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Advanced customer analytics : targeting, valuing, segmenting and loyalty techniques /

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개인저자Grigsby, Mike, author.
서명/저자사항Advanced customer analytics :targeting, valuing, segmenting and loyalty techniques /Mike Grigsby.
발행예정일자1610
형태사항1 online resource.
총서사항Marketing science series
소장본 주기eBooks on EBSCOhostAll EBSCO eBooks
ISBN9780749477165
0749477164


ISRC99972183526
서지주기Includes bibliographical references and index.
내용주기01 Overview; What is retail?; What is analytics?; Who is this book for?; Why focus on retail?; Why am I making these suggestions?; How is this book organized?; 02 Regression and Factor Analysis; Introduction; Regression 101: What is regression?; Assumptions of classical linear regression; Why is regression important and why is it used?; Factor analysis; Exploratory vs. confirmatory factor analysis; Using factor analysis; Conclusion; 03 Retail; Introduction to retail; Brief history of retail; Retail analytics; Orientation: because retail is ... this book is ...
Retail culture and corporate agilityConclusion; 04 Retail; Which CRM systems are used?; Sources of retail data; What is Big Data?; Is it important?; What does it mean for analytics? For strategy?; Why is it important?; Surviving the Big Data panic; Big Data analytics; Conclusion; 05 Understanding and estimating demand; Introduction; Business objective; Using ordinary regression to estimate demand; Properties of estimators; A note on time series data: autocorrelation; Dummy variables; Business case; Conclusion; 06 Price elasticity and discounts; Introduction to elasticity; Modelling elasticity.
Business caseConclusion; 07 Valuing marketing communications (marcomm); Business case; Conclusion; 08 Forecasting future demand; Autocorrelation; Dummy variables and seasonality; Business case; Conclusion; 09 Targeting the right customers; Introduction; Business case; Results applied to the model; A brief procedural note; Variable diagnostics; Conclusion; 10 Maximizing the impact of mailing; Introduction; Lift charts; Scoring the database with probability formula; Conclusion; 11 The benefits of product bundling; What is a market basket?; How is it usually done?; Logistic regression.
How to estimate/predict the market basketBusiness case; Conclusion; 12 Estimating time of purchase; Introduction; Conceptual overview of survival analysis; More about survival analysis; A procedure suggestion and pseudo-fit; Business case; Model output and interpretation; Conclusion; 13 Investigating the time of product purchase; Competing risks; Conclusion; 14 Increasing customer lifetime value; Descriptive analysis; Predictive analysis; Introduction to tobit analysis; Business case; Conclusion; 15 Modelling counts (transactions); Business case; Conclusion.
16 Quantifying complexity of customer behaviourIntroduction; What are simultaneous equations?; Why go to the trouble to use simultaneous equations?; Business case; A brief note on missing value imputation; Conclusion; 17 Designing effective loyalty programmes; Introduction to loyalty; Is there a range or spectrum of loyalty?; What are the 3Rs of loyalty?; Why design a programme with earn-burn measures?; Business case; Conclusion; 18 Identifying loyal customers; Structural equation modelling (SEM); Business case; Conclusion; 19 Introduction to segmentation; Overview.
요약Expert guidance in a direct and conceptual style on the analytic steps to take to resolve data-heavy retail marketing questions, taking into account consumer behaviour and multi-channel marketing scenarios.
일반주제명Marketing research.
Customer relations.
Customer loyalty.
BUSINESS & ECONOMICS -- Industrial Management.
BUSINESS & ECONOMICS -- Management.
BUSINESS & ECONOMICS -- Management Science.
BUSINESS & ECONOMICS -- Organizational Behavior.
Customer loyalty.
Customer relations.
Marketing research.
Verbraucherforschung
Kundenbindung
Marktsegmentierung
Preisbildung
언어영어
기타형태 저록Print version:Grigsby, Mike.Advanced customer analytics.London ; New York, NY : Kogan Page, [2016]9780749477158
바로가기http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1365518

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