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

No Model

Model

10

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

11

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

12

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%

13

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

14

End of Chapter 4