Machine Learning for Marketing Analytics in R Course

Name: Machine Learning for Marketing Analytics in R
Rating: 4.863636363636363 (66 reviews)

Machine Learning for Marketing Analytics in R

IntermediateSkill Level

4.8+

Updated 05/2024

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

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.

Prerequisites

Introduction to Regression in R

Modeling Customer Lifetime Value with Linear Regression

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

Customer lifetime value in CRM

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

Start Chapter

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.

Churn prevention in online marketing

50 XP

Application churn prevention

50 XP

Data discovery

100 XP

Peculiarities of the dependent variable

50 XP

Modeling and model selection

50 XP

Model specification and estimation

100 XP

Statistical significance

50 XP

Model specification

100 XP

In-sample model fit and thresholding

50 XP

In-sample fit full model

100 XP

In-sample fit restricted model

100 XP

Finding the optimal threshold

100 XP

Danger of overfitting

50 XP

Out-of-sample validation and cross validation

50 XP

Assessing out-of-sample model fit

100 XP

Cross validation

100 XP

Start Chapter

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.

Survival analysis: introduction

50 XP

Applications of survival analysis

50 XP

Data for survival analysis

100 XP

Characteristics of survival analysis

50 XP

Survival curve analysis by Kaplan Meier

50 XP

Survival function, hazard function and hazard rate

50 XP

The survival object

100 XP

Kaplan-Meier Analysis

100 XP

Cox PH model with constant covariates

50 XP

Proportional hazard assumption

50 XP

Cox Proportional Hazard Model

100 XP

Interpretation of coefficients

50 XP

Checking model assumptions and making predictions

50 XP

Violation of the PH assumption

50 XP

Model assumptions

100 XP

Predictions

100 XP

Start Chapter

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.

PCA for CRM data

50 XP

Purposes of PCA

50 XP

Getting to know the data

100 XP

Exploring the correlation structure

50 XP

PCA computation

50 XP

Standardization of data

50 XP

Compute a PCA

100 XP

The result object of a PCA

50 XP

Choosing the right number of principal components

50 XP

How many components are relevant?

100 XP

Interpretation of components

100 XP

Visualization with a biplot

100 XP

Principal components in a regression analysis

50 XP

Regression analysis with many variables

50 XP

Linear regression with principal components

100 XP

Closing

50 XP

Start Chapter

Machine Learning for Marketing Analytics in R

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FAQs

Is this course suitable for beginners?

No, this course is targeted at advanced learners.

What topics are covered in this course?

The course covers topics such as modeling customer lifetime value with linear regression, logistic regression for churn prevention, modelling time to reorder with survival analysis and reducing dimensionality with principal component analysis.

Who will benefit from this course?

This course would be beneficial for roles such as data scientists, marketing analysts, and customer insights specialists who are looking to hone their machine learning skills for marketing applications.

Will I receive a certificate at the end of the course?

Yes, upon successful completion of all course tasks, you will receive a DataCamp certificate verifying your knowledge and skills.

What will I learn to do in this course?

By the end of the course, you will have learned to build marketing model with linear regression, predict customer churn with logistic regression, model time to reorder with survival analysis, and reduce dimensionality with principal component analysis.

What programming language is used in this course?

The programming language used in this course is R.

What kind of data will I learn to use in this course?

In this course, you will use CRM data such as customer information, order history, and subscription data.

Is this course for individual users or for businesses?

This course is suitable for both individual users and businesses who want to use the power of machine learning to make better marketing decisions.

Machine Learning for Marketing Analytics in R

Training a Team?

Course Description

Prerequisites

Modeling Customer Lifetime Value with Linear Regression

Logistic Regression for Churn Prevention

Modeling Time to Reorder with Survival Analysis

Reducing Dimensionality with Principal Component Analysis

Earn Statement of Accomplishment

Don’t just take our word for it

FAQs

Is this course suitable for beginners?

What topics are covered in this course?

Who will benefit from this course?

Will I receive a certificate at the end of the course?

What will I learn to do in this course?

What programming language is used in this course?

What kind of data will I learn to use in this course?

Is this course for individual users or for businesses?

Join over 19 million learners and start Machine Learning for Marketing Analytics in R today!

Grow your data skills with DataCamp for Mobile

Course Description

Earn Statement of Accomplishment

Don’t just take our word for it

FAQs

What topics are covered in this course?

Who will benefit from this course?

Will I receive a certificate at the end of the course?

What will I learn to do in this course?

What programming language is used in this course?

What kind of data will I learn to use in this course?

Is this course for individual users or for businesses?

Join over .css-nklxlk{color:var(--wf-brand--main, #03EF62);}19 million learners and start Machine Learning for Marketing Analytics in R today!

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Grow your data skills with DataCamp for Mobile

Join over 19 million learners and start Machine Learning for Marketing Analytics in R today!