Machine Learning for Marketing Analytics in R

In this course you'll learn how to use data science for several common marketing tasks.
Start Course for Free
4 Hours17 Videos60 Exercises10,442 Learners
4200 XP

Create Your Free Account

GoogleLinkedInFacebook
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies


Course Description

This is your chance to dive into the worlds of marketing and business analytics using R. Day by day, there are a multitude of decisions that companies have to face. With the help of statistical models, you're going to be able to support the business decision-making process based on data, not your gut feeling. Let us show you what a great impact statistical modeling can have on the performance of businesses. You're going to learn about and apply strategies to communicate your results and help them make a difference.

  1. 1

    Modeling Customer Lifetime Value with Linear Regression

    Free
    How can you decide which customers are most valuable for your business? Learn how to model the customer lifetime value using linear regression.
    Play Chapter Now
  2. 2

    Logistic Regression for Churn Prevention

    Predicting if a customer will leave your business, or churn, is important for targeting valuable customers and retaining those who are at risk. Learn how to model customer churn using logistic regression.
    Play Chapter Now
  3. 3

    Modeling Time to Reorder with Survival Analysis

    Learn how to model the time to an event using survival analysis. This could be the time until next order or until a person churns.
    Play Chapter Now
  4. 4

    Reducing Dimensionality with Principal Component Analysis

    CRM data can get very extensive. Each metric you collect could carry some interesting information about your customers. But handling a dataset with too many variables is difficult. Learn how to reduce the number of variables in your data using principal component analysis. Not only does this help to get a better understanding of your data. PCA also enables you to condense information to single indices and to solve multicollinearity problems in a regression analysis with many intercorrelated variables.
    Play Chapter Now
In the following tracks
Marketing Analytics
Collaborators
Nick SolomonChester Ismay
Verena Pflieger Headshot

Verena Pflieger

Data Scientist at INWT Statistics
Data analytics was already part of Verena’s skill set during her studies of political and administrative science and statistics. Since earning her degree in 2014 Verena has been applying her experience and expertise in training and data science to her work at INWT.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA