Bhaduri / Fogarty Advanced Business Analytics

Essentials for Developing a Competitive Advantage
1. Auflage 2016
ISBN: 978-981-10-0727-9
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 163 Seiten

The present book provides an enterprise-wide guide for anyone interested in pursuing analytic methods in order to compete effectively. It supplements more general texts on statistics and data mining by providing an introduction from leading practitioners in business analytics and real case studies of firms using advanced analytics to gain a competitive advantage in the marketplace. In the era of 'big data' and competing analytics, this book provides practitioners applying business analytics with an overview of the quantitative strategies and techniques used to embed analysis results and advanced algorithms into business processes and create automated insight-driven decisions within the firm. Numerous studies have shown that firms that invest in analytics are more likely to win in the marketplace. Moreover, the Internet of Everything (IoT) for manufacturing and social-local-mobile (SOLOMO) for services have made the use of advanced business analytics even more important for firms. These case studies were all developed by real business analysts, who were assigned the task of solving a business problem using advanced analytics in a way that competitors were not. Readers learn how to develop business algorithms on a practical level, how to embed these within the company and how to take these all the way to implementation and validation.

Saumitra Bhaduri received his Master's degree in Econometric from Calcutta University, Kolkata, India, and his PhD in Financial Economics from Indira Gandhi Institute of Development Research (IGIDR), Mumbai, India. He currently works as a professor at Madras School of Economics, Chennai, India, where he regularly offers courses on Financial Economics and Econometrics, and on Advanced Quantitative Techniques. In terms of his former career he also worked at GE Capital, the financial services division of the General Electric Company, and has held various quantitative analysis roles in the company's finance services. He also founded and headed the GE - MSE decision Sciences Laboratory, where he was responsible for developing state of the art research output for GE. He has also published several research articles in various international journals. His research interests include: Financial Economics and Econometrics, Quantitative Techniques and Advanced Analytics. David Fogarty received his BS in International Relations from Connecticut State University, USA, his PhD in Applied Statistics from Leeds Metropolitan University, UK, and his MBA with a concentration in International Business from Fairfield University, USA. He also has a post-graduate qualification from Columbia University in NYC. In terms of his professional career, he currently works at a Fortune 100 health insurance company as the Chief Analytics Officer or Head of Global Customer Value Management and Growth Analytics. In terms of his former career, Dr. Fogarty also worked for 20 years at GE Capital, the financial services division of the General Electric Company, and has held various quantitative analysis roles across several functions, including risk management and marketing, both internationally and in the US. He currently holds over 10 US patents or patents pending on business analytics algorithms. In addition to his work as a practitioner Dr. Fogarty has over 10 years of teaching experience and has held various adjunct academic appointments at both the graduate and undergraduate level in statistics, international management and quantitative analysis at the University of Liverpool (UK), Trident University (USA), Manhattanville College (USA), University of New Haven (USA), SUNY Purchase College (USA), Manhattan College (USA), LIM College (USA), the University of Phoenix (USA), Chancellor University (USA), Alliant University International (USA) and the Jack Welch management Institute at Strayer University (USA). Dr. Fogarty is also an 'Honorary Professor' at the Madras School of Economics in Chennai, India and has given guest lectures in Asia at East China Normal University (Shanghai, China), Ivey Business School (Hong Kong, China), and the City University of Hong Kong. He has also taught business analytics courses at the esteemed GE Crotonville Management Development Institute in Crotonville, New York. Since obtaining his PhD, he has continued to collaborate with several universities and leading academics to pursue academic research and has several published research papers in peer-reviewed academic journals. His research interests include: how to conduct analysis with missing data, the cultural meaning of data, integrating genetic algorithms into the statistical science framework, and many other topics related to quantitative analysis in business.

Autoren/Hrsg.

Bhaduri, Saumitra N.

