Deep data analytics for new product development:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London and New York
Routledge
2020
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| Edition: | First published |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xviii, 265 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9780367077754 9780367077761 |
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MARC
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| adam_text | CONTENTS List offigures List of tables Preface Acknowledgements ix xii xiv xix 1 Introduction 1.1 New product failures 1.1.1 Design failures 1.1.2 Pricing failures 1.1.3 Messaging failures 1.2 An NPD process 1.3 The heart of the NPD process 1.3.1 Market research 1.3.2 Business analytics 1.4 Summary 1 2 4 5 7 7 9 13 14 17 2 Ideation: What do you do? 2.1 Sources for ideas 2.1.1 Traditional approaches 2.1.2 A modern approach 2.2 Big data - external and internal 2.3 Text data and text analysis 2.3.1 Documents, corpus, and corpora 2.3.2 Organizing text data 2.3.3 Text processing 2.3.4 Creating a searchable database 2.4 Call center logs and warranty claims analysis 19 20 20 22 23 24 25 26 28 40 43
vi Contents 2.5 Sentiment analysis and opinion mining 2.6 Market research: voice of the customer ( VOC) 2.6.1 Competitive assessment: the role of CEA 2.6.2 Contextual design 2.7 Machine learning methods 2.8 Managing ideas and predictive analytics 2.9 Software 2.10 Summary 2.11 Appendix 2.11.1 Matrix decomposition 2.11.2 Singular value decomposition (SVD) 2.11.3 Spectral and singular value decompositions 44 45 45 49 50 51 53 54 54 54 54 57 3 Develop: How do you do it? 3.1 Product design optimization 3.2 Conjoint analysis for product optimization 3.2.1 Conjoint framework 3.2.2 Conjoint design for new products 3.2.3 A new product design example 3.2.4 Conjoint design 3.2.5 Some problems with conjoint analysis 3.2.6 Optimal attribute levels 3.2.7 Software 3.3 Kansei engineering for product optimization 3.3.1 Study designs 3.3.2 Combining conjoint and Kansei analyses 3.4 Early-stage pricing 3.4.1 van Westendorp price sensitivity meter 3.5 Summary 3.6 Appendix 3.A 3.6.1 Brief overview of the chi-square statistic 3.7 Appendix 3.B 3.7.1 Brief overview of correspondence analysis 3.8 Appendix 3.C 3.8.1 Very brief overview of ordinary least squares analysis 3.8.2 Brief overview of principal components analysis 3.8.3 Principal components regression analysis 3.8.4 Brief overview of partial least squares analysis 59 60 61 62 63 65 65 69 70 71 72 73 83 87 88 90 91 91 96 96 98 98 101 102 102 4 Test: Will it work and sell? 4.1 Discrete choice analysis 4.1.1 Product configuration vs. competitive offerings 4.1.2 Discrete choice background - high-level view 105 106 107 108
Contents vii Test market hands-on analysis 4.2.1 Live trial tests with customers Market segmentation TURF analysis Software Summary Appendix 4.7.1 TURF calculations 114 114 120 123 127 127 127 127 5 Launch I: What is the marketing mix? 5.1 Messaging/claims analysis 5.1.1 Stages of message analysis 5.1.2 Message creation 5.1.3 Message testing 5.1.4 Message delivery 5.2 Price finalization 5.2.1 Granger-Gabor analysis 5.2.2 Price segmentation 5.2.3 Pricing in a social network 5.3 Placing the new product 5.4 Software 5.5 Summary 130 131 131 133 134 154 161 162 164 165 166 167 167 6 Launch II: How much will sell? 6.1 Predicting vs. forecasting 6.2 Forecasting responsibility 6.3 Time series and forecasting background 6.4 Data issues 6.4.1 Data availability 6.4.2 Training and testing data sets 6.5 Forecasting methods based on data availability 6.5.1 Naive methods 6.5.2 Sophisticated forecasting methods 6.5.3 Data requirements 6.6 Forecast error analysis 6.7 Software 6.8 Summary 6.9 Appendix 6.9.1 Time series definition 6.9.2 Backshift and differencing operators 6.9.3 Random walk model and naive forecast 6.9.4 Random walk with drift 6.9.5 Constant mean model 6.9.6 The ARIMA family of models 168 169 169 170 171 172 173 175 175 176 180 180 182 182 182 182 182 183 186 187 187 4.2 4.3 4.4 4.5 4.6 4.7
