Advanced Business Analytics: essentials for developing a competitive advantage
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Singapore
Springer
[2016]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | XIV, 156 Seiten Illustrationen, Diagramme |
| ISBN: | 9789811007262 |
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Datensatz im Suchindex
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| adam_text | Contents 1 Introduction and Overview........................................................................ References...................................................................................................... 2 Severity of Dormancy Model (SDM): Reckoning the Customers Before They Quiescent.................................................... With contribution from S. Raja Sethu Durai 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?....................................................................................... With contribution from S. Raja Sethu Durai 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................................................................................................. 1 16 19 19 21 21 21 23 23 23 25 25 27 30 30 33 33 34 34 34 35 36 37 37 38 38
vi Contents 3.5 Beyond Logistic Regression............................................................ 3.6 Conclusion......................................................................................... References...................................................................................................... 4 5 6 Optimizing the Media Mix—Evaluating the Impact of Advertisement Expenditures of Different Media.............................. With contribution from S. Raja Sethu Durai 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...................................................................................................... Strategic Retail Marketing Using DGP-Based Models...................... With contribution from S. Raja Sethu Durai 5.1 Introduction......................................................................................... 5.2 Methodology....................................................................................... 5.2.1 Model Likelihood Function............................................... 5.2.2 Derivation of P(act/ve|x, n, m).......................................... 5.2.3 Expected Number of Future Transaction......................... 5.2.4 Average Money Value of Future Transaction.................. 5.2.5 Prediction............................................................................. 5.2.6 Estimation............................................................................. 5.3 Data...................................................................................................... 5.3.1 Variables Used................................................................... 5.4 Results and Retail Strategy Booster............................................... 5.4.1 Model Results and Validation.......................................... 5.5 Conclusions....................................................................................... References......................................................................................................
Mitigating Sample Selection Bias Through Customer Relationship Management........................................................................ With contribution from V. Anuradha 6.1 Introduction......................................................................................... 6.2 Methodology....................................................................................... 6.2.1 Simultaneous Approach to Correct the Selection Bias.............................................................. 6.2.2 Estimation............................................................................. 42 45 45 47 48 48 49 50 50 52 52 52 53 54 54 55 56 57 58 60 60 61 62 62 63 63 63 64 64 64 69 70 71 71 73 74 75
Contents 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 8 Enabling Incremental Gains Through Customized Price Optimization................................................................................................. With contribution from V. Anuradha and Avanti George 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...................................................................................................... Customer Relationship Management (CRM) to Avoid Cannibalization: Analysis Through Spend Intensity Model............................................................................... With contribution from V. Anuradha and Avanti George 8.1 Introduction......................................................................................... 8.2 In-Store Purchase IntensityModel................................................... 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................................................................................................. VH 76 76 76 81 82 83 85 85 87 87 87 89 89 90 90 92 94 94 95 96 96 96 97 99 99 101 101 103 103 103 105 105 106 106 106
viii 9 10 11 Contents 8.5 Beyond Conventional Intensity Model.......................................... 8.6 Conclusion......................................................................................... References...................................................................................................... 109 110 Ill Estimating Price Elasticity with Sparse Data: A Bayesian Approach............................................................................... 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...................................................................................................... 113 113 114 114 118 120 121 122 125 127 128 New Methods in Ant Colony Optimization Using Multiple Foraging Approach to Increase
Stability............................................... With contribution from Avanti George 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...................................................................................................... Customer Lifecycle Value—Past, Present, and Future...................... With contribution from Avanti George 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............................................. 11.4.3 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...................................................................................................... 131 131 133 134 134 137 138 139 139 141 142 142 146 147 148 151 152 152 153 153 154 154 154
Saumura N Bhdduri - David Fogarty Advanced Business Analytics Essentials for Developing a Competitive Advantage 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.
|
| adam_txt |
Contents 1 Introduction and Overview. References. 2 Severity of Dormancy Model (SDM): Reckoning the Customers Before They Quiescent. With contribution from S. Raja Sethu Durai 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?. With contribution from S. Raja Sethu Durai 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. 1 16 19 19 21 21 21 23 23 23 25 25 27 30 30 33 33 34 34 34 35 36 37 37 38 38
vi Contents 3.5 Beyond Logistic Regression. 3.6 Conclusion. References. 4 5 6 Optimizing the Media Mix—Evaluating the Impact of Advertisement Expenditures of Different Media. With contribution from S. Raja Sethu Durai 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. Strategic Retail Marketing Using DGP-Based Models. With contribution from S. Raja Sethu Durai 5.1 Introduction. 5.2 Methodology. 5.2.1 Model Likelihood Function. 5.2.2 Derivation of P(act/ve|x, n, m). 5.2.3 Expected Number of Future Transaction. 5.2.4 Average Money Value of Future Transaction. 5.2.5 Prediction. 5.2.6 Estimation. 5.3 Data. 5.3.1 Variables Used. 5.4 Results and Retail Strategy Booster. 5.4.1 Model Results and Validation. 5.5 Conclusions. References.
