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Verena Pflieger
Verena Pflieger

Data Scientist at INWT Statistics

Data analytics was already part of Verena’s skill set during her studies of political and administrative science and statistics. Since earning her degree in 2014 Verena has been applying her experience and expertise in training and data science to her work at INWT.

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  • Chester Ismay

    Chester Ismay

  • Nick Solomon

    Nick Solomon

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.

  1. 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.

  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.