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282 CHAPTER 7 CASE STUDY: MODELING RESPONSE TO DIRECT MAIL MARKETING Figure 7.13 The response rate increases as the z ln number of purchase visits, z ln number of individual items purchased, z ln total net sales, and z ln number of different product classes increase. customers who concentrate on a particular type of clothing, buying only one or two types of clothing (e.g., blouses), tend to have a lower response rate. The raw data file contains a variable that measures product uniformity, and based on the behavior observed in Figure 7.15, we would expect the relationship between product uniformity and response to be negative. This is indeed the case, as shown by the normalized histogram in Figure 7.16. The highest response rate is shown by the customers with the lowest uniformity, that is, the highest diversity of purchasing habits, in other words, customers who purchase many different types of clothing. Next, we turn to an examination of the relationship between the response and the many flag variables in the data set. Figure 7.17 provides a directed web graph of the relationship between the response (upper right) and the following indicator variables (counterclockwise from the response): credit card holder, spending months 4, 5, and 6, spending months 2 and 3, spending last one month, spending same period last year, returns, response rate, markdown, Web buyer, and valid phone number on file. Web graphs are exploratory tools for determining which categorical variables may be of interest for further study.
DATA UNDERSTANDING AND DATA PREPARATION PHASES 283 Figure 7.14 The response rate is positively related to the z sqrt number of promotions responded to, and the z sqrt number of coupons used, but negatively related to the z ln number of days between purchases. Figure 7.15 z sqrt percentage spent on blouses, with response overlay.
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DATA MINING METHODS AND MODELS DANI
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DATA MINING METHODS AND MODELS DANI
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DEDICATION To those who have gone b
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