R Tutorial: Customer Lifetime Value in CRM

Want to learn more? Take the full course at https://learn.datacamp.com/courses/machine-learning-for-marketing-analytics-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome to this course on Marketing Analytics in R and Statistical Modeling. My name is Verena Pflieger, I am Data Scientist at the consultancy INWT Statistics in Berlin, Germany. INWT is a company that specializes in data science in the fields of online marketing, customer relation management, and business intelligence. In this course, I will introduce you to statistical methods applied to the field of marketing analytics. First, we will model customer lifetime value using linear regression. Then, we will model customer churn using logistic regression. Additionally, we will use survival analysis to predict the time until a person orders, and finally, we will use principal component analysis in order to handle high dimensional marketing data. The customer lifetime value, called CLV, describes the predicted future net-profit accumulated by a company through its relationship with a customer. Since the CLV is a forecast, there are several challenges concerning its estimation. Once estimated, we are capable of identifying customers that are likely to generate higher net-profits. Practically, this can help us to target or prioritize customers according to future profits Net-profit, also known as margin, is the metric of interest. For this reason, we want to find drivers affecting the magnitude of the margin. However, there is one tricky aspect of this. We want to predict the future margin using only data that is available at the time, not data that will be available in the future. Hence, we need a model that uses current information in order to predict the future margin. Therefore we apply a two-step procedure. First, we take the explanatory variables from year one and use them to predict the dependent variable in year two. This i