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.
Chapter 1: 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.
Chapter 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.
Chapter 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.
Chapter 4: Reducing Dimensionality with Principal Component Analysis
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.
If you are interested in learning more about marketing and data science, check out this tutorial for Python, Data Science for Search Engine Marketing.
The following R courses are prerequisites to take Marketing Analytics in R: Statistical Modeling.