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  3. Machine Learning for Marketing Analytics in R
  • 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.

  • 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.

Initializing

Exercise

Future predictions of sales

A new dataset called salesData2_4 is loaded in the working space. It contains information on the customers for the months two to four. We want to use this information in order to predict the sales for month 5.

Instructions

100 XP
  • Use the summary() command in order to get an overview of the new dataset.
  • Use the model salesModel2 and the new dataset salesData2_4 in the predict() function in order to predict the sales for month 5. Store the predictions in a vector called predSales5.
  • Calculate the mean of the expected sales. Make sure to remove missing values.