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The addition of a direct-mail ful llment specialist helped to address these issues. Its ability to work with and provide insight to the creative agency, as well as ef ciently executing the program s primary directmarketing campaigns, provided the foundation for success.

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The strategy was to use the database to send a series of direct-mail pieces that would stand out in the small-business owner s mailbox. The idea was to make them colorful and vibrant, with bold, oversized type on all outer envelopes. Many of the pieces were designed to help the customers calculate their return on investment from a Yellow Pages ad. Contact frequency and messaging were based on segments de ned by the advertisers tenure and their propensity scores on the various models. A minimum spending level was set as a quali cation, so that the marketers could focus their efforts on the top 50 percent of the customer base. All contacts were timed to match various stages of the sales cycle: Tenure. Advertisers were divided into multiple segments on the basis of seniority. Newer segments received more value-reinforcement and relationship-building messages. Segments that had been customers longer received both relationship and up-selling communications. Propensity scores. Model scores were then added to the segments in order to further target messaging. Advertisers who were at high risk for attrition received stronger reinforcement communication focusing on educating them on the value of a Yellow Pages ad and

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CRBarcodeImgDataType. 6.Add a new Report item to the project. RDLCLocalBarcodeReport . 7.Click "Data" > "Show Data Sources" to view the data source.

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how to measure the success of such an ad. Advertisers who had a high possibility of increasing spending in certain areas were given strong sales messages in those speci c areas. Those customers with high scores in both lifetime value and risk of defection received messages showing them how to optimize their product mix and encouraging trial. Those who scored low in all areas received more relationship-building messages. All mailings were sent rst class so that any pieces with bad addresses would be returned. The returned mail was keyed into the database. The impact of the direct-mail communications program was tremendous. The goal was to get a 1 percent increase in sales revenue, or $6 million in additional Yellow Pages ad sales. Control groups that did not get the mailings were set up to measure the results. These showed that the program generated a 3.8 percent increase in overall annual sales revenue, with an additional $13,361,000 in incremental Yellow Pages advertising. Comparing these results with the cost of the mailings and the database, Verizon realized a 1681 percent return on investment. The success of this program dramatically changed the way Verizon communicated with its advertisers. In subsequent sales cycles, a newsletter was added to increase communication with and education of the customer. Email retention messages were incorporated to make the program more robust. Budgets for these programs were increased in the following year. The marketing data mart became the cornerstone not just for database marketing, but for all loyalty and database marketing programs companywide. It became the preferred source for information on Verizon customers.

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Recency, frequency, monetary (RFM) analysis has been around for more than 50 years. It works wonderfully in managing offers to customers. It is worthless for prospects, however, because it is based on transactions. Companies that use RFM have saved millions of dollars by not

Table 1 l-3 contains the statistics for the nets in this model; they were trained on 25,919 facts. Again, the correlations were directly related in size to the number of connections in the net, with a larger number of connections leading to a better model fit. When mildly corrected for shrinkage, only the smaller 4-layer network deviated from this relationship by having a higher correlation than would be expected. When adjusted under the assumption of large amounts of curve-fitting and shrinkage, only the two 4-layer networks stood out, with the largest one (nn9.net) performing best. The only other high correlation obtained was for the 1% 10-l net (nn4.nef). To maximize the difference between the nets used in the trading tests, the largest 4.layer net, which was the best shrinkage-corrected performer, and the fairly small (18-10-l) net were chosen. TRADING RESULTS FOR ALL MODELS Table 11-4 provides data regarding whole portfolio performance with the best insample parameters for each test in the optimization and verification samples. The information is presented for each combination of order type, network, and model. In the table, SAM = whether the test was on the training or verification sample (Nor OUT); 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; $TRD = the average profit/loss per trade; BARS = the average number of days a trade was held; NETL = the total net profit on long trades, in thousands of dollars; NETS = the total net profit on short trades, in thousands of dollars. Columns PI, P2, and P3 represent parameter val-

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