Faculty
Professor Scott A. Neslin
Objectives
The course objective is to introduce students to the concepts and methods of database marketing. Students will work with real world applications and databases. Methods covered include lifetime value of the customer (LTV), predictive modeling (e.g., regression, logistic regression, multinomial logit, cluster analysis, decision trees, and neural nets), and experimentation/testing. Applications include list selection, prospecting, cross-selling, up-selling, market segmentation, product personalization, and multichannel customer management. Industries examined include catalogs, software, retailing, financial services, electrical equipment, consumer electronics, telecom, and retail banking. Upon completing this course, students should have a working knowledge of database marketing, its application potential, and limitations.
Requirements
Teaching Format
The class utilizes lectures, cases, and guest speakers. Students are required to conduct in-depth quantitative analysis of database marketing problems. This will involve spreadsheet analyses, SPSS to estimate regression and logistic regression response models, and commercial software to estimate neural net and other predictive models.
Several of the assignments involve teams. We will be organizing students into 3-5 person teams by the end of Friday, March 27.
Assignments
Class Presentation: Each team will present one of the 9 cases we cover on the day we discuss it in class. The class presentation will be 20 minutes long and cover the discussion questions for that case. The team is to hand in a hard copy of their presentation and send me an electronic version so I can place it in the course folder.
Case Write-Ups: Each week, each team will write up one case. If we are covering only one case in that week, that is the case that should be written up. If we are covering two cases, the team can choose which case to write up. The write-ups should be 2-3 pages long plus exhibits. There will be 8 write-ups. The group that presents a particular case needs to hand in a write-up of that case as well.
LTV Homework Exercise: Working as individuals, students will complete an exercise illustrating the calculation of lifetime value of the customer.
MovieLens: Working as individuals, students will complete an Internet-based exercise illustrating the workings of recommendation engines.
Class Participation: Students will be expected to be prepared and participate in class.
Materials
Optional Further Reading
Blattberg, Robert C., Byung-Do Kim, and Scott A. Neslin (2008) Database Marketing: Analyzing and Managing Customers, New York: Springer.
Blattberg, Robert C., and John Deighton (1996) "Manage Marketing by the Customer Equity Test," Harvard Business Review (July-August) 136-144.
David Shepard Associates, Inc. (1995) The New Direct Marketing: How to Implement A Profit-Driven Database Marketing Strategy, Burr Ridge, Illinois: Irwin Publishing.
Hughes, Arthur M. (1996) The Complete Database Marketer, Chicago: Irwin.
Journal of Direct Marketing
Journal of Interactive Marketing
Nash, Edward L. (1993) Database Marketing: The Ultimate Selling Tool, New York: McGraw Hill, Inc.
Swift, Ronald S. (2001) Accelerating Customer Relationships: Using CRM and Relationship Technologies, Upper Saddle River, NJ: Prentice Hall PTR.
Honor Code
For all case assignments, you can ask each other about technical issues, clarifications, or software. You can also ask me these questions. But you should not discuss your answers to the case questions, or what the answer should be, either with me or with your fellow students. The LTV homework is to be an individual effort where you can draw on class notes, lectures, and the spreadsheet template I will provide in the course folder.
Grading
Class presentation 17%
Exercise write-ups 56%
LVC Exercise 10%
Class participation 17%
Schedule
Session 1 - March 26
Introduction
Lecture:
Definition and importance of database marketing
Economics of database marketing
Lifetime value of the customer
Course set-up
Session 2 - March 27
Predictive Modeling I
Lecture:
Regression
Logistic regression
Decision Trees / CHAID
Scoring
Group Sign-Up Form Due: 5 PM
Session 3 - April 1
Predictive Modeling II
Lecture:
Neural Nets
Lift statistics
Validation samples
ModelMax software
LTV Exercise Due
Session 4 - April 2
Lifetime Value of the Customer I
Case:
The Independent Adviser for Vanguard Investors
Session 5 - April 8
Lifetime Value of the Customer II
Case:
The Crutchfield Corporation
Session 6 - April 9
Predictive Modeling III
Cluster Analysis
Multinomial Logit
Install ModelMax Software by this date: See ModelMax Installation Instructions in course packet
Session 7 - April 15
Cross-Selling
Lecture
Article: Next-Product-to-Buy Models for Cross-Selling Applications (Knott, Hayes, and Neslin)
April 15
INTUIT Recommendations Due, 1 PM
Session 8 - April 16
Upselling
Case:
Intuit: Quickbooks Upgrade
Download Marketing Engineering software by this date. See “Marketing Engineering Download” handout.
Monday, April 20
MovieLens Assignment Due 5 PM
Session 9 - April 22
Choice-Based Segmentation
Case:
ABB Electrical Equipment
Session 10 - April 23
Psychographic Segmentation
Case:
Conglomerate, Inc. – PDA
April 24
“The Bank” “Number Mailed as Test” Form Due, 1 PM
April 28
“The Bank” “Number Mailed on Roll-Out” Form Due, 9 AM
Session 11 - April 29
Product Personalization
Case:
Information-Based Credit Card Design
Session 12 - April 30
Multichannel Customer Management 1
Lecture
Session 13 - May 6
Churn Management
Case:
Cell2Cell
Session 14 - May 7
Acquisition and Retention 1
Lecture
Session 15 - May 13
Multichannel Customer Management 2
Case:
Pilgrim Bank (A): Customer Profitability
Session 16 - May 14
Guest Speakers
Session 17 - May 20
Acquisition and Retention 2
Case:
The King-Size Company (to be distributed)
Session 18 - May 21
Course Summary
