Transcript Chapter 4

Chapter 4
Data Mining Applications in
Marketing and Customer
Relationship Management
Business Context for DM
• Although the technical aspects of DM are
interesting and exciting (at least to geeks!), they
must be utilized in a business context to be of
value.
• Business topics addressed in this chapter are
roughly in ascending order of complexity of the
customer relationship, starting:
– Communication with prospects (little knowledge of
them)
– On-going customer relationships involving multiple:
• Products
• Communication channels/methods
• Increasingly individualized interactions
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Prospecting
• Prospect
– Noun – someone/something with possibilities
– Verb – to explore
• > 6B people worldwide
– Relatively few are prospects for a company
– Exclusion based on geography, age, ability to pay,
need for product/service, etc.
• Data mining can help in prospecting:
– Identifying good prospects
– Choosing appropriate communication channels
– Picking suitable messages
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Data Mining & Advertising
• Who fits the profile for this nationwide
publication?
Readership
YES
Score
58%
0.58
0.42
Yes
No
0.58
0.42
Prof/Exec 46%
0.46
0.54
Yes
No
0.46
0.54
$ > $75k
0.21
0.79
Yes
No
0.21
0.79
0.07
0.93
No
No
0.93
0.93
2.18
2.68
BS or >
21%
$ > $100k 7%
Total
NO
Score
Mike
Nancy
Mike
Score
Nancy
Score
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Data Mining & Advertising
• But…that might be a bit naïve; compare readership to
US population, then score Mike and Nancy
Readership
BS or >
YES
US
Pop
ReaderIndex
ship
NO
US
Pop
Index
58%
20.3%
2.86*
42%
79.7%
0.53*
Prof/Exec 46%
19.2%
2.40
54%
80.8%
0.67
$ > $75k
9.5%
2.21
79%
90.5%
0.87
2.4%
2.92
93%
97.6%
0.95
21%
$ > $100k 7%
• Mike’s score: 8.42 (2.86 + 2.40 + 2.21 + 0.95)
• Nancy’s score: 3.02 (0.53 + 0.67 + 0.87 + 0.95)
* 58% / 20.3%
* 42% / 79.7%
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TIP
• When comparing customer profiles (Mike
and Nancy), it is important to keep in mind
the profile of the population as a whole.
• For this reason, using indexes (table #2) is
often better than using raw values (table
#1)
• Review Census Tract example on pages
94-95
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Census Tract Example
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Data Mining and Direct Marketing Campaigns
• Typical mailing of 100,000 pieces costs
about $100,000 ($1/piece)
• Typical response rates < 10%
• Any list of prospects/customers that can
be ranked by likelihood of response is
good
• Campaign focused at top of list to increase
response rate %
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Consider the following…
•
•
•
•
1,000,000 prospects
Budget = $300,000
Mailing to 300,000 prospects
Rank order list (model) vs no rank order:
R
E
S
P
O
N
D
E
R
S
100%
Model
66%
No Model
30%
Benefit
0%
0%
30%
List Penetration
100%
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Consider the following…
• Is the benefit worth the cost?
• Often, smaller, better-targeted campaign can be
more profitable than a larger and more
expensive one
• Be sure to consider real revenue (for example,
10 people buy = $100 revenue; 20 people buy =
$200 revenue)
• Campaign profitability depends on many
variables that can only be estimated, hence the
need for an actual market test
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Marketing Campaign
• Goal is to change behavior (to help drive
revenue)
• How do we know if we did?
– Control Group – randomly receives mailing
– Test Group – model selected to get mailing
– Holdout Group – model selected not get
mailing
– Compare responses of the groups
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Differential Response Analysis
• How do we know if the responders actually responded because
of our campaign or would have anyway?
• Answer: Differential Response Analysis (DRA)
• DRA starts with Control & Treated groups
• Control group = no “mailing”
• Treated group = receive “mailing”
• Compare results…see if there is any “uplift”
Control Group
Treated Group
Young
Old
Young
Old
Women
0.8%
0.4%
4.1%
4.6%
Men
2.8%
3.3%
6.2%
5.2%
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DM “meets” CRM*
•
•
•
•
•
•
•
Matching campaigns to customers
Segmenting the customer base
Reducing exposure to credit risk
Determining customer value
Cross-selling and Up-selling
Retention and Churn ([in]voluntary attrition)
Different kinds of churn models – predicting who
will leave; predicting how long one will stay
* Customer Relationship Management
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End of Chapter 4
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