willing to buy packages of products such as long-distance, cellular-phone, Internet, and other services. Consequently, Worldcom started to offer more such packages.
BBC TV
TV-program schedulers would like to know the likely audience for a proposed program and the best time to show it. The data for audience prediction are fairly complex. Factors, which determine the audience share gained by a particular program, include not only the characteristics of the program itself and the time at which is shown, but also the nature of the competing programs in other channels. Using Clementine, Integral Solutions Limited developed a system to predict television audiences for the BBC. The prediction accuracy was reported to be the same as that achieved by the best performance of BBC’s planners.
Bell Atlantic
Bell Atlantic developed telephone technician dispatch system. When a customer reports a telephone problem to Bell Atlantic, the company must decide what type of technician to dispatch to resolve the issue. Starting in 1991, this decision was made using a hand-crafted expert system, but in 1999 it was replaced by another set of rules created with machine learning. The learned rules save Bell Atlantic more than 10 million dollars per year because they make fewer erroneous decisions. In addition, the original expert system had reached a stage in its evolution where it could not be maintained cost-effectively. Because the learned system was built by training it on examples, it is easy to maintain and to adapt to regional differences and changing cost structures.
B.3 DATA MINING FOR THE RETAIL INDUSTRY
Slim margins have pushed retailers into data warehousing earlier than other industries. Retailers have seen improved decision-support processes leading directly to improved efficiency in inventory management and financial forecasting. The early adoption of data warehousing by retailers has allowed them a better opportunity to take advantage of data mining. The retail industry is a major application area for data mining since it collects huge amounts of data on sales, customer-shopping history, goods transportation, consumption patterns, and service records, and so on. The quantity of data collected continues to expand rapidly, especially due to the increasing availability and popularity of business conducted on the Web, or e-commerce. Today, many stores also have Web sites where customers can make purchases online. A variety of sources