Database marketing: analyzing and managing customers
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer
2008
|
| Schriftenreihe: | International series in quantitative marketing
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturangaben S. 801 - 845 |
| Beschreibung: | XXIV, 871 S. zahlr. graph. Darst. 229 mm x 152 mm |
| ISBN: | 0387725784 9780387725789 9781441903327 |
Internformat
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| 020 | |a 9780387725789 |c Gb. : ca. EUR 155.74 (freier Pr.), ca. sfr 238.50 (freier Pr.) |9 978-0-387-72578-9 | ||
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| 100 | 1 | |a Blattberg, Robert C. |d 1942- |e Verfasser |0 (DE-588)170875407 |4 aut | |
| 245 | 1 | 0 | |a Database marketing |b analyzing and managing customers |c Robert C. Blattberg ; Byung-Do Kim ; Scott A. Neslin |
| 264 | 1 | |a New York, NY |b Springer |c 2008 | |
| 300 | |a XXIV, 871 S. |b zahlr. graph. Darst. |c 229 mm x 152 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a International series in quantitative marketing | |
| 500 | |a Literaturangaben S. 801 - 845 | ||
| 650 | 4 | |a Consumer profiling | |
| 650 | 4 | |a Database marketing | |
| 650 | 0 | 7 | |a Direktmarketing |0 (DE-588)4012421-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Database-Marketing |0 (DE-588)4263308-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenbank |0 (DE-588)4011119-2 |2 gnd |9 rswk-swf |
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| 700 | 1 | |a Kim, Pyǒng-do |d 1958- |e Verfasser |0 (DE-588)174000189 |4 aut | |
| 700 | 1 | |a Neslin, Scott A. |d 1952- |e Verfasser |0 (DE-588)170191397 |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-0-387-72579-6 |
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Datensatz im Suchindex
| _version_ | 1804136655635873792 |
|---|---|
| adam_text | Contents
Preface
.......................................................
vii
Part I Strategic Issues
1
Introduction
.............................................. 3
1.1
What Is Database Marketing?
............................ 3
1.1.1
Defining Database Marketing
...................... 4
1.1.2
Database Marketing, Direct Marketing, and Customer
Relationship Management
......................... 5
1.2
Why Is Database Marketing Becoming More Important?
.... 6
1.3
The Database Marketing Process
......................... 8
1.4
Organization of the Book
................................ 12
2
Why Database Marketing?
............................... 13
2.1
Enhancing Marketing Productivity
....................... 13
2.1.1
The Basic Argument
.............................. 13
2.1.2
The Marketing Productivity Argument in Depth
..... 15
2.1.3
Evidence for the Marketing Productivity Argument
... 19
2.1.4
Assessment
...................................... 22
2.2
Creating and Enhancing Customer Relationships
........... 23
2.2.1
The Basic Argument
.............................. 23
2.2.2
Customer Relationships and the Role of Database
Marketing
....................................... 23
2.2.3
Evidence for the Argument that Database Marketing
Enhances Customer Relationships
.................. 28
2.2.4
Assessment
...................................... 31
2.3
Creating Sustainable Competitive Advantage
.............. 32
2.3.1
The Basic Argument
.............................. 32
2.3.2
Evolution of the Sustainable Competitive Advantage
Argument
....................................... 32
2.3.3
Assessment
...................................... 44
2.4
Summary
.............................................. 45
Organizing for Database Marketing
....................... 47
3.1
The Customer-Centric Organization
...................... 47
3.2
Database Marketing Strategy
............................ 48
3.2.1
Strategies for Implementing DBM
.................. 49
3.2.2
Generating a Competitive Advantage
............... 51
3.2.3
Summary
........................................ 51
3.3
Customer Management: The Structural Foundation of the
Customer-Centric Organization
.......................... 52
3.3.1
What Is Customer Management?
................... 52
3.3.2
The Motivation for Customer Management
.......... 53
3.3.3
Forming Customer Portfolios
...................... 54
3.3.4
Is Customer Management the Wave of the Future?
... 55
3.3.5
Acquisition and Retention Departmentalization
...... 56
3.4
Processes for Managing Information: Knowledge Management
57
3.4.1
The Concept
..................................... 57
3.4.2
Does Effective Knowledge Management Enhance
Performance?
.................................... 58
3.4.3
Creating Knowledge
.............................. 59
3.4.4
Codifying Knowledge
............................. 60
3.4.5
Transferring Knowledge
........................... 61
3.4.6
Using Knowledge
................................. 62
3.4.7
Designing a Knowledge Management System
......... 63
3.4.8
Issues and Challenges
............................. 65
3.5
Compensation and Incentives
............................ 65
3.5.1
Theory
.......................................... 66
3.5.2
Empirical Findings
............................... 67
3.5.3
Summary
........................................ 69
3.6
People
................................................ 69
3.6.1
Providing Appropriate Support
.................... 69
3.6.2
Intra-Firm Coordination
.......................... 70
Customer Privacy and Database Marketing
............... 75
4.1
Background
............................................ 75
4.1.1
Customer Privacy Concerns and Their Consequences
for Database Marketers
........................... 75
4.1.2
Historical Perspective
............................. 78
4.2
Customer Attitudes Toward Privacy
...................... 79
4.2.1
Segmentation Schemes
............................ 79
4.2.2
Impact of Attitudes on Database Marketing Behaviors
81
4.2.3
International Differences in Privacy Concerns
........ 82
4.3
Current Practices Regarding Privacy
...................... 85
4.3.1
Privacy Policies
.................................. 85
4.3.2
Collecting Data
.................................. 87
4.3.3
The Legal Environment
........................... 88
4.4
Potential Solutions to Privacy Concerns
................... 91
4.4.1
Software Solutions
................................ 91
4.4.2
Regulation
...................................... 91
4.4.3
Permission Marketing
............................. 94
4.4.4
Customer Data Ownership
........................ 96
4.4.5
Focus on Trust
................................... 97
4.4.6
Top Management Support
......................... 98
4.4.7
Privacy as Profit Maximization
.................... 99
4.5
Summary and Avenues for Research
...................... 100
Part II Customer Lifetime Value (LTV)
5
Customer Lifetime Value: Fundamentals
..................105
5.1
Introduction
...........................................105
5.1.1
Definition of Lifetime Value of a Customer
..........106
5.1.2
A Simple Example of Calculating
Customer Lifetime Value
..........................106
5.2
Mathematical Formulation of LTV
........................108
5.3
The Two Primary LTV Models: Simple
Retention and Migration
................................109
5.3.1
Simple Retention Models
..........................109
5.3.2
Migration Models
................................114
5.4
LTV Models that Include Unobserved
Customer Attrition
.....................................121
5.5
Estimating Revenues
....................................130
5.5.1
Constant Revenue per Period Model
................130
5.5.2
Trend Models
....................................130
5.5.3
Causal Models
...................................130
5.5.4
Stochastic Models of Purchase Rates and Volume
.....131
6
Issues in Computing Customer Lifetime Value
............133
6.1
Introduction
...........................................133
6.2
Discount Rate and Time Horizon
......................... 134
6.2.1
Opportunity Cost of Capital Approach
