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E-raamat: Deep Data Analytics for New Product Development [Taylor & Francis e-raamat]

(Rutgers University, USA)
  • Formaat: 286 pages, 43 Tables, black and white; 105 Line drawings, black and white; 105 Illustrations, black and white
  • Ilmumisaeg: 03-Mar-2020
  • Kirjastus: Routledge
  • ISBN-13: 9780429022777
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  • Taylor & Francis e-raamat
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Deep Data Analytics for New Product DevelopmentSuurem pilt
  • Formaat: 286 pages, 43 Tables, black and white; 105 Line drawings, black and white; 105 Illustrations, black and white
  • Ilmumisaeg: 03-Mar-2020
  • Kirjastus: Routledge
  • ISBN-13: 9780429022777
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429022777

This book presents and develops the deep data analytics for providing the information needed for successful new product development.

Deep Data Analytics for New Product Development

has a simple theme: information about what customers need and want must be extracted from data to effectively guide new product decisions regarding concept development, design, pricing, and marketing. The benefits of reading this book are twofold. The first is an understanding of the stages of a new product development process from ideation through launching and tracking, each supported by information about customers. The second benefit is an understanding of the deep data analytics for extracting that information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail and illustrated at each stage of the process with simulated data. The stages of new product development and the supporting deep data analytics at each stage are not presented in isolation of each other, but are presented as a synergistic whole.

This book is recommended reading for analysts involved in new product development. Readers with an analytical bent or who want to develop analytical expertise would also greatly benefit from reading this book, as well as students in business programs.

