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The Resource Advanced Business Analytics : Essentials for Developing a Competitive Advantage

Advanced Business Analytics : Essentials for Developing a Competitive Advantage

Label
Advanced Business Analytics : Essentials for Developing a Competitive Advantage
Title
Advanced Business Analytics
Title remainder
Essentials for Developing a Competitive Advantage
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Contributor
Subject
Language
eng
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Advanced Business Analytics : Essentials for Developing a Competitive Advantage
Label
Advanced Business Analytics : Essentials for Developing a Competitive Advantage
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4591934
Publication
Copyright
Related Contributor
Related Location
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Contents -- Authors and Contributors -- List of Figures -- List of Tables -- 1 Introduction and Overview -- References -- 2 Severity of Dormancy Model (SDM): Reckoning the Customers Before They Quiescent -- Abstract -- 2.1 Introduction -- 2.2 Severity of Dormancy Model -- 2.2.1 Methodology -- 2.2.2 Severity of Dormancy Model -- 2.2.3 Prediction -- 2.2.4 Estimation -- 2.3 Data -- 2.4 Variables Used -- 2.5 Results -- 2.6 Beyond Conventional Dormancy Model -- 2.7 Conclusions -- References -- 3 Double Hurdle Model: Not if, but When Will Customer Attrite? -- Abstract -- 3.1 Introduction -- 3.2 Double Hurdle Model -- 3.2.1 Methodology -- 3.2.2 Tobit -- 3.2.3 Double Hurdle Model -- 3.2.4 Prediction -- 3.2.5 Estimation -- 3.3 Data -- 3.3.1 Variables Used -- 3.4 Results -- 3.5 Beyond Logistic Regression -- 3.6 Conclusion -- References -- 4 Optimizing the Media Mix-Evaluating the Impact of Advertisement Expenditures of Different Media -- Abstract -- 4.1 Introduction -- 4.2 Efficiency Measurement -- 4.2.1 Input-Oriented Measures -- 4.2.2 Output-Oriented Measures -- 4.2.3 Date Envelopment Analysis -- 4.2.4 Estimation -- 4.3 Data -- 4.3.1 Deseasonalization -- 4.3.2 Adjusting Spillover Effects -- 4.3.3 Model -- 4.4 Results -- 4.5 Conclusions -- References -- 5 Strategic Retail Marketing Using DGP-Based Models -- Abstract -- 5.1 Introduction -- 5.2 Methodology -- 5.2.1 Model Likelihood Function -- 5.2.2 Derivation of P(active
  • 6.2.1 Simultaneous Approach to Correct the Selection Bias -- 6.2.2 Estimation -- 6.3 Data -- 6.3.1 Variables Used -- 6.4 Results -- 6.5 Understanding and Identifying the Likely Responders from Non-selected Base -- 6.6 Conclusions -- References -- 7 Enabling Incremental Gains Through Customized Price Optimization -- Abstract -- 7.1 Introduction -- 7.2 Methodology -- 7.2.1 Customized Price Optimization Solution -- 7.2.2 The Generic Construct -- 7.2.3 Price Differentiation -- 7.3 Price Optimization Framework -- 7.3.1 Adverse Selection -- 7.3.2 The Response Model -- 7.3.3 Early Settlement -- 7.3.4 CRM Through Cross-Sell and Up-Sell -- 7.3.5 Segmentation -- 7.4 Segmentation Through GA -- 7.4.1 Optimization-Local Versus Global Optimum -- 7.4.2 Regulatory Constraints, Market Dynamics, and Competitive Conquest -- 7.5 The Optimization Model -- 7.6 Simulation -- 7.7 Summary -- References -- 8 Customer Relationship Management (CRM) to Avoid Cannibalization: Analysis Through Spend Intensity Model -- Abstract -- 8.1 Introduction -- 8.2 In-Store Purchase Intensity Model -- 8.2.1 Methodology -- 8.2.2 In-Store Intensity Model -- 8.2.3 Prediction -- 8.2.4 Estimation -- 8.3 Data -- 8.3.1 Variables Used -- 8.4 Results -- 8.5 Beyond Conventional Intensity Model -- 8.6 Conclusion -- References -- 9 Estimating Price Elasticity with Sparse Data: A Bayesian Approach -- Abstract -- 9.1 Introduction -- 9.2 Methodology -- 9.2.1 Methodology for Missing Value Techniques -- 9.2.2 Methodology for Sparse Data Techniques -- 9.3 Empirical Model -- 9.4 Data -- 9.5 Results -- 9.6 Distribution of Price Elasticities -- 9.7 Conclusion -- References -- 10 New Methods in Ant Colony Optimization Using Multiple Foraging Approach to Increase Stability -- Abstract -- 10.1 Introduction -- 10.2 k-Means and Ant Colony Optimization as Clustering Techniques -- 10.3 Methodology
  • 10.4 Algorithm Details -- 10.5 Conclusion -- References -- 11 Customer Lifecycle Value-Past, Present, and Future -- Abstract -- 11.1 Introduction -- 11.2 Fundamentals of CLV -- 11.3 CLV Approaches in Literature -- 11.3.1 Probability Based Models -- 11.4 Econometric Models -- 11.4.1 Customer Acquisition -- 11.4.2 Customer Retention/Activity -- 11.4.2.1 "Lost for Good"-Hazard-Based Models -- 11.4.2.2 "Always a Share"-Markov Models -- 11.4.3 Customer Margin and Expansion -- 11.5 The Future of CLV -- 11.5.1 Moving Beyond Static Hazard Models -- 11.5.2 Reconciling Future Uncertainties Using Fuzzy Logic -- 11.5.3 Recognizing the Need to Model Rare Events -- 11.5.4 Scope of Bayesian Framework to Overcome Future Uncertainties -- 11.6 Conclusion -- References
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{'f': 'http://opac.lib.rpi.edu/record=b4385678'}
Extent
1 online resource (163 pages)
Form of item
online
Isbn
9789811007279
Media category
computer
Media MARC source
rdamedia
Media type code
c
Sound
unknown sound
Specific material designation
remote

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