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and conference proceedings. Lively discussions on all aspects of genetic optimization take place in several Internet newsgroups of which compaigenetic is the most noteworthy. A basic exposition of simulated annealing can be found in Numericnl Recipes in C (Press et al., 1992), as can C functions implementing optimizers for both combinatorial and real-valued problems. Neural, Novel & Hybrid Algorithms for Time Series Prediction (Masters, 1995) also discusses annealing-based optimization and contains relevant C+ + code on the included CD-ROM. Like genetic optimization, simulated annealing is the focus of many research studies, conference presentations, journal articles, and Internet newsgroup discussions. Algorithms and code for conjugate gradient and variable metric optimization, two fairly sophisticated analytic methods, can be found in Numerical Recipes in C (Press et al., 1992) and Numerical Recipes (Press et al., 1986). Masters (1995) provides an assortment of analytic optimization procedures in C+ + (on the CDROM that comes with his book), as well as a good discussion of the subject. Additional procedures for analytic optimization are available in the IMSL and the NAG library (from Visual Numerics, Inc., and Numerical Algorithms Group, respectively) and in the optimization toolbox for MATLAB (a general-purpose mathematical package from The MathWorks, 508-647-7000, that has gamed popularity in the financial engineering community). Finally, Microsoft s Excel spreadsheet contains a built-in analytic optimizer-the Solver-that employs conjugate gradient or Newtonian methods. As a source of general information about optimization applied to trading system development, consult Design, Testing and Optimization qf Trading Systems by Robert Pardo (1992). Among other things, this book shows the reader how to optimize profitably, how to avoid undesirable curve-fitting, and how to carry out walkforward tests.

Golden Casket Lottery Corporation Limited is a world leader in providing innovative lottery games. It has offered entertaining lotteries in Queensland, Australia, for 85 years and today operates the popular games of Wednesday and Saturday Gold Lotto, Oz Lotto, Powerball, The Pools, Casket, and an extensive range of Instant Scratch-Its tickets. The games are sold through an agency network of approximately 1150 retailers. Golden Casket s loyalty program is called Winners Circle. The program has more than 1 million unique Winners Circle members, or 38 percent of the adult population of Queensland. Players use the Winners Circle card to register their Lotto purchases for the bene ts of security (unclaimed prize checks are mailed to them), convenience (the card can store up to 20 sets of their favorite numbers), and reward (every $1 spent gives them another chance to win the weekly $10,000 bonus draw). The loyalty program captures 80 percent of all Lotto revenue and provides Golden Casket with invaluable marketing information. The gaming market in Queensland is very competitive, with scores of gaming options available casinos, gaming machines, horse racing, sports betting, keno, and so on. Analysis of Golden Casket s customer database showed that its approach to branding was eroding player loyalty. The brands were competing with one another for the same customers, which undermined player loyalty to the corporation as a whole. Golden Casket asked Acxiom to build computer models that would assist Golden Casket in building customer loyalty. Acxiom built three customer models, which are now rescored every quarter. They are Future value model Using a neural network, 2 years worth of data, and 133 variables, Acxiom built a model that predicted the combined

used, again a sure sign that the more responsive, modified exit is better. As usual, and across both samples, the long side was more profitable (lost less) than the short side. Surprisingly, out-of-sample, the long side actually turned a very small whole-portfolio profit with this exit strategy; it is, however, nothing one would want to trade.

future value of 96.2 percent of all customers on the database with 96.8 percent accuracy. Acxiom allocated the customers to low, medium, and high future value bands. Using these segments, Golden Casket: Created a retention strategy for customers whose future value was likely to decrease. Continued the current strategy for those whose future value was not likely to change. Took a stronger focus to build a relationship with customers whose future value was likely to increase. Behavioral segmentation model Acxiom clustered Golden Casket s customers within value bands. Thirteen behavioral clusters emerged. Acxiom found that six of these variables accurately de ned customer behavior: The number of games played The number of certi cates used Brand The total amount spent The number of games played per week Methods of play chosen This model told Golden Casket which customers could be in uenced to move to a more desirable cluster. Cross-sell model Using a neural network to track historical behavior, each customer was scored with a percentage probability of playing another of the seven games. Table 10-11 shows the way it worked (all names are ctional). Since Jane Smith is 95 percent likely to play Saturday Gold Lotto, the strategy is to reinforce her behavior. Trying to cross-sell other Table 10-11 Lotto Results

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