An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 28

Table 2.4 The gains, response, and lift table.

Model tiles Cumulative % Gain % Response % Lift (%)

of records

1 10 37.1 10.7 371.4

2 20 56.9 8.2 284.5

3 30 69.6 6.7 232.1

4 40 79.6 5.7 199.0

5 50 87.0 5.0 174.1

6 60 91.6 4.4 152.7

7 70 94.6 3.9 135.2

8 80 96.4 3.5 120.6

9 90 98.2 3.1 109.2

10 100 100.0 2.9 100.0

The concentration of the actual churners is also expected to decrease. Indeed,
the first two tiles, which jointly account for the top 20% of customers with the
highest estimated churn scores, have a smaller percentage of actual churners
(8.2%). This percentage is still 2.8 times higher than randomness, though.
• Gain %: ''How many of the target population fall in the quantiles?'' Gain %
is defined as the percentage of the total target population that belongs in the
quantiles. In our example, the top 10% model tile contains 37.1% of all actual
churners, yielding a gain%of the same value. A random list containing 10%of the
customers would normally capture about 10%of all observed churners. However,
the top model tile contains more than a third (37.1%) of all observed churners.
Once again we come to the lift concept. The top 10% model tile identifies about
four times more target customers than a random list of the same size.
• Lift: ''How much better are the model results compared to randomness?'' The
lift or index assesses the improvement in predictive ability due to the model. It
is defined as the ratio of the response % to the prior probability. In other words,
it compares the model quantiles to a random list of the same size in terms of
the probability of the target category. Therefore it represents how much a data
mining model exceeds the baseline model of random selection.

The gain, response, and lift evaluation measures can also be depicted in
corresponding charts such as those shown below. The two added reference lines
correspond to the top 5% and the top 10% tiles. The diagonal line in the gains
chart represents the baseline model of randomness.
The response chart (Figure 2.4) visually illustrates the estimated churn
probability among the mode tiles. As we move to the left of the X -axis and toward
the top tiles, we have increased churn probabilities. These tiles would result inmore