List of figures
ix
List of tables
xii
Preface xiv
Acknowledgements xix
1 Introduction
1(18)
1.1 New product failures
2(5)
1.1.1 Design failures
4(1)
1.1.2 Pricing failures
5(2)
1.1.3 Messaging failures
7(1)
1.2 An NPD process
7(2)
1.3 The heart of the NPD process
9(8)
1.3.1 Market research
13(1)
1.3.2 Business analytics
14(3)
1.4 Summary
17(2)
2 Ideation: What do you do?
19(40)
2.1 Sources for ideas
20(3)
2.1.1 Traditional approaches
20(2)
2.1.2 A modern approach
22(1)
2.2 Big data --- external and internal
23(1)
2.3 Text data and text analysis
24(19)
2.3.1 Documents, corpus, and corpora
25(1)
2.3.2 Organizing text data
26(2)
2.3.3 Text processing
28(12)
2.3.4 Creating a searchable database
40(3)
2.4 Call center logs and warranty claims analysis
43(1)
2.5 Sentiment analysis and opinion mining
44(1)
2.6 Market research: voice of the customer (VOC)
45(5)
2.6.1 Competitive assessment: the role of CEA
45(4)
2.6.2 Contextual design
49(1)
2.7 Machine learning methods
50(1)
2.8 Managing ideas and predictive analytics
51(2)
2.9 Software
53(1)
2.10 Summary
54(1)
2.11 Appendix
54(5)
2.11.1 Matrix decomposition
54(1)
2.11.2 Singular value decomposition (SVD)
54(3)
2.11.3 Spectral and singular value decompositions
57(2)
3 Develop: How do you do it?
59(46)
3.1 Product design optimization
60(1)
3.2 Conjoint analysis for product optimization
61(11)
3.2.1 Conjoint framework
62(1)
3.2.2 Conjoint design for new products
63(2)
3.2.3 A new product design example
65(1)
3.2.4 Conjoint design
65(4)
3.2.5 Some problems with conjoint analysis
69(1)
3.2.6 Optimal attribute levels
70(1)
3.2.7 Software
71(1)
3.3 Kansei engineering for product optimization
72(15)
3.3.1 Study designs
73(10)
3.3.2 Combining conjoint and Kansei analyses
83(4)
3.4 Early-stage pricing
87(3)
3.4.1 Van Westendorp price sensitivity meter
88(2)
3.5 Summary
90(1)
3.6 Appendix 3.A
91(5)
3.6.1 Brief overview of the chi-square statistic
91(5)
3.7 Appendix 3.B
96(2)
3.7.1 Brief overview of correspondence analysis
96(2)
3.8 Appendix 3.C
98(7)
3.8.1 Very brief overview of ordinary least squares analysis
98(3)
3.8.2 Brief overview of principal components analysis
101(1)
3.8.3 Principal components regression analysis
102(1)
3.8.4 Brief overview of partial least squares analysis
102(3)
4 Test: Will it work and sell?
105(25)
4.1 Discrete choice analysis
106(8)
4.1.1 Product configuration vs. competitive offerings
107(1)
4.1.2 Discrete choice background --- high-level view
108(6)
4.2 Test market hands-on analysis
114(6)
4.2.1 Live trial tests with customers
114(6)
4.3 Market segmentation
120(3)
4.4 TURF analysis
123(4)
4.5 Software
127(1)
4.6 Summary
127(1)
4.7 Appendix
127(3)
4.7.1 TURF calculations
127(3)
5 Launch I: What is the marketing mix?
130(38)
5.1 Messaging/claims analysis
131(30)
5.1.1 Stages of message analysis
131(2)
5.1.2 Message creation
133(1)
5.1.3 Message testing
134(20)
5.1.4 Message delivery
154(7)
5.2 Price finalization
161(5)
5.2.1 Granger-Gabor analysis
162(2)
5.2.2 Price segmentation
164(1)
5.2.3 Pricing in a social network
165(1)
5.3 Placing the new product
166(1)
5.4 Software
167(1)
5.5 Summary
167(1)
6 Launch II: How much will sell?
168(23)
6.1 Predicting vs. forecasting
169(1)
6.2 Forecasting responsibility
169(1)
6.3 Time series and forecasting background
170(1)
6.4 Data issues
171(4)
6.4.1 Data availability
172(1)
6.4.2 Training and testing data sets
173(2)
6.5 Forecasting methods based on data availability
175(5)
6.5.1 Naive methods
175(1)
6.5.2 Sophisticated forecasting methods
176(4)
6.5.3 Data requirements
180(1)
6.6 Forecast error analysis
180(2)
6.7 Software
182(1)
6.8 Summary
182(1)
6.9 Appendix
182(9)
6.9.1 Time series definition
182(1)
6.9.2 Backshift and differencing operators
182(1)
6.9.3 Random walk model and naive forecast
183(3)
6.9.4 Random walk with drift
186(1)
6.9.5 Constant mean model
187(1)
6.9.6 The ARIMA family of models
187(4)
7 Track: Did you succeed?
191(47)
7.1 Transactions analysis
193(34)
7.1.1 Business intelligence vs. business analytics
195(1)
7.1.2 Business intelligence dashboards
196(2)
7.1.3 The limits of business intelligence dashboards
198(1)
7.1.4 Casestudy
199(1)
7.1.5 Case study data sources
200(1)
7.1.6 Case study data analysis
201(11)
7.1.7 Predictive modeling
212(13)
7.1.8 New product forecast error analysis
225(2)
7.1.9 Additional external data --- text once more
227(1)
7.2 Sentiment analysis and opinion mining
227(6)
7.2.1 Sentiment methodology overview
228(5)
7.3 Software
233(1)
7.4 Summary
233(1)
7.5 Appendix
233(5)
7.5.1 Demonstration of linearization using log transformation
233(1)
7.5.2 Demonstration of variance stabilization using log transformation
234(1)
7.5.3 Constant elasticity models
235(1)
7.5.4 Total revenue elasticity
236(1)
7.5.5 Effects tests F-ratios
236(2)
8 Resources: Making it work
238(14)
8.1 The role and importance of organizational collaboration
238(3)
8.2 Analytical talent
241(5)
8.2.1 Technology skill sets
241(2)
8.2.2 Data scientists, statisticians, and machine learning experts
243(2)
8.2.3 Constant training
245(1)
8.3 Software issues
246(6)
8.3.1 Downplaying spreadsheets
246(1)
8.3.2 Open source software
246(3)
8.3.3 Commercial software
249(1)
8.3.4 SQL: A must-know language
250(1)
8.3.5 Overall software recommendation
250(1)
8.3.6 Jupyter/Jupyter Lab
250(2)
Bibliography 252(7)
Index 259
Walter R. Paczkowski worked at AT&T, AT&T Bell Labs, and AT&T Labs. He founded Data Analytics Corp., a statistical consulting company, in 2001. Dr. Paczkowski is also a part-time lecturer of economics at Rutgers University.
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