Fogarty, David

Inhaltsverzeichnis

1;Contents;5
2;Authors and Contributors;9
3;List of Figures;11
4;List of Tables;12
5;1 Introduction and Overview;14
5.1;References;29
6;2 Severity of Dormancy Model (SDM): Reckoning the Customers Before They Quiescent;31
6.1;Abstract;31
6.2;2.1 Introduction;31
6.3;2.2 Severity of Dormancy Model;33
6.3.1;2.2.1 Methodology;33
6.3.2;2.2.2 Severity of Dormancy Model;33
6.3.3;2.2.3 Prediction;35
6.3.4;2.2.4 Estimation;35
6.4;2.3 Data;35
6.5;2.4 Variables Used;37
6.6;2.5 Results;37
6.7;2.6 Beyond Conventional Dormancy Model;39
6.8;2.7 Conclusions;42
6.9;References;42
7;3 Double Hurdle Model: Not if, but When Will Customer Attrite?;44
7.1;Abstract;44
7.2;3.1 Introduction;44
7.3;3.2 Double Hurdle Model;45
7.3.1;3.2.1 Methodology;45
7.3.2;3.2.2 Tobit;45
7.3.3;3.2.3 Double Hurdle Model;46
7.3.4;3.2.4 Prediction;47
7.3.5;3.2.5 Estimation;48
7.4;3.3 Data;48
7.4.1;3.3.1 Variables Used;49
7.5;3.4 Results;49
7.6;3.5 Beyond Logistic Regression;53
7.7;3.6 Conclusion;56
7.8;References;56
8;4 Optimizing the Media Mix—Evaluating the Impact of Advertisement Expenditures of Different Media;58
8.1;Abstract;58
8.2;4.1 Introduction;59
8.3;4.2 Efficiency Measurement;59
8.3.1;4.2.1 Input-Oriented Measures;60
8.3.2;4.2.2 Output-Oriented Measures;61
8.3.3;4.2.3 Date Envelopment Analysis;61
8.3.4;4.2.4 Estimation;63
8.4;4.3 Data;63
8.4.1;4.3.1 Deseasonalization;63
8.4.2;4.3.2 Adjusting Spillover Effects;64
8.4.3;4.3.3 Model;65
8.5;4.4 Results;65
8.6;4.5 Conclusions;66
8.7;References;67
9;5 Strategic Retail Marketing Using DGP-Based Models;68
9.1;Abstract;68
9.2;5.1 Introduction;69
9.3;5.2 Methodology;71
9.3.1;5.2.1 Model Likelihood Function;71
9.3.2;5.2.2 Derivation of P(active|x, n, m);72
9.3.3;5.2.3 Expected Number of Future Transaction;73
9.3.4;5.2.4 Average Money Value of Future Transaction;73
9.3.5;5.2.5 Prediction;74
9.3.6;5.2.6 Estimation;74
9.4;5.3 Data;74
9.4.1;5.3.1 Variables Used;75
9.5;5.4 Results and Retail Strategy Booster;75
9.5.1;5.4.1 Model Results and Validation;75
9.6;5.5 Conclusions;80
9.7;References;81
10;6 Mitigating Sample Selection Bias Through Customer Relationship Management;82
10.1;Abstract;82
10.2;6.1 Introduction;82
10.3;6.2 Methodology;84
10.3.1;6.2.1 Simultaneous Approach to Correct the Selection Bias;85
10.3.2;6.2.2 Estimation;86
10.4;6.3 Data;87
10.4.1;6.3.1 Variables Used;87
10.5;6.4 Results;87
10.6;6.5 Understanding and Identifying the Likely Responders from Non-selected Base;92
10.7;6.6 Conclusions;93
10.8;References;94
11;7 Enabling Incremental Gains Through Customized Price Optimization;95
11.1;Abstract;95
11.2;7.1 Introduction;95
11.3;7.2 Methodology;97
11.3.1;7.2.1 Customized Price Optimization Solution;97
11.3.2;7.2.2 The Generic Construct;97
11.3.3;7.2.3 Price Differentiation;99
11.4;7.3 Price Optimization Framework;99
11.4.1;7.3.1 Adverse Selection;100
11.4.2;7.3.2 The Response Model;100
11.4.3;7.3.3 Early Settlement;102
11.4.4;7.3.4 CRM Through Cross-Sell and Up-Sell;104
11.4.5;7.3.5 Segmentation;104
11.5;7.4 Segmentation Through GA;105
11.5.1;7.4.1 Optimization—Local Versus Global Optimum;106
11.5.2;7.4.2 Regulatory Constraints, Market Dynamics, and Competitive Conquest;106
11.6;7.5 The Optimization Model;106
11.7;7.6 Simulation;107
11.8;7.7 Summary;109
11.9;References;109
12;8 Customer Relationship Management (CRM) to Avoid Cannibalization: Analysis Through Spend Intensity Model;110
12.1;Abstract;110
12.2;8.1 Introduction;110
12.3;8.2 In-Store Purchase Intensity Model;112
12.3.1;8.2.1 Methodology;112
12.3.2;8.2.2 In-Store Intensity Model;112
12.3.3;8.2.3 Prediction;114
12.3.4;8.2.4 Estimation;114
12.4;8.3 Data;115
12.4.1;8.3.1 Variables Used;115
12.5;8.4 Results;115
12.6;8.5 Beyond Conventional Intensity Model;118
12.7;8.6 Conclusion;119
12.8;References;120
13;9 Estimating Price Elasticity with Sparse Data: A Bayesian Approach;121
13.1;Abstract;121
13.2;9.1 Introduction;121
13.3;9.2 Methodology;122
13.3.1;9.2.1 Methodology for Missing Value Techniques;122
13.3.2;9.2.2 Methodology for Sparse Data Techniques;126
13.4;9.3 Empirical Model;128
13.5;9.4 Data;129
13.6;9.5 Results;130
13.7;9.6 Distribution of Price Elasticities;133
13.8;9.7 Conclusion;135
13.9;References;136
14;10 New Methods in Ant Colony Optimization Using Multiple Foraging Approach to Increase Stability;138
14.1;Abstract;138
14.2;10.1 Introduction;138
14.3;10.2 k-Means and Ant Colony Optimization as Clustering Techniques;140
14.4;10.3 Methodology;141
14.5;10.4 Algorithm Details;141
14.6;10.5 Conclusion;144
14.7;References;145
15;11 Customer Lifecycle Value—Past, Present, and Future;146
15.1;Abstract;146
15.2;11.1 Introduction;146
15.3;11.2 Fundamentals of CLV;148
15.4;11.3 CLV Approaches in Literature;149
15.4.1;11.3.1 Probability Based Models;149
15.5;11.4 Econometric Models;153
15.5.1;11.4.1 Customer Acquisition;154
15.5.2;11.4.2 Customer Retention/Activity;155
15.5.2.1;11.4.2.1 “Lost for Good”—Hazard-Based Models;156
15.5.2.2;11.4.2.2 “Always a Share”—Markov Models;157
15.5.3;11.4.3 Customer Margin and Expansion;158
15.6;11.5 The Future of CLV;159
15.6.1;11.5.1 Moving Beyond Static Hazard Models;159
15.6.2;11.5.2 Reconciling Future Uncertainties Using Fuzzy Logic;160
15.6.3;11.5.3 Recognizing the Need to Model Rare Events;160
15.6.4;11.5.4 Scope of Bayesian Framework to Overcome Future Uncertainties;161
15.7;11.6 Conclusion;161
15.8;References;161