Berry

Data Mining Applications 103
How the Model Affects Profitability
How does the model whose lift and benefit are characterized by Figure 4.2
affect the profitability of a campaign? The answer depends on the start-up cost
for the campaign, the underlying prevalence of responders in the population
and on the cutoff penetration of people contacted. Recall that SAC had a budget
of $300,000. Assume that the underlying prevalence of responders in the
population is 1 percent. The budget is enough to contact 300,000 prospects, or
30 percent of the prospect pool. At a depth of 30 percent, the model provides lift
of about 2, so SAC can expect twice as many responders as they would have
without the model. In this case, twice as many means 2 percent instead of 1 percent,
yielding 6,000 (2% * 300,000) responders each of whom is worth $44 in net
revenue. Under these assumptions, SAC grosses $600,000 and nets $264,000
from responders. Meanwhile, 98 percent of prospects or 294,000 do not
respond. Each of these costs a dollar, so SAC loses $30,000 on the campaign.
Table 4.4 shows the data used to generate the concentration chart in Figure
4.2. It suggests that the campaign could be made profitable by spending less
money to contact fewer prospects while getting a better response rate. Mailing
to only 10,000 prospects, or the top 10 percent of the prospect list, achieves a
lift of 3. This turns the underlying response rate of 1 percent into a response
rate of 3 percent. In this scenario, 3,000 people respond yielding revenue of
$132,000. There are now 97,000 people who fail to respond and each of them
costs one dollar. The resulting profit is $35,000. Better still, SAC has $200,000
left in the marketing budget to use on another campaign or to improve the
offer made in this one, perhaps increasing response still more.
Table 4.4 Lift and Cumulative Gains by Decile
CUMULATIVE
PENETRATION GAINS GAINS LIFT
0% 0% 0% 0
10% 30% 30% 3.000
20% 20% 50% 2.500
30% 15% 65% 2.167
40% 13% 78% 1.950
50% 7% 85% 1.700
60% 5% 90% 1.500
70% 4% 94% 1.343
80% 4% 96% 1.225
90% 2% 100% 1.111
100% 0% 100% 1.000