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Chapter 4 Data Mining Applications in Marketing and Customer Relationship Management

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.

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Chapter 4 Data Mining Applications in Marketing and Customer Relationship Management

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  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

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