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KnowledgeBase s real-time database was a very effective system for this long-distance provider. However, there were opportunities to improve costs and deepen the customer relationship by bringing the loyalty initiative directly to the rewards membership. Therefore, in Phase II, KnowledgeBase Marketing created an online redemption functionality for the loyalty platform, with the following objectives: Provide a Web-enabled interactive interface for the rewards program through the company s own Web site, crossing through rewalls, to the KnowledgeBase loyalty database. Deliver the new system in 30 days. Increase retention and cross-selling opportunities with the new system. Reduce the operating costs of the loyalty program.

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With the new system, customers could go online to obtain information, review their transaction history, and redeem loyalty points. Customers received email con rmation of their redemption seconds after the transaction, further creating a live link with the customer. Analysis of the rewards program, such as determining the type, frequency, and medium (Web, IVR, call center, etc.) of redemptions, is performed using a reporting database into which customer redemptions and other data are fed. IVR, of course, is Interactive Voice Response, the process of pushing buttons on a telephone keypad to get the information or the person you are seeking from an automatic phone answering system. In the rst week after the program was launched, the company had a 40 percent increase in point redemption. In subsequent weeks, Web redemptions grew by 5 to 10 percent. Web redemptions, of course, replaced phone redemptions. The average cost of a phone redemption was about $6. The marginal cost of a Web redemption was only a few cents. In the rst year of the Web redemption program, the long-distance provider s costs for maintaining the loyalty program were cut in half. Churn was reduced, and cross-selling initiatives were enabled through customer-friendly Web marketing efforts assisted by KnowledgeBase Marketing.

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1 e1ae if~entryposted < 0) ( /I initialization and exits for shorts on entry day limprice - entryprice - ptlim * exitatr[cbl; stpprice = entryprice + mmstp * exiratr[cbl; ta.exitshortlimit('o', limprice); t~.exiCshortst~p('E', atpprice); if(prdLcbl < loo.o-thresh, te.exirshortclose('P'l: ) else (

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The code fragment above implements the logic of the exit strategy. The parameters ptlim and mmsrp are set to 4.5 and 1.5, respectively; these are the values that gave the best overall portfolio performance (see Table 14-1, 14). The thresh parameter, i.e., the threshold used to generate exits based on the neural forecasts, is optimized. The logic of the additional exit can be seen in the if statements that compare the prediction of the network with the threshold and that post an exit at close order based on the comparison. The parameter thresh is stepped from 50 to 80 in increments of 2. RESULTS OF THE NEURAL EXIT TEST Baseline Results Table 15-l contains data on the baseline behavior of the MSES. The threshold ..was set high enough to prevent any net-based exits from occurring. The numbers in this table are the same as those reported in 14 (Table 14-I) for an opti-

Web response beats phone, mail, email, and fax in Reduction in cost of ful llment Data captured along with the sale Immediacy of response information Personalization and speed of response Accuracy of the data captured

ma1 fixed stop and profit target. The abbreviations in Table 15-l may be interpreted as follows: SAMP = whether the test was on the training or verification sample (IN or OUT); NETL = the total net profit on long trades, in thousands of dollars; NETS = the total net profit on short trades, in thousands of dollars; PFAC = the profit factor; ROA% = the annualized return-on-account; ARRR = the annualized risk-to-reward ratio; PROB = the associated probability or statistical significance; TRDS = the number of trades taken across all commodities in the portfolio; WIN% = the percentage of winning trades; AVTR = the average profit/loss per trade; and TRDB = the average number of bars or days a trade was held. There was great consistency between in- and out-of-sample performance: The average trade lost $1,581 in-sample and $1,580 out-of-sample; both samples had 39% winning trades; and the risk-to-reward ratios were - 1.46 in-sample and - 1.45 out-of-sample. Neural Exlt Porlfolio Results

Web response involves the creation of a special microsite where customers can be recognized and greeted by name, using a PIN or a cookie.

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