Database Marketing: Analyzing and Managing Customers

Front Cover
Springer Science & Business Media, Jan 23, 2008 - Business & Economics - 872 pages

Database marketing is at the crossroads of technology, business strategy, and customer relationship management. Enabled by sophisticated information and communication systems, today’s organizations have the capacity to analyze customer data to inform and enhance every facet of the enterprise—from branding and promotion campaigns to supply chain management to employee training to new product development. Based on decades of collective research, teaching, and application in the field, the authors present the most comprehensive treatment to date of database marketing, integrating theory and practice. Presenting rigorous models, methodologies, and techniques (including data collection, field testing, and predictive modeling), and illustrating them through dozens of examples, the authors cover the full spectrum of principles and topics related to database marketing.

"This is an excellent in-depth overview of both well-known and very recent topics in customer management models. It is an absolute must for marketers who want to enrich their knowledge on customer analytics." (Peter C. Verhoef, Professor of Marketing, Faculty of Economics and Business, University of Groningen)

"A marvelous combination of relevance and sophisticated yet understandable analytical material. It should be a standard reference in the area for many years." (Don Lehmann, George E. Warren Professor of Business, Columbia Business School)

"The title tells a lot about the book's approach—though the cover reads, "database," the content is mostly about customers and that's where the real-world action is. Most enjoyable is the comprehensive story – in case after case – which clearly explains what the analysis and concepts really mean. This is an essential read for those interested in database marketing, customer relationship management and customer optimization." (Richard Hochhauser, President and CEO, Harte-Hanks, Inc.)

"In this tour de force of careful scholarship, the authors canvass the ever expanding literature on database marketing. This book will become an invaluable reference or text for anyone practicing, researching, teaching or studying the subject." (Edward C. Malthouse, Theodore R. and Annie Laurie Sills Associate Professor of Integrated Marketing Communications, Northwestern University)

 

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Contents

Introduction
3
111 Defining Database Marketing
4
112 Database Marketing Direct Marketing and Customer Relationship Management
5
12 Why Is Database Marketing Becoming More Important?
6
13 The Database Marketing Process
8
14 Organization of the Book
12
Why Database Marketing?
13
212 The Marketing Productivity Argument in Depth
15
163 Applying Cluster Analysis
419
1632 Targeting the Desired Cluster
420
Decision Trees
423
172 Fundamentals of Decision Trees
424
173 Finding the Best Splitting Rule
427
1732 Entropy and Information Theoretic Measures
429
1733 ChiSquare Test
430
1734 Other Splitting Rules
432

