Ads mailed – This is the cumulative number of ads mailed starting with the best
prospects and advancing to less well qualified prospects.
Cum. % class – This is the cumulative percentage of the sales expected from ads sent to
prospects in the bins up to and including the one with the percentage.For example, we
expect to receive 50% of total sales from ads sent to the prospects in the tow highest-priority bins.
Expected sales – This is the total number of sales that can be expected from the
cumulative number of ads mailed to customers in bins up to and including the current
one. In this example, it is believed that of the total population (100,000) about 10% will
respond resulting in sales of 10,000 units if all customers are targeted. So the expected
cumulative sales for a bin are calculated by multiplying the expected total sales (10,000)
by the cumulative percentage of the class up to and including the bin (“Cum. % class”).
For example, if ads are mailed to customers falling in bins 1 and 2, then about 50% of the
10,000 expected sales will be achieved resulting in cumulative expected sales of 5,000
units.
Cum. Gain – This is the ratio of the expected sales using the model to prioritize the
prospects divided by the expected sales if a random mailing was done. In this example
we see that by targeting the customers in bins 1 and 2, we will get about 2.50 times as
many sales as if we mailed the same number of ads to a random set of customers. Thus
our return on investment (ROI) is increased by 2.5 if we target this group. Note that if we
increase the number of ads mailed to include less qualified customers in higher bins, the
gain decreases because we are now mailing to people who are less likely to respond. If
we send ads to all 100,000 potential customers then the gain is 1.00 because are not doing
any selective targeting
Lift – This is the ratio of the expected sales for the prospects in a bin (“% of class”)
divided by the percent of the population in the bin (“% of population”). As you send ads
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