Marketing Intelligent Systems Using Soft Computing: Managerial and Research Applications (Studies in Fuzziness and Soft Computing, 258)

✍ Scribed by Jorge Casillas (editor), Francisco J. Martínez López (editor)

• Publisher: Springer
• Year: 2010
• Tongue: English
• Leaves: 476
• Category: Library

✦ Synopsis

Dr. Jay Liebowitz Orkand Endowed Chair in Management and Technology University of Maryland University College Graduate School of Management & Technology 3501 University Boulevard East Adelphi, Maryland 20783-8030 USA jliebowitz@umuc. edu When I first heard the general topic of this book, Marketing Intelligent Systems or what I’ll refer to as Marketing Intelligence, it sounded quite intriguing. Certainly, the marketing field is laden with numeric and symbolic data, ripe for various types of mining―data, text, multimedia, and web mining. It’s an open laboratory for applying numerous forms of intelligentsia―neural networks, data mining, expert systems, intelligent agents, genetic algorithms, support vector machines, hidden Markov models, fuzzy logic, hybrid intelligent systems, and other techniques. I always felt that the marketing and finance domains are wonderful application areas for intelligent systems, and this book demonstrates the synergy between marketing and intelligent systems, especially soft computing. Interactive advertising is a complementary field to marketing where intelligent systems can play a role. I had the pleasure of working on a summer faculty f- lowship with R/GA in New York City―they have been ranked as the top inter- tive advertising agency worldwide. I quickly learned that interactive advertising also takes advantage of data visualization and intelligent systems technologies to help inform the Chief Marketing Officer of various companies. Having improved ways to present information for strategic decision making through use of these technologies is a great benefit.

