課程資訊
課程名稱
 網路資料庫行銷研究
Internet Database Marketing Research 
開課學期
 101-2 
授課對象
 管理學院  國際企業學研究所  
授課教師
 任立中 
課號
 IB7056 
課程識別碼
 724 M4110 
班次
  
學分
 3 
全/半年
 半年 
必/選修
 選修 
上課時間
 星期五2,3,4(9:10~12:10) 
上課地點
  
備註
 上課教室：管二301。
限學士班三年級以上 且 限本系所學生(含輔系、雙修生)
總人數上限：30人 
Ceiba 課程網頁
 http://ceiba.ntu.edu.tw/1012IB_DBM 
課程簡介影片
 
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課程概述

Database Marketing is a study of recent data/model-driven research in the academic marketing literature and in the practice of e-commerce. The perspective developed in Marketing Management, Statistical Decision Science, and Computer Science (esp. in Web Warehousing) provides a useful base for the investigation of research literature.  

課程目標
 The objectives of this course are the following:
1. Develop an awareness of the current level of understanding and state of research in several areas of database marketing study. It is hoped that pursuit of this goal will provide the student with a greater understanding of database marketing itself, as new questions are posed from the practice perspective.
2. Develop the ability to read and understand the current research literature. Pursuit of this goal will provide a familiarity with research procedure as it is applied to database marketing. This background should be very useful as the student begins to design and execute research program in the content of e-commerce.
 
課程要求
 CLASS SESSIONS
In each week, the class will be divided into two sessions. A Lecture Session will be given so that the students will get familiar with the ideas and methods related to the certain topics. A Panel Session will be followed and it will be discussion oriented with each student exploring ideas and questions proposed by others and exposing his/her own ideas and questions for investigation by others. Each session will focus on one topic area as indicated in the following class schedule. Discussion will center on the reading assignments, computer programming, and practice simulation. Each student is expected to have thought seriously about both the modeling and the marketing issues relevant to each piece. Every student is expected to participate in the discussion of all materials, and not just a "show and tell" for the article you reviewed.

LITERATURE PRESENTATION/DISCUSSION:
Great emphases will be placed on literature reviews in terms of grading. For the literature reviews grade, students will be evaluated at both group and individual level. In addition to oral presentation/discussion, each group is subject to submit a written report summarizing the discussion of the literature review. At the group level, the group’s performance will be evaluated based on the submitted written report. At the individual level, each student will peer-evaluate other group members’ performance based on his/her i) attendance in paper discussion; ii) preparation for paper preview and review; and iii) contributions to the discussions. The given group grade will be weighted based on individual grade. In other words, individual grade is the weighted group grade using peer-assessment as the weighting factor. This part of excise will count as 30% of the final mark. Students who voluntarily deliver their insights to the class will be given extra credit based on the quality of content, organization and clarity as part of the 10% class participation mark.

TERM PAPER

Each group (or student) will be required to turn in a typed formal research proposal designed to advance a major theoretical or methodological issue considered in this course. It is to be a proposal for research that can actually be carried out by you while you are in the MBA program. This proposal will be expected to include an up-to-date literature review and hypotheses (e.g. a better research framework or model), which follow directly from that literature review. In addition, a research design should be specified which provides a test of the implications of these hypotheses. A section on limitations may be advisable to treat weaknesses in the design as well as to incorporate changes or additions that would strengthen the design. A discussion section could be used to spell out the implications of the student’s proposed research for understanding marketing models and to suggest implications for further research. The paper will be due, with enough copies for distribution to the other members of the class, at the beginning of class on the 16th week (June 7). An oral defense of the proposal will be presented in class at the last session (June 14).

MIDTERM AND FINAL EXAM:
There are two exams during the semester, a midterm and a final exam. Each exam will comprise of 4 to 6 short essay questions. The short essay questions will focus on the various topics discussed throughout the semester. Therefore, in order to get a good mark, you should not limit yourself to those topics you presented in class, but also need to study the rest of the topics. The Midterm and the Final Exam will each count towards 20% of the final mark.
 
