Building Data Mining Applications for CRM |
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| Alex Berson, Kurt Thearling, Stephen J. Smith |
| December 1999, McGraw Hill, Paperback, 488 pages, ISBN 0071344446
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Building Data Mining Applications for CRM, arms IT managers with
the information they need to make informed decisions in purchasing the
data mining and warehousing solutions they need. Provides comparison and
contrast to approaches and tools available for today's data mining and
helps the reader develop a step-by-step plan for their own organization.
Berson and Smith are well known and respected authors in the data mining
and data warehousing fields. |
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Preface
Acknowledgements
Part 1 THE IMPACT OF DATA MINING ON CRM
Introduction
Customer Relationships
Introduction
What Is Data Mining?
An Example
Relevance to a Business Process
Data Mining and Customer Relationship
Management
How Data Mining Helps Database Marketing
Scoring
The Role of Campaign Management Software
Increasing Customer Lifetime Value
Combining Data Mining and Campaign
Management
Evaluating the Benefits of a Data Mining
Model
Data Mining and Data Warehousing --- A
Connected View
Introduction
Data Mining and Data Warehousing --- the
Connection
Data Warehousing Overview
Data Warehousing ROI
Operational and Informational Data Stores
Definition and Characteristics of a Data Warehouse
Data Warehouse Architecture
Data Mining
Data Mining Defined
Data Mining Application Domains
Data Mining Categories and Research Focus
Customer Relationship Management
Introduction
The Most Profitable Customer
Customer Relationship Management
The Customer Centered Database
Managing Campaigns
The Evolution of Marketing
Closed Loop Marketing
The CRM Architecture
Next Generation CRM
Foundation --- The Technologies and Tools
Part 2 FOUNDATION---THE TECHNOLOGIES AND TOOLS
Introduction
DataWarehousing Components
Introduction
Overall Architecture
Data Warehouse Database
Sourcing, Acquisition, Cleanup and Transformation Tools
Metadata
Access Tools
Accessing and Visualizing Information
Tool Taxonomy
Query and Reporting Tools
Applications
OLAP Tools
Data Mining Tools
Data Marts
Data Warehouse Administration and Management
Impact of the Web
Approaches to Using the Web
Design Options and Issues
XML
Data Mining
What Is Data Mining?
The Mining Analogy
What Data Mining Isn't
Statistics
OLAP
Data Warehousing
Data Mining Has Come of Age
The Motivation for Data Mining Is Tremendous
Learning from Your Past Mistakes
Data Mining? Don't Need It --- I've Got Statistics
Measuring Data Mining Effectiveness ---
Accuracy, Speed, Cost
Embedding Data Mining into Your Business Process
The More Things Change, the More They Remain the Same
Discovery versus Prediction
Gold in Them Thar Hills
Discovery---Finding Something That You
Weren't Looking for
Prediction
Overfitting
State of the Industry
Targeted Solutions
Business Tools
Business Analyst Tools
Research Analyst Tools
Data Mining Methodology
What Is a Pattern? What Is a Model?
Visualizing a Pattern
A Note on Terminology
A Note on Terminology
A Note on Knowledge and Wisdom
Sampling
Random Sampling
Validating the Model
Picking the Best Model
The Types of Data Mining Applications
Classical Techniques: Statistics,
Neighborhoods, and Clustering
The Classics
What Is Different between Statistics and
Data Mining?
What Is Statistics?
Data, Counting, and Probability
Histograms
Statistics for Prediction
Linear Regression
What If the Pattern In My Data Doesn't
Look Like a Straight Line?
Nearest Neighbor
A Simple Example of Clustering
A Simple Example of Nearest Neighbor
How to Use Nearest Neighbor for Prediction
Where Is the Nearest Neighbor Technique
Used In Business?
Using Nearest Neighbor for Stock Market
Data
Why Voting Is Better --- K Nearest Neighbors
How Can the Nearest Neighbor Tell You How
Confident It Is with the Prediction?
Clustering
Clustering for Clarity
Finding the Ones That Don't Fit
In---Clustering for Outliers
How Is Clustering Like the Nearest Neighbor Technique?
How to Put Clustering and Nearest Neighbor to Work for Prediction
Is There Another Correct Way to Cluster?
How Are Tradeoffs Made When Determining
Which Records Fall into Which Clusters?
Clustering Is the Happy Medium between
Homogeneous Clusters and the Fewest
Number of Clusters
What Is the Difference between Clustering and the Nearest Neighbor Prediction?
What Is an n-Dimensional Space? Do I Really Need to Know This?
How Is the Space for Clustering and
Nearest Neighbor Defined?
Hierarchical and Non-Hierarchical
Clustering
Non-Hierarchical Clustering
Hierarchical Clustering
Choosing the Classics
Next Generation Techniques: Trees, Networks and Rules
The Next Generation
Decision Trees
What Is a Decision Tree?
Viewing Decision Trees as Segmentation with a Purpose
Applying Decision Trees to Business
Where Can Decision Trees Be Used?