Mitigating Sample Selection Bias Through Customer Relationship Management. With contribution from V. Anuradha 6.1 Introduction. 6.2 Methodology. 6.2.1 Simultaneous Approach to Correct the Selection Bias. 6.2.2 Estimation. 42 45 45 47 48 48 49 50 50 52 52 52 53 54 54 55 56 57 58 60 60 61 62 62 63 63 63 64 64 64 69 70 71 71 73 74 75
Contents 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 8 Enabling Incremental Gains Through Customized Price Optimization. With contribution from V. Anuradha and Avanti George 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. Customer Relationship Management (CRM) to Avoid Cannibalization: Analysis Through Spend Intensity Model. With contribution from V. Anuradha and Avanti George 8.1 Introduction. 8.2 In-Store Purchase IntensityModel. 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. VH 76 76 76 81 82 83 85 85 87 87 87 89 89 90 90 92 94 94 95 96 96 96 97 99 99 101 101 103 103 103 105 105 106 106 106
viii 9 10 11 Contents 8.5 Beyond Conventional Intensity Model. 8.6 Conclusion. References. 109 110 Ill Estimating Price Elasticity with Sparse Data: A Bayesian Approach. 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. 113 113 114 114 118 120 121 122 125 127 128 New Methods in Ant Colony Optimization Using Multiple Foraging Approach to Increase
Stability. With contribution from Avanti George 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. Customer Lifecycle Value—Past, Present, and Future. With contribution from Avanti George 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. 11.4.3 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. 131 131 133 134 134 137 138 139 139 141 142 142 146 147 148 151 152 152 153 153 154 154 154
Saumura N Bhdduri - David Fogarty Advanced Business Analytics Essentials for Developing a Competitive Advantage 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. |
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| id | DE-604.BV047161622 |
| illustrated | Illustrated |
| index_date | 2024-07-03T16:40:48Z |
| indexdate | 2024-07-10T09:04:21Z |
| institution | BVB |
| isbn | 9789811007262 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032567230 |
| oclc_num | 964658241 |
| open_access_boolean | |
| owner | DE-384 |
| owner_facet | DE-384 |
| physical | XIV, 156 Seiten Illustrationen, Diagramme |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Springer |
| record_format | marc |
| spelling | Bhaduri, Saumitra ca. 20./21. Jh. Verfasser (DE-588)171831640 aut Advanced Business Analytics essentials for developing a competitive advantage Saumitra N. Bhaduri, David Fogarty Singapore Springer [2016] © 2016 XIV, 156 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Business Leadership Management information systems Industrial management Operations research Management science Statistics Econometrics Business and Management Business Process Management Operations Research, Management Science Business Strategy/Leadership Statistics for Business/Economics/Mathematical Finance/Insurance Führung Statistik Wirtschaft Wettbewerbsvorteil (DE-588)4219652-8 gnd rswk-swf Operations Research (DE-588)4043586-6 gnd rswk-swf Managementinformationssystem (DE-588)4074518-1 gnd rswk-swf Business Intelligence (DE-588)4588307-5 gnd rswk-swf Business Intelligence (DE-588)4588307-5 s Operations Research (DE-588)4043586-6 s Managementinformationssystem (DE-588)4074518-1 s Wettbewerbsvorteil (DE-588)4219652-8 s DE-604 Fogarty, David Verfasser aut Erscheint auch als Online-Ausgabe 978-981-10-0726-2 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032567230&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032567230&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Bhaduri, Saumitra ca. 20./21. Jh Fogarty, David Advanced Business Analytics essentials for developing a competitive advantage Business Leadership Management information systems Industrial management Operations research Management science Statistics Econometrics Business and Management Business Process Management Operations Research, Management Science Business Strategy/Leadership Statistics for Business/Economics/Mathematical Finance/Insurance Führung Statistik Wirtschaft Wettbewerbsvorteil (DE-588)4219652-8 gnd Operations Research (DE-588)4043586-6 gnd Managementinformationssystem (DE-588)4074518-1 gnd Business Intelligence (DE-588)4588307-5 gnd |
| subject_GND | (DE-588)4219652-8 (DE-588)4043586-6 (DE-588)4074518-1 (DE-588)4588307-5 |
| title | Advanced Business Analytics essentials for developing a competitive advantage |
| title_auth | Advanced Business Analytics essentials for developing a competitive advantage |
| title_exact_search | Advanced Business Analytics essentials for developing a competitive advantage |
| title_exact_search_txtP | Advanced Business Analytics essentials for developing a competitive advantage |
| title_full | Advanced Business Analytics essentials for developing a competitive advantage Saumitra N. Bhaduri, David Fogarty |
| title_fullStr | Advanced Business Analytics essentials for developing a competitive advantage Saumitra N. Bhaduri, David Fogarty |
| title_full_unstemmed | Advanced Business Analytics essentials for developing a competitive advantage Saumitra N. Bhaduri, David Fogarty |
| title_short | Advanced Business Analytics |
| title_sort | advanced business analytics essentials for developing a competitive advantage |
| title_sub | essentials for developing a competitive advantage |
| topic | Business Leadership Management information systems Industrial management Operations research Management science Statistics Econometrics Business and Management Business Process Management Operations Research, Management Science Business Strategy/Leadership Statistics for Business/Economics/Mathematical Finance/Insurance Führung Statistik Wirtschaft Wettbewerbsvorteil (DE-588)4219652-8 gnd Operations Research (DE-588)4043586-6 gnd Managementinformationssystem (DE-588)4074518-1 gnd Business Intelligence (DE-588)4588307-5 gnd |
| topic_facet | Business Leadership Management information systems Industrial management Operations research Management science Statistics Econometrics Business and Management Business Process Management Operations Research, Management Science Business Strategy/Leadership Statistics for Business/Economics/Mathematical Finance/Insurance Führung Statistik Wirtschaft Wettbewerbsvorteil Operations Research Managementinformationssystem Business Intelligence |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032567230&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032567230&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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