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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

Estimating simple linear regression

Back to our sales dataset, salesData, which is already loaded in the workspace. We saw that the sales in the last three months are strongly positively correlated with the sales in this month. Hence we will start off including that as an explanatory variable in a linear regression.

Instructions

100 XP

  • Use the lm() function in order to specify a linear regression model using salesLast3Mon as explanatory variable. Assign it to an object salesSimpleModel.

  • Then use the summary() function in order to look at the results.

  • Look at the regression coefficient. Is there a positive or negative relationship between the two variables?

  • About how much of the variation in the sales in this month can be explained by the sales of the previous three months?