213 Evidence for the Marketing Productivity Argument
19
214 Assessment
22
22 Creating and Enhancing Customer Relationships
23
223 Evidence for the Argument that Database Marketing Enhances Customer Relationships
28
224 Assessment
31
23 Creating Sustainable Competitive Advantage
32
233 Assessment
44
24 Summary
45
Organizing for Database Marketing
47
32 Database Marketing Strategy
48
321 Strategies for Implementing DBM
49
322 Generating a Competitive Advantage
51
The Structural Foundation of the CustomerCentric Organization
52
332 The Motivation for Customer Management
53
333 Forming Customer Portfolios
54
334 Is Customer Management the Wave of the Future?
55
335 Acquisition and Retention Departmentalization
56
Knowledge Management
57
342 Does Effective Knowledge Management Enhance Performance?
58
343 Creating Knowledge
59
344 Codifying Knowledge
60
345 Transferring Knowledge
61
346 Using Knowledge
62
347 Designing a Knowledge Management System
63
348 Issues and Challenges
65
351 Theory
66
352 Empirical Findings
67
353 Summary
69
362 IntraFirm Coordination
70
Customer Privacy and Database Marketing
75
412 Historical Perspective
78
42 Customer Attitudes Toward Privacy
79
422 Impact of Attitudes on Database Marketing Behaviors
81
423 International Differences in Privacy Concerns
82
43 Current Practices Regarding Privacy
85
432 Collecting Data
87
433 The Legal Environment
88
44 Potential Solutions to Privacy Concerns
91
443 Permission Marketing
94
444 Customer Data Ownership
96
445 Focus on Trust
97
446 Top Management Support
98
447 Privacy as Profit Maximization
99
45 Summary and Avenues for Research
100
Customer Lifetime Value LTV
103
Customer Lifetime Value Fundamentals
105
511 Definition of Lifetime Value of a Customer
106
52 Mathematical Formulation of LTV
108
Simple Retention and Migration
109
532 Migration Models
114
54 LTV Models that Include Unobserved Customer Attrition
121
55 Estimating Revenues
130
554 Stochastic Models of Purchase Rates and Volume
131
Issues in Computing Customer Lifetime Value
133
62 Discount Rate and Time Horizon
134
622 Discount Rate Based on the SourceofRisk Approach
140
63 Customer Portfolio Management
142
64 Cost Accounting Issues
145
642 Variable Costs and Allocating Fixed Overhead
148
65 Incorporating Marketing Response
154
66 Incorporating Externalities
158
Customer Lifetime Value Applications
161
72 Using LTV to Guide Customer Reactivation Strategies
163
73 Using SMCs Model to Value Customers
164
74 A Case Example of Applying LTV Modeling
168
75 Segmentation Methods Using Variants of LTV
172
752 Creating Customer Portfolios Using LTV Measures
174
76 Drivers of the Components of LTV
175
77 Forcasting Potential LTV
176
78 Valuing a Firms Customer Base
178
Database Marketing Tools The Basics
181
Sources of Data
183
82 Types of Data for Describing Customers
184
822 Demographic Data
186
824 Transaction Data
188
825 Marketing Action Data
190
826 Other Types of Data
191
831 Internal Secondary Data
192
832 External Secondary Data
193
833 Primary Data
211
84 The Destination Marketing Company
213
Test Design and Analysis
215
92 To Test or Not to Test
216
922 Assessing Mistargeting Costs
221
93 Sampling Techniques
223
931 Probability Versus Nonprobability Sampling
224
933 Systematic Random Sampling
225
934 Other Sampling Techniques
226
94 Determining the Sample Size
227
942 Decision Theoretic Approach
229
95 Test Designs
235
Full Factorials
238
Orthogonal Designs
241
954 QuasiExperiments
243
The Predictive Modeling Process
245
Overview
248
1032 Prepare the Data
250
1033 Estimate the Model
256
1034 Evaluate the Model
259
1035 Select Customers to Target
267
104 A Predictive Modeling Example
275
105 LongTerm Considerations
280
1053 Learning from the Interpretation of Predictive Models
284
1054 Predictive Modeling Is a Process to Be Managed
285
106 Future Research
286
Database Marketing Tools Statistical Techniques
289
Statistical Issues in Predictive Modeling
291
111 Economic Justification for Building a Statistical Model
292
112 Selection of Variables and Models
293
1122 Variable Transformations
299
113 Treatment of Missing Variables
301
1131 Casewise Deletion
302
1134 Multiple Imputation
303
1135 Data Fusion
305
1136 Missing Variable Dummies
307
114 Evaluation of Statistical Models
308
1141 Dividing the Sample into the Calibration and Validation Sample
309
1142 Evaluation Criteria
312
Evolutionary ModelBuilding
321
RFM Analysis
323
122 The Basics of the RFM Model
