Java Reference
In-Depth Information
Table 7-5
Lift computations table
Number of
customers likely
to attrite
Cumulative number
of customers likely
to attrite
Quantile
number
Cumulative
quantile lift
Cumulative gain
1
70
70
70/19
3.684
70/190
36.8%
2
40
110
110/38
3.289
110/190
57.9%
3
25
135
135/57
2.368
135/190
71.5%
4
15
150
150/76 1.974
150/190 79.4%
5
12
162
162/95 1.705
162/190 85.7%
6
8
170
170/114 1.491
170/190 89.8%
7
7
177
177/133
1.331
177/190
93.4%
8
5
183
183/152
1.204
183/190
96.5%
9
5
188
188/171
1.099
188/190
99.0%
10
3
190
190/190
1.000
190/190
100%
Using the classification model, the first quantile contains the top
100 customers that are predicted to be attriters. Comparing the pre-
diction against the known actual values, we find that the algorithm
was correct for 70 of these 100 customers. Therefore, the lift for the
first quantile is 70/19
3.684, where 70 is the number of attriters
found using the classification model and 19 is the number of customers
that would have been found given a random selection of customers.
Similarly, the cumulative gain value for this first quantile is the
percentage of the attriters in this quantile, that is, 70/190
0.368. In
Table 7-5, observe that the cumulative quantile lift values gradually
decrease, because the addition of each quantile includes fewer probable
cases, and the last quantile has lift value 1 because it includes all
1,000 cases. Cumulative gain values gradually increase, because the
addition of each quantile increases the proportion of attriters, and the
last quantile has cumulative gain of 100 percent because it includes all
1,000 cases.
In this example, suppose that ABCBank wants to launch a cus-
tomer retention campaign with a limited budget that can retain at
least 50 percent of the attriters. Here, the user can select the 200
customers in the first two quantiles whose cumulative gain is 57.9
percent and has a lift of 3.289.
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