預期每週課後學習時數
  
Office Hours
  
指定閱讀
 1. Wulf, Kristof De, Gaby Odekerken-Schröder, and Dawn Iacobucci (2001), “Investments in Customer Relationships: A Cross-Country and Cross-Industry Exploration,” Journal of Marketing, 65(4), 33-50.
2. Chandon, Pierre, Vicki G. Morwitz, and Werner J. Reinartz (2005), “Do Intentions Really Predict Behavior? Self-Generated Validity Effects in Survey Research,” Journal of Marketing, 69(2), 1-14.
3. Allenby, Greg M., Lichung Jen, and Robert P. Leone (1996), “Economic Trends and Being Trendy: The Influence of Consumer Confidence on Retail Fashion Sales,” Journal of Business & Economic Statistics, 14(1), 103-111.
4. Colombo, Richard and Weina Jiang (1999), “A Stochastic RFM Model,” Journal of Interactive Marketing, 13(3), 2-12.
5. Ansari, Asim, Skander Essegaier, and Rajeev Kohli (2000), “Internet Recommendation Systems,” Journal of Marketing Research, 37 (August), 363–75.
6. Jen, Lichung, Chien-Heng Chou, and Greg M. Allenby (2003), “A Bayesian Approach to Modeling Purchase Frequency,” Marketing Letters, 14(1), 5-20.
7. Allenby, Greg M., Robert P. Leone, and Lichung Jen (1999), “A Dynamic Model of Purchase Timing with Application to Direct Marketing,” Journal of the American Statistical Association, 93 (June), 365-374.
8. Jen, Lichung, Demetrios Vakratsas, and Wei-Lin Wang (2009). "Regular and Irregular Purchase Timing Behaviors," working paper, National Taiwan University.
9. Jen, Lichung, Chien-Heng Chou, and Greg M. Allenby (2009), "The Importance of Modeling Temporal Dependence of Timing and Quantity in Direct Marketing," Journal of Marketing Research, 46(4), 482-493.
10. Venkatesan, Rajkumar and V. Kumar (2004), “A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy,” Journal of Marketing, 68(4), 106-125.
11. Rust, Roland T., Katherine N. Lemon, and Valarie A. Zeithaml (2004), “Return on Marketing: Using Customer Equity to Focus Marketing Strategy,” Journal of Marketing, 68(1), 109-127.
12. Gustafssopm, Anders, Michael D. Johnson, and Inger Roos (2005), “The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention,” Journal of Marketing, 69(4), 210-218.
13. Homburg, Christian, Nicole Koschate, and Wayne D. Hoyer (2005), “Do Satisfied Customers Really Pay More? A Study of the Relationship between Customer Satisfaction and Willingness to Pay,” Journal of Marketing, 69(2), 84-96.
14. Boulding, William, Richard Staelin, Michael Ehret, and Wesley J. Johnston (2005), “A Customer Relationship Management Roadmap: What is Known, Potential Pitfalls, and Where to Go,” Journal of Marketing, 69(4), 155-166. 
參考書目
 Reference Books
1. Rossi, Peter E., Greg Allenby, and Rob McCulloch (2005), Bayesian Statistics and Marketing, John Wiley and Sons, New York, NY.
2. Leeflang, Peter S.H., Dick R. Wittink, Michel Wedel, and Philippe A. Naert (2000), Building Models for Marketing Decisions, Lower Academic Publishers, Norwell, MA.
3. Blattberg, Robert C., Gary Getz, and Jacquelyn S. Thomas (2001), Customer Equity: Building and Managing Relationships as Valuable Assets, Harvard Business School Press, Boston, Massachusetts.
4. Lilien, Gary L. and Arvind Rangaswamy (2003), Marketing Engineering: Computer- Assisted Marketing Analysis and Planning, Pearson Education, NJ.
5. Koch, Richard (1998), The 80/20 Principle: The Secret of Achieving More with Less, Doubleday, New York, NY.

Articles from:
Journal of Database Marketing
Journal of Marketing Research
Journal of Marketing
Marketing Science
Sloan Management Review
Journal of the American Statistical Association
Selected Master Theses or Doctorial Dissertations and other journals
 
評量方式
(僅供參考)
  
No.
項目
百分比
說明
1. 
 Contribution to Class Discussion (extra credit) 
10% 
  
2. 
 Group Presentation and Written Report 
30% 
  
3. 
 Midterm Exam  
20% 
 in class on April 19 
4. 
 Term Paper 
30% 
 due on June 7 and Presentation on June 14 
5. 
 Final Exam  
20% 
 in class on June 21 
 
