
Cumulative Gains and Lift
Charts 


Lift is a measure of the effectiveness of a predictive model calculated
as the ratio between the results obtained with and without the predictive
model.

Cumulative gains and lift charts are visual aids for measuring model performance

Both charts consist of a lift curve and a baseline

The greater the area between the lift curve and the baseline, the better
the model
Example Problem 1
A company wants to do a mail marketing campaign. It costs the company $1
for each item mailed. They have information on 100,000 customers. Create
a cumulative gains and a lift chart from the following data.

Overall Response Rate: If we assume we have no model other than
the prediction of the overall response rate, then we can predict the number
of positive responses as a fraction of the total customers contacted. Suppose
the response rate is 20%. If all 100,000 customers are contacted we will
receive around 20,000 positive responses.
Cost ($) 
Total Customers Contacted 
Positive Responses 
100000

100000

20000


Prediction of Response Model: A response model predicts who will
respond to a marketing campaign. If we have a response model, we can make
more detailed predictions. For example, we use the response model to assign
a score to all 100,000 customers and predict the results of contacting
only the top 10,000 customers, the top 20,000 customers, etc.
Cost ($) 
Total Customers Contacted 
Positive Responses 
10000

10000

6000

20000

20000

10000

30000

30000

13000

40000

40000

15800

50000

50000

17000

60000

60000

18000

70000

70000

18800

80000

80000

19400

90000

90000

19800

100000

100000

20000

Cumulative Gains Chart:

The yaxis shows the percentage of positive responses. This is a
percentage of the total possible positive responses (20,000 as the overall
response rate shows).

The xaxis shows the percentage of customers contacted, which is
a fraction of the 100,000 total customers.

Baseline (overall response rate): If we contact X% of customers
then we will receive X% of the total positive responses.

Lift Curve: Using the predictions of the response model, calculate
the percentage of positive responses for the percent of customers contacted
and map these points to create the lift curve.
Lift Chart:

Shows the actual lift.

To plot the chart: Calculate the points on the lift curve by determining
the ratio between the result predicted by our model and the result using
no model.

Example: For contacting 10% of customers, using no model we should get
10% of responders and using the given model we should get 30% of responders.
The yvalue of the lift curve at 10% is 30 / 10 = 3.
Analyzing the Charts: Cumulative gains and lift charts are a
graphical representation of the advantage of using a predictive model to
choose which customers to contact. The lift chart shows how much more likely
we are to receive respondents than if we contact a random sample of customers.
For example, by contacting only 10% of customers based on the predictive
model we will reach 3 times as many respondents as if we use no model.
Evaluating a Predictive Model
We can assess the value of a predictive model by using the model to score
a set of customers and then contacting them in this order. The actual response
rates are recorded for each cutoff point, such as the first 10% contacted,
the first 20% contacted, etc. We create cumulative gains and lift charts
using the actual response rates to see how much the predictive model would
have helped in this situation. The information can be used to determine
whether we should use this model or one similar to it in the future.
Example Problem 2
Using the response model P(x)=100AGE(x) for customer x
and the data table shown below, construct the cumulative gains and lift
charts. Ties in ranking should be arbitrarily broken by assigning a higher
rank to who appears first in the table.
Customer Name

Height

Age

Actual Response

Alan

70

39

N

Bob

72

21

Y

Jessica

65

25

Y

Elizabeth

62

30

Y

Hilary

67

19

Y

Fred

69

48

N

Alex

65

12

Y

Margot

63

51

N

Sean

71

65

Y

Chris

73

42

N

Philip

75

20

Y

Catherine

70

23

N

Amy

69

13

N

Erin

68

35

Y

Trent

72

55

N

Preston

68

25

N

John

64

76

N

Nancy

64

24

Y

Kim

72

31

N

Laura

62

29

Y

1. Calculate P(x) for each person x
2. Order the people according to rank P(x)
Customer Name 
P(x) 
Actual Response 
Alex 
88 
Y 
Amy 
87 
N 
Hilary 
81 
Y 
Philip 
80 
Y 
Bob 
79 
Y 
Catherine 
77 
N 
Nancy 
76 
Y 
Jessica 
75 
Y 
Preston 
75 
N 
Laura 
71 
Y 
Elizabeth 
70 
Y 
Kim 
69 
N 
Erin 
65 
Y 
Alan 
61 
N 
Chris 
58 
N 
Fred 
52 
N 
Margot 
49 
N 
Trent 
45 
N 
Sean 
35 
Y 
John 
24 
N 
3. Calculate the percentage of total responses for each cutoff
point

Response Rate = Number of Responses / Total Number of Responses (10)
Total Customers Contacted

Number of Responses

Response Rate

2

1

10%

4

3

30%

6

4

40%

8

6

60%

10

7

70%

12

8

80%

14

9

90%

16

9

90%

18

9

90%

20

10

100%

4. Create the cumulative gains chart:

The lift curve and the baseline have the same values for 10%20% and 90%100%.
5. Create the lift chart: