Chapter 4 Data Mining Applications in Marketing and Customer Relationship Management

1. Chapter 4Data 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?

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) * 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: 100% R E S P O N D E R S Model 66% No Model 30% Benefit 0% 0% 30% List Penetration 100%

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”

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