..............134
6.2.2
Discount Rate Based on the
Source-of-Risk Approach
..........................140
6.3
Customer Portfolio Management
.........................142
6.4
Cost Accounting Issues
..................................145
6.4.1
Activity-Based Costing (ABC)
.....................145
6.4.2
Variable Costs and Allocating Fixed Overhead
.......148
6.5
Incorporating Marketing Response
........................154
6.6
Incorporating Externalities
..............................158
Customer Lifetime Value Applications
....................161
7.1
Using LTV to Target Customer Acquisition
................161
7.2
Using LTV to Guide Customer Reactivation Strategies
......163
7.3
Using SMC s Model to Value Customers
...................164
7.4
A Case Example of Applying LTV Modeling
...............168
7.5
Segmentation Methods Using Variants of LTV
.............172
7.5.1
Customer Pyramids
..............................172
7.5.2
Creating Customer Portfolios Using LTV Measures
. .. 174
7.6
Drivers of the Components of LTV
.......................175
7.7 Forcasting
Potential LTV
................................176
7.8
Valuing a Firm s Customer Base
.........................178
Part III Database Marketing Tools: The Basics
8
Sources of Data
...........................................183
8.1
Introduction
...........................................183
8.2
Types of Data for Describing Customers
...................184
8.2.1
Customer Identification Data
...................... 184
8.2.2
Demographic Data
............................... 186
8.2.3
Psychographic or Lifestyle Data
.................... 186
8.2.4
Transaction Data
................................. 188
8.2.5
Marketing Action Data
........................... 190
8.2.6
Other Types of Data
.............................. 191
8.3
Sources of Customer Information
......................... 191
8.3.1
Internal (Secondary) Data
.........................192
8.3.2
External (Secondary) Data
........................193
8.3.3
Primary Data
....................................211
8.4
The Destination Marketing Company
.....................213
9
Test Design and Analysis
.................................215
9.1
The Importance of Testing
...............................215
9.2
To Test or Not to Test
..................................216
9.2.1
Value of Information
..............................216
9.2.2
Assessing Mistargeting Costs
......................221
9.3
Sampling Techniques
....................................223
9.3.1
Probability Versus Nonprobability Sampling
.........224
9.3.2
Simple Random Sampling
.........................224
9.3.3
Systematic Random Sampling
......................225
9.3.4
Other Sampling Techniques
........................226
9.4
Determining the Sample Size
.............................227
9.4.1
Statistical Approach
..............................227
9.4.2
Decision Theoretic Approach
......................229
9.5
Test Designs
...........................................235
9.5.1
Single Factor Experiments
.........................235
9.5.2
Multifactor
Experiments:
Full Factorials
.............238
9.5.3
Multifactor Experiments: Orthogonal Designs
........241
9.5.4
Quasi-Experiments
...............................243
10
The Predictive Modeling Process
.........................245
10.1
Predictive Modelling and the Quest for
Marketing Productivity
.................................245
10.2
The Predictive Modeling Process: Overview
................248
10.3
The Process in Detail
...................................248
10.3.1
Define the Problem
...............................248
10.3.2
Prepare the Data
.................................250
10.3.3
Estimate the Model
...............................256
10.3.4
Evaluate the Model
...............................259
10.3.5
Select Customers to Target
........................267
10.4
A Predictive Modeling Example
..........................275
10.5
Long-Term Considerations
...............................280
10.5.1
Preaching to the Choir
............................280
10.5.2
Model Shelf Life and Selectivity Bias
...............280
10.5.3
Learning from the Interpretation of
Predictive Models
................................284
10.5.4
Predictive Modeling Is a Process
to Be Managed
...................................285
10.6
Future Research
........................................286
Part IV Database Marketing Tools: Statistical Techniques
11
Statistical Issues in Predictive Modeling
..................291
11.1
Economic Justification for Building a Statistical Model
......292
11.2
Selection of Variables and Models
........................293
11.2.1
Variable Selection
................................293
11.2.2
Variable Transformations
..........................299
11.3
Treatment of Missing Variables
...........................301
11.3.1
Casewise Deletion
................................302
11.3.2
Pairwise Deletion
.................................302
11.3.3
Single Imputation
................................302
11.3.4
Multiple Imputation
..............................303
11.3.5
Data Fusion
.....................................305
11.3.6
Missing Variable Dummies
........................307
11.4
Evaluation of Statistical Models
..........................308
11.4.1
Dividing the Sample into the Calibration and
Validation Sample
................................309
11.4.2
Evaluation Criteria
...............................312
11.5
Concluding Note: Evolutionary Model-Building
.............321
12
RFM Analysis
............................................323
12.1
Introduction
...........................................323
12.2
The Basics of the RFM Model
...........................324
12.2.1
Definition of Recency, Frequency, and
Monetary Value
..................................324
12.2.2
RFM for Segment-Level Prediction
.................326
12.3
Breakeven Analysis: Determining the Cutoff Point
..........327
12.3.1
Profit Maximizing Cutoff Response Probability
.......328
12.3.2
Heterogeneous Order Amounts
.....................329
12.4
Extending the RFM Model
..............................331
12.4.1
Treating the RFM Model as ANOVA
...............331
12.4.2
Alternative Response Models Without Discretization.
. 334
12.4.3
A Stochastic RFM Model by Colombo and
Jiang
(1999).....................................336
13
Market Basket Analysis
..................................339
13.1
Introduction
...........................................339
13.2
Benefits for Marketers
...................................340
13.3
Deriving Market Basket Association Rules
.................341
13.3.1
Setup of a Market Basket Problem
.................341
13.3.2
Deriving Interesting Association Rules
............342
13.3.3
Zhang
(2000)
Measures of Association
and Dissociation
.................................345
13.4
Issues in Market Basket Analysis
.........................346
13.4.1
Using Taxonomies to Overcome the Dimensionality
Problem
.........................................346
13.4.2
Association Rules for More than Two Items
.........347
13.4.3
Adding Virtual Items to Enrich the Quality of the
Market Basket Analysis
...........................348
13.4.4
Adding Temporal Component to the Market Basket
Analysis
.........................................349
13.5
Conclusion
............................................350
14
Collaborative Filtering
....................................353
14.1
Introduction
...........................................353
14.2
Memory-Based Methods
.................................354
14.2.1
Computing Similarity Between Users
................356
14.2.2
Evaluation Metrics
...............................360
14.3
Model-Based Methods
..................................363
14.3.1
The Cluster Model
...............................364
14.3.2
Item-Based Collaborative Filtering
.................364
14.3.3
A Bayesian Mixture Model by
Chien
and George
(1999)..........................366
14.3.4
A Hierarchical Bayesian Approach by Ansari
et al.