vili Contents 7 Track: Did you succeed? 7.1 Transactions analysis 7.1.1 Business intelligence vs. business analytics 7.1.2 Business intelligence dashboards 7.1.3 The limits of business intelligence dashboards 7.1.4 Casestudy 7.1.5 Case study data sources 7.1.6 Case study data analysis 7.1.7 Predictive modeling 7.1.8 New product forecast error analysis 7.1.9 Additional external data — text once more 7.2 Sentiment analysis and opinion mining 7.2.1 Sentiment methodology overview 7.3 Software 7.4 Summary 7.5 Appendix 7.5.1 Demonstration of linearization using log transformation 7.5.2 Demonstration of variance stabilization using log transformation 7.5.3 Constant elasticity models 7.5.4 Total revenue elasticity 7.5.5 Effects tests F-ratios 191 193 195 196 198 199 200 201 212 225 227 227 228 233 233 233 233 8 Resources: Making it work 8.1 The role and importance of organizational collaboration 8.2 Analytical talent 8.2.1 Technology skill sets 8.2.2 Data scientists, statisticians, and machine learning experts 8.2.3 Constant training 8.3 Software issues 8.3.1 Downplaying spreadsheets 8.3.2 Open source software 8.3.3 Commercial software 8.3.4 SQL: A must-know language 8.3.5 Overall software recommendation 8.3.6 Jupyter/Jupyter Lab 238 238 241 241 243 245 246 246 246 249 250 250 250 Bibliography Index 252 259 , 234 235 236 236
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| adam_txt |
CONTENTS List offigures List of tables Preface Acknowledgements ix xii xiv xix 1 Introduction 1.1 New product failures 1.1.1 Design failures 1.1.2 Pricing failures 1.1.3 Messaging failures 1.2 An NPD process 1.3 The heart of the NPD process 1.3.1 Market research 1.3.2 Business analytics 1.4 Summary 1 2 4 5 7 7 9 13 14 17 2 Ideation: What do you do? 2.1 Sources for ideas 2.1.1 Traditional approaches 2.1.2 A modern approach 2.2 Big data - external and internal 2.3 Text data and text analysis 2.3.1 Documents, corpus, and corpora 2.3.2 Organizing text data 2.3.3 Text processing 2.3.4 Creating a searchable database 2.4 Call center logs and warranty claims analysis 19 20 20 22 23 24 25 26 28 40 43
vi Contents 2.5 Sentiment analysis and opinion mining 2.6 Market research: voice of the customer ( VOC) 2.6.1 Competitive assessment: the role of CEA 2.6.2 Contextual design 2.7 Machine learning methods 2.8 Managing ideas and predictive analytics 2.9 Software 2.10 Summary 2.11 Appendix 2.11.1 Matrix decomposition 2.11.2 Singular value decomposition (SVD) 2.11.3 Spectral and singular value decompositions 44 45 45 49 50 51 53 54 54 54 54 57 3 Develop: How do you do it? 3.1 Product design optimization 3.2 Conjoint analysis for product optimization 3.2.1 Conjoint framework 3.2.2 Conjoint design for new products 3.2.3 A new product design example 3.2.4 Conjoint design 3.2.5 Some problems with conjoint analysis 3.2.6 Optimal attribute levels 3.2.7 Software 3.3 Kansei engineering for product optimization 3.3.1 Study designs 3.3.2 Combining conjoint and Kansei analyses 3.4 Early-stage pricing 3.4.1 van Westendorp price sensitivity meter 3.5 Summary 3.6 Appendix 3.A 3.6.1 Brief overview of the chi-square statistic 3.7 Appendix 3.B 3.7.1 Brief overview of correspondence analysis 3.8 Appendix 3.C 3.8.1 Very brief overview of ordinary least squares analysis 3.8.2 Brief overview of principal components analysis 3.8.3 Principal components regression analysis 3.8.4 Brief overview of partial least squares analysis 59 60 61 62 63 65 65 69 70 71 72 73 83 87 88 90 91 91 96 96 98 98 101 102 102 4 Test: Will it work and sell? 4.1 Discrete choice analysis 4.1.1 Product configuration vs. competitive offerings 4.1.2 Discrete choice background - high-level view 105 106 107 108
Contents vii Test market hands-on analysis 4.2.1 Live trial tests with customers Market segmentation TURF analysis Software Summary Appendix 4.7.1 TURF calculations 114 114 120 123 127 127 127 127 5 Launch I: What is the marketing mix? 5.1 Messaging/claims analysis 5.1.1 Stages of message analysis 5.1.2 Message creation 5.1.3 Message testing 5.1.4 Message delivery 5.2 Price finalization 5.2.1 Granger-Gabor analysis 5.2.2 Price segmentation 5.2.3 Pricing in a social network 5.3 Placing the new product 5.4 Software 5.5 Summary 130 131 131 133 134 154 161 162 164 165 166 167 167 6 Launch II: How much will sell? 6.1 Predicting vs. forecasting 6.2 Forecasting responsibility 6.3 Time series and forecasting background 6.4 Data issues 6.4.1 Data availability 6.4.2 Training and testing data sets 6.5 Forecasting methods based on data availability 6.5.1 Naive methods 6.5.2 Sophisticated forecasting methods 6.5.3 Data requirements 6.6 Forecast error analysis 6.7 Software 6.8 Summary 6.9 Appendix 6.9.1 Time series definition 6.9.2 Backshift and differencing operators 6.9.3 Random walk model and naive forecast 6.9.4 Random walk with drift 6.9.5 Constant mean model 6.9.6 The ARIMA family of models 168 169 169 170 171 172 173 175 175 176 180 180 182 182 182 182 182 183 186 187 187 4.2 4.3 4.4 4.5 4.6 4.7
