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Science has answered our modern needs with processed, preserved, and packaged convenience foods. Unfortunately it has done so at the expense of our health. That s why the traditional diets of our ancestors are so instructive today even if we can t fully implement them anymore. The rates of cancer and other degenerative diseases are lower in countries where people observe more traditional diets and eat in moderation. It s as simple as that.

e, market behavior is anticipated rather than responded to As with any predictive system, the forecasts may be more or less correct Trades resulting from incorrect predictions can be incredibly bad, even if many trades resulting from correct predictions are perfect, as is often the case with predictive systems Being anticipatory, the models lend themselves to countertrend entries, which means better tills, less slippage, and better risk control, assuming appropriate stops are used (not done here because of the need to maintain the standard exit)..

8. If analysis shows that LTV will go down in future years, what should you do a. Revise your marketing program. b. Redo your LTV tables, since future LTV always goes up. c. Revise the discount rate. d. Add more marketing dollars. e. Reduce the retention rate. 9. Suppose you have two segments. Segment A has an LTV of $150 in 3 years, and segment B has an LTV of $200 in 3 years. What should your strategy be a. Work to retain segment B and increase the LTV of segment A. b. Get rid of segment A and nd more people that t into segment B. c. You can t decide based on what we know at present. d. Retain both, since the LTV is positive. e. None of the above.

The code for these tests is similar to that for lunar analysis and so is not presented here. It is available on the companion software CD (see offer at back of book).

his is my fth book on database marketing. My rst, The Complete Database Marketer, was published in 1991. As I write each subsequent book, I am constantly amazed at the changes that have taken place in the techniques and the uses of database marketing. Today is no exception. There is so much that is new and different that we are almost talking about an entirely new marketing method.

Tests were performed on the solar entry model using entry at open (Test 1) on a limit (Test 2), and on a stop (Test 3) The breakout look-back (Ibl) was stepped from 25 to 40 in increments of 5; the repeat prevention look-back (lb, ) from 10 to 15 by 5; and the displacement (disp) from 0 to 2 by 1 For entry at the open, the best in-sample performance was with a breakout look-back of 35, a repeat prevention look-back of 15, and a displacement of 2 For the limit, a breakout lookback of 40, a repeat prevention look-back of 15, and a displacement of 0 were optimal For the stop, the best values were: for the breakout look-back, 35; for the repeat prevention look-back, 15; and for the displacement, 2 Table 9-4 shows both the in- and out-of-sample performance for specific commodities.

I like to repeat the story of the genesis of database marketing. It got its start in a big way in the early 1980s, when mass mailers such as American Express and State Farm Insurance started using their customer lists to build ongoing relationships with their customers after the initial sale, leading to increased retention and cross sales. But the roots of database marketing go back to a period in the United States before there were supermarkets. Back in those days, all the groceries in the United States were sold in small corner grocery stores. The proprietors knew their customers names. They would stand at the door and greet their customers by name as they entered, asking them about their families and their concerns. They put things aside for customers, helped them carry heavy packages out to their cars, and built strong and lasting relationships. They built their businesses by developing and cultivating the loyalty of their customers.

The columns and rows contain the same kinds of data as in Tables 9-2 and 9-3 above The solar model performed about as well as the lunar ones and, in sample, did best with a stop order In-sample, entry at the open was worst (-$I631 per trade); a limit was slightly better (-$1519), substantially so when only long trades were considered The stop order was the best, losing only $52 per trade; were it not for transaction costs, this order would have been very profitable in-sample The mode1 required the theoretically expected displacement The best displacement for the limit was zero, understandable since the goal is to respond quickly, before the market turns, when a limit can enter on the noise With the stop, a displacement of 2 was best: Movement needs to begin to trigger the stop and cause entry.