DATA MINING TECHNIQUES FOR MARKETING SALES AND CUSTOMER RELATIONSHIP MANAGEMENT THIRD EDITION

ness context of data mining, starting with a chapter that introduces data min-ing and explains what it is used for and why. The second chapter introduces the virtuous cycle of data mining — the ongoing process by which data min-ing is us[r]

back when they were acquired. Certainly it is not safe to assume that the char-acteristics of someone who got a cellular subscription in 1990 are good predic-tors of which of today’s new customers will keep their service for many years. A better approach is to use survival analysis techniq[r]

mance less obvious. In short, the model gradually expires and it is not always clear exactly when to update it. 470643 c07.qxd 3/8/04 11:36 AM Page 220220 Chapter 7 The solution is to incorporate more recent data into the neural network. One way is to take the same neural network back[r]

out. As with radio reception, too many competing signals add up to noise. Clustering provides a way to learn about the structure of complex data, to break up the cacophony of competing signals into its components. When human beings try to make sense of complex questions, our natural tendency[r]

lenger may only accept responses via telephone, but the champion may accept them by telephone or on the Web. In such a case, the challenger response may be dampened because of the lack of a Web channel. Or, there might need to be special training for the inbound telephone service reps to hand[r]

470643 c03.qxd 3/8/04 11:09 AM Page 7474 Chapter 3 When missing values must be replaced, the best approach is to impute them by creating a model that has the missing value as its target variable. Values with Meanings That Change over Time When data comes from several different points in histo[r]

profitability, 100–104 proof-of-concept projects, 600 response modeling, 96–97 as statistical analysis acuity of testing, 147–148 confidence intervals, 146 proportion, standard error of, 139–141 results, comparing, using confi-dence bounds, 141–143 sample sizes, 145 targeted acquisition campaigns, 3[r]

470643 c01.qxd 3/8/04 11:08 AM Page 13Why and What Is Data Mining? 13 many transaction records, but it is only relatively recently that the automation of everyday life has become so pervasive. Today, the rise of supermarket point-of-sale scanners, automatic teller machines, cred[r]

606 Chapter 18 less likely to churn. The additional requirement to identify separate segments of subscribers at risk and understand what motivates each group to leave sug-gests the use of decision trees and clever derived variables. Each leaf of the decision tree has a label, which in[r]

make use of a validation set to choose from among candidate subtrees; the same data used to grow the tree is also used to decide how the tree should be pruned. This may reflect the algorithm’s origins in the academic world, where in the past, university researchers had a hard time getting the[r]

shaped curves turn up in other domains. As mentioned earlier, the bathtub haz-ard initially starts out quite high, then it goes down and flattens out for a long time, and finally, the hazards increase again. One phenomenon that causes this is when customers are on contracts ([r]

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customer acquisition. Past researches2 have shown that typically 6% of such target customers respond to these direct mails. Banks use their credit risk models to classify these respondents in good credit risk and bad credit risk classes. The proportion of good credit risk respondents i[r]

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