(2000) ..........................................366
14.4
Current Issues in Collaborative Filtering
..................368
14.4.1
Combining Content-Based Information Filtering with
Collaborative Filtering
............................368
14.4.2
Implicit Ratings
..................................372
14.4.3
Selection Bias
....................................374
14.4.4
Recommendations Across Categories
................375
15
Discrete Dependent Variables and Duration Models
......377
15.1
Binary Response Model
.................................378
15.1.1
Linear Probability Model
.......................... 378
15.1.2
Binary Logit (or Logistic Regression) and
Probit
Models
.......................................... 379
15.1.3
Logistic Regression with Rare Events Data
.......... 382
15.1.4
Discriminant Analysis
............................. 385
15.2
Multinomial Response Model
............................ 386
15.3
Models for Count Data
.................................. 388
15.3.1
Poisson
Regression
............................... 388
15.3.2
Negative Binomial Regression
...................... 389
15.4
Censored Regression (Tobit) Models and Extensions
........ 390
15.5
Time Duration (Hazard) Models
.......................... 392
15.5.1
Characteristics of Duration Data
...................393
15.5.2
Analysis of Duration Data Using a Classical Linear
Regression
.......................................394
15.5.3
Hazard Models
...................................395
15.5.4
Incorporating Covariates into the Hazard Function
... 398
16
Cluster Analysis
..........................................401
16.1
Introduction
...........................................401
16.2
The Clustering Process
..................................402
16.2.1
Selecting Clustering Variables
...................... 403
16.2.2
Similarity Measures
............................... 404
16.2.3
Clustering Methods
............................... 408
16.2.4
The Number of Clusters
........................... 418
16.3
Applying Cluster Analysis
............................... 419
16.3.1
Interpreting the Results
...........................419
16.3.2
Targeting the Desired Cluster
......................420
17
Decision Trees
............................................423
17.1
Introduction
...........................................423
17.2
Fundamentals of Decision Trees
..........................424
17.3
Finding the Best Splitting Rule
..........................427
17.3.1
Gini
Index of Diversity
............................427
17.3.2
Entropy and Information Theoretic Measures
........429
17.3.3
Chi-Square Test
..................................430
17.3.4
Other Splitting Rules
.............................432
17.4
Finding the Right Sized Tree
.............................432
17.4.1
Pruning
.........................................432
17.4.2
Other Methods for Finding the Right Sized Tree
.....434
17.5
Other Issues in Decision Trees
............................435
17.5.1
Multivariate Splits
................................436
17.5.2
Cost Considerations
..............................436
17.5.3
Finding an Optimal Tree
..........................436
17.6
Application to a Direct Mail Offer
........................437
17.7
Strengths and Weaknesses of Decision Trees
...............438
18
Artificial Neural Networks
................................443
18.1
Introduction
...........................................443
18.1.1
Historical Remarks
...............................443
18.1.2
ANN Applications in Database Marketing
...........444
18.1.3
Strengths and Weaknesses
.........................445
18.2
Models of Neurons
......................................447
18.3
Multilayer Perceptrons
..................................450
18.3.1
Network Architecture
.............................451
18.3.2
Back-Propagation Algorithm
.......................454
18.3.3
Application to Credit Scoring
......................455
18.3.4
Optimal Number of Units in the Hidden Layer,
Learning-Rate, and Momentum Parameters
..........457
18.3.5
Stopping Criteria
.................................457
18.3.6
Feature (Input Variable) Selection
..................458
18.3.7
Assessing the Importance of the Input Variables
......459
18.4
Radial-Basis Function Networks
..........................460
18.4.1
Background
.....................................460
18.4.2
A Curve-Fitting (Approximation) Problem
..........461
18.4.3
Application Example
.............................463
19
Machine Learning
........................................465
19.1
Introduction
...........................................465
19.2
1-Rule
................................................466
19.3
Rule Induction by Covering Algorithms
...................468
19.3.1
Covering Algorithms and Decision Trees
.............469
19.3.2
PRISM
.........................................470
19.3.3
A Probability Measure for Rule Evaluation
and the INDUCT Algorithm
.......................474
19.4
Instance-Based Learning
................................477
19.4.1
Strengths and Limitations
.........................478
19.4.2
A Brief Description of an Instance-Based Learning
Algorithm
.......................................478
19.4.3
Selection of Exemplars
............................479
19.4.4
Attribute Weights
................................481
19.5
Genetic Algorithms
.....................................481
19.6
Bayesian
Networks
.....................................484
19.7
Support Vector Machines
................................486
19.8
Combining Multiple Models: Committee Machines
..........489
19.8.1
Bagging
.........................................490
19.8.2
Boosting
........................................491
19.8.3
Other Committee Machines
........................492
Part V Customer Management
20
Acquiring Customers
..................................... 495
20.1
Introduction
........................................... 495
20.2
The Fundamental Equation of Customer Equity
............ 496
20.3
Acquisition Costs
....................................... 497
20.4
Strategies for Increasing Number of
Customers Acquired
.................................... 499
20.4.1
Increasing Market Size
............................ 499
20.4.2
Increasing Marketing Acquisition Expenditures
....... 500
20.4.3
Changing the Shape of the Acquisition Curve
........ 501
20.4.4
Using Lead Products
............................. 503
20.4.5
Acquisition Pricing and Promotions
................ 504
20.5
Developing a Customer Acquisition Program
............... 505
20.5.1
Framework
...................................... 505
20.5.2
Segmentation, Targeting and Positioning (STP)
...... 506
20.5.3
Product/Service Offering
.......................... 507
20.5.4
Acquisition Targeting
............................. 508
20.5.5
Targeting Methods for Customer Acquisition
......... 510
20.6
Research Issues in Acquisition Marketing
.................. 514
21
Cross-Selling and Up-Selling
.............................. 515
21.1
The Strategy
.......................................... 515
21.2
Cross-Selling Models
.................................... 516
21.2.1
Next-Product-to-Buy Models
...................... 517
21.2.2
Next-Product-to-Buy Models with Explicit
Consideration of Purchase Timing
.................. 529
21.2.3
Next-Product-to-Buy with Timing and Response
..... 534
21.3
Up-Selling
............................................. 537
21.3.1
A Data Envelope Analysis Model
................... 538
21.3.2
A Stochastic Frontier Model
....................... 540
21.4
Developing an Ongoing Cross-Selling Effort
................ 541
21.4.1
Process Overview
................................. 541
21.4.2
Strategy
......................................... 541
21.4.3
Data Collection
.................................. 544
21.4.4
Analytics
........................................ 544
21.4.5
Implementation
.................................. 546
21.4.6 Evaluation ......................................546
21.5 Research
Needs ........................................
547
22
Frequency Reward Programs
.............................549
22.1
Definition and Motivation
...............................549
22.2
How Frequency Reward Programs Influence Customer
Behavior
..............................................550
22.2.1
Mechanisms for Increasing Sales
....................550
22.2.2
What We Know About How Customers Respond to
Reward Programs
................................552
22.3
Do Frequency Reward Programs Increase Profits in a
Competitive Environment?
..............................562
22.4
Frequency Reward Program Design
.......................565
22.4.1
Design Decisions
.................................565
22.4.2
Infrastructure
....................................565
22.4.3
Enrollment Procedures
............................566
22.4.4
Reward Schedule
.................................566
22.4.5
The Reward
.....................................569
22.4.6
Personalized Marketing
...........................571
22.4.7
Partnering
.......................................572
22.4.8
Monitor and Evaluate
.............................573
22.5
Frequency Reward Program Examples
....................573
22.5.1
Harrah s Entertainment1
..........................573
22.5.2
The UK Supermarket Industry: Nectar
Versus
Clubcard .................................574
22.5.3
Cingular Rollover Minutes
.........................576
22.5.4
Hilton Hotels
....................................576
22.6
Research Needs
........................................578
23
Customer Tier Programs
.................................579
23.1
Definition and Motivation
...............................579
23.2
Designing Customer Tier Programs
.......................581
23.2.1
Overview
........................................581
23.2.2
Review Objectives
................................582
23.2.3
Create the Customer Database
.....................582
23.2.4
Define Tiers
.....................................582
23.2.5
Determine Acquisition Potential for Each Tier
.......584
23.2.6
Determine Development Potential for Each Tier
......585
23.2.7
Allocate Funds to Tiers
...........................588
23.2.8
Design Tier-Specific Programs
.....................595
23.2.9
Implement and Evaluate
..........................596
23.3
Examples of Customer Tier Programs
.....................597
23.3.1
Bank One
(Hartfeil 1996)..........................597
23.3.2
Royal Bank of Canada
(Rasmusson
1999)............598
23.3.3
Thomas Cook Travel
(Rasmusson
1999).............598
23.3.4
Canadian
Grocery Store Chain (Grant and
Schlesinger
1995).................................598
23.3.5
Major US Bank (Rust
et al.
