Profit driven business analytics : a practitioner's guide to transforming big data into added value / Wouter Verbeke, Bart Baesens, Cristián Bravo.

Format:

Book

Author/Creator:

Verbeke, Wouter, 1983- author.

Bravo, Cristián, 1983- author.

Baesens, Bart, author.

Series:

Wiley and SAS business series.

Wiley & SAS business series

Language:

English

Subjects (All):

Management--Statistical methods.

Management.

Management--Data processing.

Big data.

Value added.

Strategic planning.

Physical Description:

1 online resource (419 pages) : illustrations (some color).

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, Incorporated, [2018]

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. 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.

Contents:

Cover

Title Page

Copyright

Contents

Foreword

Acknowledgments

Chapter 1: A Value-Centric Perspective Towards Analytics

Introduction

Business Analytics

Profit-Driven Business Analytics

Analytics Process Model

Analytical Model Evaluation

Analytics Team

Profiles

Data Scientists

Conclusion

Review Questions

Multiple Choice Questions

Open Questions

References

Chapter 2: Analytical Techniques

Data Preprocessing

Denormalizing Data for Analysis

Sampling

Exploratory Analysis

Missing Values

Outlier Detection and Handling

Principal Component Analysis

Types of Analytics

Predictive Analytics

Linear Regression

Logistic Regression

Decision Trees

Neural Networks

Ensemble Methods

Bagging

Boosting

Random Forests

Evaluating Ensemble Methods

Evaluating Predictive Models

Splitting Up the Dataset

Performance Measures for Classification Models

Performance Measures for Regression Models

Other Performance Measures for Predictive Analytical Models

Descriptive Analytics

Association Rules

Sequence Rules

Clustering

Survival Analysis

Survival Analysis Measurements

Kaplan Meier Analysis

Parametric Survival Analysis

Proportional Hazards Regression

Extensions of Survival Analysis Models

Evaluating Survival Analysis Models

Social Network Analytics

Social Network Definitions

Social Network Metrics

Social Network Learning

Relational Neighbor Classifier

Probabilistic Relational Neighbor Classifier

Relational Logistic Regression

Collective Inferencing

Notes

Chapter 3: Business Applications

Introduction.

Marketing Analytics

RFM Analysis

Response Modeling

Churn Prediction

X-selling

Customer Segmentation

Customer Lifetime Value

Customer Journey

Recommender Systems

Fraud Analytics

Credit Risk Analytics

HR Analytics

Note

Chapter 4: Uplift Modeling

The Case for Uplift Modeling: Response Modeling

Effects of a Treatment

Experimental Design, Data Collection, and Data Preprocessing

Experimental Design

Campaign Measurement of Model Effectiveness

Uplift Modeling Methods

Two-Model Approach

Regression-Based Approaches

Tree-Based Approaches

Ensembles

Continuous or Ordered Outcomes

Evaluation of Uplift Models

Visual Evaluation Approaches

Performance Metrics

Practical Guidelines

Two-Step Approach for Developing Uplift Models

Implementations and Software

Chapter 5: Profit-Driven Analytical Techniques

Profit-Driven Predictive Analytics

The Case for Profit-Driven Predictive Analytics

Cost Matrix

Cost-Sensitive Decision Making with Cost-Insensitive Classification Models

Cost-Sensitive Classification Framework

Cost-Sensitive Classification

Pre-Training Methods

During-Training Methods

Post-Training Methods

Evaluation of Cost-Sensitive Classification Models

Imbalanced Class Distribution

Implementations

Cost-Sensitive Regression

The Case for Profit-Driven Regression

Cost-Sensitive Learning for Regression

During Training Methods

Profit-Driven Descriptive Analytics

Profit-Driven Segmentation

Profit-Driven Association Rules

Review Questions.

Multiple Choice Questions

Chapter 6: Profit-Driven Model Evaluation and Implementation

Profit-Driven Evaluation of Classification Models

Average Misclassification Cost

Cutoff Point Tuning

ROC Curve-Based Measures

Profit-Driven Evaluation with Observation-Dependent Costs

Profit-Driven Evaluation of Regression Models

Loss Functions and Error-Based Evaluation Measures

REC Curve and Surface

Chapter 7: Economic Impact

Economic Value of Big Data and Analytics

Total Cost of Ownership (TCO)

Return on Investment (ROI)

Key Economic Considerations

In-Sourcing versus Outsourcing

On Premise versus the Cloud

Open-Source versus Commercial Software

Improving the ROI of Big Data and Analytics

New Sources of Data

Data Quality

Management Support

Organizational Aspects

Cross-Fertilization

About the Authors

Index

EULA.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781119286981

1119286980

9781119286998

1119286999

9781119443179

1119443172

OCLC:

1001968535