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 two-fold. 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.
Author(s): Walter R. Paczkowski
Publisher: Routledge
Year: 2020
Language: English
Pages: xx+266
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
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
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
4.2 Test market hands-on analysis
4.2.1 Live trial tests with customers
4.3 Market segmentation
4.4 TURF analysis
4.5 Software
4.6 Summary
4.7 Appendix
4.7.1 TURF calculations
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
5.2 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
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
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 Case study
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
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
Bibliography
Index