✦ Table of Contents

Title
Foreword
Preface
Contents
Essays
Marketing and Artificial Intelligence: Great Opportunities, Reluctant Partners
Introduction
Marketing Problem-Solving Modes
Marketing Problem-Solving Modes and Decision Support Technologies
The State of Artificial Intelligence (AI) in Marketing
Applications of AI in Marketing
Perspective
References
Data Mining and Scientific Knowledge: Some Cautions for Scholarly Researchers
Introduction
The Data Mining Method
Data Mining and Scientific Knowledge
Data Mining in a Practical Context
Discussion and Conclusions
References
Observations on Soft Computing in Marketing
References
Soft Computing Methods in Marketing: Phenomena and Management Problems
Introduction
Marketplace Phenomena
Management Problems
Summary
References
User-Generated Content: The “Voice of the Customer” in the 21st Century
Introduction
Marketing Scientists Should Care about UGC or Should They?
Marketing Scientists and Data Mining Experts Need Each Other Now More Than Ever
References
Fuzzy Networks
References
KDD: Applying in Marketing Practice Using Point of Sale Information
Introduction
The Store Layout
The Buying Association: A Way to Measure the Relationship among Products
Method and Results
Discussion
References
Marketing – Sales Interface and the Role of KDD
Marketing Versus Sales
A Call for Change
KDD as a Catalyst for Paradigmatic Change
References
Segmentation and Targeting
Applying Soft Cluster Analysis Techniques to Customer Interaction Information
Introduction
Literature Review
Background of Cluster Analysis
Applications in the Financial Services Industry and Customer Relationship Management
Business Context and Data Used for Analysis
Business Context
Synthetic Data Structure and Design
Synthetic Data Group Characteristics
Research Approach
Variable Selection
Data Validation
Data Standardization
Addressing Outliers
Decide Number of Clusters
Validate Clustering Results
Results
Variable Selection
Standardize Data
Decide Number of Clusters
Validate Clustering Results
Discussion and Analysis
Conclusion
References
Marketing Intelligent System for Customer Segmentation
Introduction
Components of Marketing Intelligent System
Operational Data
Derived Data
Customers’ Segmentation Using Partitioning Method
Interpretation of Intelligent Systems Outputs by Expert Systems
Marketing Intelligent Sytems for Customers Clustering Using Fuzzy C-Means Clustering
Experimental Results
Collaboration of Knowledge Based System and the Fuzzy C-Means Clustering Implementation Results
Conclusion
Practical Utilities for Marketing Management
References
Using Data Fusion to Enrich Customer Databases with Survey Data for Database Marketing
Introduction and Motivation
DataFusion
Data Fusion Concepts
Core Data Fusion Algorithms
Data Fusion Evaluation and Deployment
Case Study: Cross Selling Credit Cards
Internal Evaluation
External Evaluation
Case Discussion
A Process Model for a Fusion Factory
Conclusion
References
Collective Intelligence in Marketing
Introduction
Data Mining Technology in Marketing
Business Applications of Data Mining
Predicting Customer Preferences
Finding Similar Customers or Consumers
Applying Collective Intelligence in Marketing
Challenges of Applying Collective Intelligence in Marketing
Case Study: Keyword-Based Product Suggestions
Summary and Conclusions
References
Marketing Modelling
Predictive Modeling on Multiple Marketing Objectives Using Evolutionary Computation
Introduction
Background
Non-dominated Solutions
Genetic Search
Multi-objective Evolutionary Computation
Multi-objective Models Using Genetic Search
Model Representation
Genetic Search Operators
Multi-objective Models Using Pareto-Selection
Fitness Function
Performance Measures
Data and Multi-objective Model Performance
Data
Models along the Pareto-Frontier
Conclusions
References
Automatic Discovery of Potential Causal Structures in Marketing Databases Based on Fuzzy Association Rules
Introduction
Previous Considerations on the Adaptation of Marketing Data
Data Collection in Marketing
The Classical Approach to Deal with Marketing Data
Transformation of Marketing Scales into Fuzzy Semantic
Application of Machine Learning to the Marketing Data
Mining Association Rules
Association Rules: The Beginning
Association Rule Mining with Continuous Variables
Description of Fuzzy-CSar
Knowledge Representation
Multi-item Fuzzification
Process Organization
Problem Description and Methodology
Problem Description
Experimental Methodology
Analysis of the Results
Analysis of the Rules in the Objective Space
Analysis of the Utility of the Rules from the Marketing Expert Perspective
Conclusions and Further Work
References
Fuzzy–Evolutionary Modeling of Customer Behavior for Business Intelligence
Introduction
TheContext
Application Scenarios
Related Work
Data Mining
Fuzzy Rule-Based Systems
Fuzzy Sets
Operations on Fuzzy Sets
Fuzzy Propositions and Predicates
Fuzzy Rulebases
Evolutionary Algorithms
An Island-Based Evolutionary Algorithm for Fuzzy Rule-Base Optimization
Genetic Operators
Fitness
Selection and Overfitting Control
A Case Study on Customer Revenue Modeling