Using Decision Trees for Exploration
Using Decision Trees for Data Preprocessing
Decision Trees for Prediction
The First Step Is Growing the Tree
The Difference between a Good Question and a Bad Question
When Does the Tree Stop Growing?
Why Would a Decision Tree Algorithm Stop
Growing the Tree If There Wasn't Enough
Data?
Decision Trees Aren't Necessarily
Finished after the Tree Is Grown
ID3 and an Enhancement---C4.5
CART---Growing a Forest and Picking the Best Tree
CART Automatically Validates the Tree
CART Surrogates Handle Missing Data
CHAID
Neural Networks
What Is a Neural Network?
Don't Neural Networks Learn to Make Better Predictions?
Are Neural Networks Easy to Use?
Applying Neural Networks to Business
Where to Use Neural Networks
Neural Networks for Clustering
Neural Networks for Outlier Analysis
Neural Networks for Feature Extraction
What Does a Neural Net Look Like?
How Does a Neural Net Make a Prediction?
How Is the Neural Net Model Created?
How Complex Can the Neural Network Model Become?
Hidden Nodes Are Like Trusted Advisors to the Output Nodes
The Learning That Goes On in the Hidden Nodes
Sharing the Blame and the Glory throughout the Organization
Different Tuypes of Neural Networks
Kohonen Feature Maps
How Much Like a Human Brain Is the Neural Network?
Combatting Overfitting---Getting a Model
You Can Use Somewhere Else
Explaining the Network
Rule Induction
Applying Rule Induction to Business
What Is a Rule?
What to Do with a Rule
Caveat: Rules Do Not Imply Causality
Types of Databases Used for Rule Induction
The General Idea
The Business Importance of Accuracy and
Coverage
Trading Off Accuracy and Coverage Is Like Betting at the Track
How to Evaluate the Rule
Defining ``Interestingness''
Other Measures of Usefulness
Rules versus Decision Trees
Another Commonality between Decision
Trees and Rule Induction Systems
Which Technique and When?
Balancing Exploration and Exploitation
When to Use Data Mining
Introduction
Using the Right Technique
The Data Mining Process
How Decision Trees Are Like Nearest Neighbor
How Rule Induction Is Like Decision Trees
How to Do Link Analysis with a Neural Network
Data Mining in the Business Process
Avoiding Some Big Mistakes in Data Mining
Understanding the Data
The Case for Embedded Data Mining
The Cost of a Distributed Business Process
The Best Way to Measure a Data Mining Tool
The Case for Embedded Data Mining
How to Measure Accuracy, Explanation, and
Integration
Measuring Accuracy
Measuring Explanation
Measuring Integration
What the Future Holds for Embedded Data
Mining
Part 3 THE BUSINESS VALUE
Introduction
Customer Profitability
Introduction
Why Calculate Customer Profitability?
The Effect of Loyalty on Customer Profitability
Customer Loyalty and the Law of Compound Effect
What Is Customer Relationship Management?
Optimizing Customer Profitability through
Data Mining
Predicting Future Profitability
Predicting Customer Profitability Transitions
Using Customer Profitability to Guide
Marketing
Why Revenue Isn't Enough
Incremental Customer Profitability
What Is Incremental Customer Profitability?
Telling Your Sales Force to Stop Selling
How Do I Get Organizational Buy-in?
Surrogates Are Often Worse Than Nothing at All
The Holy Grail
How Do You Measure the Value of Data Mining?
Customer Acquisition
Introduction
How Data Mining and Statistical Modeling
Change Things
Defining Some Key Acquisition Concepts
It All Begins with the Data
Test Campaigns
Evaluating Test Campaign Responses
Building Data Mining Models Using Response Behaviors
Cross-selling
Introduction
How Cross-selling Works
Steps in the Process
The Analysis Begins
Modeling
Scoring
Optimization
Multiple Offers |
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Alex Berson is a Director of Technology for a global management consulting
firm. Dr. Berson holds a Ph.D. in Computer Science and M.S. in Applied
Mathematics, and is an internationally recognized expert, author, educator
and practitioner who has over 20 years of experience in information technologies.
He has published numerous technical articles in trade magazines, and is
a best-selling author of a number of professional books including "Data
Warehousing, Data Mining and OLAP" and "Client/Server Architecture."
Stephen Smith is the President and CEO of Optas, Inc. the leading provider
of web-based Customer Relationship Management tools for the Pharmaceutical
and Healthcare industries. Mr. Smith holds a BSEE from the Massachusetts
Institute of Technology and an MS from Harvard University. He has been
working in the fields of Data Mining and Data Warehousing for the past
15 years. Mr. Smith also co-authored the book "Data Warehousing,
Data Mining and OLAP."
Kurt Thearling has spent much of the last decade designing, using, and
evaluating data mining and customer relationship management technologies.
Dr. Thearling holds a Ph.D. in Electrical Engineering from the University
of Illinois. He is currently Senior Director of Development for Wheelhouse,
a CRM services company based in the Boston area. |
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