Applied Marketing Analytics Using R

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.

Klappentext:

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.

Inhaltsverzeichnis:

Chapter 1: Introduction
Chapter 2: Customer Segmentation
Chapter 3: Marketing Mix Modelling
Chapter 4: Attribution Modelling
Chapter 5: User Generated Data Analytics
Chapter 6: Customer Mindset Metrics
Chapter 7: Text Mining
Chapter 8: Churn Prediction and Marketing Classification Models With Supervised Learning
Chapter 9: Demand Forecasting
Chapter 10: Image Analytics
Chapter 11: Data Project Management and General Recommendations

Rezension:

There are good books on marketing principles, on analytical models and on statistical software, but not on the combination of these three areas. This is where Applied Marketing Analytics Using R breaks new ground and offers exceptional value to the practice of marketing model building. The marketing decision areas are carefully selected, the modeling principles are well explained, and the case studies offer relevant applications of the R software modules. I recommend this book with enthusiasm! Dominique M. Hanssens 20230616