The Company
Aim of the Study
The Data
Discussion of the Results
Validation of the Model
Conclusions
References
Communication/Direct Marketing
An Evaluation Model for Selecting Integrated Marketing Communication Strategies for Customer Relationship Management
Introduction
Literature Review
Customer Relationship Management (CRM)
Customer Relational Benefit
Integrated Marketing Communication Strategy
Quality Function Deployment
Fuzzy Analytic Hierarchy Process
Construction of an Evaluation Model for Selecting IMC Strategy on CRM
Empirical Illustrations
The Hierarchy Construction of Customer Relational Benefit
The Relative Importance Weights of Categories and Attributes for Customer Relational Benefit
The Relationship Matrix between Customer Relational Benefit and IMC Strategy
The Completed Evaluation Model for Selecting IMC Strategy on CRM
Conclusion
Discussion and Implications
References
Direct Marketing Based on a Distributed Intelligent System
Introduction
Formation of Clusters to Boost Direct Marketing
Formal Presentation of Methods
The Analytical Hierarchy Process
Fuzzy C Means Clustering Algorithm
The Hybrid Approach to Process Customers Evaluations
The Multi-Agent System
Experimental Results
Clients’ Evaluation
Concluding Remarks
References
Direct Marketing Modeling Using Evolutionary Bayesian Network Learning Algorithm
Introduction
Background
Direct Marketing Modeling
Bayesian Networks
The Missing Value Problem
Basic SEM Algorithm
HEA
Learning Bayesian Networks from Incomplete Databases
The EBN Algorithm
The EM Procedure in EBN
The Initial Network Structure for G$_best$
Data Completing Procedure
HEA Search Procedure
Application in Direct Marketing Modeling
Methodology
Cross-Validation Results
Conclusion
References
Product
Designing Optimal Products: Algorithms and Systems
Introduction
The Optimal Product (Line) Design Problem
Choice Rule
Optimization Criteria
Number of Products to be Designed
Procedure Steps
Optimization Algorithm
Problem Formulation
Deterministic Choice Rules
Probabilistic Choice Rules
Optimization Algorithms Applied to the Problem
Greedy Heuristic
Interchange Heuristic
Divide and Conquer
Coordinate Ascent
Dynamic Programming
Beam Search
Nested Partitions
Genetic Algorithms
Lagrangian Relaxation with Branch and Bound
Comparison of the Algorithms
A Comparison of Genetic Algorithm to Simulated Annealing
Genetic Algorithm Implementation
Simulated Annealing Implementation
Monte Carlo Simulation
A Real World Case
Programs and Systems
DESOP-LINEOP
SIMOPT
GENESYS
MDSS
Advanced Simulation Module
Discussion
Conclusions
References
PRODLINE: Architecture of an Artificial Intelligence Based Marketing Decision Support System for PRODuct LINE Designs
Introduction
The Product Line Problem
Existing Approaches to Product Line Design Problem
Architecture of PRODLINE
Database
Model Base
PRODLINE: User Interaction
Inputs
Discussion and Future Directions
References
A Dempster-Shafer Theory Based Exposition of Probabilistic Reasoning in Consumer Choice
Introduction
Background
Dempster-Shafer Theory
Formulisation of DS/AHP and Consumer Choice
DS/AHP Analysis of Car Choice Problem
Future Trends
Conclusions
References
E-Commerce
Decision Making in Multiagent Web Services Based on Soft Computing
Introduction
Fundamentals for Web Services
E-Services and Web Services
Parties in Web Services
SESS: A Unified Multilayer Architecture for E-Services
First Layer: Infrastructure Services
Second Layer: Web Services
Third Layer: E-Services
SESS: A Service Centered System Architecture
Web Service Lifecycle
Introduction
Provider's Demand Driven Web Service Lifecycle
Requester's Demand Driven Web Service Lifecycle
Broker's Demand Driven Web Service Lifecycle
Summary of Demand Driven Web Service Lifecycle
Decision Making in Web Services
Decision Making
Decision Making in Web Services
Soft Computing for Web Services
WUDS: A Unified Decision Support System for Web Services
Agents within WUDS
WS Decision Supporter
Agents Workflowing in WUDS
Case Based Web Services
Introduction
Web Services vs. CBR
A Unified Treatment of Case Based Web Services
Conclusions and Future Work
References
Dynamic Price Forecasting in Simultaneous Online Art Auctions
Introduction
Simultaneous Online Auctions
Data Used in This Study
Bidder Competition in Simultaneous Online Auctions
Dynamic Price Forecasting
Model Formulation
Benchmark Models
Model Estimation and Evaluation
Results
Estimated Models
Forecasting Performance
Bidder Competition and Price Forecasting
Bidder Competition as Time-Varying Predictors
Bidder Competition as a Conditioning Variable
Conclusion and Future Direction
References
Analysing Incomplete Consumer Web Data Using the Classification and Ranking Belief Simplex (Probabilistic Reasoning and Evolutionary Computation)
Introduction
Background
Measuring Consumer Web Purchasing Attitudes and CaRBS Analyses
Conceptualisation and Operationalisation of Web Purchasing Involvement
Internet Survey Design
CaRBS Analysis of ‘Incomplete’ Web Experience Data Set
CaRBS Analysis of ‘Completed’ Web Experience Data Set
Future Trends
Conclusions
References
Author Index