課程進度
週次
日期
單元主題
第1週
   Lecture:(1)The Course Philosophy, Structure, and Policy(2)The Paradigm Shift in Modern Marketing Thoughts(83e7)The Heterogeneous and Dynamic in Consumer Behavior(83e7)Satisfy Consumer Needs – Reactive Marketing Paradigm(83e7)Shape Consumer Needs – Proactive Marketing Paradigm(83e7)Customize Consumer Needs – Interactive Marketing Paradigm(83e7)Individualize Consumer Needs – Chain-Reactive Marketing Paradigm(3)Group assignments and free discussionHomework Assignment:(1)Wulf, Kristof De, Gaby Odekerken-Schroder, and Dawn Iacobucci (2001), “Investments in Customer Relationships: A Cross-Country and Cross-Industry Exploration,” Journal of Marketing, 65(4), 33-50. 
第2週
   Lecture:(1)Strategic Planning in Database Marketing(2)Organizing for Database Marketing (BKN 3)(83e7)The Customer-Centric Organization(83e7)Processes for Managing Information: Knowledge Management(3)Customer Privacy and Database Marketing (BKN 4)(83e7)Customer Attitudes Toward Privacy(83e7)Current Practices Regarding Privacy(83e7)Potential Solutions to Privacy Concerns(4)Sources of Data (BKN 8)(83e7)Types of Data for Describing Customers(83e7)Sources of Customer InformationGroup (1) Presentation:Wulf, Odekerken-Schroder, and Iacobucci (2001)Homework Assignment:(2)Chandon, Pierre, Vicki G. Morwitz, and Werner J. Reinartz (2005), “Do Intentions Really Predict Behavior? Self-Generated Validity Effects in Survey Research,” Journal of Marketing, 69(2), 1-14. 
第3週
   Lecture:(1)Data Collection and Questionnaire Design(83e7)Survey Methods(83e7)Measurements(83e7)Questionnaire Design(2)Test Design and Analysis (BKN 9)(83e7)Sampling Techniques(83e7)Determining the sample size(83e7)Test DesignsGroup (2) Presentation:Chandon, Morwitz, and Reinartz (2005)Homework Assignment:(3)Allenby, Greg M., Lichung Jen, and Robert P. Leone (1996), “Economic Trends and Being Trendy: The Influence of Consumer Confidence on Retail Fashion Sales,” Journal of Business  
第4週
   Lecture:(1)The Predictive Modeling Process (BKN 10)(83e7)Predictive Modeling and the Quest for Marketing Productivity(83e7)The Predictive Modeling Process: Overview(83e7)The Process in Detail(83e7)A Predictive Modeling Example(83e7)Long-Term Considerations(2)Statistical Issues in Predictive Modeling (BKN 11)(83e7)Economic Justification for Building a Statistical Model(83e7)Selection of Variables and Models(83e7)Treatment of Missing Variables(83e7)Evaluation of Statistical Models(83e7)Concluding Note: Evolutionary Model-BuildingGroup (3) Presentation:Allenby, Jen, and Leone (1996).Homework Assignment:(4)Colombo, Richard and Weina Jiang (1999), “A Stochastic RFM Model,” Journal of Interactive Marketing, 13(3), 2-12. 
第5週
   Lecture:
(1) FRM Analysis (BKN 12)
(83e7) The Basics of the RFM Model
(83e7) Definition of Recency, Frequency, and Monetary Value
(83e7) RFM for Segment-Level Prediction
(83e7) Breakeven Analysis: Determining the Cutoff Point
(83e7) Profit Maximizing Cutoff Response Probability
(83e7) Heterogeneous Order Amounts
(83e7) Extending the RFM Model
(83e7) Treating the RFM Model as ANOVA
(83e7) Alternative Response Models Without Discretization
(2) Marketing Basket Analysis (BKN 13)
(83e7) Deriving Market Basket Association Rules
(83e7) Setup of a Market Basket Problem
(83e7) Deriving "Interesting" Association Rules
(83e7) Zhang (2000) Measures of Association and Dissociation
(83e7) Issues in Market Basket Analysis
(83e7) Using Taxonomies to Overcome the Dimensionality Problem
(83e7) Association Rules for More than Two Items
(83e7) Adding Virtual Items to Enrich the Quality of the Market Basket Analysis
(83e7) Adding Temporal Component to the Market Basket Analysis
Group (4) Presentation: Colombo and Jiang (1999)
Homework Assignment:
