Who is this course for?
This course is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts. For example, it may be suited to experienced statisticians, analysts, engineers who want to move more into a business role, in particular in marketing.
You will find this course exciting and rewarding if you already have a background in statistics, can use R or another programming language and are familiar with databases and data analysis techniques such as regression, classification, and clustering.
However, it contains a number of recitals and R Studio tutorials which will consolidate your competences, enable you to play more freely with data and explore new features and statistical functions in R.
Business Analytics, Big Data and Data Science are very hot topics today, and for good reasons. Companies are sitting on a treasure trove of data, but usually lack the skills and people to analyze and exploit that data efficiently. Those companies who develop the skills and hire the right people to analyze and exploit that data will have a clear competitive advantage.
It's especially true in one domain: marketing. About 90% of the data collected by companies today are related to customer actions and marketing activities.The domain of Marketing Analytics is absolutely huge, and may cover fancy topics such as text mining, social network analysis, sentiment analysis, real-time bidding, online campaign optimization, and so on.
But at the heart of marketing lie a few basic questions that often remain unanswered: (1) who are my customers, (2) which customers should I target and spend most of my marketing budget on, and (3) what's the future value of my customers so I can concentrate on those who will be worth the most to the company in the future.
That's exactly what this course will cover: segmentation is all about understanding your customers, scorings models are about targeting the right ones, and customer lifetime value is about anticipating their future value. These are the foundations of Marketing Analytics. And that's what you'll learn to do in this course.
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.
Jason Michael Cherry completed this course, spending 3 hours a week on it and found the course difficulty to be medium.
An exceptionally good course, giving a good business relevance for analytical techniques, and providing just enough technical details to do said analyses. This is a great course for analysts with technical expertise who want to expand the business relevance of their work for marketing uses.