Marketing Analytics: A Modern Toolkit
1 Welcome in the World of Marketing Analytics
This book is the result of more than six years of lecture notes, project work, and hands-on experience in setting up data analytics environments — recently overhauled to reflect today’s tools, practices, and even AI-assisted workflows. To make that update happen, we worked closely with ChatGPT as a writing and structuring partner, drafting, refining, and reorganising material so it’s both relevant and immediately usable. All examples, insights, and perspectives are our own.
Whether you’re running an academic study, preparing a paper for submission, or delivering analytics in a business setting, you’ll face the same recurring challenges: messy, inconsistent data; tools that don’t “talk” to each other; and projects that are hard to reproduce months later. In our own work — from scraping Spotify data for music consumption research to building large-scale retail panel datasets — we’ve learned that these problems aren’t solved by “just knowing R” or “just knowing Excel.” They’re solved by combining coding, collaboration, and automation into a professional workflow.
This book shows you how to structure your projects so they can be understood, extended, and reproduced — by you, your collaborators, or anyone else — whether you revisit them in a week or five years later. We’ll walk through the tools and practices that make this possible, using examples inspired by real marketing analytics and data science projects.
1.1 Who This Book Is For
This book is for university students who want to professionalise the way they work with data — whether in academic research, marketing analytics, or other data-driven fields. It draws heavily on examples and practices developed within the Marketing Analytics program at the Tilburg School of Economics and Management (TiSEM), but the tools and workflows are applicable far beyond that context.
It will be especially valuable if you:
- Already have basic familiarity with R and want to integrate it into a more organised, reproducible workflow.
- Would like to work with complex datasets that require cleaning, transformation, and integration from multiple sources.
- Need to collaborate on data projects, whether in research teams or professional analytics settings.
- Want to adopt automation and reproducibility practices to save time and reduce errors.
1.2 What to Expect
Think of this book as a guided build. We’ll start by assembling your personal toolkit — the essential software and services that will make your analytics work smoother, faster, and easier to reproduce. You’ll meet R as your main data analysis companion, but also learn how to connect it with version control, automation tools, and even AI-assisted coding.
From there, we’ll move into core programming concepts and hands-on data preparation — the gritty but rewarding work of turning messy, inconsistent inputs into analysis-ready datasets. Along the way, you’ll explore how to collaborate effectively using GitHub, manage project versions without chaos, and introduce lightweight automation so your workflow can run on autopilot.
To give you a preview, here’s the path we’ll follow:
- Building Your Tool Stack – Choosing, installing, and connecting the essential software for analytics work.
- Meet R – Your main language for data analysis, with examples tailored to marketing analytics problems.
- Coding with an AI – Using tools like GitHub Copilot to speed up coding, spot errors, and explore new solutions.
- Programming Concepts – Core ideas like loops, functions, and conditionals explained with hands-on examples.
- Data Preparation with R – Techniques for cleaning, transforming, and combining datasets so they’re ready for analysis.
- Versioning and Branching – Using Git and GitHub to track changes, collaborate, and manage different project versions.
- Docker and Makefiles – Scaling your workflow with containerisation and automation tools for reproducibility.
By the time you finish, you won’t just have learned a set of tools — you’ll have a way of working that you can carry into research, business, or any data-driven career.
1.3 Why This Matters
At first glance, things like version control, automation, and “connecting tools” might sound like something only big tech companies care about. But in reality, the need for these skills shows up very quickly — even in small student projects.
1. The Solo Project That Gets Messy Fast Imagine you’re writing your MSc thesis and decide to analyse product pricing from bol.com. Every week, you download a new file and save it somewhere on your laptop: data1.csv, data_new.csv, data_final.csv. Your R scripts multiply too: clean.R, clean_new.R, clean_final_v2.R. A month later, your supervisor asks for an extra figure — but you can’t remember which version produced the results in your draft. You spend hours retracing steps instead of doing new analysis.
2. Working in a Small Team Now imagine your first role after graduation, at a local marketing analytics agency in the Netherlands. You and two colleagues are working for the same client, but on different parts of the same data project. Without version control (like Git) and clear workflows, you might overwrite each other’s changes, lose track of who did what, or waste time redoing work that’s already been done. A shared, structured way of working means you can actually work together without creating chaos.
3. Scaling Up Inside a Growing Company A few years later, you join Coolblue to help analyse customer behaviour across their entire webshop. Your code now runs on a company server every night, automatically pulling in new data, running the analysis, and updating dashboards for the marketing team each morning. Here, automation isn’t about fancy pipelines — it’s simply about making sure the process runs on its own, the same way every time, without you clicking buttons. If the setup is fragile or disorganised, one small error can break the whole thing and delay important decisions.
From a single thesis project to a growing business, the challenges change — but the solution is the same: a professional, reproducible way of working that keeps your projects running smoothly, no matter how big they get. That’s exactly what you’ll learn in this book.