HomeRMachine Learning for Marketing Analytics in R

# Machine Learning for Marketing Analytics in R

In this course you'll learn how to use data science for several common marketing tasks.

4 Hours17 Videos60 Exercises

or

Training 2 or more people?Try DataCamp For Business

## 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.

### .css-1goj2uy{margin-right:8px;}Group.css-gnv7tt{font-size:20px;font-weight:700;white-space:nowrap;}.css-12nwtlk{box-sizing:border-box;margin:0;min-width:0;color:#05192D;font-size:16px;line-height:1.5;font-size:20px;font-weight:700;white-space:nowrap;}Training 2 or more people?

Try DataCamp for BusinessFor a bespoke solution book a demo.

Go To Track
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
50 xp
Benefits of knowing CLV
50 xp
Looking at data
100 xp
Simple linear regression
50 xp
Understanding residuals
50 xp
Estimating simple linear regression
100 xp
Multiple linear regression
50 xp
Avoiding multicollinearity
100 xp
Interpretation of coefficients
50 xp
Model validation, model fit, and prediction
50 xp
Interpretation of model fit
50 xp
Future predictions of sales
100 xp
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.

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.

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.

### In the following Tracks

#### Marketing Analytics with R

Go To Track

Datasets

Churn dataSales dataSales data, months 2-4Survival dataDefault dataNews dataFirst CLV datasetSecond CLV datasetCustomer data

Collaborators

Verena Pflieger

Data Scientist at INWT Statistics

See More