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Management, once it was sold on the program, demanded an aggressive implementation process, with not enough time for testing.

Code listings (Cont.): neural exit strategy, 337-339 neural networ!c% 233-237 oscillator-based entries. 140-143 pmfit target (fixed stop). 311-313 reverse slow %K model, 233-237 seasonaMy, W-163 shrinkage profit target, 326328 standard exit strategy (SES), 295-297 turning-point *odds, 241,242 Commodities channel index (021). 135, 136 Co lmodities pricing data, 3,4 Commodities Systems Incqwrafed (CSI), 1 I Conlpanion software a ailahle, 364 C mpurerized ml& * (lurik,, 227 Conclusions, 353-363 Confidence interval, 60 Confim*ltion-and-inversion model: lunar activity, 182, 196 seasonality, 156, 173 Constant-i estr e t model, 8 1 Continuous contract, 3 Co tralim crosso er model. 125 CO trtia. tmiing, 289 Correlational statistics, 52 Cost function, 30 Counter-trend moving average entry models, 113, 125-130 CRlTBWOM function, 60 Critical threshold exits, 283 C~OSSO ~~: genetic algorithms, 258,259 lunar activity. 181, 82 seasonality, 155, 156 (See also Basic crn~~~ver model) Crossover-with-confirmation mdel: lunar activity, 182, 195, 196 seasonality. 156. 171-173 CSI K2xnmodities systems Incorporated), 11 C-Trader toolkit, 14, 15, 19,X,. 36,99. L14.214 Cumulative t-distribution, 58, 59 Curve-flttmg: bad. 54 gmd, 54 neural networks. 230,255 optimization. and 54-57 Cycle, 203 Cycle-based entries, 76.203-226 B tterwo* filters, 206,207 characteristics. 2,3, 214 code testing, 2, &2,9 filter banks. 206213

Entry methods (Conr.): dollar volatility equalization. 78-81 genetic algorithms. 257-280 good entry, 71 inmoduction, 71-82 lunar/solar phenomena. 179-202 moving average models, 109-132 CUEi, networks, 227-256 orders used in entries, 72-74 osciUators, 133-152 seasonality, 153-177 standard portfolio, 81. 82 standardized exits, 77.78 Equis International. 25,47 Evol~ti0n.q model building (see Genetic algorithms) Evolver, 47.49

To set up the new system, Franks and the SmartDM team set up a number of new communications aimed at building relationships with brokers: Welcome kit mailings to new brokers when they rst register on the system. Activation emails encouraging brokers to make their rst quotes and sales. New customer thank-you mailings to thank brokers for their rst Bene tMall sale. Thank-you satisfaction surveys emailed to each broker after each sale. The email links the broker to a Web microsite that asked questions about the experience. The emails came from the regional vice president of sales for the broker s region. Reactivation emails, letters, and phone calls of two types: one for those who stopped selling, and one for those who stopped quoting. Broker newsletter emails: nine regional monthly versions. Special regional postcard/email/fax promotions. These were special communications aimed at getting information such as new carrier announcements, rate changes, and so on to brokers in a short period of time.

Excalih,41,48

For its marketing database, Bene tMall used SmartDM s DirectTR@K system, which got updates every 2 weeks from the Bene tMall system. This database stored all the information about each broker: past transactions, past marketing communications received, when the broker became a customer, and so on. The DirectTR@K system sent the emails automatically. It tracked who opened the emails, who clicked on the various links, and what emails bounced. While the DirectTR@K system sent the email communications directly, lists for the direct-mail and fax

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Exit strategies, 28L-351 barrier exits. 287 best exit stn,egy (market-by-marke,

communications were extracted and sent to SmartDM s production division for execution. The communications were personalized and customized to include transaction data, sign-up date, and the sales rep and sales manager and their contact information. In this way, the communications appeared to have been sent to the broker by the rep whom the broker knew.

330-332

This program was entirely new for Bene tMall. It required setting up A Web-based, multichannel, marketing system that tracked customer activity and response. Data of much higher integrity than had previously been maintained. It was necessary to pull lists on the same day as the request, which was very new to Bene tMall. User interfaces for generating queries, examining data on individual brokers, managing campaigns, and viewing marketing reports. Broker pro les, including type of practice, product sales, and transaction types. This information had not been available before. Customer satisfaction data from the Web surveys, which enabled Bene tMall to identify strong and weak personnel within the company. One-on-one direct marketing communications across several channels (email, direct mail, fax, phone, and Web microsite). With the new program, the company added a Bene tMall custom proposal, a Microsoft Excel based software tool designed to make it easier for brokers to customize insurance coverage proposals for clients. The goal of this free software was to help brokers customize proposals so that they could sell more insurance. The Web system (see Figure 9-2) permitted brokers to View side-by-side rate and bene ts comparisons for multiple policies. Add coverage options to help clients compare rates and bene ts.

results),

Figure 9-2