2000)..................599
23.3.6
Viking Office Products (Miller
2001)................600
23.3.7
Swedbank
(Storbacka
and Luukinen
1994,
see also
Storbacka
1993)..................................600
23.4
Risks in Implementing Customer Tier Programs
............601
23.5
Future Research Requirements
...........................604
24
Churn Management
......................................607
24.1
The Problem
...........................................607
24.2
Factors that Cause Churn
...............................611
24.3
Predicting Customer Churn
..............................615
24.3.1
Single Future Period Models
.......................616
24.3.2
Time Series Models
...............................622
24.4
Managerial Approaches to Reducing Churn
................625
24.4.1
Overview
........................................625
24.4.2
A Framework for Proactive Churn Management
......627
24.4.3
Implementing a Proactive Churn
Management Program
............................631
24.5
Future Research
........................................633
25
Multichannel Customer Management
.....................635
25.1
The Emergence of Multichannel
Customer Management
..................................636
25.1.1
The Push Toward Multichannel
....................636
25.1.2
The Pull of Multichannel
..........................636
25.2
The Multichannel Customer
.............................637
25.2.1
A Framework for Studying the Customer s Channel
Choice Decision
..................................637
25.2.2
Characteristics of Multichannel Customers
...........638
25.2.3
Determinants of Channel Choice
...................641
25.2.4
Models of Customer Channel Migration
.............647
25.2.5
Research Shopping
...............................652
25.2.6
Channel Usage and Customer Loyalty
...............655
25.2.7
The Impact of Acquisition Channel
on Customer Behavior
............................655
25.2.8
The Impact of Channel Introduction
on Firm Performance
.............................657
25.3
Developing Multichannel Strategies
.......................659
25.3.1
Framework for the Multichannel Design Process
......659
25.3.2
Analyze Customers
...............................659
25.3.3
Design Channels
.................................661
25.3.4
Implementation
..................................667
25.3.5
Evaluation
......................................668
25.4
Industry Examples
.....................................672
25.4.1
Retail Best Practice (Crawford
2002) .............672
25.4.2
Waters Corporation
(CRM ROI
Review
2003)........672
25.4.3
The Pharmaceutical Industry (Boehm
2002).........673
25.4.4
Circuit City (Smith
2006;
Wolf
2006) ...............674
25.4.5
Summary
........................................674
26
Acquisition and Retention Management
..................675
26.1
Introduction
...........................................675
26.2
Modeling Acquisition and Retention
......................676
26.2.1
The
Blattberg
and Deighton
(1996)
Model
...........676
26.2.2
Cohort Models
...................................682
26.2.3
Type II Tobit Models
.............................682
26.2.4
Competitive Models
..............................687
26.2.5
Summary: Lessons on How to Model Acquisition and
Retention
.......................................689
26.3
Optimal Acquisition and Retention Spending
..............690
26.3.1
Optimizing the Blattberg/Deighton Model with No
Budget Constraint
................................691
26.3.2
The Relationship Among Acquisition and Retention
Costs, LTV, and Optimal Spending: If Acquisition
Costs Exceed Retention Costs
,
Should the Firm
Focus on Retention?
..............................695
26.3.3
Optimizing the Budget-Constrained
Blattberg/Deighton Model
.........................698
26.3.4
Optimizing a Multi-Period, Budget-Constrained
Cohort Model
....................................702
26.3.5
Optimizing the Reinartz
et al.
(2005)
Tobit Model
.....................................705
26.3.6
Summary: When Should We Spend More on
Acquisition or Retention?
.........................706
26.4
Acquisition and Retention Budget Planning
................708
26.4.1
The Customer Management Marketing Budget
(CMMB)
........................................708
26.4.2
Implementation Issues
............................709
26.5
Acquisition and Retention Strategy: An Overall Framework
.. 710
Part VI Managing the Marketing Mix
27
Designing Database Marketing Communications
..........715
27.1
The Planning Process
...................................715
27.2
Setting the Overall Plan
.................................716
27.2.1
Objectives
.......................................716
27.2.2
Strategy
.........................................717
27.2.3 Budget.......................................... 717
27.2.4
Summary
........................................ 718
27.3
Developing Copy
....................................... 719
27.3.1
Creative Strategy
................................ 719
27.3.2
The Offer
....................................... 723
27.3.3
The Product
..................................... 726
27.3.4
Personalizing Multiple Components of the
Communication
.................................. 736
27.4
Selecting Media
........................................ 737
27.4.1
Optimization
.................................... 737
27.4.2
Integrated Marketing Communications
.............. 739
27.5
Evaluating Communications Programs
.................... 739
28
Multiple Campaign Management
.........................743
28.1
Overview
..............................................743
28.2
Dynamic Response Phenomena
...........................744
28.2.1
Wear-in, Wear-out, and Forgetting
..................744
28.2.2
Overlap
.........................................749
28.2.3
Purchase Acceleration, Loyalty,
and Price Sensitivity Effects
.......................750
28.2.4
Including Wear-in, Wear-out, Forgetting, Overlap,
Acceleration, and Loyalty
.........................752
28.3
Optimal Contact Models
................................753
28.3.1
A Promotions Model (Ching
et al
2004) ............755
28.3.2
Using a Decision Tree Response Model
(Simester
et al.
2006).............................756
28.3.3
Using a Hazard Response Model
(Gönül et
al.
2000) ...............................758
28.3.4
Using a Hierarchical
Bayes
Model (Rust and
Verhoef
2005)...........................................760
28.3.5
Incorporating Customer and Firm Dynamic
Rationality
(Gönül
and Shi
1998)...................763
28.3.6
Incorporating Inventory Management (Bitran and
Mondschein 1996)................................765
28.3.7
Incorporating a Variety of Catalogs
(Campbell
et al.
2001) ............................768
28.3.8
Multiple Catalog Mailings (Eisner
et al.
2003, 2004).................................772
28.3.9
Increasing Response to Online Panel
Surveys (Neslin
et al.
2007)........................774
28.4
Summary
..............................................777
29
Pricing
...................................................781
29.1
Overview
-
Customer-based Pricing
.......................781
xxiv Contents
29.2
Customer Pricing when Customers Can Purchase Multiple
One-Time Products from the Firm
........................783
29.2.1
Case
1:
Only Product
1
Is Purchased
...............786
29.2.2
Case
2:
Two Product Purchase Model with Lead
Product
1.......................................786
29.3
Pricing the Same Products/Services to Customers
over Two Periods
.......................................788
29.3.1
Pessimistic Case:
R
<
q
-
Expectations of Quality
are Less than Actual Quality
......................789
29.3.2
Optimistic Case:
R
>
q
-
Expectations of
Quality are Greater than Actual Quality
............790
29.3.3
Research Issues
..................................790
29.4
Acquisition and Retention Pricing Using the Customer
Equity Model
..........................................791
29.5
Pricing to Recapture Customers
..........................794
29.6
Pricing Add-on Sales
....................................796
29.7
Price Discrimination Through Database Targeting Models
... 797
References
....................................................801
Author Index
.................................................847
Subject Index
................................................859
|
| adam_txt |
Contents
Preface
.
vii
Part I Strategic Issues
1
Introduction
. 3
1.1
What Is Database Marketing?
. 3
1.1.1
Defining Database Marketing
. 4
1.1.2
Database Marketing, Direct Marketing, and Customer
Relationship Management
. 5
1.2
Why Is Database Marketing Becoming More Important?
