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who had registered within the previous 2 years. When Terri started to assemble the rst house list using the new database, she assigned a source code to each customer for tracking purposes. The addition of the source code meant that the list and the individual customer cells could be tracked separately. She reviewed the customer records and was able to locate and include a group of customers who had a high lifetime value (LTV) but had not purchased courses for 3 or 4 years. Normally this group of customers would be excluded, since it is too costly to mail calendars to customers who haven t purchased in 3 or 4 years. But by targeting only the customers in this group who had a high LTV, Terri was able to keep the mailing costs manageable while she attempted to reactivate their business. The result was that 8 percent of those who were targeted purchased courses, and their purchasing habits were as high the second time around as they had been originally. The reactivated HV group bought an average of three courses each per year compared to the school s overall average of 1.45. The impact of recency on course purchases is obvious in Figure 8-3. By assigning RFM indicators to the customer base, Terri was able to nd new revenue from lost customers and to gain some valuable insights into the school s best customers. Marketing to customer segments by direct-mail promotions is relatively easy. Going beyond that by appointing segment managers and

Portfolio Performance of the Modified Standard Exit Strategy with an Added Neural Signal Exit Evaluated over a Range of Threshold Parameter Values

50% 40% 30% 20% 10% 0% 1mo. <12mo. 12mo. <24mo. <48mo. Cal Req Alumni Test NR NR never registered Reactivated HV Grp

Table 15-3 shows the performance data for the portfolio, broken down by market, for the optimal MSES with the added neural signal exit. The results are for tire composite exit with the best threshold value (54) found in the optimization presented in Table 15-2. Live Hogs was the only market that was substantially profitable in both samples. A number of markets (e.g., the Deutschemark and Japanese Yen) showed strong profitability in-sample that was not evident out-of-sample. On the long side, the NYFE and Unleaded Gasoline were profitable in both samples. This could easily be a statistical artifact since, in-sample, a large number of markets had profitable performance on the long side. TEST METHODOLOGY FOR THE GENETIC EXIT COMPONENT Since it is almost certain that different rules are required for the long side, as opposed to the short side, two tests are rtm. In the first test, random entries are generated for long positions using the standard random entry strategy. Any short trades generated are simply not taken. Rules are genetically evolved for inclusion in the MSES for the long positions. In the second test, only short entries are taken. Any long entries generated by the random entry strategy are ignored. An attempt is made to evolve rules that work well as additional elements to the MSES for the short side.

managing segments on a daily basis is much more complicated and seldom pro table. Let s take a look at one enterprise that succeeded.

I S - 3

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While many companies have failed at managing customer segments on a day-to-day basis, National Australia Bank (NAB) succeeded at this task. The lead franchise in NAB was National, a Melbourne-based bank serving more than 4.5 million customers. With the help of Beth Ann Konves of the Teradata Division of NCR, Fernando Ricardo, head of customer knowledge management at the bank, identi ed six major customer segments composed of individuals and businesses. Each segment was managed as a global business. The system, called National Leads, identi ed leads within the bank s database based on signi cant changes (events) in a customer s transaction pattern, such as balance changes, large transactions, or loan renewals. The system also calculated each customer s propensity to buy a product or to respond to an offer. The bank s database was maintained on an NCR Teradata computer using Teradata s CRM analytical software, which was used for the National Leads system. The National Leads system prioritized events and alerted the appropriate bankers each morning so that they could take appropriate action. The system also served as a communications gatekeeper, managing the frequency, content, and channel used for customer interactions during marketing campaigns. The goal was to ensure that customers were contacted with meaningful opportunities at the right times through the right channels. The six major customer segments used by the bank were common to many other banks and nancial institutions: Custom business Packaged business Agribusiness Private Premium Retail

Market-by-Market Performance for the Modified Standard Exit with a Neural Signal Addition Using Random Entries

The code above shows the logic of both the entries and exits. The modeltype parameter controls whether the longs or shorts are tested. Parameters ptlim and mmsfp are for the profit target and stop (respectively). They are fixed at the same optimal values used in the neural network test earlier, Each of the three rules is calculated as a series of TRUE/FALSE values, and if all three rules are TRUE, a rulebased exit signal (exitrig) is generated. In the exit code, if clauses have been added. For example, an exit at the close is generated ifan exit signal is produced by all three rules being TRUE (exitsig = TRUE). The evolution of rules for the long and short sides follow the same steps as in the chapter on genetics, in which similar rules were evolved for use in entries. The same 12 parameter chromosomes, broken into three genes (each specifying a rule), are employed, and there is no change in that logic in the current test, Rules for the short side and for the long side are produced by allowing 2,500 generations to pass (2,500 runs using OptEvolve). The top 10 solutions for the longs and for the shorts are then tested on both the in-sample and out-of-sample data.