Applied Marketing Analytics Using R
eBook - ePub

Applied Marketing Analytics Using R

  1. 392 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Applied Marketing Analytics Using R

About this book

Marketing has become increasingly data-driven in recent years as a result of new emerging technologies such as AI, granular data availability and ever-growing analytics tools. With this trend only set to continue, it's vital for marketers today to be comfortable in their use of data and quantitative approaches and have a thorough grounding in understanding and using marketing analytics in order to gain insights, support strategic decision-making, solve marketing problems, maximise value and achieve success.

Taking a very hands-on approach with the use of real-world datasets, case studies and R (a free statistical package), this book supports students and practitioners to explore a range of marketing phenomena using various applied analytics tools, with a balanced mix of technical coverage alongside marketing theory and frameworks. Chapters include learning objectives, figures, tables and questions to help facilitate learning.

Supporting online resources are available to instructors to support teaching, including datasets and software codes and solutions (R Markdowns, HTML files) as well as PowerPoint slides, a teaching guide and a testbank.

This book is essential reading for advanced level marketing students and marketing practitioners who want to become cutting-edge marketers.

Dr. Gokhan Yildirim is an Associate Professor of Marketing at Imperial College Business School, London.

Dr. Raoul V. Kübler is an Associate Professor of Marketing at ESSEC Business School, Paris.

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Yes, you can access Applied Marketing Analytics Using R by Gokhan Yildirim,Raoul Kübler in PDF and/or ePUB format, as well as other popular books in Business & Marketing. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Contents
  6. Acknowledgements
  7. About the Authors
  8. Overview
  9. Endorsements
  10. Online Resources
  11. 1 Introduction
  12. 2 Customer Segmentation
  13. 3 Marketing Mix Modelling
  14. 4 Attribution Modelling
  15. 5 User-generated data analytics
  16. 6 Customer mindset metrics
  17. 7 Text Mining
  18. 8 Churn prediction and marketing classification models with supervised learning
  19. 9 Demand Forecasting
  20. 10 Image Analytics
  21. 11 Data project management and general recommendations
  22. Index