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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Lecture 10
MARK2039
Summer 2006
George Brown College
Wednesday 912
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
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%
1
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?