Preface

When I first started as a marketing data analyst, I felt lost. I already knew how to program in Python, and the basics of statistics and econometrics, but marketing analytics is a surprisingly deceptive field. It feels easy, but due to the nature of the data we work with, it involves more complex models than you initially thought; correlation is often confused with causation, and sometimes, you just feel like you are flying blind. Either that or you feel like a glorified pivot table maker. A lot of my knowledge then came from trial and error: testing techniques, reading up on new methods, and making mistakes… a lot of mistakes.

When I started managing and hiring a team of analysts, I sometimes felt that it would make my life easier if I could just point them to a book that gave them the basics instead of spending hours in one-on-one sessions explaining methods or techniques. This book is my attempt at that: a summary of the fundamentals of marketing analytics, above simple pivot table making.

Marketing analytics is an incredibly complex field, and it is impossible to encapsulate all of it in one book. This book aims to give you, the reader, a grounding understanding of the techniques and tools most used in marketing analytics. The aim is to provide a practical, no-nonsense approach. You will have to have some basic understanding of the theoretical aspects surrounding tools and models so that you know what you are using and why, but we will quickly shift to a practical approach. The ultimate goal of this book is to equip you with the practical knowledge to get operational as a marketing data analyst quickly. This book will present open source libraries that allow you to derive insights quickly and use examples of common questions you will face daily as a marketing analyst.

There are gaps and techniques we will not explore. Marketing analytics is an ever-evolving field, and writing a book to cover everything would take more than 5,000 pages. Each chapter could be, and sometimes is, its own book. This book aims to equip you with fundamental knowledge, give you an overview of what is available, and provide some understanding of how to apply it. It also aims to give you the biggest asset an experienced analyst can have – knowing what to look for and research when facing a new problem. This last point is, for me, the most important. If this book achieves nothing else, let it be that it provides you with the compass to find the tools and techniques you need in your daily life. Even if that means you will disagree with me on a specific technique, that will be a win.

Throughout the book, we will use Python and its rich data analysis and statistics package ecosystem.