Foundations of marketing analytics
- Module 0 : Introduction to Foundation of Marketing Analytics
In this short module, we will introduce the field of marketing analytics, and layout the structure of this course.
We will also take that opportunity to explore a retailing data set that we’ll be using throughout this course. We will setup the environment, load the data in R (we’ll be using the RStudio environment), and explore it using simple SQL statements.
- Module 1 : Statistical segmentation
In this module, you will learn the inner workings of statistical segmentation, how to compute statistical indicators about customers such as recency or frequency, and how to identify homogeneous groups of customers within a database.
We will alternate lectures and R tutorials, making sure that, by the end of this module, you will be able to apply every concept we will cover.
- Module 2 : Managerial segmentation
Statistical segmentation is an invaluable tool, especially to explore, summarize, or make a snapshot of an existing database of customers. But what most academics will fail to tell you is that this kind of segmentation is not the method of choice for many companies, and for good reasons.
In this module, you will learn to perform managerial segmentations, which are not built upon statistical techniques, but are an essential addition to your toolbox of marketing analyst.
You will also learn how to segment a database now, but also at any point in time in the past, and why it is useful to managers to do so.
- Module 3 : Targeting and scoring models
How can Target predict which of its customers are pregnant? How can a bank predict the likelihood you will default on their loan, or crash your car within the next five years, and price accordingly? And if your firm only has the budget to reach a few customers during a marketing campaign, who should it target to maximize profit?
The answer to all these questions is… by building a scoring model, and targeting your customers accordingly.
In this module, you will learn how to build a customer score, which in marketing usually combines two predictions in one : what is the likelihood that a customer will buy something, and if he does, how much will he buy for?
- Module 4 : Customer lifetime value
- In this module, you will learn how to use R to execute lifetime value analyses. You will learn to estimate what is called a transition matrix -which measures how customers transition from one segment to another- and use that information to make invaluable predictions about how a customer database is likely to evolve over the next few years, and how much money it should be worth.