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Apigenin is a flavonoid found in parsley, artichokes, basil, celery, apples, onions, and other plants. It appears to counteract the induction of tumors by its effect on Phase I and Phase II detoxification, by increasing glutathione levels and by its antioxidizing activity. In the past several years, there have been many published reports on the protective effects of apigenin on the induction of cancer, especially skin cancer. Doctors Pelling and Birt, while conducting research at the Eppley Cancer Research Center, found that apigenin was a potent inhibitor of ultraviolet light induced skin cancer.

The countertrend moving average models were more variable than the trendfollowing ones Many of them showed much smaller losses or even small profits, in-sample A similar picture was seen out-of-sample, especially with the simple moving average support/resistance model Except for the MACD divergence model, which behaved differently from the others, oscillators performed very poorly There was a lot of variability, but on the whole, these models gave per-trade profits that were worse than expected by chance both in-sample and out-of-sample The RSI overbought/oversold model was the worst of them all In both samples, it provided staggering losses that were (statistically) significantly worse than those that would have been achieved with a random entry The seasonal models, on the whole, were clearly better than chance.

Number promoted Teens Control 20s and 30s Control 40s and 50s Control Seniors Control Total 115,000 20,000 145,000 20,000 75,000 20,000 65,000 20,000 480,000 Sales rate 9.50% 6.00% 7.70% 5.80% 5.00% 5.70% 4.00% 3.10% Lift 3.50% 1.90% 0.70% 0.90% Lift in sales 4,025 2,755 (525) 585 6,840

Although only one of these models actually provided profits in both samples, two of them were profitable out-of-sample, and several had only very small losses (much less than would be expected by chance using random entries) across samples The basic lunar model had mixed findings Most of the in-sample results were slightly positive when compared with chance (the random entry), but not profitable The basic crossover model, however, was decidedly biased above chance in both samples Although the solar models performed slightly better than chance in-sample, they were mixed and variable out-of-sample This was also true for the cycle models However, the cycle models, when using entry at open or on limit, actually performed significantly worse in recent years than a random entry As with breakouts, the findings are not due to optimization; significant curve-fitting was only detected with the genetic and neural network models.

Based on this discussion, how do you go about pro table segment marketing There are several rules: Build a customer database with demographics and transaction history. Find a reliable way to get regular updates on transactions and responses. Come up with a pro table method for creating customer segments that you can market to. Select just a few segments and design a marketing program for each segment. Set up control groups that have exactly the same characteristics as the groups selected for marketing, but that do not get the promotional material. Measure the performance of the segment that received the promotion against that of the control group. In the marketing campaign for each segment, use the database to create personalized content: emails, direct-mail pieces, telephone calls, Web response pages, and so on. Monitor the response from each segment and each response method and record them in the database. Constantly test new methods of marketing to each segment, recording the results and increasing the resources devoted to those methods that work.

Because of the tremendous sample involved in the portfolio, the optimization of one or two parameters, necessary for most models (other than the genetic and neural ones), had minimal curve-fitting effect Surprisingly, the neural network models showed a fairly consistent bias to perform better than chance out-of-sample In-sample, of course, performance was stellar across all tests There was shrinkage (evidence of curve-fitting), but the shrinkage was not complete, leaving some predictive utility in the out-of-sample data The results for the genetically evolved rules were the best In-sample, performance was excellent, Out-of-sample, performance was exceptional for models involving long positions Summary Many of the models described as significantly better than chance (ie, better than what would be produced by a random entry) would likely become profitable if coupled with a better exit strategy.

From iDine, we learned that you can track consumer behavior by using registered credit cards. It is easy to do. You can get many people to participate if you make it worth their while. From the National Australian Bank, we learned that Teradata computers and software permit you to trawl customer accounts every night to nd people who have had signi cant changes in their bank accounts. We use segment marketing to develop rules to market to each situation. From Kmart, we learned that you can identify mothers-to-be based on their shopping behavior. Marketing to this segment was very pro table for Kmart, and was worth the effort. From the University of Toronto, we learned that you can reactivate lost customers using a creative database marketing strategy.