Read more
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.
List 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
<
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.
Summary
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.
