Foundations of marketing analytics

ESSEC Business School via Coursera

Go to Course: https://www.coursera.org/learn/foundations-marketing-analytics

Syllabus

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.

Overview

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 databa

Skills

Business Analysis Data Analysis Marketing Data Management Probability & Statistics

Reviews

Marketing concepts are clearly and concisely explained. Data analytics part is nicely blended with those concepts. BIG Thumps Up from my side...!!!

This Course was really an advanced level. The tests made us think and apply the learnt concepts. I am glad that i have opt for it. Overall, beautifully interpreted.

I was expecting a lot from this course. from basic to advanced fundamentals. this course should be floated as full specialization rather than as a part of other specialization.

Excellent course and perfectly taught by the professor. The course is easy to understand. With a very basic knowledge of R, one can understand a lot about marketing analytics.

The course is very useful and is simply fantastic. The explanations were structured well and were easy to understand. I have gained a lot of knowledge. Thank you.