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The table divides the total prospect set into 10 “buckets” by cumulative percentage of mailings, with the best 10% of the prospects in the first bucket, the secondbest 10% in the second bucket, and so forth. The table has five columns: 1. Cumulative % of Mailings  This is the cumulative percentage of ads mailed starting with the best prospects and advancing to the least qualified prospects as a result of the scoring within the predictive model. 2. % of Total Responders  This is the cumulative percentage of the total sales expected from ads sent to prospects in the buckets up to and including the one with the percentage being reviewed. For example, we expect to receive 50% of total sales (10,000 expected) from ads sent to the prospects in the two highestpriority buckets. 3. Expected sales  This is the total number of sales that can be expected from the cumulative number of ads mailed. If no model was used, the expected sales would always be 10% of the ads mailed. With the model, we see that expected sales are considerably better for the best prospects. The cumulative expected sales for a bucket are calculated by multiplying the total expected sales (10,000) by the cumulative percent of total responders figure. 4. Cumulative Gain Ratio  This is the ratio of the projected sales using the model to prioritize the prospects divided by the expected sales if a random mailing was done. For example, we expect to receive sales of 7,200 with 40,000 ads mailed using the model versus sales of 4,000 with 40,000 ads mailed randomly resulting in a cumulative gain of 1.80%. 5. Lift Ratio  This is the ratio of the change in expected sales for the modeled prospects in a bucket divided by the expected sales for the random prospects in the same bucket. For example, we have a change in expected sales of 700 when we go from expected sales of 6,500 to 7,200 so we divide by the expected sales of 10,000 for the random prospects resulting in a lift of .70%. What we learn from the table is that by targeting the campaign at the best 10% of the prospects, we can expect sales of 3,000 which constitute 30% of the total expected sales. By targeting the best 50,000 prospects, we can expect 8,000 sales which constitute 80% of the total. The mailings done to the 10,000 prospects in the last (worst) bin are likely to yield only 200 sales for a return of only 2%. 
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Healy List Marketing • 153 Andover Street • Suite 108A • Danvers, MA 019231450 Phone: 8002818956 • Fax: 9783360463 • info@healylistmarketing.com 

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