| Description |
1 online resource. |
| Series |
Marketing science series
|
|
Marketing science series.
|
| Bibliography |
Includes bibliographical references and index. |
| Contents |
01 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 ... |
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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. |
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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. |
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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. |
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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. |
| Summary |
Expert 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. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Marketing research.
|
|
Customer relations.
|
|
Customer loyalty.
|
| Genre/Form |
Electronic books.
|
| Other Form: |
Print version: Grigsby, Mike. Advanced customer analytics. London ; New York, NY : Kogan Page, [2016] 9780749477158 (DLC) 2016033366 (OCoLC)957696196 |
| ISBN |
9780749477165 (electronic bk.) |
|
0749477164 (electronic bk.) |
|
9780749477158 (alk. paper) |
|
0749477156 |
| Standard No. |
99972183526 |
|