vili Contents 7 Track: Did you succeed? 7.1 Transactions analysis 7.1.1 Business intelligence vs. business analytics 7.1.2 Business intelligence dashboards 7.1.3 The limits of business intelligence dashboards 7.1.4 Casestudy 7.1.5 Case study data sources 7.1.6 Case study data analysis 7.1.7 Predictive modeling 7.1.8 New product forecast error analysis 7.1.9 Additional external data — text once more 7.2 Sentiment analysis and opinion mining 7.2.1 Sentiment methodology overview 7.3 Software 7.4 Summary 7.5 Appendix 7.5.1 Demonstration of linearization using log transformation 7.5.2 Demonstration of variance stabilization using log transformation 7.5.3 Constant elasticity models 7.5.4 Total revenue elasticity 7.5.5 Effects tests F-ratios 191 193 195 196 198 199 200 201 212 225 227 227 228 233 233 233 233 8 Resources: Making it work 8.1 The role and importance of organizational collaboration 8.2 Analytical talent 8.2.1 Technology skill sets 8.2.2 Data scientists, statisticians, and machine learning experts 8.2.3 Constant training 8.3 Software issues 8.3.1 Downplaying spreadsheets 8.3.2 Open source software 8.3.3 Commercial software 8.3.4 SQL: A must-know language 8.3.5 Overall software recommendation 8.3.6 Jupyter/Jupyter Lab 238 238 241 241 243 245 246 246 246 249 250 250 250 Bibliography Index 252 259 , 234 235 236 236 |
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| spelling | Paczkowski, Walter R. ca. 20./21. Jh. Verfasser (DE-588)116780757X aut Deep data analytics for new product development Walter R. Paczkowski First published London and New York Routledge 2020 xviii, 265 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Big Data (DE-588)4802620-7 gnd rswk-swf Neues Produkt (DE-588)4171552-4 gnd rswk-swf Management (DE-588)4037278-9 gnd rswk-swf Produktentwicklung (DE-588)4139402-1 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf New products / Management Big data Quantitative research Neues Produkt (DE-588)4171552-4 s Produktentwicklung (DE-588)4139402-1 s Management (DE-588)4037278-9 s Big Data (DE-588)4802620-7 s Datenanalyse (DE-588)4123037-1 s b DE-604 Erscheint auch als Online-Ausgabe 978-0-429-02277-7 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032064507&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Paczkowski, Walter R. ca. 20./21. Jh Deep data analytics for new product development Big Data (DE-588)4802620-7 gnd Neues Produkt (DE-588)4171552-4 gnd Management (DE-588)4037278-9 gnd Produktentwicklung (DE-588)4139402-1 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4171552-4 (DE-588)4037278-9 (DE-588)4139402-1 (DE-588)4123037-1 |
| title | Deep data analytics for new product development |
| title_auth | Deep data analytics for new product development |
| title_exact_search | Deep data analytics for new product development |
| title_exact_search_txtP | Deep data analytics for new product development |
| title_full | Deep data analytics for new product development Walter R. Paczkowski |
| title_fullStr | Deep data analytics for new product development Walter R. Paczkowski |
| title_full_unstemmed | Deep data analytics for new product development Walter R. Paczkowski |
| title_short | Deep data analytics for new product development |
| title_sort | deep data analytics for new product development |
| topic | Big Data (DE-588)4802620-7 gnd Neues Produkt (DE-588)4171552-4 gnd Management (DE-588)4037278-9 gnd Produktentwicklung (DE-588)4139402-1 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Big Data Neues Produkt Management Produktentwicklung Datenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032064507&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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