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E-grāmata: Advanced Business Analytics: Essentials for Developing a Competitive Advantage

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  • Formāts: PDF+DRM
  • Izdošanas datums: 12-Jul-2016
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789811007279
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Advanced Business Analytics: Essentials for Developing a Competitive AdvantagePalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 12-Jul-2016
  • Izdevniecība: Springer Verlag, Singapore
  • Valoda: eng
  • ISBN-13: 9789811007279
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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.

1 Introduction and Overview
1(18)
References
16(3)
2 Severity of Dormancy Model (SDM): Reckoning the Customers Before They Quiescent
19(14)
S. Raja Sethu Durai
2.1 Introduction
19(2)
2.2 Severity of Dormancy Model
21(2)
2.2.1 Methodology
21(1)
2.2.2 Severity of Dormancy Model
21(2)
2.2.3 Prediction
23(1)
2.2.4 Estimation
23(1)
2.3 Data
23(2)
2.4 Variables Used
25(1)
2.5 Results
25(2)
2.6 Beyond Conventional Dormancy Model
27(3)
2.7 Conclusions
30(3)
References
30(3)
3 Double Hurdle Model: Not if, but When Will Customer Attrite?
33(14)
S. Raja Sethu Durai
3.1 Introduction
33(1)
3.2 Double Hurdle Model
34(3)
3.2.1 Methodology
34(1)
3.2.2 Tobit
34(1)
3.2.3 Double Hurdle Model
35(1)
3.2.4 Prediction
36(1)
3.2.5 Estimation
37(1)
3.3 Data
37(1)
3.3.1 Variables Used
38(1)
3.4 Results
38(4)
3.5 Beyond Logistic Regression
42(3)
3.6 Conclusion
45(2)
References
45(2)
4 Optimizing the Media Mix---Evaluating the Impact of Advertisement Expenditures of Different Media
47(10)
S. Raja Sethu Durai
4.1 Introduction
48(1)
4.2 Efficiency Measurement
48(4)
4.2.1 Input-Oriented Measures
49(1)
4.2.2 Output-Oriented Measures
50(1)
4.2.3 Date Envelopment Analysis
50(2)
4.2.4 Estimation
52(1)
4.3 Data
52(2)
4.3.1 Deseasonalization
52(1)
4.3.2 Adjusting Spillover Effects
53(1)
4.3.3 Model
54(1)
4.4 Results
54(1)
4.5 Conclusions
55(2)
References
56(1)
5 Strategic Retail Marketing Using DGP-Based Models
57(14)
S. Raja Sethu Durai
5.1 Introduction
58(2)
5.2 Methodology
60(3)
5.2.1 Model Likelihood Function
60(1)
5.2.2 Derivation of P(active|x, n, m)
61(1)
5.2.3 Expected Number of Future Transaction
62(1)
5.2.4 Average Money Value of Future Transaction
62(1)
5.2.5 Prediction
63(1)
5.2.6 Estimation
63(1)
5.3 Data
63(1)
5.3.1 Variables Used
64(1)
5.4 Results and Retail Strategy Booster
64(5)
5.4.1 Model Results and Validation
64(5)
5.5 Conclusions
69(2)
References
70(1)
6 Mitigating Sample Selection Bias Through Customer Relationship Management
71(14)
V. Anuradha
6.1 Introduction
71(2)
6.2 Methodology
73(3)
6.2.1 Simultaneous Approach to Correct the Selection Bias
74(1)
6.2.2 Estimation
75(1)
6.3 Data
76(1)
6.3.1 Variables Used
76(1)
6.4 Results
76(5)
6.5 Understanding and Identifying the Likely Responders from Non-selected Base
81(1)
6.6 Conclusions
82(3)
References
83(2)
7 Enabling Incremental Gains Through Customized Price Optimization
85(16)
V. Anuradha
Avanti George
7.1 Introduction
85(2)
7.2 Methodology
87(2)
7.2.1 Customized Price Optimization Solution
87(1)
7.2.2 The Generic Construct
87(2)
7.2.3 Price Differentiation
89(1)
7.3 Price Optimization Framework
89(6)
7.3.1 Adverse Selection
90(1)
7.3.2 The Response Model
90(2)
7.3.3 Early Settlement
92(2)
7.3.4 CRM Through Cross-Sell and Up-Sell
94(1)
7.3.5 Segmentation
94(1)
7.4 Segmentation Through GA
95(1)
7.4.1 Optimization---Local Versus Global Optimum
96(1)
7.4.2 Regulatory Constraints, Market Dynamics, and Competitive Conquest
96(1)
7.5 The Optimization Model
96(1)
7.6 Simulation
97(2)
7.7 Summary
99(2)
References
99(2)
8 Customer Relationship Management (CRM) to Avoid Cannibalization: Analysis Through Spend Intensity Model
101(12)
V. Anuradha
Avanti George
8.1 Introduction
101(2)
8.2 In-Store Purchase Intensity Model
103(3)
8.2.1 Methodology
103(1)
8.2.2 In-Store Intensity Model
103(2)
8.2.3 Prediction
105(1)
8.2.4 Estimation
105(1)
8.3 Data
106(1)
8.3.1 Variables Used
106(1)
8.4 Results
106(3)
8.5 Beyond Conventional Intensity Model
109(1)
8.6 Conclusion
110(3)
References
111(2)
9 Estimating Price Elasticity with Sparse Data: A Bayesian Approach
113(18)
9.1 Introduction
113(1)
9.2 Methodology
114(6)
9.2.1 Methodology for Missing Value Techniques
114(4)
9.2.2 Methodology for Sparse Data Techniques
118(2)
9.3 Empirical Model
120(1)
9.4 Data
121(1)
9.5 Results
122(3)
9.6 Distribution of Price Elasticities
125(2)
9.7 Conclusion
127(4)
References
128(3)
10 New Methods in Ant Colony Optimization Using Multiple Foraging Approach to Increase Stability
131(8)
Avanti George
10.1 Introduction
131(2)
10.2 k-Means and Ant Colony Optimization as Clustering Techniques
133(1)
10.3 Methodology
134(1)
10.4 Algorithm Details
134(3)
10.5 Conclusion
137(2)
References
138(1)
11 Customer Lifecycle Value---Past, Present, and Future
139(1)
Avanti George
11.1 Introduction
139(2)
11.2 Fundamentals of CLV
141(1)
11.3 CLV Approaches in Literature
142(4)
11.3.1 Probability Based Models
142(4)
11.4 Econometric Models
146(6)
11.4.1 Customer Acquisition
147(1)
11.4.2 Customer Retention/Activity
148(3)
11.4.3 Customer Margin and Expansion
151(1)
11.5 The Future of CLV
152(2)
11.5.1 Moving Beyond Static Hazard Models
152(1)
11.5.2 Reconciling Future Uncertainties Using Fuzzy Logic
153(1)
11.5.3 Recognizing the Need to Model Rare Events
153(1)
11.5.4 Scope of Bayesian Framework to Overcome Future Uncertainties
154(1)
11.6 Conclusion
154(1)
References
154
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.
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