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Sejal projected that in the second year, the cataloger could gain from email marketing to its existing customers. The use of email marketing was possible only when the cataloger had a substantial number of customer records that included email names and permission to use them. Since the cataloger did not have many email names to start with, Sejal suggested that it become aggressive about capturing email names on all future sales, thus rapidly building up a base of customer email names. What could be done with email marketing Sejal suggested several techniques: Regular promotions of existing products, particularly before and after the catalog arrives Promotions of sale items Last-minute specials Retention messages (thank you for your business, customer surveys, etc.) Follow-up messages (thank you for your order; your order will be shipped on . . .) Table 12-6 One-Time Setup Costs

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Another statistical technique frequently used is multiple regression. Consider intermarket analysis: The purpose of intermarket analysis is to find measures of behaviors in other markets that are predictive of the future behavior of the market being studied. Running various regressions is an appropriate technique for analyzing such potential relationships; moreover, there are excellent statistics to use for testing and setting confidence intervals on the correlations and regression (beta) weights generated by the analyses. Due to lack of space and the limited scope of this chapter, no examples are presented, but the reader is referred to Myers (1986), a good basic text on multiple regression. A problem with most textbooks on multiple regression analysis (including the one just mentioned) is that they do not deal with the issue of serial correlation in time series data, and its effect on the statistical inferences that can be made from regression analyses using such data. The reader will need to take the effects of serial correlation into account: Serial correlation in a data sample has the effect of reducing the effective sample size, and statistics can be adjusted (at least in a rough-and-ready manner) based on this effect. Another trick that can be used in some cases is to perform some transformations on the original data series to make the time series more stationary and to remove the unwanted serial correlations.

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These cold-water fish are among my favorite sources of protein and two omega-3 fatty acids, EPA and DHA, which I have already mentioned are critical for good health and detoxification.

One powerful, unique approach to making statistical inferences is known as the Monte Carlo Simulation, which involves repeated tests on synthetic data that are constructed to have the properties of samples taken from a random population. Except for randomness, the synthetic data are constructed to have the basic characteristics of the population from which the real sample was drawn and about which inferences must be made. This is a very powerful method. The beauty of Monte Carlo Simulations is that they can be performed in a way that avoids the dangers of assumptions (such as that of the normal distribution) being violated, which would lead to untrustworthy results.

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Viral marketing ( New product specs are available. Send them to a coworker. ) Email newsletters with product information What results might the cataloger get from email marketing The experience of other catalogers suggests a lift in sales of between 5 and 20 percent using well-targeted and appreciated emails. For this cataloger, Sejal conservatively projected only 5 percent. He used that gure to estimate the increased sales and pro ts from emails (Table 12-7). By adding the email pro ts to the prospecting pro ts, Sejal was able to predict the cataloger s pro ts for the second year, as shown in Table 12-8. The second year s response rate was projected to increase

Another way to evaluate a system is to perform out-of-sample testing. Several time periods are reserved to test a model that has been developed or optimized on some other time period. Out-of-sample testing helps determine how the model behaves

Conservative Old customers New customers Total Sales @ $250 5% increase Pro t @ 35% 10,000 18,600 28,600 $7,150,000 $357,500 $125,125 Bold 10,000 30,000 40,000 $10,000,000 $500,000 $175,000 Aggressive 10,000 46,400 56,400 $14,100,000 $705,000 $246,750

on data it had not seen during optimization or development. This approach is strongly recommended. In fact, in the examples discussed above, both in-sample and out-of-sample tests were analyzed. No corrections to the statistics for the process of optimization are necessary in out-of-sample testing. Out-of-sample and multiple-sample tests may also provide some information on whether the market has changed its behavior over various periods of time.

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