ESSEC Business School - France - Foundations of marketing analytics
- Offered byCoursera
Foundations of marketing analytics at Coursera Overview
Duration | 6 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Foundations of marketing analytics at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 4 in the Strategic Business Analytics Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 6 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Foundations of marketing analytics at Coursera Course details
- 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.
Foundations of marketing analytics at Coursera Curriculum
Module 0 : Introduction to Foundation of Marketing Analytics
Foundations of Marketing Analytics
Setting up the environment and exploring the data (recital)
.R files and dataset
Module 1 : Statistical segmentation
Introduction
Hierarchical segmentation
Selecting the "right" number of segments
Segmentation variables
Recency, frequency, and monetary value
Computing recency, frequency and monetary value with R (Recital 1)
Data transformation
Preparing and transforming your data in R (Recital 2)
Running a hierarchical segmentation in R (Recital 3)
Acxiom URL
Instructions before starting the quiz 1
Quiz module 1 - 20% of final grade
Module 2 : Managerial segmentation
Limitations of statistical segmentation
Developing a managerial segmentation
Coding a managerial segmentation in R (Recital 1)
Describing segments
Segmenting a database retrospectively in R (Recital 2)
Segments and revenue generation
R tutorial (Recital 3)
Instructions before starting quiz 2
Quiz module 2 - 20% of final grade
Module 3 : Targeting and scoring models
Can Target predict a customer is pregnant?
What you need to develop a scoring model
Calibration data and statistical model
Building a predictive model in R (Recital)
Instructions before starting quiz 3
Quiz module 3 - 20% of final grade
Module 4 : Customer lifetime value
What is customer lifetime value and why it matters
Transition probabilities and transition matrix
How to compute a transition matrix in R (Recital 1)
Using the transition matrix to estimate how customers will evolve
Using the transition matrix to make predictions in R (Recital 2)
Assigning and discounting revenue
Computing customer lifetime value in R (Recital 3)
Instructions before starting the quiz 4
Quiz module 4
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