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Web personalization. All good Web sites gain customers permission to use cookies by giving them a chance to opt out. More than 95 percent of customers agree to their use. By using cookies, a visitor returning to a catalog Web site sees Welcome Back, Susan. Susan can see items that the cataloger thinks (or she has told them) she is interested in based on her past purchases and Web activity. What catalogers tried and failed to do in catalog personalization is now economically possible using the Web. Control groups. Control groups can be created in the database to test customers reactions to innovations in the Web site. Such groups were almost impossible to use effectively with mass mailings of catalogs. Online focus groups. In 6 we describe how Universal Music used online focus groups to determine customers reaction to the release of a new music CD. Catalogers can now use online focus groups to determine customers reactions to new products. Measuring multibuyer performance. The Web offers the opportunity to track what customers are looking at, which is not possible with a paper catalog. However, few catalogers have yet taken advantage of this feature, since it requires not just sophisticated software, but creative marketers who can look at the data and draw pro table conclusions from what they see. The scarcest commodity in all of database marketing is the creative imagination needed to drive new marketing programs. Marketers are usually awash with data that they cannot gure out how to use pro tably.

In a very real sense, a neural network is actually a multiple regression, albeit, nonlinear, and the correlation of a neural net s output with the target may be construed as a multiple correlation coefficient The multiple correlation obtained between a net s output and the target may be corrected for shrinkage to obtain some idea of how the net might perform on out-of-sample data Such shrinkage-corrected multiple correlations should routinely be computed as a means of determining whether a network has merely curve-fit the data or has discovered something useful The formula for correcting a multiple correlation for shrinkage is as follows:.

A FORTRAN-style expression was used for reasons of typsetting. In this formula, SQRT represents the square root operator; N is the number of data points or, in the case of neural networks, facts; P is the number of regression coefticients or, in the case of neural networks, connection weights; R represents the uncorrected multiple correlation; and RC is the multiple correlation corrected for shrinkage. Although this formula is strictly applicable only to linear multiple regression (for which it was originally developed), it works well with neural networks and may be used to estimate how much performance was inflated on the in-sample data due to curve-fitting. The formula expresses a relationship between sample size, number of parameters, and deterioration of results. The

Once the landsend.com Web site was constructed, the company wanted to try additional methods besides the paper catalog that would induce shoppers to come to its site and buy. It introduced several new ideas that seemed to work. Live customer service that allowed customers to send messages and receive replies in real time (see Figure 12-1). The customer support agents could synchronize their screens with customers so that

statistical correction embodied in the shrinkage formula is used in the chapter on neural network entry models.

they could see the same screen the customer was seeing at home. They could help shoppers with their choices and in selecting cross sales. A virtual model that customers could use to try on clothes (see Figure 12-2). My Personal Shopper, who helps you the way a good clerk in a department store would (see Figure 12-3). How well did these things work Conversion rates among those who used the virtual model were 26 percent higher than among those Figure 12-1 Lands End Live. Lands End, Inc. Used with permission.

Although, for statistical reasons, the system developer should seek the largest sam ple possible, there is a trade-off between sample size and representativeness when dealing with the financial markets. Larger samples mean samples that go farther back in time, which is a problem because the market of years ago may be fundamentally different from the market of today-remember the S&P 500 in 1983 This means that a larger sample may sometimes be a less representative sample, or one that confounds several distinct populations of data! Therefore, keep in mind that, although the goal is to have the largest sample possible, it is equally important to try to make sure the period from which the sample is drawn is still representative of the market being predicted.

Figure 12-2