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Gain a thorough grounding in marketing analytics using Python with this practical guide which balances applied analytics tools with marketing theory.
List 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
About the author
Dr. Gokhan Yildirim is an Associate Professor of Marketing at Imperial College Business School where he teaches on the full-time MBA, executive MBA and MSc Business Analytics programs. Gokhan's research is at the intersection of marketing effectiveness, metrics and models. He quantifies how marketing actions impact offline and online consumer behaviour, and how changes in consumer attitudes in turn drive company performance. Specifically, his research concerns short vs. long-term effectiveness of digital and non-digital marketing activities, cross-channel marketing resource allocation, and consumer attitudinal metrics for guiding marketing decisions. He uses applied time-series econometrics and machine learning tools to offer managerial insights in these areas. Gokhan's academic work appeared in leading journals of the field such as Journal of Marketing, Marketing Science and International Journal of Research in Marketing. He is one of the recipients of the prestigious ISMS-MSI Gary Lilien Practice Prize award.
Summary
Gain a thorough grounding in marketing analytics using Python with this practical guide which balances applied analytics tools with marketing theory.
