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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