324
1222 RFM for SegmentLevel Prediction
326
Determining the Cutoff Point
327
1231 Profit Maximizing Cutoff Response Probability
328
1232 Heterogeneous Order Amounts
329
124 Extending the RFM Model
331
1242 Alternative Response Models Without Discretization
334
1243 A Stochastic RFM Model by Colombo and Jiang 1999
336
Market Basket Analysis
339
132 Benefits for Marketers
340
133 Deriving Market Basket Association Rules
341
1332 Deriving Interesting Association Rules
342
1333 Zhang 2000 Measures of Association and Dissociation
345
134 Issues in Market Basket Analysis
346
1342 Association Rules for More than Two Items
347
1343 Adding Virtual Items to Enrich the Quality of the Market Basket Analysis
348
1344 Adding Temporal Component to the Market Basket Analysis
349
135 Conclusion
350
Collaborative Filtering
353
142 MemoryBased Methods
354
1421 Computing Similarity Between Users
356
1422 Evaluation Metrics
360
143 ModelBased Methods
363
1431 The Cluster Model
364
1433 A Bayesian Mixture Model by Chien and George 1999
366
144 Current Issues in Collaborative Filtering
368
1442 Implicit Ratings
372
1443 Selection Bias
374
1444 Recommendations Across Categories
375
Discrete Dependent Variables and Duration Models
377
151 Binary Response Model
378
1512 Binary Logit or Logistic Regression and Probit Models
379
1513 Logistic Regression with Rare Events Data
382
1514 Discriminant Analysis
385
152 Multinomial Response Model
386
153 Models for Count Data
388
1532 Negative Binomial Regression
389
154 Censored Regression Tobit Models and Extensions
390
155 Time Duration Hazard Models
392
1551 Characteristics of Duration Data
393
1552 Analysis of Duration Data Using a Classical Linear Regression
394
1553 Hazard Models
395
1554 Incorporating Covariates into the Hazard Function
398
Chapter 16 Cluster Analysis
401
162 The Clustering Process
402
1621 Selecting Clustering Variables
403
1622 Similarity Measures
404
1623 Clustering Methods
408
1624 The Number of Clusters
418
1742 Other Methods for Finding the Right Sized Tree
434
175 Other Issues in Decision Trees
435
1751 Multivariate Splits
436
176 Application to a Direct Mail Offer
437
177 Strengths and Weaknesses of Decision Trees
438
Artificial Neural Networks
443
1812 ANN Applications in Database Marketing
444
1813 Strengths and Weaknesses
445
182 Models of Neurons
447
183 Multilayer Perceptrons
450
1831 Network Architecture
451
1832 BackPropagation Algorithm
454
1833 Application to Credit Scoring
455
1834 Optimal Number of Units in the Hidden Layer LearningRate and Momentum Parameters
457
1836 Feature Input Variable Selection
458
1837 Assessing the Importance of the Input Variables
459
184 RadialBasis Function Networks
460
1842 A CurveFitting Approximation Problem
461
1843 Application Example
463
Machine Learning
465
192 1Rule
466
193 Rule Induction by Covering Algorithms
468
1931 Covering Algorithms and Decision Trees
469
1932 PRISM
470
1933 A Probability Measure for Rule Evaluation and the INDUCT Algorithm
474
194 InstanceBased Learning
477
1941 Strengths and Limitations
478
1943 Selection of Exemplars
479
1944 Attribute Weights
481
196 Bayesian Networks
484
197 Support Vector Machines
486
Committee Machines
489
1981 Bagging
490
1982 Boosting
491
1983 Other Committee Machines
492
Customer Management
493
Acquiring Customers
495
202 The Fundamental Equation of Customer Equity
496
203 Acquisition Costs
497
204 Strategies for Increasing Number of Customers Acquired
499
2042 Increasing Marketing Acquisition Expenditures
500
2043 Changing the Shape of the Acquisition Curve
501
2044 Using Lead Products
503
2045 Acquisition Pricing and Promotions
504
205 Developing a Customer Acquisition Program
505
2052 Segmentation Targeting and Positioning STP
506
2053 ProductService Offering
507
2054 Acquisition Targeting
508
2055 Targeting Methods for Customer Acquisition
510
206 Research Issues in Acquisition Marketing
514
CrossSelling and UpSelling
515
212 CrossSelling Models
516
2121 NextProducttoBuy Models
517
2122 NextProducttoBuy Models with Explicit Consideration of Purchase Timing
529
2123 NextProducttoBuy with Timing and Response
534
213 UpSelling
537
2131 A Data Envelope Analysis Model
538
2132 A Stochastic Frontier Model
540
214 Developing an Ongoing CrossSelling Effort
541
2143 Data Collection
544
2145 Implementation
546
215 Research Needs
547
Frequency Reward Programs
549
222 How Frequency Reward Programs Influence Customer Behavior
550
