Deep Data Analytics for New Product Development

Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- List of Figures -- List of Tables -- Preface -- Acknowledgements -- 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 -- Notes -- 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 -- 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 -- 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.