Grigsby, Mike
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자료유형 | E-BOOK |
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서명/저자사항 | Marketing analytics : a practical guide to improving consumer insights using data techniques/ Mike Grigsby. |
개인저자 | Grigsby, Mike,author. |
판사항 | Second edition. |
발행사항 | London ; New York: Kogan Page, 2018. |
형태사항 | 1 online resource. |
기타형태 저록 | Print version: Grigsby, Mike. Marketing analytics. Second edition. London ; New York : Kogan Page, 2018 9780749482169 |
ISBN | 9780749482176 0749482176 |
서지주기 | Includes bibliographical references and index. |
내용주기 | Cover; Contents; Foreword to the first edition; Foreword to the second edition; Preface; Introduction to marketing analytics; PART ONE Overview -- how can marketing analytics help you?; 01 A brief statistics review; Measures of central tendency; Measures of dispersion; The normal distribution; Confidence intervals; Relations among two variables: covariance and correlation; Probability and the sampling distribution; Conclusion; Checklist: You'll be the smartest person in the room if you...; 02 Brief principles of consumer behaviour and marketing strategy; Introduction Consumer behaviour as the basis for marketing strategyOverview of consumer behaviour; Overview of marketing strategy; Conclusion; Checklist: You'll be the smartest person in the room if you...; 03 What is an insight?; Introduction; Insights tend not to be used by executives; Is this an insight?; So, what is an insight?; Ultimately, an insight is about action-ability; Checklist: You'll be the smartest person in the room if you...; PART TWO Dependent variable techniques; 04 What drives demand? Modelling dependent variable techniques; Introduction Dependent equation type vs inter-relationship type statisticsDeterministic vs probabilistic equations; Business case; Results applied to business case; Modelling elasticity; Technical notes; Highlight: Segmentation and elasticity modelling can maximize revenue in a retail/medical clinic chain:field test results; Abstract; The problem and some background; Description of the dataset; First: segmentation; Then: elasticity modelling; Last: test vs control; Discussion; Conclusion; Checklist: You'll be the smartest person in the room if you...; 05 Who is most likely to buy and how do I target them? IntroductionConceptual notes; Business case; Results applied to the model; Lift charts; Using the model -- collinearity overview; Variable diagnostics; Highlight: Using logistic regression for market basket analysis; Abstract; What is a market basket?; Logistic regression; How to estimate/predict the market basket; Conclusion; Checklist: You'll be the smartest person in the room if you...; 06 When are my customers most likely to buy?; Introduction; Conceptual overview of survival analysis; Business case; More about survival analysis; Model output and interpretation; Conclusion Highlight: Lifetime value: how predictive analysis is superior to descriptive analysisAbstract; Descriptive analysis; Predictive analysis; An example; Checklist: You'll be the smartest person in the room if you...; 07 Panel regression -- how to use a cross-sectional time series; Introduction; What is panel regression?; Panel regression: details; Business case; Insights about marcom (direct mail, e-mail and SMS); Insights about time period (quarters); Insights about cross sections (counties); Conclusion; Checklist: You'll be the smartest person in the room if you... |
요약 | Understand how to apply marketing science techniques fearlessly, to improve consumer insights and compete more effectively in the marketplace. |
일반주제명 | Marketing research. Marketing. Marketing. Marketing research. BUSINESS & ECONOMICS / Industrial Management. BUSINESS & ECONOMICS / Management. BUSINESS & ECONOMICS / Management Science. BUSINESS & ECONOMICS / Organizational Behavior. |
언어 | 영어 |
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