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Machine Learning for Marketing Analytics in R

Choosing the right number of principal components

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Machine Learning for Marketing Analytics in R

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How can you decide which customers are most valuable for your business? Learn how to model the customer lifetime value using linear regression.

Exercise 1: Customer lifetime value in CRM Exercise 2: Benefits of knowing CLV Exercise 3: Looking at data Exercise 4: Simple linear regression Exercise 5: Understanding residuals Exercise 6: Estimating simple linear regression Exercise 7: Multiple linear regression Exercise 8: Avoiding multicollinearity Exercise 9: Interpretation of coefficients Exercise 10: Model validation, model fit, and prediction Exercise 11: Interpretation of model fit Exercise 12: Future predictions of sales

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.

Exercise 1: Churn prevention in online marketing Exercise 2: Application churn prevention Exercise 3: Data discovery Exercise 4: Peculiarities of the dependent variable Exercise 5: Modeling and model selection Exercise 6: Model specification and estimation Exercise 7: Statistical significance Exercise 8: Model specification Exercise 9: In-sample model fit and thresholding Exercise 10: In-sample fit full model Exercise 11: In-sample fit restricted model Exercise 12: Finding the optimal threshold Exercise 13: Danger of overfitting Exercise 14: Out-of-sample validation and cross validation Exercise 15: Assessing out-of-sample model fit Exercise 16: Cross validation

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.

Exercise 1: Survival analysis: introduction Exercise 2: Applications of survival analysis Exercise 3: Data for survival analysis Exercise 4: Characteristics of survival analysis Exercise 5: Survival curve analysis by Kaplan Meier Exercise 6: Survival function, hazard function and hazard rate Exercise 7: The survival object Exercise 8: Kaplan-Meier Analysis Exercise 9: Cox PH model with constant covariates Exercise 10: Proportional hazard assumption Exercise 11: Cox Proportional Hazard Model Exercise 12: Interpretation of coefficients Exercise 13: Checking model assumptions and making predictions Exercise 14: Violation of the PH assumption Exercise 15: Model assumptions Exercise 16: Predictions

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.

Exercise 1: PCA for CRM data Exercise 2: Purposes of PCA Exercise 3: Getting to know the data Exercise 4: Exploring the correlation structure Exercise 5: PCA computation Exercise 6: Standardization of data Exercise 7: Compute a PCA Exercise 8: The result object of a PCA Exercise 9: Choosing the right number of principal components

Exercício atual

Exercise 10: How many components are relevant?Exercise 11: Interpretation of components Exercise 12: Visualization with a biplot Exercise 13: Principal components in a regression analysis Exercise 14: Regression analysis with many variables Exercise 15: Linear regression with principal components Exercise 16: Closing