. 6
1.3
The Database Marketing Process
. 8
1.4
Organization of the Book
. 12
2
Why Database Marketing?
. 13
2.1
Enhancing Marketing Productivity
. 13
2.1.1
The Basic Argument
. 13
2.1.2
The Marketing Productivity Argument in Depth
. 15
2.1.3
Evidence for the Marketing Productivity Argument
. 19
2.1.4
Assessment
. 22
2.2
Creating and Enhancing Customer Relationships
. 23
2.2.1
The Basic Argument
. 23
2.2.2
Customer Relationships and the Role of Database
Marketing
. 23
2.2.3
Evidence for the Argument that Database Marketing
Enhances Customer Relationships
. 28
2.2.4
Assessment
. 31
2.3
Creating Sustainable Competitive Advantage
. 32
2.3.1
The Basic Argument
. 32
2.3.2
Evolution of the Sustainable Competitive Advantage
Argument
. 32
2.3.3
Assessment
. 44
2.4
Summary
. 45
Organizing for Database Marketing
. 47
3.1
The Customer-Centric Organization
. 47
3.2
Database Marketing Strategy
. 48
3.2.1
Strategies for Implementing DBM
. 49
3.2.2
Generating a Competitive Advantage
. 51
3.2.3
Summary
. 51
3.3
Customer Management: The Structural Foundation of the
Customer-Centric Organization
. 52
3.3.1
What Is Customer Management?
. 52
3.3.2
The Motivation for Customer Management
. 53
3.3.3
Forming Customer Portfolios
. 54
3.3.4
Is Customer Management the Wave of the Future?
. 55
3.3.5
Acquisition and Retention Departmentalization
. 56
3.4
Processes for Managing Information: Knowledge Management
57
3.4.1
The Concept
. 57
3.4.2
Does Effective Knowledge Management Enhance
Performance?
. 58
3.4.3
Creating Knowledge
. 59
3.4.4
Codifying Knowledge
. 60
3.4.5
Transferring Knowledge
. 61
3.4.6
Using Knowledge
. 62
3.4.7
Designing a Knowledge Management System
. 63
3.4.8
Issues and Challenges
. 65
3.5
Compensation and Incentives
. 65
3.5.1
Theory
. 66
3.5.2
Empirical Findings
. 67
3.5.3
Summary
. 69
3.6
People
. 69
3.6.1
Providing Appropriate Support
. 69
3.6.2
Intra-Firm Coordination
. 70
Customer Privacy and Database Marketing
. 75
4.1
Background
. 75
4.1.1
Customer Privacy Concerns and Their Consequences
for Database Marketers
. 75
4.1.2
Historical Perspective
. 78
4.2
Customer Attitudes Toward Privacy
. 79
4.2.1
Segmentation Schemes
. 79
4.2.2
Impact of Attitudes on Database Marketing Behaviors
81
4.2.3
International Differences in Privacy Concerns
. 82
4.3
Current Practices Regarding Privacy
. 85
4.3.1
Privacy Policies
. 85
4.3.2
Collecting Data
. 87
4.3.3
The Legal Environment
. 88
4.4
Potential Solutions to Privacy Concerns
. 91
4.4.1
Software Solutions
. 91
4.4.2
Regulation
. 91
4.4.3
Permission Marketing
. 94
4.4.4
Customer Data Ownership
. 96
4.4.5
Focus on Trust
. 97
4.4.6
Top Management Support
. 98
4.4.7
Privacy as Profit Maximization
. 99
4.5
Summary and Avenues for Research
. 100
Part II Customer Lifetime Value (LTV)
5
Customer Lifetime Value: Fundamentals
.105
5.1
Introduction
.105
5.1.1
Definition of Lifetime Value of a Customer
.106
5.1.2
A Simple Example of Calculating
Customer Lifetime Value
.106
5.2
Mathematical Formulation of LTV
.108
5.3
The Two Primary LTV Models: Simple
Retention and Migration
.109
5.3.1
Simple Retention Models
.109
5.3.2
Migration Models
.114
5.4
LTV Models that Include Unobserved
Customer Attrition
.121
5.5
Estimating Revenues
.130
5.5.1
Constant Revenue per Period Model
.130
5.5.2
Trend Models
.130
5.5.3
Causal Models
.130
5.5.4
Stochastic Models of Purchase Rates and Volume
.131
6
Issues in Computing Customer Lifetime Value
.133
6.1
Introduction
.133
6.2
Discount Rate and Time Horizon
. 134
6.2.1
Opportunity Cost of Capital Approach
.134
6.2.2
Discount Rate Based on the
Source-of-Risk Approach
.140
6.3
Customer Portfolio Management
.142
6.4
Cost Accounting Issues
.145
6.4.1
Activity-Based Costing (ABC)
.145
6.4.2
Variable Costs and Allocating Fixed Overhead
.148
6.5
Incorporating Marketing Response
.154
6.6
Incorporating Externalities
.158
Customer Lifetime Value Applications
.161
7.1
Using LTV to Target Customer Acquisition
.161
7.2
Using LTV to Guide Customer Reactivation Strategies
.163
7.3
Using SMC's Model to Value Customers
.164
7.4
A Case Example of Applying LTV Modeling
.168
7.5
Segmentation Methods Using Variants of LTV
.172
7.5.1
Customer Pyramids
.172
7.5.2
Creating Customer Portfolios Using LTV Measures
. . 174
7.6
Drivers of the Components of LTV
.175
7.7 Forcasting
Potential LTV
.176
7.8
Valuing a Firm's Customer Base
.178
Part III Database Marketing Tools: The Basics
8
Sources of Data
.183
8.1
Introduction
.183
8.2
Types of Data for Describing Customers
.184
8.2.1
Customer Identification Data
. 184
8.2.2
Demographic Data
. 186
8.2.3
Psychographic or Lifestyle Data
. 186
8.2.4
Transaction Data
. 188
8.2.5
Marketing Action Data
. 190
8.2.6
Other Types of Data
. 191
8.3
Sources of Customer Information
. 191
8.3.1
Internal (Secondary) Data
.192
8.3.2
External (Secondary) Data
.193
8.3.3
Primary Data
.211
8.4
The Destination Marketing Company
.213
9
Test Design and Analysis
.215
9.1
The Importance of Testing
.215
9.2
To Test or Not to Test
.216
9.2.1
Value of Information
.216
9.2.2
Assessing Mistargeting Costs
.221
9.3
Sampling Techniques
.223
9.3.1
Probability Versus Nonprobability Sampling
.224
9.3.2
Simple Random Sampling
.224
9.3.3
Systematic Random Sampling
.225
9.3.4
Other Sampling Techniques
.226
9.4
Determining the Sample Size
.227
9.4.1
Statistical Approach
.227
9.4.2
Decision Theoretic Approach
.229
9.5
Test Designs
.235
9.5.1
Single Factor Experiments
.235
9.5.2
Multifactor
Experiments:
Full Factorials
.238
9.5.3
Multifactor Experiments: Orthogonal Designs
.241
9.5.4
Quasi-Experiments
.243
10
The Predictive Modeling Process
.245
10.1
Predictive Modelling and the Quest for
Marketing Productivity
.245
10.2
The Predictive Modeling Process: Overview
.248
10.3
The Process in Detail
.248
10.3.1
Define the Problem
.248
10.3.2
Prepare the Data
.250
10.3.3
Estimate the Model
.256
10.3.4
Evaluate the Model
.259
10.3.5
Select Customers to Target
.267
10.4
A Predictive Modeling Example
.275
10.5
Long-Term Considerations
.280
10.5.1
Preaching to the Choir
.280
10.5.2
Model Shelf Life and Selectivity Bias
.280
10.5.3
Learning from the Interpretation of
Predictive Models
.284
10.5.4
Predictive Modeling Is a Process
to Be Managed
.285
10.6
Future Research
.286
Part IV Database Marketing Tools: Statistical Techniques
11
Statistical Issues in Predictive Modeling
.291
11.1
Economic Justification for Building a Statistical Model
.292
11.2
Selection of Variables and Models
.293
11.2.1
Variable Selection
.293
11.2.2
Variable Transformations
.299
11.3
Treatment of Missing Variables
.301
11.3.1
Casewise Deletion
.302
11.3.2
Pairwise Deletion
.302
11.3.3
Single Imputation
.302
11.3.4
Multiple Imputation
.303
11.3.5
Data Fusion
.305
11.3.6
Missing Variable Dummies
.307
11.4
Evaluation of Statistical Models
.308
11.4.1
Dividing the Sample into the Calibration and
Validation Sample
.309
11.4.2
Evaluation Criteria
.312
11.5
Concluding Note: Evolutionary Model-Building
.321
12
RFM Analysis
.323
12.1
Introduction
.323
12.2
The Basics of the RFM Model
.324
12.2.1
Definition of Recency, Frequency, and
Monetary Value
.324
12.2.2
RFM for Segment-Level Prediction
.326
12.3
Breakeven Analysis: Determining the Cutoff Point
.327
12.3.1
Profit Maximizing Cutoff Response Probability
.328
12.3.2
Heterogeneous Order Amounts
.329
12.4
Extending the RFM Model
.331
12.4.1
Treating the RFM Model as ANOVA
.331
12.4.2
Alternative Response Models Without Discretization.
