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Chapter 4
Data Mining Applications in Marketing and Customer
Relationship Management
2
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
3
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
4
Data Mining & Advertising
• Who fits the profile for this nationwide publication?
Reader-
ship
YES
Score
NO
Score Mike Nancy
Mike
Score
Nancy
Score
BS or > 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 21% 0.21 0.79 Yes No 0.21 0.79
$ > $100k 7% 0.07 0.93 No No 0.93 0.93
Total 2.18 2.68
5
Data Mining & Advertising
• But…that might be a bit naïve; compare readership to US population, then score Mike and Nancy
• 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)
Reader-
ship
YES
US
Pop
Index
Reader-
ship
NO
US
Pop
Index
BS or > 58% 20.3% 2.86* 42% 79.7% 0.53*
Prof/Exec 46% 19.2% 2.40 54% 80.8% 0.67
$ > $75k 21% 9.5% 2.21 79% 90.5% 0.87
$ > $100k 7% 2.4% 2.92 93% 97.6% 0.95
* 58% / 20.3%* 42% / 79.7%
6
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
7
Census Tract Example
8
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 %
9
Consider the following…
• 1,000,000 prospects
• Budget = $300,000
• Mailing to 300,000 prospects
• Rank order list (model) vs no rank order:
0%
0% 100%
100%
30%
30%
RESPONDERS
List Penetration
66%
Benefit (66/30=2.17)
No Model
ModelThe ratio of concentration to penetration is the lift (2.17) (= model performance against no model).
10
Consider the following…
ROC chart / curveThe false positive rate is plotted on the X-axis and one minus the false negative rate is plotted on the Y-axis.
The area under the ROC curve is a measure of the model’s ability to differentiate between two outcomes. This measure is called discrimination. A perfect test has discrimination of 1 and a useless test for two outcomes has discrimination 0.5 since that is the area under the diagonal line that represents no model.
11
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
12
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
13
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)– reaching prospects who are more likely to make purchases because of
having been contacted
• 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%
14
Differential Response Analysis
15
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
16
RapidMiner Practice
• To see:
– Training Videos\01 - Ralf Klinkenberg –RapidMinerResources\...
• 1 - Introduction -2- GUI Advanced.mp4
• 3 - Data Visualisation & Exploration -1-Introduction.mp4
• 3 - Data Visualisation & Exploration -4- Meta Data.mp4
• To practice:
– Do the exercises presented in the movies using the files “Iris.arff” / “Iris.ioo” (RapidMiner data file) and “Labor-Negociations.ioo”.