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Other seasonal models also did okay, with out-of-sample profits being made using the simple seasonal crossover model, Next down the list was the short turning-point model that used the small 1610-1 network This model was profitable in both samples across all orders: out-ofsample, at open, a 93% annualized return, with $580 per trade; in-sample, a 352% return and $8,448 per trade profit While still on the subject of neural networks, the reverse Slow %K model was a profitable performer, especially when a stop order was used Out-of-sample, the annualized return was 61%, with $362 per trade In-sample, there was a 225% return, with a $6,764per-trade profit Note the large shrinkage from in-sample to out-of-sample for these models: While this is evidence of curve-fitting, enough real curves were caught for profits to be made in the verification sample.

Segments are easy to create. They don t know how to do one-to-one marketing. Segments cost less but produce similar results. One-to-one marketing produces a much higher lift in sales. Which of the following are not considered demographic data a. Age b. Preferences c. Ethnicity d. Income e. Value of home What technique does iDine use that most marketers are not using today a. Creating customer segments b. Credit card matching c. Air miles as a reward d. Cash as a reward e. Transactions viewed on the Web Which is not listed as a feature of the Australian National Leads program a. Web customer response b. Customer segmentation c. Nightly trawling of the data warehouse d. Prediction of customer propensity to purchase e. Offers based on transactions What is the best strategy for Quadrant 3 the low lows a. Retention b. Expansion c. Ignoring them d. Repricing their services e. Rewards for good behavior Which was not a result of the Kmart mothers-to-be program a. Hispanic baby product sales were up 25 percent. b. Cross shopping was reduced by 35 percent. c. Twenty-one percent of top-decile shoppers increased their spending by 40 percent.

Another model that produced profits in both samples was the MACD divergence model, especially with entry on limit This model had a 61% annualized return, out-of-sample, and a $9X5-per-trade profit In-sample, the figures were a 67% return and a $1,250 profit per trade..

Finally, among the models that were profitable in both samples was the simple moving-average suppotVresistance model with entry on stop: It took 6.4% out of the market, with $482 per trade, out-of-sample; and $5.8%, with $227 per trade, in-sample. Almost all other models lost money out-of-sample, and often in-sample, as well, The only exception was the volatility breakout model restricted to the currencies, which performed fairly well. Out-of-sample, it had an 8.5% return and made $2,106 per trade. In sample, it had a 12.4% return, with $3,977 profit per trade.

Until 10 years ago, few companies used their databases to compute customer lifetime value. Banks began the process. They were able to determine the pro tability of every account on a monthly basis, and to roll that up to compute household pro tability. The results were used to segment customers in order to learn which ones were useful to the bank and which ones were reducing the bank s pro ts. I like to use the chart shown in Figure 1-1. It comes from a major bank in the South that ranked all its customers by pro tability. It shows that 80 percent of the bank s pro ts came from the top 5 percent of its customers. The bottom 28 percent lost 22 percent of its pro ts. This is very powerful information that was just not available before database marketing came along. What was even more powerful was what the bank was able to do with this information. Clearly the top 5 percent of customers had to be retained. The bank s whole future rested on these people. In addition, something had to change in the bank s marketing strategy for the bottom 28 percent. Why try to retain people who are costing the bank money Not just banks, but insurance companies, business-to-business marketers, and scores of other enterprises began to compute numbers like these and to develop strategies that made pro table use of them. Many companies went on to determine potential lifetime value by using computer models to determine all the other products that each customer Figure 1-1

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Summary

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Even though most of the other model-order combinations lost out-of-sample, in many cases, the losses were much less than would be expected with a totally random entry. In a number of instances, however, they were worse. It seems evident that there are a number of models that, although not ideal and in need of further development, do yield profitable trading that holds up in a verification sample and yields reasonable statistics.

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