After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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
Curriculum
Course outline
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.
- Customer lifetime value in CRM50 XP
- Benefits of knowing CLV50 XP
- Looking at data100 XP
- Simple linear regression50 XP
- Understanding residuals50 XP
- Estimating simple linear regression100 XP
- Multiple linear regression50 XP
- Avoiding multicollinearity100 XP
- Interpretation of coefficients50 XP
- Model validation, model fit, and prediction50 XP
- Interpretation of model fit50 XP
- Future predictions of sales100 XP
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
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
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.
R
Machine Learning for Marketing Analytics in R
Course
Complete