(5) Ansari, Asim, Skander Essegaier, and Rajeev Kohli (2000), “Internet Recommendation Systems,” Journal of Marketing Research, 37 (August), 363–75. 
第6週
   Lecture:
(1) Profiling and Cloning Customers (BKN 16)
(83e7) The Clustering Process
(83e7) Applying Cluster Analysis
(2) Targeting the Best/Right Customers (BKN 17)
(83e7) Fundamentals of Decision Trees
(83e7) Finding the Best Splitting Rule
(83e7) Finding the Right Sized Tree
(83e7) Other Issues in Decision Trees
(83e7) Application to a Direct Mail Offer
(83e7) Strengths and Weaknesses of Decision Trees
Group (5) Presentation: Ansari, Essegaier, and Kohli (2000)
Homework Assignment:
(6) Jen, Lichung, Chien-Heng Chou, and Greg M. Allenby (2003), “A Bayesian Approach to Modeling Purchase Frequency,” Marketing Letters, 14(1), 5-20. 
第7週
   National Holiday/No Class 
第8週
   Lecture:
(1) Collaborative Filtering (BKN 14)
(83e7) Introduction
(83e7) Memory-Based Methods
(83e7) Model-Based Methods
(83e7) Current Issues in Collaborative Filtering
(2) Discrete Dependent Variables (BKN 15)
(83e7) Binary Response Model
(83e7) Linear Probability Model
(83e7) Binary Logit and Probit Models
(83e7) Logistic Regression with Rare Events Data
(83e7) Discriminant Analysis
(83e7) Multinomial Response Model
(83e7) Models for Count Data
(83e7) Poisson Regression
(83e7) Negative Binomial Regression
(83e7) Censored Regression (Tobit) Models and Extensions
Group (6) Presentation: Jen, Chou, and Allenby (2003)
Homework Assignment:
(7) Allenby, Greg M., Robert P. Leone, and Lichung Jen (1999), “A Dynamic Model of Purchase Timing with Application to Direct Marketing,” Journal of the American Statistical Association, 93 (June), 365-374. 
第9週
   Midterm Exam 
第10週
   Lecture:
(1) Lecturing Bayesian Statistical Decision Analysis
(83e7) Review Classical Statistics
(83e7) Distribution Theory
(83e7) Bayes Theorem and Inferences
(83e7) Hierarchical Bayes Models
Group (7) Presentation: Allenby, Leone, and Jen (1999)
Homework Assignment:
(8) Jen, Lichung, Demetrios Vakratsas, and Wei-Lin Wang (2009). "Regular and Irregular Purchase Timing Behaviors," working paper, National Taiwan University. 
第11週
   Lecture:
(1) Purchase Timing Models and Duration (Hazard) Models (BKN 15)
(83e7) Characteristics of Duration Data
(83e7) Analysis of Duration Data Using a Classical Linear Regression
(83e7) Hazard Models
(83e7) Incorporating Covariates into the Hazard Function
(2) Patterns in Time Series
(3) Dependency Derivation
Group (8) Presentation: Jen, Vakratsas, and Wang (2009)
Homework Assignment:
(9) Jen, Lichung, Chien-Heng Chou, and Greg M. Allenby (2009), "The Importance of Modeling Temporal Dependence of Timing and Quantity in Direct Marketing," Journal of Marketing Research, 46(4), 482-493. 
第12週
   Lecture:
(1) Customer Lifetime Value: Fundamentals (BKN 5)
(83e7) Mathematical Formulation of LTV
(83e7) The Two Primary LTV Models: Simple Retention and Migration
(83e7) LTV Models that Include Unobserved Customer Attrition
(83e7) Estimating Revenues
Group (9) Presentation: Jen, Chou, and Allenby (2009)
Homework Assignment:
(10) Venkatesan, Rajkumar and V. Kumar (2004), “A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy,” Journal of Marketing, 68(4), 106-125. 
第13週
   Lecture:
(1) Issues in Computing Customer Lifetime Value (BKN 6)
(83e7) Discount Rate and Time Horizon
(83e7) Customer Portfolio Management
(83e7) Cost Accounting Issues
(83e7) Incorporating Marketing Response
(83e7) Incorporating Externalities
(2) Customer Lifetime Value Applications (BKN 7)
(83e7) Using LTV to Target Customer Acquisition
(83e7) Using LTV to Guide Customer Reactivation Strategies
(83e7) Using SMC's Model to Value Customers
(83e7) A Case Example of Applying LTV Modeling
(83e7) Segmentation Methods Using Variants of LTV
(83e7) Drivers of the Components of LTV
(83e7) Forecasting Potential LTV
