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Marketing analytics a practical guide to real marketing science [electronic resource]
Grigsby, Mike
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| 자료유형 | E-BOOK |
|---|---|
| 서명/저자사항 | Marketing analytics [electronic resource]: a practical guide to real marketing science/ Mike Grigsby. |
| 개인저자 | Grigsby, Mike,author, |
| 발행사항 | London : Philadelphia: Kogan Page, 2015. |
| 형태사항 | 1 online resource. |
| 총서사항 | Marketing science series |
| 기타형태 저록 | Print version: Grigsby, Mike. Marketing analytics 9780749474171 |
| ISBN | 9780749474188 0749474181 |
| 서지주기 | Includes bibliographical references and index. |
| 내용주기 | Machine generated contents note: Foreword -- PrefaceIntroduction Part One: Overview01 A (little) statistical review -- Measures of central tendency -- Measures of dispersion -- The normal distribution -- 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 strategy -- Overview of consumer behaviour -- Overview of marketing strategy -- Conclusion -- Checklist: You'll be the smartest person in the room if you...Part Two Dependent variable techniques 03 Modelling dependent variable techniques (with one equation): what are the things that drive demand? -- Introduction -- Dependent equation type vs inter-relationship type statistics -- Deterministic 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/clinic chain: field test results -- Abstract -- The problem and some background -- Description of the data set -- First: segmentation -- Then: elasticity modelling -- Last: test vs control -- Discussion -- Conclusion -- Checklist: You'll be the smartest person in the room if you...04 Who is most likely to buy and how do I target? -- Introduction -- Conceptual 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...05 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 analysis -- Abstract -- Descriptive analysis -- Predictive analysis -- An example -- Checklist: You'll be the smartest person in the room if you...06 Modelling-dependent variable techniques (with more than one equation) -- Introduction -- What are simultaneous equations? -- Why go to the trouble to use simultaneous equations? -- Desirable properties of estimators -- Business case -- Checklist: You'll be the smartest person in the room if you...Part Three Inter-relationship techniques 07 Modelling inter-relationship techniques: what does my (customer) market look like? -- Introduction -- Introduction to segmentation -- What is segmentation? What is a segment? -- Why segment? Strategic uses of segmentation -- The four Ps of strategic marketing -- Criteria for actionable segmentation -- A priori or not? -- Conceptual process -- Checklist: You'll be the smartest person in the room if you...08 Segmentation tools and techniques -- Overview -- Metrics of successful segmentation -- General analytic techniques -- Business case -- Analytics -- Comments/details on individual segments -- K-means compared to LCA -- Highlight: Why Go Beyond RFM? -- Abstract -- What is RFM? -- What is behavioural segmentation? -- What does behavioural segmentation provide that RFM does not? -- Conclusion -- Sidebar: Segmentation techniques -- Checklist: You'll be the smartest person in the room if you...Part Four Other -- 09 Marketing Research -- Introduction -- How is survey data different than database data? -- Missing value imputation -- Combating respondent fatigue -- A far too brief account of conjoint analysis -- Structural equation modelling (SEM) -- Checklist: You'll be the smartest person in the room if you...10 Statistical testing: how do I know what works? -- Everyone wants to test -- Sample size equation: use the lift measure -- A/B testing and full factorial differences -- Business case -- Checklist: You'll be the smartest person in the room if you...Part Five Capstone 11 Capstone: focusing on digital analytics -- Introduction -- Modelling engagement -- Business case -- Model conception -- How do I model multiple channels? -- ConclusionPart Six Conclusion 12 The Finale: What should you take away from this? Any other stories/soap box rants? -- What things have I learned that I'd like to pass on to you? -- What other things should you take away from all this?Glossary -- Bibliography and further reading -- Index . |
| 요약 | "Mike Grigsby provides business analysts and marketers with the marketing science understanding and techniques they need to solve real-world marketing challenges, such as pulling a targeted list, segmenting data, testing campaign effectiveness, and forecasting demand.Assuming no prior knowledge, Marketing Analytics introduces concepts relating to statistics, marketing strategy, and consumer behavior and then works through a series of problems by providing various data modeling options as solutions. By using this format of presenting a problem and multiple ways to solve it, this book both makes marketing science accessible to beginners and aids the more experienced practitioner in understanding the more complex aspects of data analytics to refine their skills and compete more effectively in the workplace"-- |
| 요약 | "Marketing Analytics arms business analysts and marketers with the marketing science understanding and techniques they need to solve real-world marketing problems, from pulling a targeted list and segmenting data to testing campaign effectiveness and forecasting demand. Assuming no prior knowledge, this book outlines everything practitioners need to 'do' marketing science and demonstrate value to their organization. It introduces concepts relating to statistics, marketing strategy and consumer behaviour and then works through a series of marketing problems in a straightforward, jargon-free way. It demonstrates solutions for various data modelling scenarios and includes full workings and critical analyses to reinforce the key concepts. By starting with the marketing problem and then sharing a series of data modelling options on how to solve it, Marketing Analytics both makes marketing science accessible for beginners and aids the more seasoned practitioner in getting to grips with the trickier technical aspects of data analytics to refine their marketing skills and toolkit and compete more effectively in the marketplace. About the series: The Marketing Science series makes difficult topics accessible to marketing students and practitioners by grounding them in business reality. Each book is written by an expert in the field and includes case studies and illustrations so marketers can gain confidence in applying the tools and techniques and commission external research"-- |
| 해제 | Provided by publisher. |
| 해제 | Provided by publisher. |
| 일반주제명 | Marketing research. Marketing. BUSINESS & ECONOMICS / Marketing / Research. COMPUTERS / Database Management / Data Mining. BUSINESS & ECONOMICS / E-Commerce / Internet Marketing. BUSINESS & ECONOMICS / Industrial Management BUSINESS & ECONOMICS / Management BUSINESS & ECONOMICS / Management Science BUSINESS & ECONOMICS / Organizational Behavior |
| 언어 | 영어 |
| 바로가기 | URL |
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