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Machine Learning for Marketing Analytics in R

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

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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.
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In the following Tracks

Marketing Analytics in R

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  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
    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
  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
    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
  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
    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
  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
    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
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

In the following Tracks

Marketing Analytics in R

Go To Track

datasets

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

collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Nick Solomon

prerequisites

Introduction to Regression in R
Verena Pflieger HeadshotVerena Pflieger

Data Scientist at INWT Statistics

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