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Applied Marketing Analytics Using Python
- Gokhan Yildirim - Imperial College Business School, UK
- Raoul V. Kübler - ESSEC Business School, France
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March 2025 | 336 pages | SAGE Publications Ltd
It is 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 Python, 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.
Supporting online resources include datasets and software codes and solutions as well as PowerPoint slides, a teaching guide and a testbank. This book is essential reading for advanced level marketing students and 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.
Taking a very hands-on approach with the use of real-world datasets, case studies and Python, 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.
Supporting online resources include datasets and software codes and solutions as well as PowerPoint slides, a teaching guide and a testbank. This book is essential reading for advanced level marketing students and 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.
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
Sage College Publishing