. 334
12.4.3
A Stochastic RFM Model by Colombo and
Jiang
(1999).336
13
Market Basket Analysis
.339
13.1
Introduction
.339
13.2
Benefits for Marketers
.340
13.3
Deriving Market Basket Association Rules
.341
13.3.1
Setup of a Market Basket Problem
.341
13.3.2
Deriving "Interesting" Association Rules
.342
13.3.3
Zhang
(2000)
Measures of Association
and Dissociation
.345
13.4
Issues in Market Basket Analysis
.346
13.4.1
Using Taxonomies to Overcome the Dimensionality
Problem
.346
13.4.2
Association Rules for More than Two Items
.347
13.4.3
Adding Virtual Items to Enrich the Quality of the
Market Basket Analysis
.348
13.4.4
Adding Temporal Component to the Market Basket
Analysis
.349
13.5
Conclusion
.350
14
Collaborative Filtering
.353
14.1
Introduction
.353
14.2
Memory-Based Methods
.354
14.2.1
Computing Similarity Between Users
.356
14.2.2
Evaluation Metrics
.360
14.3
Model-Based Methods
.363
14.3.1
The Cluster Model
.364
14.3.2
Item-Based Collaborative Filtering
.364
14.3.3
A Bayesian Mixture Model by
Chien
and George
(1999).366
14.3.4
A Hierarchical Bayesian Approach by Ansari
et al.
(2000) .366
14.4
Current Issues in Collaborative Filtering
.368
14.4.1
Combining Content-Based Information Filtering with
Collaborative Filtering
.368
14.4.2
Implicit Ratings
.372
14.4.3
Selection Bias
.374
14.4.4
Recommendations Across Categories
.375
15
Discrete Dependent Variables and Duration Models
.377
15.1
Binary Response Model
.378
15.1.1
Linear Probability Model
. 378
15.1.2
Binary Logit (or Logistic Regression) and
Probit
Models
. 379
15.1.3
Logistic Regression with Rare Events Data
. 382
15.1.4
Discriminant Analysis
. 385
15.2
Multinomial Response Model
. 386
15.3
Models for Count Data
. 388
15.3.1
Poisson
Regression
. 388
15.3.2
Negative Binomial Regression
. 389
15.4
Censored Regression (Tobit) Models and Extensions
. 390
15.5
Time Duration (Hazard) Models
. 392
15.5.1
Characteristics of Duration Data
.393
15.5.2
Analysis of Duration Data Using a Classical Linear
Regression
.394
15.5.3
Hazard Models
.395
15.5.4
Incorporating Covariates into the Hazard Function
. 398
16
Cluster Analysis
.401
16.1
Introduction
.401
16.2
The Clustering Process
.402
16.2.1
Selecting Clustering Variables
. 403
16.2.2
Similarity Measures
. 404
16.2.3
Clustering Methods
. 408
16.2.4
The Number of Clusters
. 418
16.3
Applying Cluster Analysis
. 419
16.3.1
Interpreting the Results
.419
16.3.2
Targeting the Desired Cluster
.420
17
Decision Trees
.423
17.1
Introduction
.423
17.2
Fundamentals of Decision Trees
.424
17.3
Finding the Best Splitting Rule
.427
17.3.1
Gini
Index of Diversity
.427
17.3.2
Entropy and Information Theoretic Measures
.429
17.3.3
Chi-Square Test
.430
17.3.4
Other Splitting Rules
.432
17.4
Finding the Right Sized Tree
.432
17.4.1
Pruning
.432
17.4.2
Other Methods for Finding the Right Sized Tree
.434
17.5
Other Issues in Decision Trees
.435
17.5.1
Multivariate Splits
.436
17.5.2
Cost Considerations
.436
17.5.3
Finding an Optimal Tree
.436
17.6
Application to a Direct Mail Offer
.437
17.7
Strengths and Weaknesses of Decision Trees
.438
18
Artificial Neural Networks
.443
18.1
Introduction
.443
18.1.1
Historical Remarks
.443
18.1.2
ANN Applications in Database Marketing
.444
18.1.3
Strengths and Weaknesses
.445
18.2
Models of Neurons
.447
18.3
Multilayer Perceptrons
.450
18.3.1
Network Architecture
.451
18.3.2
Back-Propagation Algorithm
.454
18.3.3
Application to Credit Scoring
.455
18.3.4
Optimal Number of Units in the Hidden Layer,
Learning-Rate, and Momentum Parameters
.457
18.3.5
Stopping Criteria
.457
18.3.6
Feature (Input Variable) Selection
.458
18.3.7
Assessing the Importance of the Input Variables
.459
18.4
Radial-Basis Function Networks
.460
18.4.1
Background
.460
18.4.2
A Curve-Fitting (Approximation) Problem
.461
18.4.3
Application Example
.463
19
Machine Learning
.465
19.1
Introduction
.465
19.2
1-Rule
.466
19.3
Rule Induction by Covering Algorithms
.468
19.3.1
Covering Algorithms and Decision Trees
.469
19.3.2
PRISM
.470
19.3.3
A Probability Measure for Rule Evaluation
and the INDUCT Algorithm
.474
19.4
Instance-Based Learning
.477
19.4.1
Strengths and Limitations
.478
19.4.2
A Brief Description of an Instance-Based Learning
Algorithm
.478
19.4.3
Selection of Exemplars
.479
19.4.4
Attribute Weights
.481
19.5
Genetic Algorithms
.481
19.6
Bayesian
Networks
.484
19.7
Support Vector Machines
.486
19.8
Combining Multiple Models: Committee Machines
.489
19.8.1
Bagging
.490
19.8.2
Boosting
.491
19.8.3
Other Committee Machines
.492
Part V Customer Management
20
Acquiring Customers
. 495
20.1
Introduction
. 495
20.2
The Fundamental Equation of Customer Equity
. 496
20.3
Acquisition Costs
. 497
20.4
Strategies for Increasing Number of
Customers Acquired
. 499
20.4.1
Increasing Market Size
. 499
20.4.2
Increasing Marketing Acquisition Expenditures
. 500
20.4.3
Changing the Shape of the Acquisition Curve
. 501
20.4.4
Using Lead Products
. 503
20.4.5
Acquisition Pricing and Promotions
. 504
20.5
Developing a Customer Acquisition Program
. 505
20.5.1
Framework
. 505
20.5.2
Segmentation, Targeting and Positioning (STP)
. 506
20.5.3
Product/Service Offering
. 507
20.5.4
Acquisition Targeting
. 508
20.5.5
Targeting Methods for Customer Acquisition
. 510
20.6
Research Issues in Acquisition Marketing
. 514
21
Cross-Selling and Up-Selling
. 515
21.1
The Strategy
. 515
21.2
Cross-Selling Models
. 516
21.2.1
Next-Product-to-Buy Models
. 517
21.2.2
Next-Product-to-Buy Models with Explicit
Consideration of Purchase Timing
. 529
21.2.3
Next-Product-to-Buy with Timing and Response
. 534
21.3
Up-Selling
. 537
21.3.1
A Data Envelope Analysis Model
. 538
21.3.2
A Stochastic Frontier Model
. 540
21.4
Developing an Ongoing Cross-Selling Effort
. 541
21.4.1
Process Overview
. 541
21.4.2
Strategy
. 541
21.4.3
Data Collection
. 544
21.4.4
Analytics
. 544
21.4.5
Implementation
. 546
21.4.6 Evaluation .546
21.5 Research
Needs .
