Profit Driven Business Analytics. A Practitioner's Guide to Transforming Big Data into Added Value. Wiley and SAS Business Series

  • ID: 3822518
  • Book
  • 416 Pages
  • John Wiley and Sons Ltd
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GENERATE REAL VALUE FROM YOUR BUSINESS ANALYTICS

Profit Driven Business Analytics details a progressive value–centric strategy for using analytics to heighten the accuracy of your business decisions and skyrocket your bottom line. Based on the authorial team′s worldwide consulting experience and high–quality research, this step–by–step guide opens up a road map to handling data, optimizing data analytics for specific companies, and continuously evaluating and improving the entire process. Written to give leadership teams the tools and know–how to transform their organizations into forward–edge mechanisms of profitable analytics, this complete, practical approach enables you to:

  • Customize the latest analytical business solutions into a reliable system for creating real, value–increasing insight into every part of an organization
  • Gain a real–world perspective on advanced analytics through illustrative case studies and online access to practice materials and tools
  • Confidently roll out the covered methodology at any company, from conception through ongoing management—including tips for hiring a data scientist

See the dollar signs in your data with Profit Driven Business Analytics.

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Foreword xv

Acknowledgments xvii

Chapter 1 A Value–Centric Perspective Towards Analytics 1

Introduction 1

Business Analytics 3

Profit–Driven Business Analytics 9

Analytics Process Model 14

Analytical Model Evaluation 17

Analytics Team 19

Profiles 19

Data Scientists 20

Conclusion 23

Review Questions 24

Multiple Choice Questions 24

Open Questions 25

References 25

Chapter 2 Analytical Techniques 28

Introduction 28

Data Preprocessing 29

Denormalizing Data for Analysis 29

Sampling 30

Exploratory Analysis 31

Missing Values 31

Outlier Detection and Handling 32

Principal Component Analysis 33

Types of Analytics 37

Predictive Analytics 37

Introduction 37

Linear Regression 38

Logistic Regression 39

Decision Trees 45

Neural Networks 52

Ensemble Methods 56

Bagging 57

Boosting 57

Random Forests 58

Evaluating Ensemble Methods 59

Evaluating Predictive Models 59

Splitting Up the Dataset 59

Performance Measures for Classification Models 63

Performance Measures for Regression Models 67

Other Performance Measures for Predictive Analytical

Models 68

Descriptive Analytics 69

Introduction 69

Association Rules 69

Sequence Rules 72

Clustering 74

Survival Analysis 81

Introduction 81

Survival Analysis Measurements 83

Kaplan Meier Analysis 85

Parametric Survival Analysis 87

Proportional Hazards Regression 90

Extensions of Survival Analysis Models 92

Evaluating Survival Analysis Models 93

Social Network Analytics 93

Introduction 93

Social Network Definitions 94

Social Network Metrics 95

Social Network Learning 97

Relational Neighbor Classifier 98

Probabilistic Relational Neighbor Classifier 99

Relational Logistic Regression 100

Collective Inferencing 102

Conclusion 102

Review Questions 103

Multiple Choice Questions 103

Open Questions 108

Notes 110

References 110

Chapter 3 Business Applications 114

Introduction 114

Marketing Analytics 114

Introduction 114

RFM Analysis 115

Response Modeling 116

Churn Prediction 118

X–selling 120

Customer Segmentation 121

Customer Lifetime Value 123

Customer Journey 129

Recommender Systems 131

Fraud Analytics 134

Credit Risk Analytics 139

HR Analytics 141

Conclusion 146

Review Questions 146

Multiple Choice Questions 146

Open Questions 150

Note 151

References 151

Chapter 4 Uplift Modeling 154

Introduction 154

The Case for Uplift Modeling: Response Modeling 155

Effects of a Treatment 158

Experimental Design, Data Collection, and Data

Preprocessing 161

Experimental Design 161

Campaign Measurement of Model Effectiveness 164

Uplift Modeling Methods 170

Two–Model Approach 172

Regression–Based Approaches 174

Tree–Based Approaches 183

Ensembles 193

Continuous or Ordered Outcomes 198

Evaluation of Uplift Models 199

Visual Evaluation Approaches 200

Performance Metrics 207

Practical Guidelines 210

Two–Step Approach for Developing Uplift Models 210

Implementations and Software 212

Conclusion 213

Review Questions 214

Multiple Choice Questions 214

Open Questions 216

Note 217

References 217

Chapter 5 Profit–Driven Analytical Techniques 220

Introduction 220

Profit–Driven Predictive Analytics 221

The Case for Profit–Driven Predictive Analytics 221

Cost Matrix 222

Cost–Sensitive Decision Making with Cost–Insensitive

Classification Models 228

Cost–Sensitive Classification Framework 231

Cost–Sensitive Classification 234

Pre–Training Methods 235

During–Training Methods 247

Post–Training Methods 253

Evaluation of Cost–Sensitive Classification Models 255

Imbalanced Class Distribution 256

Implementations 259

Cost–Sensitive Regression 259

The Case for Profit–Driven Regression 259

Cost–Sensitive Learning for Regression 260

During Training Methods 260

Post–Training Methods 261

Profit–Driven Descriptive Analytics 267

Profit–Driven Segmentation 267

Profit–Driven Association Rules 280

Conclusion 283

Review Questions 284

Multiple Choice Questions 284

Open Questions 289

Notes 290

References 291

Chapter 6 Profit–Driven Model Evaluation

and Implementation 296

Introduction 296

Profit–Driven Evaluation of Classification Models 298

Average Misclassification Cost 298

Cutoff Point Tuning 303

ROC Curve–Based Measures 310

Profit–Driven Evaluation with Observation–Dependent

Costs 334

Profit–Driven Evaluation of Regression Models 338

Loss Functions and Error–Based Evaluation Measures 339

REC Curve and Surface 341

Conclusion 345

Review Questions 347

Multiple Choice Questions 347

Open Questions 350

Notes 351

References 352

Chapter 7 Economic Impact 355

Introduction 355

Economic Value of Big Data and Analytics 355

Total Cost of Ownership (TCO) 355

Return on Investment (ROI) 357

Profit–Driven Business Analytics 359

Key Economic Considerations 359

In–Sourcing versus Outsourcing 359

On Premise versus the Cloud 361

Open–Source versus Commercial Software 362

Improving the ROI of Big Data and Analytics 364

New Sources of Data 364

Data Quality 367

Management Support 369

Organizational Aspects 370

Cross–Fertilization 371

Conclusion 372

Review Questions 373

Multiple Choice Questions 373

Open Questions 376

Notes 377

References 377

About the Authors 378

Index 381

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WOUTER VERBEKE is assistant professor of Business Informatics and Data Analytics at Vrije Universiteit Brussel (Belgium). He is the coauthor of Fraud Analytics using Descriptive, Predictive, and Social Network Techniques.

BART BAESENS is a professor at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He is the author of Credit Risk Management and Analytics in a Big Data World, as well as coauthor of Fraud Analytics using Descriptive, Predictive, and Social Network Techniques.

CRISTIÁN BRAVO is a lecturer in business analytics in the department of Decision Analytics and Risk at the University of Southampton.

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