Lecture 10. MARK2039 Summer 2006 George Brown College Wednesday 912. Assignment 8: Geocoding example. Example: A retailer has the following information: Name and address of its customers Address of its stores Stats Can Information
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How is the above derived?
From the partial R2 of each variable divided by the total R2 of the equation.
Untargetted/
Targetted/
Type of Activity
Benchmark
Challenger
Lift
Acquisition Campaign
Response Rate
1%
2%
200.
Retention Campaign
Churn Rate
15%
25%
166
Credit Card Loss Rate
5%
8%
160
Product Affinity Rate
10%
30%
300
The targetted group represents those names as determined by a
data mining tool such as a predictive model.
Untargetted/
Targetted/
Type of Activity
Benchmark
Challenger
Lift
Acquisition
Campaign
Response Rate
1%
.5%
50
Retention
Campaign
Churn Rate
15%
10%
66
Credit Card
Loss Rate
5%
2%
40
Product Affinity
Rate
10%
6%
60
% of List
Validation
Cum.
Cum. %
Cum.
Interval
Benefits
(Ranked by
Resp.
of all
Lift
ROI
Model
Quantity
Rate
Resp
Score)
0

10%
20000
3.50%
23.33%
233
145%
$22799
10

20%
40000
3.00%
40%
200
75%
$34200
20

30%
60000
2.75%
55%
183
58%
$42750
30

40%
80000
2.50%
67%
167
23%
$45600
40

50%
100000
2.25%
75%
150

12.2%
$42750
.
.
.
90

100%
20,0000
1.50%
100%
100

58%
$0
How might this be plotted?in class we saw this as a straight decreasing linear slope if we were plotting interval resp. rate against the deciles. If we plot the Cum % of responders, then the shape would be a parobola type curve with a larger parobola representing a better model. Meanwhile, a steeper slope if we plotted interval response rate against deciles would represent a stronger model.
Cum. # of Names charts
Cum. Response
Mailed
Rate
Interval Resp.Rate
Interval Lift
Benefits
Interval ROI
10000
2.50%
20000
2.25%
30000
2.10%
40000
1.80%
.
.
.
.
100000
1%
Gains Chart Examples1
25%
0
10%
55%
$15,000
$25,000
$33,000
$32,000
2.5%
250
2.5%
200
2.5%
1.8%
180
0.9%
90
Assume a mail cost of $1.00 per piece and a revenue per order of $50.00.
IntervalResp.Rate
10,000*0.025=250=2.5%
20,000*0.
Please fill in the blanks for the first 4 rows.
What does this look like if we plot it on a lift curve
A line rather than a parobola if we plot cum % of responders
What is the best model?Model 1
What is the worst model?Model 4
What are the Model 3 results telling you. –we have some rank ordering all the way down to 70000 names and then the model flattens outmay need a strategy herefor this bottom segment.
Model is rank ordering names quite well for campaign B(1.2% overall) while the better campaign overall(3.5%) exhibits no rank ordering of response rate between deciles.
What criteria determine the end nodes?