547
22
Frequency Reward Programs
.549
22.1
Definition and Motivation
.549
22.2
How Frequency Reward Programs Influence Customer
Behavior
.550
22.2.1
Mechanisms for Increasing Sales
.550
22.2.2
What We Know About How Customers Respond to
Reward Programs
.552
22.3
Do Frequency Reward Programs Increase Profits in a
Competitive Environment?
.562
22.4
Frequency Reward Program Design
.565
22.4.1
Design Decisions
.565
22.4.2
Infrastructure
.565
22.4.3
Enrollment Procedures
.566
22.4.4
Reward Schedule
.566
22.4.5
The Reward
.569
22.4.6
Personalized Marketing
.571
22.4.7
Partnering
.572
22.4.8
Monitor and Evaluate
.573
22.5
Frequency Reward Program Examples
.573
22.5.1
Harrah's Entertainment1
.573
22.5.2
The UK Supermarket Industry: Nectar
Versus
Clubcard .574
22.5.3
Cingular Rollover Minutes
.576
22.5.4
Hilton Hotels
.576
22.6
Research Needs
.578
23
Customer Tier Programs
.579
23.1
Definition and Motivation
.579
23.2
Designing Customer Tier Programs
.581
23.2.1
Overview
.581
23.2.2
Review Objectives
.582
23.2.3
Create the Customer Database
.582
23.2.4
Define Tiers
.582
23.2.5
Determine Acquisition Potential for Each Tier
.584
23.2.6
Determine Development Potential for Each Tier
.585
23.2.7
Allocate Funds to Tiers
.588
23.2.8
Design Tier-Specific Programs
.595
23.2.9
Implement and Evaluate
.596
23.3
Examples of Customer Tier Programs
.597
23.3.1
Bank One
(Hartfeil 1996).597
23.3.2
Royal Bank of Canada
(Rasmusson
1999).598
23.3.3
Thomas Cook Travel
(Rasmusson
1999).598
23.3.4
Canadian
Grocery Store Chain (Grant and
Schlesinger
1995).598
23.3.5
Major US Bank (Rust
et al.
2000).599
23.3.6
Viking Office Products (Miller
2001).600
23.3.7
Swedbank
(Storbacka
and Luukinen
1994,
see also
Storbacka
1993).600
23.4
Risks in Implementing Customer Tier Programs
.601
23.5
Future Research Requirements
.604
24
Churn Management
.607
24.1
The Problem
.607
24.2
Factors that Cause Churn
.611
24.3
Predicting Customer Churn
.615
24.3.1
Single Future Period Models
.616
24.3.2
Time Series Models
.622
24.4
Managerial Approaches to Reducing Churn
.625
24.4.1
Overview
.625
24.4.2
A Framework for Proactive Churn Management
.627
24.4.3
Implementing a Proactive Churn
Management Program
.631
24.5
Future Research
.633
25
Multichannel Customer Management
.635
25.1
The Emergence of Multichannel
Customer Management
.636
25.1.1
The Push Toward Multichannel
.636
25.1.2
The Pull of Multichannel
.636
25.2
The Multichannel Customer
.637
25.2.1
A Framework for Studying the Customer's Channel
Choice Decision
.637
25.2.2
Characteristics of Multichannel Customers
.638
25.2.3
Determinants of Channel Choice
.641
25.2.4
Models of Customer Channel Migration
.647
25.2.5
Research Shopping
.652
25.2.6
Channel Usage and Customer Loyalty
.655
25.2.7
The Impact of Acquisition Channel
on Customer Behavior
.655
25.2.8
The Impact of Channel Introduction
on Firm Performance
.657
25.3
Developing Multichannel Strategies
.659
25.3.1
Framework for the Multichannel Design Process
.659
25.3.2
Analyze Customers
.659
25.3.3
Design Channels
.661
25.3.4
Implementation
.667
25.3.5
Evaluation
.668
25.4
Industry Examples
.672
25.4.1
Retail "Best Practice" (Crawford
2002) .672
25.4.2
Waters Corporation
(CRM ROI
Review
2003).672
25.4.3
The Pharmaceutical Industry (Boehm
2002).673
25.4.4
Circuit City (Smith
2006;
Wolf
2006) .674
25.4.5
Summary
.674
26
Acquisition and Retention Management
.675
26.1
Introduction
.675
26.2
Modeling Acquisition and Retention
.676
26.2.1
The
Blattberg
and Deighton
(1996)
Model
.676
26.2.2
Cohort Models
.682
26.2.3
Type II Tobit Models
.682
26.2.4
Competitive Models
.687
26.2.5
Summary: Lessons on How to Model Acquisition and
Retention
.689
26.3
Optimal Acquisition and Retention Spending
.690
26.3.1
Optimizing the Blattberg/Deighton Model with No
Budget Constraint
.691
26.3.2
The Relationship Among Acquisition and Retention
Costs, LTV, and Optimal Spending: If Acquisition
"Costs" Exceed Retention "Costs"
,
Should the Firm
Focus on Retention?
.695
26.3.3
Optimizing the Budget-Constrained
Blattberg/Deighton Model
.698
26.3.4
Optimizing a Multi-Period, Budget-Constrained
Cohort Model
.702
26.3.5
Optimizing the Reinartz
et al.
(2005)
Tobit Model
.705
26.3.6
Summary: When Should We Spend More on
Acquisition or Retention?
