Title Advanced Customer Analytics
Subtitle Targeting, Valuing, Segmenting and Loyalty Techniques
Author Mike Grigsby
ISBN 9780749477158
List price GBP 29.99
Price outside India Available on Request
Original price
Binding Paperback
No of pages 264
Book size 153 x 229 mm
Publishing year 2016
Original publisher Kogan Page Limited
Published in India by Kogan Page Limited
Exclusive distributors Viva Books Private Limited
Sales territory India, Sri Lanka, Bangladesh, Pakistan, Nepal, .
Status New Arrival
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Reviews:

“I strongly recommend this book to anyone interested in data-based marketing decision making.”
— Michael Haenlein, Professor of Marketing, ESCP Europe

“This book gives the reader a truly enjoyable, very user-friendly explanation of several advanced techniques needed to assess marketing performance in the retail industries. It is written clearly enough for non-statisticians, but the analytics, with applications and results, are unerring, appropriate and educational in those areas  that many retail managers and executives need to get up to speed with.”
— Mike Morgan, President and CEO, Morgan Analytics Inc

Description:

Advanced Customer Analytics provides a clear guide to the specific analytical challenges faced by the retail sector. It covers the nature and scale of data obtained in transactions, relative proximity to the consumer and the need to monitor customer behaviour across multiple channels. The book advocates a category management approach, taking into account the need to understand the consumer mindset through elasticity modelling and discount strategies, as well as targeted marketing and loyalty design.
Not skirting around the complexities of the subject, Advanced Customer Analytics offers conceptual support to steer retail marketers towards making the right choices for analysing their data. Rich in case studies and examples, the book considers the specific features of retail that set it apart from other disciplines, such as proximity to customers and the interface with merchandising, putting forward effective solutions to the challenges these present. Taking a practical, no- nonsense approach to complex scenarios, Advanced Customer Analytics provides expert guidance on the analytic steps to take to resolve data- heavy retail marketing questions.
Datasets relating to chapters are available online at: www.koganpage.com/Advanced-Customer-Analytics
About the series: the Marketing Scienceseries 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 enabling marketers to gain confidence in applying the tools and techniques and in commissioning external research.

Contents:

Chapter 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?
Chapter 02: Regression and factor analysis: an introduction • 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 
Chapter 03: Retail: industry uniqueness• Introduction to retail • Brief history of retail • Retail analytics • Orientation: because retail is ... this book is... • Retail culture and corporate agility • Conclusion
Chapter 04: Retail: data uniqueness• Which CRM systems are used? • Sources of retail data • What is Big Data? • Is it imnortant?  • What does it mean for analytics? For strategy? • Why is it important? • Surviving the Big Data panic • Big Data analytics • Conclusion

INTERLUDE

Chapter 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
Chapter 06: Price elasticity and discounts • Introduction to elasticity • Modelling elasticity • Business case • Conclusion
Chapter 07: Valuing marketing communications (marcomm) • Business case • Conclusion
Chapter 08: Forecasting future demand • Autocorrelation • Dummy variables and seasonality • Business case • Conclusion
Chapter 09 Targeting the right customers • Introduction • Business case • Results applied to the model • A brief procedural note • Variable diagnostics • Conclusion
Chapter 10: Maximizing the impact of mailing • Introduction • Lift charts • Scoring the database with probability formula • Conclusion
Chapter 11: The benefits of product bundling• What is a market basket? • How is it usually done? • Logistic regression • How to estimate/predict the market basket • Business case  • Conclusion
Chapter 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 
Chapter 13: Investigating the time of product purchase • Competing risks • Conclusion
Chapter 14: Increasing customer lifetime value • Descriptive analysis  • Predictive analysis • Introduction to tobit analysis  • Business case  • Conclusion 
Chapter 15: Modelling counts (transactions) • Business case  • Conclusion 
Chapter 16: Quantifying complexity of customer behaviour• Introduction • What are simultaneous equations? • Why go to the trouble to use simultaneous equations? • Business case • A brief note on missing value imputation • Conclusion 
Chapter 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
Chapter 18: Identifying loyal customers • Structural equation modelling (SEM) • Business case • Conclusion 
Chapter 19: Introduction to segmentation• Overview • Introduction to segmentation • What is segmentation? What is a segment? • Strategic uses of segmentation • A priori or not? • Conceptual process • Conclusion
Chapter 20: Tools for successful segmentation • Overview • Metrics of successful segmentation• General analytic techniques • CHAID • Conclusion 
Chapter 21: Drawing insights from segmentation • Business case • Analytics • Comments/details on individual segments • Conclusion
Chapter 22 Creating targeted messages • Overview  • Conclusion
Chapter 23: RFM vs. segmentation• Introduction • What is RFM?• What is behavioural segmentation?• What does behavioural segmentation provide that RFM does not? • Conclusion
Chapter 24:  Marketing strategy: customers not competitors • Customer-centricity • Conclusion
References and further reading 
Index 

About the Author:

Mike Grigsby has been involved in marketing science for more than 25 years. He was marketing research director at Millward Brown and has held leadership positions at Hewlett-Packard and Gap. With a wealth of experience at the forefront of marketing science and analytics, he now heads up the strategic retail analysis practice at Targetbase. Mike is also known for academic work, having written articles for academic and trade journals and taught at postgraduate and undergraduate levels. He is a regular speaker at trade conventions and seminars, and is the author ofMarketing Analytics, also published by Kogan Page in the Marketing Science series

Target Audience:

Marketing professional, and senior leaders in developing the most effective programmes for their organization.

 
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