Chapter 14 Data Mining throughout the Customer Life Cycle 447

Levels of the Customer Relationship 448

Deep Intimacy 449

Mass Intimacy 451

In-between Relationships 453

Indirect Relationships 453

Customer Life Cycle 454

The Customers Life Cycle: Life Stages 455

Customer Life Cycle 456 Subscription Relationships versus Event-Based Relationships 458

Event-Based Relationships 458

Subscription-Based Relationships 459 Business Processes Are Organized around

the Customer Life Cycle 461

Customer Acquisition 461

Who Are the Prospects? 462

When Is a Customer Acquired? 462

What Is the Role of Data Mining? 464

Customer Activation 464

Relationship Management 466

Retention 467

Winback 470

Lessons Learned 470

Chapter 15 Data Warehousing, OLAP, and Data Mining 473

The Architecture of Data 475

Transaction Data, the Base Level 476

Operational Summary Data 477

Decision-Support Summary Data 477

Database Schema 478

Metadata 483

Business Rules 484

A General Architecture for Data Warehousing 484

Source Systems 486

Extraction, Transformation, and Load 487

Central Repository 488

Metadata Repository 491

Data Marts 491

Operational Feedback 492

End Users and Desktop Tools 492

Analysts 492

Application Developers 493

Business Users 494

Where Does OLAP Fit In? 494

Whats in a Cube? 497

Three Varieties of Cubes 498

Facts 501

Dimensions and Their Hierarchies 502

Conformed Dimensions 504

Star Schema 505

OLAP and Data Mining 507

Where Data Mining Fits in with Data Warehousing 508

Lots of Data 509

Consistent, Clean Data 510

Hypothesis Testing and Measurement 510

Scalable Hardware and RDBMS Support 511

Lessons Learned 511

Chapter 16 Building the Data Mining Environment 513

A Customer-Centric Organization 514

An Ideal Data Mining Environment 515

The Power to Determine What Data Is Available 515

The Skills to Turn Data into Actionable Information 516

All the Necessary Tools 516

Back to Reality 516

Building a Customer-Centric Organization 516

Creating a Single Customer View 517

Defining Customer-Centric Metrics 519

Collecting the Right Data 520

From Customer Interactions to Learning Opportunities 520

Mining Customer Data 521

The Data Mining Group 521

Outsourcing Data Mining 522

Outsourcing Occasional Modeling 522

Outsourcing Ongoing Data Mining 523

Insourcing Data Mining 524

Building an Interdisciplinary Data Mining Group 524

Building a Data Mining Group in IT 524

Building a Data Mining Group in the Business Units 525

What to Look for in Data Mining Staff 525

Data Mining Infrastructure 526

The Mining Platform 527

The Scoring Platform 527

One Example of a Production Data Mining Architecture 528

Architectural Overview 528

Customer Interaction Module 529

Analysis Module 530

Data Mining Software 532

Range of Techniques 532

Scalability 533

Support for Scoring 534

Multiple Levels of User Interfaces 535

Comprehensible Output 536

Ability to Handle Diverse Data Types 536

Documentation and Ease of Use 536

Availability of Training for Both Novice and

Advanced Users, Consulting, and Support 537

Vendor Credibility 537

Lessons Learned 537

Chapter 17 Preparing Data for Mining 539

What Data Should Look Like 540

The Customer Signature 540

The Columns 542

Columns with One Value 544

Columns with Almost Only One Value 544

Columns with Unique Values 546

Columns Correlated with Target 547

Model Roles in Modeling 547

Variable Measures 549

Numbers 550

Dates and Times 552

Fixed-Length Character Strings 552

IDs and Keys 554

Names 555

Addresses 555

Free Text 556

Binary Data (Audio, Image, Etc.) 557

Data for Data Mining 557

Constructing the Customer Signature 558

Cataloging the Data 559

Identifying the Customer 560

First Attempt 562

Identifying the Time Frames 562

Taking a Recent Snapshot 562

Pivoting Columns 563

Calculating the Target 563

Making Progress 564

Practical Issues 564

Exploring Variables 565

Distributions Are Histograms 565

Changes over Time 566

Crosstabulations 567

Deriving Variables 568

Extracting Features from a Single Value 569

Combining Values within a Record 569

Looking Up Auxiliary Information 569

Pivoting Regular Time Series 572

Summarizing Transactional Records 574

Summarizing Fields across the Model Set 574