.706
26.4
Acquisition and Retention Budget Planning
.708
26.4.1
The Customer Management Marketing Budget
(CMMB)
.708
26.4.2
Implementation Issues
.709
26.5
Acquisition and Retention Strategy: An Overall Framework
. 710
Part VI Managing the Marketing Mix
27
Designing Database Marketing Communications
.715
27.1
The Planning Process
.715
27.2
Setting the Overall Plan
.716
27.2.1
Objectives
.716
27.2.2
Strategy
.717
27.2.3 Budget. 717
27.2.4
Summary
. 718
27.3
Developing Copy
. 719
27.3.1
Creative Strategy
. 719
27.3.2
The Offer
. 723
27.3.3
The Product
. 726
27.3.4
Personalizing Multiple Components of the
Communication
. 736
27.4
Selecting Media
. 737
27.4.1
Optimization
. 737
27.4.2
Integrated Marketing Communications
. 739
27.5
Evaluating Communications Programs
. 739
28
Multiple Campaign Management
.743
28.1
Overview
.743
28.2
Dynamic Response Phenomena
.744
28.2.1
Wear-in, Wear-out, and Forgetting
.744
28.2.2
Overlap
.749
28.2.3
Purchase Acceleration, Loyalty,
and Price Sensitivity Effects
.750
28.2.4
Including Wear-in, Wear-out, Forgetting, Overlap,
Acceleration, and Loyalty
.752
28.3
Optimal Contact Models
.753
28.3.1
A Promotions Model (Ching
et al
2004) .755
28.3.2
Using a Decision Tree Response Model
(Simester
et al.
2006).756
28.3.3
Using a Hazard Response Model
(Gönül et
al.
2000) .758
28.3.4
Using a Hierarchical
Bayes
Model (Rust and
Verhoef
2005).760
28.3.5
Incorporating Customer and Firm Dynamic
Rationality
(Gönül
and Shi
1998).763
28.3.6
Incorporating Inventory Management (Bitran and
Mondschein 1996).765
28.3.7
Incorporating a Variety of Catalogs
(Campbell
et al.
2001) .768
28.3.8
Multiple Catalog Mailings (Eisner
et al.
2003, 2004).772
28.3.9
Increasing Response to Online Panel
Surveys (Neslin
et al.
2007).774
28.4
Summary
.777
29
Pricing
.781
29.1
Overview
-
Customer-based Pricing
.781
xxiv Contents
29.2
Customer Pricing when Customers Can Purchase Multiple
One-Time Products from the Firm
.783
29.2.1
Case
1:
Only Product
1
Is Purchased
.786
29.2.2
Case
2:
Two Product Purchase Model with Lead
Product
1.786
29.3
Pricing the Same Products/Services to Customers
over Two Periods
.788
29.3.1
Pessimistic Case:
R
<
q
-
Expectations of Quality
are Less than Actual Quality
.789
29.3.2
Optimistic Case:
R
>
q
-
Expectations of
Quality are Greater than Actual Quality
.790
29.3.3
Research Issues
.790
29.4
Acquisition and Retention Pricing Using the Customer
Equity Model
.791
29.5
Pricing to Recapture Customers
.794
29.6
Pricing Add-on Sales
.796
29.7
Price Discrimination Through Database Targeting Models
. 797
References
.801
Author Index
.847
Subject Index
.859 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Blattberg, Robert C. 1942- Kim, Pyǒng-do 1958- Neslin, Scott A. 1952- |
| author_GND | (DE-588)170875407 (DE-588)174000189 (DE-588)170191397 |
| author_facet | Blattberg, Robert C. 1942- Kim, Pyǒng-do 1958- Neslin, Scott A. 1952- |
| author_role | aut aut aut |
| author_sort | Blattberg, Robert C. 1942- |
| author_variant | r c b rc rcb p d k pdk s a n sa san |
| building | Verbundindex |
| bvnumber | BV022541770 |
| callnumber-first | H - Social Science |
| callnumber-label | HF5415 |
| callnumber-raw | HF5415.126 |
| callnumber-search | HF5415.126 |
| callnumber-sort | HF 45415.126 |
| callnumber-subject | HF - Commerce |
| classification_rvk | QP 621 QP 650 |
| classification_tum | WIR 803f |
| ctrlnum | (OCoLC)212409900 (DE-599)DNB983878633 |
| dewey-full | 658.8/72 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.8/72 |
| dewey-search | 658.8/72 |
| dewey-sort | 3658.8 272 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV022541770 |
| illustrated | Illustrated |
| index_date | 2024-07-02T18:10:31Z |
| indexdate | 2024-07-09T20:59:51Z |
| institution | BVB |
| isbn | 0387725784 9780387725789 9781441903327 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015748193 |
| oclc_num | 212409900 |
| open_access_boolean | |
| owner | DE-N2 DE-703 DE-1049 DE-92 DE-739 DE-945 DE-573 DE-91 DE-BY-TUM DE-1050 DE-355 DE-BY-UBR DE-384 DE-19 DE-BY-UBM |
| owner_facet | DE-N2 DE-703 DE-1049 DE-92 DE-739 DE-945 DE-573 DE-91 DE-BY-TUM DE-1050 DE-355 DE-BY-UBR DE-384 DE-19 DE-BY-UBM |
| physical | XXIV, 871 S. zahlr. graph. Darst. 229 mm x 152 mm |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Springer |
| record_format | marc |
| series2 | International series in quantitative marketing |
| spelling | Blattberg, Robert C. 1942- Verfasser (DE-588)170875407 aut Database marketing analyzing and managing customers Robert C. Blattberg ; Byung-Do Kim ; Scott A. Neslin New York, NY Springer 2008 XXIV, 871 S. zahlr. graph. Darst. 229 mm x 152 mm txt rdacontent n rdamedia nc rdacarrier International series in quantitative marketing Literaturangaben S. 801 - 845 Consumer profiling Database marketing Direktmarketing (DE-588)4012421-6 gnd rswk-swf Database-Marketing (DE-588)4263308-4 gnd rswk-swf Datenbank (DE-588)4011119-2 gnd rswk-swf Database-Marketing (DE-588)4263308-4 s DE-604 Direktmarketing (DE-588)4012421-6 s Datenbank (DE-588)4011119-2 s 1\p DE-604 Kim, Pyǒng-do 1958- Verfasser (DE-588)174000189 aut Neslin, Scott A. 1952- Verfasser (DE-588)170191397 aut Erscheint auch als Online-Ausgabe 978-0-387-72579-6 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015748193&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Blattberg, Robert C. 1942- Kim, Pyǒng-do 1958- Neslin, Scott A. 1952- Database marketing analyzing and managing customers Consumer profiling Database marketing Direktmarketing (DE-588)4012421-6 gnd Database-Marketing (DE-588)4263308-4 gnd Datenbank (DE-588)4011119-2 gnd |
| subject_GND | (DE-588)4012421-6 (DE-588)4263308-4 (DE-588)4011119-2 |
| title | Database marketing analyzing and managing customers |
| title_auth | Database marketing analyzing and managing customers |
| title_exact_search | Database marketing analyzing and managing customers |
| title_exact_search_txtP | Database marketing analyzing and managing customers |
| title_full | Database marketing analyzing and managing customers Robert C. Blattberg ; Byung-Do Kim ; Scott A. Neslin |
| title_fullStr | Database marketing analyzing and managing customers Robert C. Blattberg ; Byung-Do Kim ; Scott A. Neslin |
| title_full_unstemmed | Database marketing analyzing and managing customers Robert C. Blattberg ; Byung-Do Kim ; Scott A. Neslin |
| title_short | Database marketing |
| title_sort | database marketing analyzing and managing customers |
| title_sub | analyzing and managing customers |
| topic | Consumer profiling Database marketing Direktmarketing (DE-588)4012421-6 gnd Database-Marketing (DE-588)4263308-4 gnd Datenbank (DE-588)4011119-2 gnd |
| topic_facet | Consumer profiling Database marketing Direktmarketing Database-Marketing Datenbank |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015748193&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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