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

Maximize profit and optimize decisions with advanced business analytics Profit-Driven Business Analytics provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics. Despite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business. Reinforce basic analytics to maximize profits Adopt the tools and techniques of successful integration Implement more advanced analytics with a value-centric approach Fine-tune analytical information to optimize business decisions Both data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. Profit-Driven Business Analytics provides a practical guidebook and reference for adopting real business analytics techniques.

Table of Contents:
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

About the Author :
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 vin business analytics in the department of Decision Analytics and Risk at the University of Southampton.