2222 What We Know About How Customers Respond to Reward Programs
552
223 Do Frequency Reward Programs Increase Profits in a Competitive Environment?
562
224 Frequency Reward Program Design
565
2243 Enrollment Procedures
566
2245 The Reward
569
2246 Personalized Marketing
571
2247 Partnering
572
2248 Monitor and Evaluate
573
Nectar Versus Clubcard
574
2253 Cingular Rollover Minutes
576
226 Research Needs
578
Customer Tier Programs
579
232 Designing Customer Tier Programs
581
2322 Review Objectives
582
2325 Determine Acquisition Potential for Each Tier
584
2327 Allocate Funds to Tiers
588
2328 Design TierSpecific Programs
595
2329 Implement and Evaluate
596
233 Examples of Customer Tier Programs
597
2332 Royal Bank of Canada Rasmusson 1999
598
2335 Major US Bank Rust et al 2000
599
2336 Viking Office Products Miller 2001
600
234 Risks in Implementing Customer Tier Programs
601
235 Future Research Requirements
604
Churn Management
607
242 Factors that Cause Churn
611
243 Predicting Customer Churn
615
2431 Single Future Period Models
616
2432 Time Series Models
622
244 Managerial Approaches to Reducing Churn
625
2442 A Framework for Proactive Churn Management
627
2443 Implementing a Proactive Churn Management Program
631
245 Future Research
633
Multichannel Customer Management
635
251 The Emergence of Multichannel Customer Management
636
252 The Multichannel Customer
637
2522 Characteristics of Multichannel Customers
638
2523 Determinants of Channel Choice
641
2524 Models of Customer Channel Migration
647
2525 Research Shopping
652
2526 Channel Usage and Customer Loyalty
655
253 Developing Multichannel Strategies
659
2533 Design Channels
661
2534 Implementation
667
2535 Evaluation
668
254 Industry Examples
672
2543 The Pharmaceutical Industry Boehm 2002
673
2544 Circuit City Smith 2006 Wolf 2006
674
Acquisition and Retention Management
675
262 Modeling Acquisition and Retention
676
2622 Cohort Models
682
2624 Competitive Models
687
Lessons on How to Model Acquisition and Retention
689
263 Optimal Acquisition and Retention Spending
690
2631 Optimizing the BlattbergDeighton Model with No Budget Constraint
691
Retention Costs LTV and Optimal Spending If Acquisition Costs Exceed Retention Costs Should the Firm Focus on Retention?
695
2633 Optimizing the BudgetConstrained BlattbergDeighton Model
698
2634 Optimizing a MultiPeriod BudgetConstrained Cohort Model
702
2635 Optimizing the Reinartz et al 2005 Tobit Model
705
When Should We Spend More on Acquisition or Retention?
706
264 Acquisition and Retention Budget Planning
708
2642 Implementation Issues
709
An Overall Framework
710
Managing the Marketing Mix
713
Designing Database Marketing Communications
715
272 Setting the Overall Plan
716
2722 Strategy
717
2724 Summary
718
273 Developing Copy
719
2732 The Offer
723
2733 The Product
726
2734 Personalizing Multiple Components of the Communication
736
274 Selecting Media
737
2742 Integrated Marketing Communications
739
Multiple Campaign Management
743
282 Dynamic Response Phenomena
744
2822 Overlap
749
2823 Purchase Acceleration Loyalty and Price Sensitivity Effects
750
2824 Including Wearin Wearout Forgetting Overlap Acceleration and Loyalty
752
283 Optimal Contact Models
753
2831 A Promotions Model Ching et al 2004
755
2832 Using a Decision Tree Response Model Simester et al 2006
756
2833 Using a Hazard Response Model Goniil et al 2000
758
2834 Using a Hierarchical Bayes Model Rust and Verhoef 2005
760
2835 Incorporating Customer and Firm Dynamic Rationality Gdniil and Shi 1998
763
2836 Incorporating Inventory Management Bitran and Mondschein 1996
765
2837 Incorporating a Variety of Catalogs Campbell et al 2001
768
2838 Multiple Catalog Mailings Eisner et al 2003 2004
772
2839 Increasing Response to Online Panel Surveys Neslin et al 2007
774
284 Summary
777
Pricing
781
292 Customer Pricing when Customers Can Purchase Multiple OneTime Products from the Firm
783
Only Product 1 Is Purchased
786
293 Pricing the Same ProductsServices to Customers over Two Periods
788
R q Expectations of Quality are Less than Actual Quality
789
R q Expectations of Quality are Greater than Actual Quality
790
294 Acquisition and Retention Pricing Using the Customer Equity Model
791
295 Pricing to Recapture Customers
794
296 Pricing Addon Sales
796
297 Price Discrimination Through Database Targeting Models
797
References
801
Author Index
847
Subject Index
859
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