(83e7) Valuing a Firm's Customer Base
Group (10) Presentation: Venkatesan and Kumar (2004)
Homework Assignment:
(11) Rust, Roland T., Katherine N. Lemon, and Valarie A. Zeithaml (2004), “Return on Marketing: Using Customer Equity to Focus Marketing Strategy,” Journal of Marketing, 68(1), 109-127. 
第14週
   Lecture:
(1) Acquiring Customers (BKN 20)
(83e7) The Fundamental Equation of Customer Equity
(83e7) Acquisition Costs
(83e7) Strategies for Increasing Number of Customers Acquired
(83e7) Developing a Customer Acquisition Program
(83e7) Research Issues in Acquisition Marketing
(2) Cross-Selling and Up-Selling (BKN 21)
(83e7) The Strategy
(83e7) Cross-Selling Models
(83e7) Up-Selling
(83e7) Developing an Ongoing Cross-Selling Effort
(3) Frequency Reward Programs (BKN 22)
(83e7) How Frequency Reward Programs Influence Customer Behavior
(83e7) Do Frequency Reward Programs Increase Profits in a Competitive Environment?
(83e7) Frequency Reward Programs Design
(83e7) Frequency Reward Programs Examples
(4) Customer Tier Programs (BKN 23)
(83e7) Design Customer Tier Programs
(83e7) Examples of Customer Tier Programs
(83e7) Risks in Implementing Customer Tier Programs
Group (11) Presentation: Rust, Lemon, and Zeithaml (2004)
Homework Assignment:
(12) Gustafssopm, Anders, Michael D. Johnson, and Inger Roos (2005), “The Effects of Customer Satisfaction, Relationship commitment Dimensions, and Triggers on Customer Retention,” Journal of Marketing, 69(4), 210-218. 
第15週
   Lecture:
(1) Churn Management (BKN 24)
(83e7) Factors that Cause Churn
(83e7) Predicting Customer Churn
(83e7) Managerial Approaches to Reducing Churn
(2) Multichannel Customer Management (BKN 25)
(83e7) The Emergence of Multichannel Customer Management
(83e7) The Multichannel Customer
(83e7) Developing Multichannel Strategies
(83e7) Industry Examples
(3) Acquisition and Retention Management (BKN 26)
(83e7) Modeling Acquisition and Retention
(83e7) Optimal Acquisition and Retention Spending
(83e7) Acquisition and Retention Budget Planning
(83e7) Acquisition and Retention Strategy: An Overall Framework
Group (12) Presentation: Gustafssopm, Johnson, and Roos (2005)
Homework Assignment:
(13) Homburg, Christian, Nicole Koschate, and Wayne D. Hoyer (2005), “Do Satisfied Customers Really Pay More? A Study of the Relationship Between Customer Satisfaction and Willingness to Pay,” Journal of Marketing, 69(2), 84-96.
(14) Boulding, William, Richard Staelin, Michael Ehret, and Wesley J. Johnston (2005), “A Customer Relationship Management Roadmap: What is Known, Potential Pitfalls, and Where to Go,” Journal of Marketing, 69(4), 155-166. 
第16週
   Lecture:
(1) Database Marketing Communications (BKN 27)
(83e7) The Planning Process
(83e7) Setting the Overall Plan
(83e7) Developing Copy
(83e7) Selecting Media
(83e7) Evaluating Communications Programs
(2) Multiple Campaign Management (BKN 28)
(83e7) Dynamic Response Phenomena
(83e7) Optimal Contact Models
(3) Pricing (BKN 29)
(83e7) Overview -- Customer-based Pricing
(83e7) Customer Pricing when Customers Can Purchase Multiple One-Time Products from the Firm
(83e7) Pricing the Same Products/Services to Customers over Two Periods
(83e7) Acquisition and Retention Pricing Using the Customer Equity Model
(83e7) Pricing to Recapture Customers
(83e7) Pricing Add-on Sales
(83e7) Price Discrimination Through Database Targeting Models
Group (13) Presentation: Homburg, Koschate, and Hoyer (2005)
Group (14) Presentation: Boulding, Staelin, Ehret, and Johnston (2005) 
第17週
   Term Paper Presentation
You must each read and be prepared to discuss each student’s written research proposal. Each proposal writer is expected to briefly present key aspects of the proposal, explain its contribution and respond to questions, criticisms and suggestions. 
第18週
   Final Exam