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”.