Applied Marketing Analytics Using Python
Gokhan Yildirim
- Imperial College Business School, UK
Raoul V. Kübler
- ESSEC Business School, France
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Go to College Publishing WebsiteDescription
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.
Contents
Chapter 1: Introduction
Chapter 1: Introduction
Chapter 2: Customer Segmentation
Chapter 2: Customer Segmentation
Chapter 3: Marketing Mix Modelling
Chapter 3: Marketing Mix Modelling
Chapter 4: Attribution Modelling
Chapter 4: Attribution Modelling
Chapter 5: User-Generated Data Analytics
Chapter 5: User-Generated Data Analytics
Chapter 6: Customer Mindset Metrics
Chapter 6: Customer Mindset Metrics
Chapter 7: Text Mining
Chapter 7: Text Mining
Chapter 8: Churn Prediction and Marketing Classification Models with Supervised Learning
Chapter 8: Churn Prediction and Marketing Classification Models with Supervised Learning
Chapter 9: Demand Forecasting
Chapter 9: Demand Forecasting
Chapter 10: Image Analytics
Chapter 10: Image Analytics
Chapter 11: Data Project Management and General Recommendations
Chapter 11: Data Project Management and General Recommendations
Additional materials
Description
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.
Contents
Chapter 1: Introduction
Chapter 1: Introduction
Chapter 2: Customer Segmentation
Chapter 2: Customer Segmentation
Chapter 3: Marketing Mix Modelling
Chapter 3: Marketing Mix Modelling
Chapter 4: Attribution Modelling
Chapter 4: Attribution Modelling
Chapter 5: User-Generated Data Analytics
Chapter 5: User-Generated Data Analytics
Chapter 6: Customer Mindset Metrics
Chapter 6: Customer Mindset Metrics
Chapter 7: Text Mining
Chapter 7: Text Mining
Chapter 8: Churn Prediction and Marketing Classification Models with Supervised Learning
Chapter 8: Churn Prediction and Marketing Classification Models with Supervised Learning
Chapter 9: Demand Forecasting
Chapter 9: Demand Forecasting
Chapter 10: Image Analytics
Chapter 10: Image Analytics
Chapter 11: Data Project Management and General Recommendations
Chapter 11: Data Project Management and General Recommendations
Additional materials
March 2025 | 384 pages | Sage UK
| Format | Published Date | ISBN | Price |
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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.
Table Of Contents:
- 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