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.
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.
Chapter
1. Introduction and Overview (by David J Fogarty).
Chapter
2.
Severity of Dormancy Model (SDM): Reckoning the Customers before they
Quiescent (by Saumitra N Bhaduri, S Raja Sethu Durai and David J Fogarty).-
Chapter
3. Double Hurdle Model: Not if, but when will Customer Attrite? (by
Saumitra N Bhaduri, S Raja Sethu Durai and David J Fogarty).
Chapter
4.
Optimizing the Media Mix- Evaluating the Impact of Advertisement Expenditures
of Different Media (by Saumitra N Bhaduri, S Raja Sethu Durai and David J
Fogarty).
Chapter
5. Strategic Retail Marketing through DGP Based Models (by
Saumitra N Bhaduri, Anuradha V., S. Raja Sethu Durai and David J Fogarty).-
Chapter
6. Mitigating Sample Selection Bias through Customer Relationship
Management (by Saumitra N Bhaduri, Anuradha V. and David J Fogart).
Chapter
7. Enabling Incremental Gains through Customized Price Optimization (by
Saumitra N Bhaduri, Anuradha V. Avanti George and DavidJ Fogarty).
Chapter
8. Customer Relationship Management (CRM) to Avoid Cannibalization: Analys
Through Spend Intensity Model (by Saumitra N Bhaduri, Anuradha V. Avanti
George and David J Fogarty).
Chapter
9. Estimating Price Elasticity with
Sparse Data: A Bayesian Approach (by David J Fogarty and Saumitra N
Bhaduri).
Chapter
10. New Methods in Ant Colony Optimization using Multiple
Foraging Approach to Increase Stability (by David J Fogarty, Avanti George
and Saumitra N Bhaduri).
Chapter
11. Customer Lifecycle Management Past,
Present and Future (by Avanti George, Saumitra Bhaduri and David J Fogarty).
Saumitra Bhaduri received his Masters 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 companys 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 withseveral 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.
